ࡱ>  7bjbj 3<~%t t 8dg\Z$"   OQQQQQQ$ru]u  ҠEEE  OEOEE _m'\Y;0,Bl&˜pEuuClt }: Project Final Report Template Reporting Years: October 1, 2003 August 1, 2010 GENERAL INFORMATION This form contains 4 sections Project & Personnel Information Executive Summary and Research Information Educational Information, and Outreach information. Each section has multiple questions that will help us generate an integrated report for both the RESCUE and Responsphere Annual and Final Reports. Please answer them as succinctly as possible. However, the content should contain enough details for a scientifically-interested reader to understand the scope of your work and importance of the achievements. As this form covers both an annual and final report, the form asks you to provide input on the past years progress as well as overall progress for the entire 7-year program. DEADLINE The RESCUE and Responsphere reports are due to NSF by June 30, 2010. Completed forms MUST be submitted by May 15th, 2010. (Obviously, publications can be submitted through the website ( HYPERLINK "http://www.itr-rescue.org" www.itr-rescue.org) as you get papers accepted.). It is crucial you have this finished by this date, as the Ex-Com will be meeting (some are flying in) to finalize the report. SUBMISSION INSTRUCTIONS The completed forms must be submitted via email to: Chris Davison  HYPERLINK "mailto:cbdaviso@uci.edu" cbdaviso@uci.edu Publications need to be submitted to our website in order for us to upload to the NSF:  HYPERLINK "http://www.itr-rescue.org/pubs/pub_submit.php" http://www.itr-rescue.org/pubs/pub_submit.php Auxiliary Material To help you complete this form, you should refer to both the RESCUE Strategic Plan which identifies the overall goal of the program (this information is needed in order for you to explain how your research helps to achieve the goals of the RESCUE program) and the RESCUE annual reports for Years 1 through 6, plus the strategic plan. You can find these documents on the RESCUE projects website Intranet:  HYPERLINK "http://www.itr-rescue.org" http://www.itr-rescue.org SECTION A: Project & Personnel Information Project Title: Robust Networking and Information Collection Names of Team Members: (Include Faculty/Senior Investigators, Graduate/Undergraduate Students, Researchers; which institution theyre from; and their function [grad student, researcher, etc]) Principal Investigator: Ramesh R. Rao Project Lead and Research Scientist: Manoj Balakrishnan Senior personnel(s): Ganapathy Chockalingam; Babak Jafarian, Per Johansson, and John Zhu Post-doc(s): Bheemarjuna Reddy Tamma Graduate student(s): Ping Zhou, Raheleh Dilmaghani, Abhijeet A Bhorkar, Parul Gupta, and Salih Ergut Research Experience for Undergraduates(s): Paul Baumgart Technician, programmer(s): Javier Rodriguez Molina Other -- specify(s): Alexandra H Baker, Vanessa Pool, and Maureen C. Curran Technician, programmer(s); Antony Nwokafor, Jeoff Cuenco, and Javier Rodriguez Molina. Collaborator(s): Shannon Spanhake and Michael Ye, Nick Hill, Vikram Rao, and Sam Fernald List of Collaborators on Project: (List all collaborators [industrial, government, academic] their affiliation, title, role in the project [e.g., member of Community Advisory Board, Industry Affiliate, testbed partner, etc.], and briefly discuss their participation in your project) Government Partners: (Please list) San Diego Police Department: Collaborative Research City of San Diego: Collaborative Research UCSD Environmental Health & Safety: Facilities; Collaborative Research UCSD Police Department: Facilities; Collaborative Research Academic Partners: (Please list) San Diego State University: Facilities; Collaborative Research NSF SGER-CogNet project: collaborative research on using cognitive principles for emergency response networking research, data and information sharing on wireless network characterization, information analysis for the emergency response drills. WIISARD Project: Joint participation of drills and other larger scale emergency response preparatory activities. Industry Partners: (Please list) CALTRANS Corporation: Collaborative Research ( HYPERLINK "http://www.dot.ca.gov/dist11/d11tmc/sdmap/showmap.html" http://www.dot.ca.gov/dist11/d11tmc/sdmap/showmap.html) Voxeo Corporation  HYPERLINK "http://www.voxeo.com" http://www.voxeo.com KPBS Radio: Collaborative Research on traffic information dissemination. San Diego MMST: Collaborative Research on emergency response drills, facilitating interaction with first responder community, feedback and other information sharing on emergency response requirements. SECTION B: Executive Summary and Research-Related Information (2 pages per project/area e.g., SAMI, PISA, networks, dissemination, privacy, metasim, social science contributions, artifacts, testbeds) (This summary needs to cover the entire 7-year period of the grant. However, information on recent research progress must also be provided. Please discuss the progress of your research within the context of the following questions. Where possible, please include graphics or tables to help answer these questions.) Executive Summary Executive Summary: Describe major research activities, major achievements, goals, and new problems identified over the entire seven-year period: (This will be the MAJOR section of your report. The rest of this template will provide more detailed information for the subsections of the final report). The section should answer the following questions: 1) What was the major challenge that your project was addressing and what were your goals? Example: Creating on site networks and bi-directional data communication instantaneously which can meet the needs of data transmission both from first responders to the incident commanders and from incident commanders to the first responders. 2) What major technological/social science research questions were identified and what approach did you identify to solve the research question? Example: The research question in the above challenge could be (a) reliability of communication in mesh environments and in multi-carrier networks, and (b) building capacity by exploiting multiple networks. An example of approach could be exploiting multiple carriers, and of building mechanisms for prioritization of messaging to meet application quality. 3) What were your achievements in meeting the goals and addressing the research questions which you would like to highlight? Example: Theoretical analysis of network capacities in such networks. One can quote the main result in such a theoretical analysis. Engineering such multinetworks, coming up with mechanisms for data collection in such networks, etc. Products and Contributions: (Artifacts, 1st Responder adopted technologies, impact, and outreach). This section should answer the following questions: What products/systems did you develop? How were these products /ideas tested? What were the lessons learned? Project Achievements: (This is where you get to tout the success of your project as well as new problems identified): Please address following questions: How did your work change the state-of-the-art in the area of your project? That is, what new scientific achievements can we attribute to your work? How did the achievement lead to impact on first responders if any? Clear examples of such impact would be very useful. SECTION C: Research Activities (this section will provide us information for the detailed appendix that will be included along with the executive summary) (Please summarize major research activities over the past 7 years using the following points as a guide) Project Name : Robust Networking and Information Collection Project Summary --- The grand challenge of this project is to develop research solutions and artifacts that can make todays communication networks perform better during crises situations. To achieve these objectives, the overall project is reorganized into the following four sub-groups: (1) Theoretical Research group; (2) Extreme Networks System (ENS); (3) Adaptive Information Collection System (AICS); and (5) System Integration. ENS, Peer-to-Peer Information Collection and Dissemination systems and Rich Feeds (System Integration) are the three artifacts developed under this project. This is more or less a cut and paste from Section B that goes to executive summary. Feel free to elaborate a bit more about the project and its scope and in addition address the following questions. Describe how your research supports the RESCUE vision (Please provide a concise statement of how your research helps to meet RESCUEs objectives and overarching and specific strategies for reference, please refer to the Strategic Plan). The main objective of this project is to provide research solutions that can enable restoration of computing, communication, and higher layer services at a crisis site in a manner that is focused on the needs and opportunities that arise proximate to the crisis (in both time and space dimensions.) Commercial systems are often based on assumptions that fall apart during a crisis when large-scale loss of power, destruction of antenna masts and servers are common. Commercial services also incorporate elements important for day-to-day business (such as the need to compete with other similar providers) that are largely irrelevant during a crisis. In addition, self-contained relief organizations that arrive at a crisis site often carry communication equipment that fail to interoperate, are inadequate for the needs at the scene, and may even interfere with each other making the task of forming an ad-hoc organization harder. In summary, the challenge is to compose a set of research solutions to assist in crisis response that is designed to serve the dynamically evolving situation at the crisis site. How did you specifically engage the end-user community in your research? In RESCUE Robust Networking and Information Collection research, the user community includes the first-responder community including law enforcement and public safety agencies, responding officers, and volunteers. Their participation was ensured in every stage of RESCUE research. The user community was engaged in the following ways: (i) interactions to seek direct input for our research, (ii) periodically discussing research and design choices with them, and (iii) involving the user community in using the end results of our research during many large-scale drills and exercises. In the interaction with the user community, we had several interactions with UCSD and San Diego police officers, members of the fire department, and UCSD environment, health, and safety. In addition, RESCUE participated in two emergency response drills one conducted by UCSD police and another conducted by Metropolitan Medical Strike Team (MMST), where all local, as well as some regional, city and county law enforcement and public safety agencies were participants An example of user participation is as follows: there are thousands of commuters of California who use the RESCUE developed traffic notification system on an everyday basis to get personalized traffic reports. They also act as sensors who report and share highway incident information on an everyday basis. The reported incidents are available at the traffic notification website at http://traffic.calit2.net/sd/ireport.jsp by selecting the from and to dates on the page. How did your research address the social, organizational, and cultural contexts associated with technological solutions to crisis response? Our research created knowledge on various social behavioral patterns using our research prototypes. One example is the Adaptive Information Collection System. The AICS collected aggregate traffic information from various cities such as San Diego, Los Angeles, and San Francisco and created information on the traffic congestion as a function of time and space. Such information is made available to the rest of the user population. The city-wide traffic behavior that can be obtained by our system is enormously useful for handing future disaster management operations. Research Findings (Summarize major research findings over the past 7 years).) Describe major findings highlighting what you consider to be groundbreaking scientific findings of your research. (Especially emphasize research results that you consider to be translational, i.e., changing a major perspective of research in your area). Highlight major research findings in this final year (Year 7). Please discuss how the efficacy of your research was evaluated. Through testbeds? Through interactions with end-users? Was there any quantification of benefits performed to assess the value of your technology or research? Please summarize the outcome of this quantification. Responsphere - Please discuss how the Responsphere facilities (servers, storage, networks, testbeds, and drill activities) assisted your research. Research Contributions (The emphasis here is on broader impacts. How did your research contribute to advancing the state-of-knowledge in your research area? Please use the following questions to guide your response). What products or artifacts have been developed as a result of your research? ENS, Peer-to-Peer traffic information collection and dissemination systems and Rich Feeds system integration are the three artifacts developed under this project. Web sites developed in Year 5:  HYPERLINK "http://calnode.calit2.net" http://calnode.calit2.net CalNode is a prototype Cognitive Network Access Point (CogNet AP), which has the unique capability to observe and learn from the network traffic in order to optimize itself. Unlike traditional devices which require significant amount of network planning prior to deployment, a cognitive network system based on CalNodes can be deployed with no prior channel planning. Even with no planning, the cognitive ability of these devices enables them to converge to the optimal network configuration over a period of time. These devices collect, compact, repositorize, and analyze wireless network traffic in order to extract crucial spatio-temporal network traffic patterns. The information gained from the spatio-temporal network traffic patterns will be used to reconfigure the network elements for optimal performance. In addition, information gathered by CalNodes will be shared among themselves for improving system efficiency. CalNodes can be used in either centralized or autonomous mode. In the autonomous node, decisions are taken within a device with the help of local information. However, in the centralized mode, a collection of CalNodes are controlled by a centralized CogNet controller in order to achieve network-wide optimal configuration. In the emerging heterogeneity of wireless networking environment, cognitive networking capability of CalNodes help design new network forms that can achieve higher network capacity while minimizing the effort needed to deploy them.  HYPERLINK "http://rescue.calit2.net" http://rescue.calit2.net Rich Feeds: Rich Feeds is a system that demonstrates how unconventional data feeds and emergent data feeds can be captured, preserved, integrated, and exposed in either real time or after the fact. Rich Feeds promotes situational awareness during a disaster by integrating and displaying these feeds on a Google map in real time. To meet these challenges, Rich Feeds? design is based on a Service Oriented Architecture (SOA) pattern called Rich Services, which delivers the benefits of SOA in a system-of-systems framework using an agile development process. Rich Feeds is a hierarchically decomposed system that integrates data producers, data consumers, and data storage and streaming facility into a structure that services crosscutting concerns such as authorization, authentication, and governance flexibly and reliably. Rich Feeds? service oriented architecture allows the addition of new data producers and consumers quickly and with low risk to existing functionality while providing clear paths to high scalability. Rich Feeds provides users with the opportunity to integrate research and emergent feeds to create novel presentations and gain novel insights both in emergency and research settings. This system has integrated several products developed within the RESCUE project, including the Calit2 Peer to Peer Wireless traffic system, Cellular based vehicle tracking and telematics system, video feeds from Gizmo (a remote controlled vehicular CalMesh access point/sensor node), and the Cal-Sat multimodal situational awareness mobiquitous computing platform. We were also given access to cameras located on campus through the UCSD campus police; they also supplied credentials to enable us to begin implementation of a crosscutting concern processing authorization/authentication/policy evaluation. Based on user-supplied credentials, the feed list presented to the user is determined - lack of credentials filtered out the UCSD camera feeds. The Rich Feeds system can be accessed at  HYPERLINK "http://rescue.calit2.net" http://rescue.calit2.net. Web Portals for Peer-to-Peer Networking:  HYPERLINK "http://traffic.calit2.net" http://traffic.calit2.net  HYPERLINK "http://traffic.calit2.net/sd" http://traffic.calit2.net/sd HYPERLINK "http://traffic.calit2.net/la"http://traffic.calit2.net/la HYPERLINK "http://traffic.calit2.net/bayarea"http://traffic.calit2.net/bayarea Fully automated peer to peer information dissemination system: this system collects is based upon the Calit2 Wireless Traffic system and relays customized (targeted) highway incident information to the general public and to the first responders. Though government agencies and the private sector have some of the basic data needed for effective highway incident collection, the means to effectively disseminate the data in an intelligent manner (i.e., delivery of relevant and timely information to the right segment) is lacking. Typically the data is disseminated in a broadcast mode, with unacceptable latencies. Also, in many situations, there is significant lag in the collection of crisis related data by the government agencies. This lag can be eliminated by empowering the general public to report relevant information. The pilot system was deployed in the San Diego Area in 2006, and it currently gets over 20,000 unique users per month. We have since expanded the system and have deployed the peer-to-peer wireless traffic information system in the Los Angeles/Orange County areas (2007) and also the San Francisco Bay Area (2007). This system may be accessed through its website or by calling the telephone number for the corresponding geographic area: Voice Portals: (866) 500 0977 - San Diego (888) 9Calit2 - Los Angeles (888) 4Calit2 - Bay Area How has your research contributed to knowledge within your discipline? Our research contributed to the advancement of knowledge in general in the area of communication networking and in particular in the areas of emergency response communication, peer-to-peer information collection and dissemination, theoretical modeling and analysis of communication systems and protocols for emergency response communication. For example, the ENS system has several novel aspects including its ease of deployment, reconfigurability and its efficient routing operation. Similarly, the opportunistic MAC protocol for MIMO communications provides significant performance improvement and flexibility. The peer-to-peer traffic incident notification system is the first of its kind in incident reporting in a peer-to-peer fashion. It has shed new light into how commuters could act as sensors in crisis situations. How has your research contributed to knowledge in other disciplines? What human resource development contributions did your research project result in (e.g., students graduated, Ph.D., MS, contributions in placement of students in industry, academia, etc.) Our research contributed to the advancement of knowledge in general in the area of communication networking and in particular in the areas of emergency response communication, peer-to-peer information collection and dissemination, theoretical modeling and analysis of communication systems and protocols for emergency response communication. For example, the ENS system has several novel aspects including its ease of deployment, reconfigurability and its efficient routing operation. Similarly, the opportunistic MAC protocol for MIMO communications provides significant performance improvement and flexibility. The peer-to-peer traffic incident notification system is the first of its kind in incident reporting in a peer-to-peer fashion. It has shed new light into how commuters could act as sensors in crisis situations. Contributions beyond science and engineering (e.g., to industry, current practice, to first responders, etc.) Contribution beyond science and engineering includes the joint exercises and the potential training that caused to the first responder community as well as law enforcement agencies as a result of RESCUEs participation with several major drills in San Diego. Please update your publication list for this project by going to:  HYPERLINK "http://www.itr-rescue.org/pubs/pub_submit.php" http://www.itr-rescue.org/pubs/pub_submit.php (Include journal publications, technical reports, books, or periodicals). NSF must be referenced in each publication. DO NOT LIST YOUR PUBLICATIONS HERE. PLEASE PUT THEM ON THE WEBSITE. Remaining Research Questions or Challenges (In order to help develop a research agenda based on RESCUE after the project ends, please list remaining research questions or challenges and why they are significant within the context of the work you have done in RESCUE. Please also explain how the research that has been performed under the current RESCUE project has been used to identify these research opportunities). Success Stories / Major Scientific Achievements (Use this section to highlight what your project has achieved over the last 7 years. This is your opportunity to publicize your advancements and look back over our many years together and find those nuggets that really made a difference to science, first responders, etc.) SECTION D: Education-Related Information Educational activities: (RESCUE-related activities you and members of your team are involved in. Include courses, projects in your existing courses, etc. Descriptions must have [if applicable] the following: quarter/semester during which the course was taught, the course name and number, university this course was taught in, course instructor, course project name) Graduate and Undergraduate Education: RESCUE continues to have an impact on course curriculum throughout all the universities involved in the project. Specific courses and class projects have been designed to have a direct tie to the research being done at RESCUE. Throughout the year, the RESCUE project has encouraged undergraduate students to be a part of ongoing research through individual study courses, honors courses, the NSF-funded California Alliance for Minority Program (CAMP), and undergraduate research appointments. Ramesh Rao, BS Manoj, Rajesh Hegde, Javier Rodriguez, Don Kimball and Per Johansson supervised more than a dozen and a half undergraduate and graduate group design courses in electrical or mechanical engineering and computer science during the 2007-2008 academic year. Ramesh Rao advised an undergraduate student for three quarters in the academic year 2007-2008 for a self study course which significantly overlaps with RESCUE research area. Rajesh Hegde and BS Manoj taught graduate/undergraduate level courses where topics directly from RESCUE research were covered. Ingolf Krger covered the RESCUE integration architecture in two computer science & engineering graduate courses he taught. K-12 Education: RESCUE continues to reach out to the K-12 community by sponsoring high school interns and participating in campus events for high school students. During the 2007-2008 academic year, UCSD hosted 12 high school senior interns from the Preuss School, a charter school under the San Diego Unified School District whose mission is to provide an intensive college preparatory curriculum to low-income student populations and to improve educational practices in grades 6-12. Training and development: (Internships, seminars, workshops, etc., provided by your project. Seminars/workshops should include date, location, and presenter. Internships should include intern name, duration, and project topic.) What PhD students have graduated? Education Materials: (Please list courses introduced, taught, tutorials, data sets, creation of any education material of pedagogical significance that is a direct result of the RESCUE project). Internships: (Please list) Internships and Student Exchange Programs: Students participated in the creation and evaluation of several RESCUE technologies, several students from the senior group design courses in electrical or mechanical engineering went on to intern on various subprojects. At UCSD, Prof. Ramesh Rao hosted several visiting graduate students, including one from the University of Karlsruhe, Germany, who is studying disaster response there. SECTION E: Outreach Related Information Additional outreach activities: (RESCUE-related conference presentations, participation in community activities, workshops, products or services provided to the community, etc.) At UCSD we have been active in outreach efforts with the academic community, organizing the following conferences and workshops: Ramesh Rao, Manoj Balakrishnan and Alexandra Hubenko co-chaired a special session at the 5th Conference of the International Community on Information Systems for Crisis Response and Management (ISCRAM) titled Technology Showcase: Communication Systems and Technologies for Crisis and Disaster Response Other outreach activities at UCSD included demonstrating our infrastructure and research technologies for industry groups, domestic and international governmental delegations, and conferences that take place at Calit2; including Future in Review (FiRE) RESCUE researchers and technologists from UCSD campus gave a number of keynote addresses and invited talks. These addresses provide the Responsphere team the opportunity to engage a number of stakeholders (Government, industry, academia, and First Responders) within the emergency response domain. We list a sample of such talks below. R. B. Dilmaghani attended IEEE Homeland Security conference held in Boston, MA, during May 2008 and presented a paper titled A Wireless Mesh Infrastructure Deployment with Application to Emergency Scenarios. R. B. Dilmaghani attended International conference on Information Systems for Crisis Response and Management (ISCRAM) 2008 Conference, in Washington D.C., during May 2008 and presented a paper titled A Wireless Mesh Infrastructure Deployment with Application for Emergency Scenarios. Bheemarjuna Tamma attended IEEE WCNC 2008, during March 2008 and presented a paper titled On the Accuracy of Sampling Schemes for Wireless Network Characterization. B. S. Manoj attended IEEE CCNC 2008 in Las Vegas, NV, during January 2008 and presented two papers titled On Optimizing Non-Asymptotic Throughput of Wireless Mesh Network and On the Use of Information Sharing in Wireless Networks. Among this, the paper titled On Optimizing Non-Asymptotic Throughput of Wireless Mesh Network received the Best Paper Award at IEEE CCNC 2008. Don Kimball attended ICAST 2007 in Ghana, during December 2007 and presented a paper, authored as part of this project, titled On the Viability of Wireless Mesh Networks as a Next Generation Wireless Networking. B. S. Manoj attended IEEE Globecom 2007 in Washington, D.C., during November 2007 and presented two papers titled On Adding Link Dimensional Dynamism to CSMA/CA based MAC protocols and On the Use of Higher Layer Information for Cognitive Networking. Ping Zhou attended ACM WICON 2007, in Austin, TX, during October 2007 and presented a paper titled On Gateway Placement in Wireless Mesh Networks. Salih Ergut attended WCCI 2008, held during June 2008 in Hong Kong, and presented a paper titled "Localization Via Multipath Strengths in a Cdma2000 Cellular Network Using Neural Networks." Salih Ergut attended IEEE International conference on Communication (ICC 2008) and presented a paper titled "Localization via TDOA in a UWB sensor network using Neural Networks" in May 2008. The conference was held in Beijing, P. R. China. Salih Ergut attended IEEE CCNC 2008, held during January 2008, in Las Vegas, and presented a paper titled "Packet Size Aware Path Setup For Wireless Networks". UCSD K-12 outreach activities included demonstrations at the 2008 Calit2 Take your Daughters and Sons to Work Day; and sponsoring a total of 12 student interns from the Preuss School during the 2007-2008 academic year, a charter school under the San Diego Unified School District whose mission is to provide an intensive college preparatory curriculum to low-income student populations and to improve educational practices in grades 6-12. These students worked on projects related to the Gizmo platform. In addition to the papers and presentations that we participated, RESCUE organized/participated the following large scale emergency response drills October 16, 2007: UCSD Campus Drill. UCSDs RESCUE and Responsphere projects participated in a campus drill (active shooter scenario) January 24, 2008; MMST Drill at Coors Amphitheatre, National City, CA. UCSD participated in a large-scale emergency response drill in conjunction with the San Diego Metropolitan Medical Strike Team (MMST) and the UC San Diego Police and Emergency Services departments on the UCSD campus on August 22, 2006. The ENS system was demonstrated and used as the backbone network for emergency response activities demonstrated during this event Conferences: (Please list) ACM WiCON 2007, IEEE Globecom 2007, IEEE CCNC 2008, IEEE WCNC 2008, IEEE ICC 2008, IEEE WCCI 2008, ISCRAM 2008, IEEE Homeland Security 2008, and Group Presentations: (Please list) Impact of products or artifacts created from this project on first responders, industry, etc. (Are they currently being used by a first-responder group? In what capacity? Are they industry groups that are interested in licensing the technology or investing in further development?). The traffic notification system is currently used by Caltrans ( HYPERLINK "http://www.dot.ca.gov/dist11/d11tmc/sdmap/showmap.php" http://www.dot.ca.gov/dist11/d11tmc/sdmap/showmap.php see link called Wireless Traffic Report ). A startup company in the Bay Area called IntelliTraffic is evaluating our traffic notification technology for the Indian market. Rescue Year 6 Annual Report: Networking Project 2: Robust Networking and Information Collection Project Summary The main objective of this project is to provide research solutions that enable the restoration of computing, communication, and higher-layer services at a crisis site in a manner that focuses on the needs and opportunities that arise proximate to the crisis (in both time and space dimensions). Commercial systems are often based on assumptions that fall apart during a crisis when large-scale loss of power and destruction of antenna masts and servers are common. In addition, self-contained relief organizations that arrive at a crisis site often carry communication equipment that fail to interoperate, are inadequate for the needs at the scene, and may even interfere with each other making the task of forming an ad-hoc organization harder. In summary, the challenge is to compose a set of research solutions to assist in crisis response that is designed to serve the dynamically-evolving situation at the crisis site. Activities and Findings The highlight for the UCSD RESCUE team this past year was our participation in the San Diego Science Festivals Expo Day in April, which included the largest deployment of CalMesh ever. In addition, technical progress was made in a number of areas and refinements of previous technologies continued. San Diego Science Festival - Expo Day Researchers from the UCSD ResponSphere and RESCUE projects participated in the grand finale of the month-long San Diego Science Festival: Expo Day at Balboa Park. More than 50,000 people attended the event held on Saturday, April, 4, 2009, which featured 200+ exhibition booths. Organizers called it "the largest one-day science gathering ever in the United States." More than three dozen researchers (PIs, faculty, staff, postdocs, graduate and undergraduate students) were on-hand at Balboa Park to run demonstrations, provide information to visitors, and manage the wireless network and the many experiments. A wide variety of measurements were taken, both on the day of the event and as preparation and reference samples. The huge crowds and changing network environment over the day-long event served as an excellent live real-time testbed for evaluation how our wireless technologies performed in areas with heavy pedestrian and cellular traffic. This activity has resulted in at least one new collaboration. The Balboa Park Online Collaborative (BPOC) has asked the Robust Networking team to help them wirelessly connect museums in Balboa Park to help create a more unified management and online data sharing and collaboration systems. The Calit2 San Diego Science Festival Photo Gallery is available here: http://projects.calit2.net/gallery/main.php?g2_itemId=3436. Products and Contributions 1. CalMesh At the request of the San Diego Science festival organizers, we set up a CalMesh ad-hoc wireless network covering most of the exhibit areas along the Prado (around 40+ booths), many of which would otherwise have not had WiFi connectivity or only spotty access to the Internet, much less high-speed web access. The coverage area was along the eastern part of the Prado from the Lilly Pond to the Fountain. This was the largest deployment of CalMesh ever. See Figure 1, below. The deployment also provided an excellent opportunity to collect data, to further our research on communications in cases of emergency. Many experiments were conducted and measurements taken on the network and surrounding environment. The CalMesh network was linked to the Internet via the High Performance Wireless Research and Education Network (HPWREN) and a commercial provider, Sky River, both had access points on the Natural History Museum's roof. An access bonding solution from Mushroom Network Inc. aggregated the two channels together. Combined, they provided 45 megabits per second of bandwidth. For Expo Day, 10 nodes total were deployed: 8 Mesh Nodes (one open access point [AP]) and 2 Stand-alone gateway [GW] nodes (3G access and inside the Natural History Museum). Each box (node) is a WiFi Access point, supporting data, voice and video applications with a 15-hour battery and GPS. The cost is about $1000 per node. CalMesh uses a variety of ways to connect to the Internet: Wired (DSL, Ethernet, Cable), WiFi Hotspots, Cellular (3G), and Satellite access. CalMesh has multiple interfaces: Two WiFi interfaces: 1) One for Mesh connectivity only where the actual routing protocol runs. 2) One for Access Point only, where legacy WiFi clients can connect. Two Ethernet interfaces: 1) Internet connectivity (gateway) and 2) Wired access to servers. One Cellular interface: Currently USB based cellular data devices are supported Figure 1. CalMesh deployment in Balboa Park (approximate node locations). The lily pond is where CalMesh GW4 is located (on the left of the photo), the fountain is on the right, surrounded by a circle of wide concrete. The Prado is the area in between. Measurements: The internet access providers (SkyRiver and HPWREN) stored traffic logs at their respective network operations centers (NOCs) that are available to us. Each CalMesh node stored traffic traces by the use of tcpdump where the IP addresses and type of traffic of each user can be recorded. In addition, each node stored the number of received and sent packets, sampled at programmable time intervals. In addition, some of the access points were open for the general public to study how such traffic impacted the network performance. Signal to noise ratio measurements on the move were made along the node placement route by walking up and down the Prado collecting location-aware data. Monitoring of the aggregated traffic through the backhauls was also performed to give us an idea of total bitrate for event, quality and possibly type of traffic. Data analysis is ongoing. 2. Gizmo The big hit among the crowds of families in attendance was Gizmo, Rescues family of autonomous multi-radio devices that serve as adaptable and reliable research platforms on wheels to deploy different technologies and gather sensor data in real time. They are designed to transport cameras, other sensors, and wireless access points to and around disaster sites in order to get communications going again in an emergency. They are configured with 2.4GHz WiFi communications and control, 75MHz PCM control only, Video pan tilt 640x480 60fps, and Audio ADPCM. This year a mobile touchscreen kiosk based on the Gizmo technology (the Nokia Siemens Networks Gizmo operator) was developed and made its public demo debut in the Calit2 Expo day booth. Gizmoi was upgraded for use in rough terrains. Also, a Backpack Cam video feed device, which could be worn while walking around the festival, was created from a Gizmo-based webcam. 3. Condor The project WiFli CalMesh Condor was originated with the idea of expanding and improving our network deployment capabilities. As it is often the case, in emergency response environment, not every area is accessible nor is every terrain smooth. Therefore, deployment becomes really complicated and time consuming. Gizmo and MOP projects helped in facilitating the deployment of the CalMesh network. Even though the newly upgraded Gizmo can go through rough terrains, it will always be limited by possible obstacles. The WiFli CalMesh Condor offers a faster and dependable system. 4. BlueMap Bluetooth Malware Analysis and Prevention (BlueMap). We deployed 15 Bluetooth sensor nodes within the Expo area to record Bluetooth devices in the vicinity of each sensor node and store this data in a central database that can later be used to infer, among other things, the potential spread of mobile phone virus during a highly populated event. Analysis of malware spread between mobile phones over short-range wireless links (Bluetooth, WiFi etc.) helps gain a better understanding of the potential for proximity driven malware spread and develops means to both detect and prevent such outbreaks. We saw 440 unique Bluetooth devices during the measurement period between 3 p.m.6 p.m. 5. CogNet Monitoring On Expo day we conducted network monitoring using CogNet. We observed all the channels in the 2.4GHz and 5.2GHz spectrums and sampled network traffic from all the channels. We measured the channels used for CalMesh continuous capture. The results from this measurement are mainly focused on studying the network behavior as well as studying the wireless environment behavior. In addition, we visualized the network traffic environment on a portable visualization display live at the event. Testbed and Data Stats: Four locations were used for the traffic monitoring with a visualization node in the Calit2 booth. Two types of devices: 1) CalNodes for sampling traffic across 11 channels in 2.4 GHz (1:11 ratio) and 2) CogAP for visualization of Live traffic and Network state prediction using Neural Network based Cognition engine. The experimental time period was 8:30 a.m. to 6:00 p.m. on Festival Day and noon to 1:15 p.m. on reference days. The sampling CalNodes collected 7,076,853 packets total. The complete capture CalNodes collected 15,012,407 packets on selected channels (1, 6, 10, 11). Results Summary: Significant impact on the cyber world on Expo day when compared to reference sampling prior to and afterward, due to the physical world activity, was observed across several parameters (amount of traffic, number of clients, packet length) which confirmed our past observations made in previous drills. Key observations made: Time line of active client association to CalMesh nodes and Spatio-temporal traffic characterization of 802.11 b/g channels (11 channels in 2.4 GHz spectrum). 6. Cellular Network Monitoring EVDO Cellular Network Logging. Cellular network monitoring was conducted before, during, and after the event using the Qualcomm CAIT tool. We logged physical, MAC, and upper layer information such as signal strengths, power control, frame error rates, RLP retransmission, data rates, handoff status to observe differences in the mobile users perceived performance between light load and high load conditions and to observe effects of excess number of users in an EVDO cellular network from mobile point of view. Observations: Forward Link: 1) Received power is significantly lower during the science fair (Active sector selection is dynamic) and 2) Mobile switches to more distant sectors due to load in the nearby sectors. Reverse Link: 1) Increased transmit power and 2) Mobile needs to transmit with more power due to interference in the loaded sector. Also, the mobile's battery will drain faster to maintain similar QoS on crowded day as compared to a normal day 7. Laser Scanning & Wireless Network/Coverage Simulation During the past year, the laser scanning equipment and processing continue to be extremely important and are used frequently. They are currently, or have been, used in about 10 different projects (with multiple subprojects), ranging in purpose from assessment of wireless signal transfer, to cultural heritage, art history, archaeological exploration, and structural analysis of historic buildings and locations. The data across projects has been used for the development of new, rapid meshing techniques. While cellular networks are deployed based on an extensive design process, in emergency scenarios it is impossible to have a long design/performance analysis phase before the rapid deployment. The main idea for this experiment was to define and create a process to use a 3-D laser scanner and quickly scan the deployment environment, input it into the wireless simulation tool and find the optimum locations for temporary deployment of CalMesh nodes. We used the point-cloud exemplar to model the wireless propagation environment to give a view of theoretical behavior of the system. Experiment design: The MAPTEK I-Site 4400 3-D scanner was used before the event, and the data input into the EDX Wireless simulation tool to determine the best locations for CalMesh nodes to maximize coverage and capacity for wireless network during the event. During the experiment the inter-operability between 3-D scanner (formats) and EDX tool needs to be tested. Also, real signal strength and operability of the network during event must be observed to validate accuracy of design. Outcome: The main parking area scanned successfully and relevant data collected. The size of data was around 500 Mb which was more than other experiments. This was due to extended area we intended to simulate during this event. The output format was ESRI/Arc View ASCII grid format. Since color accuracy was not important for our experiment the total scanning process took less than a day. During scanning process, 6 different points set and scanned before final interpolation to extract the actual data. The data converted to EDX .201 format using EDXCV conversion tool before feeding to EDX. The outcome of simulation indicates optimum locations for our CalMesh nodes during the event. Figure 2. Overall coverage area Observations and Analysis: As it is shown in Figure 2, most of the area had a good signal strength (>-45 dBm) and at the corners there were areas with moderate signal strength (<-55 dBm). Based on this simulation results, we were confident that CalMesh locations are optimum locations to provide the best coverage for all exhibits in the central areas of exhibition as it was identified before. Lack of color information for our simulation caused a huge reduction in scanning time; six points of scanning and each about 45 minutes and smaller file size; 500 mega versus terra bytes. EDX was able to consider interference effect of the nodes on each other during simulation. Conclusion: The value of a two-steps design and deployment for wireless networks in emergency cases was shown during the drill. Although after any disaster or unexpected events there is no time for a systematic wireless deployment, but a real-time simulation/deployment approach can be useful to maximize the coverage and reliability in these types of deployments. We did not use a real-time scan/simulation/deployment approach for this, however, different components tested separately and their interoperability tested in a non real-time scenario. 8. Wireless Mesh Network Electromagnetic Interference Analysis We have deployed dozens of wireless mesh network nodes at various locations and activities throughout San Diego, CA, including Expo Day this year. At the festival, we had complete control over our own wireless mesh network nodes (Cal_Mesh_Demo), and had access to the Balboa Park WiFi access points and cooperation back at UCSD. In this way, we could supplement the connectivity of our mesh network with the UCSD WiFi infrastructure. Previously, nearby WiFi access points presented a significant source of interference since they would consume air-time capacity without sharing their frequency spectrum. Detailed measurements of electromagnetic wireless spectrum were performed during and before these activities to improve frequency and spatial network planning. This activity included - - on site measurements around museum venues and rooftops with interviews of museum personnel who wish to use and deploy wireless networks. Most of these museums are operated by city commissions and private foundations with a strong interest in educational outreach. Most of the museums utilize 2.4GHz WiFi indoor communications, WLAN 10Mb/s indoor CAT5 networks, with ADSL backhauls up to 1.544Mb/s These medium-band wireless communications systems do not interfere with interfere with our mesh network node within a distance of 10m or more by virtue of their low transmit power (23-DBm) that are within the dynamic range of our low noise amplifiers in our receivers Surprisingly, the most significant communication degradation is the channel fading caused by the moist Benjamin Focus trees that obstruct wireless communications from rooftop to ground within 30m of ground based mesh network nodes. The solution is to site the mesh network node antennas at distance of 3m or more from the trees so that they remain away from the scattering leaves. The exceptions are numerous Lemon Eucalyptus trees that are transparent to our wireless mesh network nodes. We employed sufficiently directional antennas that are directed in corridors down the outdoor promenade to provide wireless network coverage to the science exhibitors tents. We did not use our wide area coverage nodes with our 10 meter tall antenna caddy since we had adequate roof access. Vehicles at the festival with the most broad band communications available are TV news crew vans. The vehicles may be equipped with Ethernet routers with 1Gb/s total capacity, but local on-site outbound communication is limited to a 6MHz bandwidth channel. The TV news crew vans can become a significant source of short term source of electromagnetic interference to our mesh network nodes, so we have learned to route around these vans. 9. Rich Feeds Integration and NUTSO/Optiportable Rich Feeds was used to display a variety of live and real-time data (with and without terrain overlay); clicking on the indicators (tacks) revealed the data and/or detail. The data was also archived for future analysis. The following technologies were integrated: 1) maps of node locations, 2) radio frequency (RF) spectrum sampling points, and 3) GPS location- based tracking technology in vehicles showing the location of the Calit2 vehicles at the scene. Optiportable was deployed, using both CGLX and XDMX to show off the systems capabilities, in the booth in a 5x3 configuration (5 30-screens across, 3 down). Various webpages, both static and dynamic (including live CogNet monitoring data graphs) were displayed, as well as the Backpack Cam video feed, on the XDMX side. Like NUTSO, Optiportable is portable visualization system consisting of fifteen 30-inch displays. It runs the same core system and software that NUTSO does (Rocks 5.1 x86_64, Viz 5.0, latest Hiper [CGLX]) 10. Peer to Peer Traffic System Our automated peer to peer traffic system (http://traffic.calit2.net) has been further disseminated with an iPhone app: commuters in California equipped with the Apple iPhone can now get personalized traffic information via the "California Traffic Report," the first iPhone application from Calit2 at UCSD. In the first ten days since the app became available through Apple's App Store on Feb. 7, roughly 2,650 people have downloaded the application, and downloads continue to run at a clip of roughly 250 per day. The California Traffic Report made it into the first page of "Top Free" apps in the Travel section of the App Store. (ref:  HYPERLINK "http://www.calit2.net/newsroom/release.php?id=1471" http://www.calit2.net/newsroom/release.php?id=1471) Rescue Year 5 Annual Report: Networking Project 2: Robust Networking and Information Collection Project Summary The main objective of this project is to provide research solutions that enable the restoration of computing, communication, and higher-layer services at a crisis site in a manner that focuses on the needs and opportunities that arise proximate to the crisis (in both time and space dimensions). Commercial systems are often based on assumptions that fall apart during a crisis when large-scale loss of power and destruction of antenna masts and servers are common. In addition, self-contained relief organizations that arrive at a crisis site often carry communication equipment that fail to interoperate, are inadequate for the needs at the scene, and may even interfere with each other making the task of forming an ad-hoc organization harder. In summary, the challenge is to compose a set of research solutions to assist in crisis response that is designed to serve the dynamically-evolving situation at the crisis site. This project is organized into four sub-groups: 1) Theoretical Research; 2) Extreme Networks System (ENS); 3) Adaptive Information Collection System (AICS); and 4) System Integration. ENS, AICS and Rich Feeds (system integration) are the three artifacts developed by the project. Activities and Findings Theoretical Research Group. The main contributions during the past year include the development of new solutions for network design. Existing network optimization and throughput estimation are done in an asymptotic manner where the network size, or the number of nodes in the network, is expected to be infinitely high. However, in reality, the network size is finite and therefore, as part of RESCUE research, we proposed new methods for estimating the throughput and optimization of the network design for hierarchical wireless mesh networks. In addition, we also proposed packet-size-aware path setup mechanisms for wireless mesh networks. According to this scheme, different packets belonging to the same source destination pair can traverse different paths based on their sizes. We implemented our scheme as a Linux kernel module and experimentally showed the benefits in terms of the transmission delay and the throughput. During the past year, we also proposed a new medium access control (MAC) protocol for Multiple Input Multiple Output (MIMO) ad hoc wireless networks. The key contribution is the use of a novel rank-based metric to obtain interference information, in order to enable multiple simultaneous transmissions and to make MAC decisions. Through analysis and simulation, we found that the proposed protocol significantly outperforms 802.11 MIMO and it obtained spatial degree of freedom utilization as high as 85%. We modeled and studied supervisory network management (SNM) by developing an efficient communication model that can be used among response organizations. The SNM can ensure that only one update is sent at any given time and that it is received by all designated organizations before sending another one. In a limited resource network, this is to be done by a supervising entity that knows the resource availability. The system has been modeled from real observations made during the drills and from the UCSD emergency medical department, in order to improve human communication in crisis scenarios. We created another important research result in collaboration with the CogNet project: a wireless network characterization strategy. In order to achieve efficient wireless network characterization, accurate traffic sampling strategies are required. Since the spectrum used for most wireless networks is divided into several channels, the existing count-based sampling methods are not scalable. Through experiments, we found that time-based sampling is an efficient and effective strategy for characterizing the wireless network environmental traffic. From our analysis using the Chi-Square test, we found that the Timer-driven, Time-based sampling is more accurate than existing count-based sampling for both systematic and stratified sampling schemes. We also focused localization solutions for two technological areas: first is localization using cellular network technology. For this, our research led to the proposal of a neural network-based algorithm that uses multi-path signal strengths to accurately locate a mobile user without a GPS receiver. Second, we developed a localization algorithm for ultra-wide band (UWB) technology. We proposed an artificial neural network-based localization algorithm to detect a single object in a sensor network and compared its performance to a Cramr-Rao bound and least squares estimator. We obtained good resolution when using our solution. Extreme Networking System. Many research advances were made for several components and peripherals of the ENS. Notably, a new routing protocol for the CalMesh platform, called MACRT, was developed and successfully deployed. This replaced the prior spanning tree protocol. MACRT is a layer 2 (MAC) ad hoc on-demand routing protocol and was inspired by the popular layer 3 AODV protocol. But, as the name implies, it operates on layer 2 of the protocol stack, making the mesh nodes use MAC addresses to "route" within a mesh network. MACRT also incorporates several new functions, such as: (i) control message intercepting where it intercepts the 802.11 client management messages and uses these messages to help clients roam between Access Points (AP), (ii) the ETX (Expected Transmission Count) which is used as a link metric in the routing algorithm in order to achieve better throughput, (iii) a delay algorithm which introduces very short delays before "Route Requests" are forwarded, and (iv) a neighbor subsystem that maintains the connections to its adjacent nodes by using a bounded random walk model of the RSSI (Received Signal Strength Indication) values in order to filter out unstable neighbors. Adaptive Information Collection System. As part of this project, we have created a fully automated peer-to-peer system in San Diego, Los Angeles, Orange County and the Bay Area (in northern California) that collects and disseminates highway incident information to the general public and to first responders. Commuters can report incidents via the system and other users who are calling in for traffic information will receive information about the incidents in their commute segment. We used the following strategies to validate and rank the user input: (i) access control mechanism which granted access only to frequent users (carefully fanned out over time) and (ii) a flagging mechanism using which other users can flag if they thought it was spam. More information on this system can be found in the description of Artifacts section of this report. In addition to the traffic notification system, we enhanced the cellular phone-based location tracking system to develop the Participatory Symptom Network (PSNET). It is developed for real-time disease surveillance via mobile phones and it can be used: (i) by networks of health care providers and (ii) to monitor travelers as they cross geographical boundaries. In the first case, the application focuses on a patient monitoring system where doctors and nurses can keep track of sick patients, especially the elderly and infants. In the second case, monitoring of spreading diseases by visitors to and travelers in any country can be carried out. System Integration Group. The system integration group mainly focuses on integrating various research products of the RESCUE project including Robust Networking and Information Collection. To meet these challenges, Rich Feeds design is based on a Service Oriented Architecture (SOA) pattern called Rich Services, which delivers the benefits of SOA in a systemof- systems framework using an agile development process. This architecture allows the addition of new data producers and consumers quickly and with low risk to existing functionality while providing clear paths to high scalability. More details can be found in the Artifacts section. Products and Contributions ۃ CalMesh2 - CalMesh2 is an improved version of our original CalMesh platform (http://calmesh.calit2.net) which is a mesh network platform that forms the basis for ENS. ۃ CalNode - CalNode (http://calnode.calit2.net) is a prototype Cognitive Network Access Point (CogNet AP), which has the unique capability to observe and learn from the network traffic in order to optimize itself. A CalNode can be deployed with no prior channel planning. ۃ Rich Feeds - Rich Feeds (http://rescue.calit2.net) is a system that demonstrates how unconventional data feeds and emergent data feeds can be captured, preserved, integrated, and exposed either in real time or after the fact. Rich Feeds promotes situational awareness during a disaster by integrating and displaying these feeds on a Google map in real time. ۃ Web and Voice Portals for Peer-to-Peer Networking - http://traffic.calit2.net/sd, http://traffic.calit2.net/la, and http://traffic.calit2.net/bayarea; (866) 500 0977 - San Diego, (888) 9Calit2 - Los Angeles and Orange Counties, and (888) 4Calit2 - Bay Area. This system is based upon the Calit2 Wireless Traffic system that relays customized highway incident information to the general public and to first responders. Future Research Directions In Year 6, our focus will be on refining, finalizing, improving, packaging, and disseminating the research solutions and artifacts that we created as part of this project. Rescue Year 4 Annual Report: Robust Networking Project 2: Robust Networking and Information Collection The grand challenge of this project is to develop research solutions and artifacts that can make todays communication networks perform better during crises situations. To achieve these objectives, five sub-projects were formed: (1) theoretical research; (2) Extreme Networks System (ENS); (3) Adaptive Information Collection System (AICS); (4) Adaptive Cellular Network System (ACNS); and (5) system integration. ENS and Peer-to-Peer Information Collection and Dissemination systems are the two artifacts developed under this project. Activities and Findings. As part of our theoretical research, we proposed the concept of Sentient Networking to bring humans and computer networks closer. Our example sentient system studied a new networking approach that could recognize the emotion content in voice packet streams and prioritize voice packets originating with distressed speakers. Up to 60% performance differentiation is provided to voice streams from these sources. The proposed scheme also contained an approach for detecting the packet type in wireless networks by utilizing the spectral properties of packets instead of using the traditional approach of explicit identification of the information contained in the packet. This technique was also used for detecting network data packets without using any information from packet headers. Another important theoretical research topic was the t non-asymptotic capacity analysis for wireless mesh networks. We developed two solutions: (a) Maximum Throughput Partition (MTP) and (b) Maximum Throughput Partition with Hops number Constraint (MTPHC). In MTP, the ideal throughput is achieved by optimally partitioning the network with a proper number of backbone nodes. In MTPHC solution, an additional constraint on the average number of hops in the backbone network is considered. These results showed that it is critical to find an appropriate size of the backbone network for a wireless mesh network, especially when the hops number constraint is imposed. In collaboration with CogNet project, we developed an emergency response framework for exchanging critical network environmental information across nodes in a cooperative manner. Based on this approach, we designed a MAC protocol which shares contention information to optimally choose the parameters. Using a simulator platform, we found that a system throughput improvement of 50-60% can be achieved. As part of the adaptive information collection systems research, we created a sophisticated vehicle telematics system that can track, and control vehicles or similar objects. This system is integrated and demonstrated with the Responsphere truck. The tracking and telematics system is implemented to support various location tracking technologies, including GPS, Assisted GPS (AGPS), and GPS with WAAS. A WAAS-capable receiver provides position accuracy of better than three meters 95 percent of the time. We compared the standard GPS system, AGPS, and GPS with WAAS especially the availabilities and accuracy of location tracking for both indoor and outdoor. The standard GPS does not work indoors. AGPS works indoors with an accuracy of 50 to 100 meters. GPS with WAAS works indoors when the receiver is close to window. In the outdoor environment, GPS with WAAS has better accuracy than standard GPS and AGPS. As part of our adaptive cellular networking system research, an early simulation model based on the UMTS system was built based on the OPNET simulation platform. The impact on delay and blocking on services was analyzed when base stations were put out of service due to damage to the infrastructure. As part of our system integration efforts within the research products of RESCUE networking, we created a rich service-based architecture that incorporates data acquisition services tailored to the collaborators instruments and feeds. The central data store is a service within a rich servicebased architecture. It stores data from each of the collaborator sources, and is structured so that new collaborators and feeds can be added with minimal effort. We also created a documentation framework that readily accepts data source definitions and promotes data usage by potential integrators, analyzers, and visualizers, thereby encouraging the use of this data by investigators other than the original researchers. At present, we have integrated the vehicle telematics system and peer-to-peer traffic information dissemination system in a single framework. Going forward, several challenges remain. The biggest challenge in sentient networking is the development of a generic framework for a host of sentient capabilities that can be enabled on to todays networking infrastructure. One of the challenges that adaptive information collection systems face, as part of the vehicle telematics system is the complexity of the specific vehicle electronic system which needs to be considered in order to integrate the telematics device. For example, different car manufacturers may have a different specification for their vehicle data bus. In ACNS, the biggest challenge is the design of integration interfaces that are not fully exposed. Although the mechanisms and interactions within MetaSIM have been identified, integration needs more clarification for APIs. In current networks, the main technical challenge was limitation in standard RACH mechanism in cellular network (basic Slotted-ALOHA). Although proposed solutions will theoretically sort out the problem, integration into real systems and standards within real carrier networks will remain an issue. Plans for Year 5: As part of the theoretical research, we plan to accomplish the following: (1) 3 months: evaluate throughput capacity regions for finite wireless mesh networks with fixed or random back-bone topology; optimize the design of back-bone network topology; design a first responder radio with cognitive capabilities, (2) 6 months: complete the simulation platform for cognitive first responder networks; create simulation platforms for non-asymptotic transport capacity in finite node topologies for wireless mesh networks; create new applications for networked signal processing concepts proposed as part of the sentient networks, and (3) 12 months: evaluate the potentials for early prototypes for cognitive first responder networks; capacity analysis of spatial multiplexing; and spatial diversity based MIMO ad hoc wireless networks, and build new simulation platforms and prototype models for sentient networking. We also expect a number of educational outcomes include research platforms, simulation models, publications, technical reports, and other hardware and software products. As part of the adaptive cellular network systems research, we plan to achieve the following: (1) months: Finalize the simulations;, (2) 6 months: Integrate the Cellular Simulation into MetaSIM; and (3) 12 months: Analyze and create results for a simple disaster with MetaSIM; Share the results with operators and promote its use. As part of our System Integration activity, we plan to achieve the following in the next one year: (1) 3 Months: addition of Cal-Mesh and voice systems to data source list; refactoring prototype into production system; Deployment of initial production system on public-facing servers; improvement of Google Maps ad-hoc query interface; (2) 6 Months: documentation of system; documentation system encouraging users to access data; portlets for Google and Yahoo allowing public access to data; crosscutting security/authorization functionality; direct support for Google Earth; initial implementation of ODBC external interface; demonstration of interoperability with Microsoft Office production deployment; and (3) 12 Months: video stream support; integration into Irvine-based web site; introduction to disaster community at large; completion of ODBC external interface; production deployment maintenance. Rescue Year 3 Annual Report: Robust Networking Project 2: Robust Networking and Information Collection The Robust Networking and Information Collection project is progressing well in each of its sub-projects: (a) Extreme Networking System, (b) Adaptive Cellular Networking System, and (c) Adaptive Information Collection System. In the Extreme Networking System, we designed and created the GLQ mesh networking testbed and used it successfully during the Mardi Gras event in February 2006 in downtown San Diego. As part of this deployment, we developed a hybrid wireless networking architecture with several innovations including a dynamic addressing scheme; identified important issues with the 802.11 MAC protocol that restrict the MAC protocol from being dynamic enough to work with link dimensions; made significant progress in the design of routing protocols that use dynamic diversity approach; identified essential services and design of such essential services is in progress; and identified the effect of MAC layer contention resolution mechanism on the end-to-end throughput in a wireless mesh network with string topology. Progress in the Adaptive Cellular Networking System included: design of the InLET1-based simulator for networks, now in the early stages of development and being restructured along the lines of MetaSIM development; development of resource management strategies for use in todays cellular networks during pre-, in-progress, and post-crisis situations; and development of a cellular networking simulator, also under restructuring along the lines of MetaSIM. Researchers have made significant progress in the Adaptive Information Collection System: we developed a prototype for a peer-to-peer information collection and dissemination system, and a cellular phone-based location tracking system. A significant change in the networking research group in Year 3 necessitated the addition of two project themes: theoretical research and project integration research that spanned across all the different sub-areas. The theoretical research group is studying the capacity problem in wireless mesh networks and is developing a new networking paradigm which acts on the emotional content in the data that is transferred across the network. The project integration group is effectively integrating the cellular location tracking system with a management portal. The key artifact that is being developed as part of this project is the ENS (Extreme Networking System), which is a portable, easy to deploy, reliable, and highly dynamic hybrid networking platform which can be used for facilitating communication networks on ground zero during a disaster. Our progress in this project is on schedule and is consistent with the strategic plan, as far as the three sub-projects are concerned. In sub-project (a), we plan on achieving a modular routing framework that makes the ENS artifact highly flexible and hot swappable as far as networking protocol goes. In sub-project (b), we plan on integrating the InLET platform with the cellular network simulator within MetaSIM. We also plan on developing new solutions for improving the scalability of cellular networks during crises. In sub-project (c), we plan on developing the existing prototypes into production versions with more sophisticated trust algorithms built-in. The theoretical research group will focus on both building long-term theoretical solutions and a knowledge base in wireless mesh networking. Finally, one of the goals of Year 4 is the integration of the sub-projects into a single robust networking and information collection solution. Rescue Annual Report Year 2: Robust Networking C4 ROBUST NETWORKING SYSTEMS C4.1 Always Best Connected (ABC) (UCSD/ R. Rao) The Always Best Connected (ABC) concept refers to an environment where several different types of access networks and different devices are available to a user for communication. The user can choose at any time the access network and device that best suits his or her needs depending on the applications that he or she is currently running, and change whenever something better becomes available. In recent years, the following functionalities of ABC have been built and tested in our labs: Access Discovery and Selection; Mobility Management; Bandwidth Aggregation in Ad-Hoc networks; Measurements; and Profile-Based Access Control. Access Discovery performs the task of determining what network accesses are currently available for the client and whether there is connectivity to a point in his home network. Access Selection allows the user to select different access networks depending on his predefined preference or based on the dynamic preference based on his profile stored in 31 the network. Mobility Management manages the session continuity for various services. Bandwidth Aggregation handles the use of multiple interfaces simultaneously. Measurements are done for getting the throughput, delay and some other statistics for the client machine. Profile Server helps the client to store and manage its various preferences. Some of the preferences can include the network preferences, application characteristics, device characteristics, etc. With the addition of all these features, ABC has become a versatile testbed for current and future projects. Gas Lamp Quarter (GLQ) Testbed The Gas Lamp Quarter (GLQ) Testbed consists of a rapidly deployable mobile networking, computing, and geo-localization infrastructure in the context of incident-level response to spatially-localized disasters, such as the World Trade Center attack. The testbed focuses on situations where the crisis site either does not have an existing infrastructure, or alternatively, the infrastructure is severely damaged. This testbed focuses on supporting basic services essential to the first responders that can be brought over to crisis sites for rapid deployment. Such services include communication among the first-responders, accurate geo-localization both inside and outside of buildings, in urban as well as rural areas, computation infrastructure, incidence level command center, and technology to support information flow from/to crisis sites to/from regional emergency centers. This testbed will be deployed in the Gas Lamp Quarter district in downtown San Diego. The testbed will provide a seamless Wi-Fi (802.11b) connectivity for first-responders in this area. GLQ is currently divided into three zones, where each zone has its central post in direct line of site to the top of the NBC building. The transmitter on top of NBC building provides the broadband access to these three lampposts via a 5.2 5.7 GHz backhaul. By using Tropos Networks 5110 outdoor units, the coverage of these three zones will be expanded and we are able to provide the support for standard 802.11b users. Tropos units will find each other and mesh together using a standard ISM band which will also act as an 802.11b access point for end users. By connecting these three locations to the network, the testbed will cover a large area. Also by having three entry points, the reliability of the system will be enhanced. The three lampposts with backhaul connectivity (5th and Market, 5th and E, 4th and G) will have a Motorola Canopy receiver and a Tropos 5110 unit installed. The others will just have a Tropos 5110 unit installation. The 802.11b cells communicate with each other wirelessly through a mesh routing algorithm implemented within the access points. The control protocol is part of the Tropos Sphere operating and management tool. The following discussion highlights lessons learned during design and lab trials prior to network deployment. Network Design. For this testbed, there is a need for a reliable and controlled wireless network that covers the entire GLQ area in downtown San Diego. Although there are several wireless ISPs in this area, most of have patchy coverage and are not reliable. 67 RESCUE is in a good position to design and deploy its own network to cover this area. This will provide an opportunity for researchers to have a more reliable network and to maintain control of the system. In deploying this system, the following issues are being addressed. Frequency Band. The system can deploy in an unlicensed band and use standard offthe- shelf equipment. This will reduce the cost of deployment significantly. Our approach is to have a hybrid network design. While using the standard 802.11b for reaching the end-users, 5.2 GHZ and 5.7 GHz will be used for backhauls to increase the capacity of the system. The other option is using Ensemble (a San Diego-based company)/XO partnership. Recently, Ensemble and XO announced that they are able to provide equipment and spectrum to support any wireless deployment in the downtown San Diego area. This will certainly increase the reliability of the system since that system will operate in a licensed band. Site Acquisition. Sentre Partners, one of the leading real estate companies in San Diego, has already committed itself to several projects in order to promote the city of San Diego. They own three of the tallest buildings in the downtown area. By using their rooftops, it will be easy to have a large footprint and cover the downtown area. Backhauls. The cost of the backhaul is the most critical factor affecting long-term deployment. One possible solution for meeting this need is to involve some of the local telephone companies, e.g., Verizon or SBC, in providing the backhaul. Another solution is to use the under-utilized bandwidth in the buildings to connect our base stations to the network. Sentre Partners, the owner of NBC building, is committed to provide this access and enough bandwidth for the project; therefore, this is the solution we have chosen. Figure 4. A Metro-Scale Cellular Wi-Fi Deployment 68 Throughput Analysis in Large Networks. Maximizing throughput in large networks requires minimal network bandwidth for protocol traffic control and optimal data paths for users in the face of highly variable RF conditions. Wireless link bandwidth is a finite resource and any traffic for control signals will reduce the capacity for user traffic. Traditional mesh nodes maintain routes between all nodes in the network, using either link-state or distance-vector protocols. As a result, the routing tables and information exchanged between nodes grows proportionally to the size of network. After the network reaches a certain size, the routing overhead will exceed the data traffic. In the present GLQ testbed architecture, the nodes and their routing mechanism maintain constant routing overhead as network elements grow in number. Wireless links are prone to multi-path fading and interference. These effects are dynamic, asymmetric and vary over time, particularly in a mobile environment. Ultimately, these effects manifest themselves as 802.11 packet reception errors, making them the major source of throughput loss on the wireless data link. In sharp contrast to wired networks where link-status is binary, throughput measured across one or more wireless hops can fluctuate anywhere between 0 and 100% of its theoretical maximum due to packet errors. Recent research has shown that routing algorithms that minimize hop-count or rely solely on RF signal strength to make routing decisions will fail to converge on a useful network topology, and offer poor throughput. Since these routing decisions are uncorrelated with throughput, they achieve far less throughput over time. This would include the vast majority of wired-routing protocols as well as the wirelessrouting algorithms employed by interconnected-based client mesh networks. In contrast, the Predictive Wireless Routing Protocol (PWRP), used in GLQ deployment, is sensitive to these variations in throughput, i.e., by taking bi-directional measurement samples multiple times a second across wireless links. Based on a history of these measurements, predictive algorithms dynamically tune the selection of the multi-hop paths from the available paths in the mesh network. By estimating the throughput of each alternative path using advanced multi-hop metrics, PWRP ensures that it consistently selects paths in the top few percentiles of all available paths. On average, this achieves more than twice the throughput of competing routing approaches which are, in effect, choosing their paths at random with respect to throughput. In fact, PRWP consistently ensures a stable and high level throughput for Wi-Fi clients. Network Layer Resiliency. In the initial development of this testbed for RESCUE, resiliency to unexpected failure was the main assumption in design and deployment. Cellular networks are prone to service interruption due to loss of network components (BSS and MSC). Wi-Fi networks, on the other hand, are vulnerable to severe interferences. PWRP incorporates network layer resiliency and self-healing features that enable network deployment with the desired reliability. PWRP is fully distributed and eliminates all single points of failure allowing for geographic distribution and rapid restoration. The GLQ testbed quickly detects backhaul failures and degradation, and reroutes the traffic through other nodes to other available backhaul links. The routing protocol typically relies on a small number of hello packets to detect the state of a link. It is important to discriminate between temporary wireless fades and an actual loss of link. During all these processes, the application and all active sessiones must be maintained without interruption. 69 Current Progress. In order to execute the original San Diego Gas Lamp Quarter (GLQ) plan of deploying a highly resilient wireless mesh network in downtown San Diego, we have analyzed real behavior of mesh networks in a lab environment to ensure flawless design and deployment of the testbed. We have designed and built the wireless mesh network testbed to run most of the experimental studies before actual network deployment in the GLQ. This laboratory deployment has involved four stages of implementation and evaluation; the first two stages have been completed so far. The four stages are: 1. Wireless Mesh Network Gateway. We have set up the wireless mesh network gateway which acts as a bridge for both wired and wireless parts of the mesh network, and conducted studies on bridging, routing, name resolution and Internet access across wireless and wired networks; 2. Multiple Relay Nodes and Wireless Clients. Using a linear-string topology, we have set up a wireless mesh gateway with multiple relay nodes and wireless clients, and conducted studies on relaying, detection, association, and disassociation; 3. Mesh Topologies. Current work includes setup of planned mesh topologies with as many relay nodes and clients as possible. Planned studies include path selection, load balancing, hand-off, seamless roaming, video delivery, and quality of service measurements for data and video traffic. For the video delivery performance and associated studies, we partnered with Ortiva Wireless, Inc. in order to provide performance evaluation studies of their video conditioning product Ostreamer on our testbed. A multistage evaluation sequence, including quantitative and qualitative studies in both networking parameters and multimedia parameters, will help understand delivering efficient multimedia streams to the first-responders while considering device limitations (cell phone, PDA, etc); 4. Long-haul Wireless Link. The laboratory GLQ testbed will be completed using the long-haul Canopy wireless link to finalizing the configuration and prepare it for deployment in the GLQ. We will use multiple gateways that are connected to the Canopy client transceivers and Canopy clients communicate with a Canopy server that has a wired network connection; In the first stage of this laboratory deployment, several experiments were conducted to confirm the testbeds functionality and wireless client nodes were also configured to communicate through this gateway. In the second stage of this deployment, we will set up multi-hop relaying, routing and communication to the Internet through the gateway. Currently, we are in the process of proceeding with the stages (3) and (4) to build the laboratory testbed for the GLQ wireless mesh network. Information Collection: The objective of information collection is to bring relevant crisisrelated information from a variety of information sources to decision makers in a timely and efficient manner. Our research addresses how large volumes of highly dynamic, multimodal information generated at various information sources can be effectively collected and stored over networks that might be unreliable and possibly insecure. Information sources include instrumented sensors, video cameras either embedded in civil infrastructures or dispersed at crisis sites for situation monitoring, remote sensing systems, field-based sensors such as seismic sensors, and sensor and video probes carried by first responders. Another important source of information is humans themselves. One of the key observations in our project is that first responders observations and interpretations from the scene (given the benefit of human cognizance) can provide among the most vital and dependable sources of ground reality information during a crisis. Leveraged properly, it can result in much more accurate situation awareness and hence better response. Along the same line, eye-witness accounts from the public can also play an important role in situational awareness. Yet existing crisisresponse systems do not systematically exploit such human input. An important thread of research in RESCUE is seeking scalable and robust IT solutions to enable the realization of humans as sensors. In the context of information collection research, our project is making specific contributions to Information Technology along the following directions: 121 _ We are exploring integrated distributed systems, data management and networking research that enable information to seamlessly flow in real-time from these information sources to collection points over heterogeneous network channels that may be only sporadically available and are vulnerable to further hazards or attacks. In this context, we are specifically developing techniques for adaptive data collection from micro-sensors. Another critical contribution is exploring scalable techniques to collect localization (and other types of information) from cellular/mobile devices to enable a multitude of applications ranging from awareness of where people are (at an aggregate level) to support better disaster planning during a crisis, to cellular resource reallocation (e.g., bandwidth) to meet surge demands during crisis. _ Techniques to capture, analyze and process voice input from conversational speech in order to support the realization of the human-as-sensors concept. This effort, in conjunction with the information analysis work described below, aims to extract meaningful events/information in real-time from transcriptions (possibly with the help of positioning technologies such as GPS, other sensor data and video feeds) and utilize the extracted information as input into situational monitoring, and damage and impact assessment systems. _ Another important contribution is exploring the privacy implications in the context of information collection with the objective of developing customizable solutions that explore a tradeoff between functionality and (the loss of) privacy. Spaces instrumented with video cameras, sensors, tracking systems based on RFID or similar technologies, while they facilitate surveillance and situation monitoring, leave a trace of people behind, whether this is acceptable to them or not. We envision data-collection mechanisms that are privacy-aware and adaptive. Such mechanisms will empower the users to the extent possible to control the acceptable loss of privacy, and adapt to the varying needs of the situation. Rescue Year 1 Annual Report: Robust Networking Information Collection: The objective of information collection is to bring relevant crisisrelated information from a variety of information sources to decision makers in a timely and efficient manner. Our research addresses how large volumes of highly dynamic, multimodal information generated at various information sources can be effectively collected and stored over networks that might be unreliable and possibly insecure. Information sources include instrumented sensors, video cameras either embedded in civil infrastructures or dispersed at crisis sites for situation monitoring, remote sensing systems, field-based sensors such as seismic sensors, and sensor and video probes carried by first responders. Another important source of information is humans themselves. One of the key observations in our project is that first responders observations and interpretations from the scene (given the benefit of human cognizance) can provide among the most vital and dependable sources of ground reality information during a crisis. Leveraged properly, it can result in much more accurate situation awareness and hence better response. Along the same line, eye-witness accounts from the public can also play an important role in situational awareness. Yet existing crisisresponse systems do not systematically exploit such human input. An important thread of research in RESCUE is seeking scalable and robust IT solutions to enable the realization of humans as sensors. In the context of information collection research, our project is making specific contributions to Information Technology along the following directions: 121 _ We are exploring integrated distributed systems, data management and networking research that enable information to seamlessly flow in real-time from these information sources to collection points over heterogeneous network channels that may be only sporadically available and are vulnerable to further hazards or attacks. In this context, we are specifically developing techniques for adaptive data collection from micro-sensors. Another critical contribution is exploring scalable techniques to collect localization (and other types of information) from cellular/mobile devices to enable a multitude of applications ranging from awareness of where people are (at an aggregate level) to support better disaster planning during a crisis, to cellular resource reallocation (e.g., bandwidth) to meet surge demands during crisis. _ Techniques to capture, analyze and process voice input from conversational speech in order to support the realization of the human-as-sensors concept. This effort, in conjunction with the information analysis work described below, aims to extract meaningful events/information in real-time from transcriptions (possibly with the help of positioning technologies such as GPS, other sensor data and video feeds) and utilize the extracted information as input into situational monitoring, and damage and impact assessment systems. _ Another important contribution is exploring the privacy implications in the context of information collection with the objective of developing customizable solutions that explore a tradeoff between functionality and (the loss of) privacy. Spaces instrumented with video cameras, sensors, tracking systems based on RFID or similar technologies, while they facilitate surveillance and situation monitoring, leave a trace of people behind, whether this is acceptable to them or not. We envision data-collection mechanisms that are privacy-aware and adaptive. Such mechanisms will empower the users to the extent possible to control the acceptable loss of privacy, and adapt to the varying needs of the situation. GLQ Testbed The Gas Lamp Quarter (GLQ) testbed consists of a rapidly deployable mobile networking, computing, and geo-localization infrastructure in the context of incident-level response to spatially localized disasters such as the World Trade Center attack. The testbed focuses on situations where the crisis site either does not have an existing infrastructure, or alternatively, the infrastructure is severely damaged. This testbed focuses on supporting basic services essential to the first responders that can be brought over to crisis sites for rapid deployment. Such services include communication among the first responders, accurate geo-localization both inside and outside of buildings, in urban as well as rural areas, computation infrastructure, incidence level command center, and technology to support information flow from/to crisis sites to/from regional emergency centers. The primary demonstration study will be in the context of localized incident management in the Gas Lamp district in downtown San Diego. The Gas Lamp Quarter will be 33 instrumented in partnership with the civic authorities and the San Diego Police Department. This 10 block historic region adjoins the San Diego convention center, sports stadiums as well as city, county and federal facilities and is described as Southern California's premier dining, shopping and entertainment district. Some of the key issues that will be addressed in this testbed are discussed below. Deploying a Network as a Living Lab For this testbed, there is a need to have a reliable and controlled wireless network that covers the Gas Lamp Quarter area in downtown San Diego. Although there are several wireless ISPs in that area, most of them provide patchy coverage and their systems are not always reliable. The RESCUE project is in a good position to design and deploy its own network to cover that area. This will give researchers the opportunity to have a more reliable network that is fully under their control. The following issues are being addressed as we study possible solutions for this deployment: Frequency Band: The system can be deployed in an unlicensed band and use standard off-the-shelf equipment. This will reduce costs significantly. Our approach is to have a hybrid network design. While using the standard 802.11b for reaching end-users, 5.2 GHZ and 5.7 GHz will be used for backhauls to increase capacity. The other opportunity is using Ensemble (a San Diego based company)/XO partnership. Recently, Ensemble and XO announced that they can provide equipment and spectrum to support any wireless deployment in the Downtown San Diego area. This can increase the reliability of the system since this system can be operated in the license band. Site Acquisition: Sentre Partners, one of the leading real estate companies in San Diego, has already committed itself to a related project to promote the city. They have three of the tallest buildings in downtown area. By using their rooftops, it will be easy to have a large footprint and cover the downtown area. Backhauls: The cost of the backhaul is the most important factor that should be considered for the long-term. It is possible to involve some of the telephone companies, i.e., Verizon or SBC, to provide the backhaul. However, there is also a possibility for using under-utilized bandwidth in the buildings above to connect our base stations to the network. Sentre Partners, the owner of NBC building, is committed to providing access and enough bandwidth for this testbed. Network Architecture: As it is shown in Figure 2-3, the GLQ is divided into three different zones. Each zone has its central post that has LoS to the top of NBC building. The transmitter on top of NBC building provides the broadband access to these three lampposts via a 5.2 5.7 GHz backhaul (the red points). By using Tropos Networks 5110 outdoor units (the green points), the coverage of three zones will be expanded and we are able to provide the support for standard 802.11b users. 5110 units will find each other and mesh together using standard ISM band and they will also act as an 802.11b access point for the end users. Therefore, it will be possible (by connecting three locations to the network) to cover a large area. Also by having three entry points, the reliability of the system will increase in case of outage for one of the backhauls. Three lampposts with backhaul 34 NBC Building connectivity (5th and Market, 5th and E, and 4th and G) will have a Motorola Canopy receiver and a Tropos 5110 unit installed, see Figure 2-4. The others will just have a Tropos 5110 unit installation. Provision of power is the only major requirement for the operation and it has been tested on these same lampposts as part of a Super Bowl experiment. The 802.11b cells communicate with each other wirelessly through a mesh routing algorithm implemented within the access points. The control protocol is part of Tropos Sphere operating and management tool. The network topology is based on standard centralized routing. All the routing and switching is managed centrally. The backhaul links will act as pure second layer channels, whereas the 802.11b access points will do the DHCP server functionality locally. The system will support mobility based on TCP and VPN session persistent roaming without client software upgrade. The system will be managed remotely through a web-based management tool for the Tropos and standard SNMP. Figure 2-3. 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Central Posts to be used in GLQ Testbed The deployment plan is divided into three phases: Phase 1 (Proof of concept): In this phase, the inter-operability of the system will be tested in a controlled environment (lab). This includes setting up one zone and using the backhaul radio link and networking gear. Phase 2 (Test deployment): In this phase, the tested zone will be deployed and real measurements will be performed to check and confirm the operational parameters of the testbed. The monitoring software will be also tested in the real environment. Phase 3 (Final deployment): In this phase, the other two phases will be deployed. 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