ebiquity.umbc/

1. UMBC and CBMANET http://ebiquity.umbc.edu/ Anupam Joshi and Tim FininEbiquity Research GroupUniversity of Maryland, Baltimore CountyBaltimore MD 21250

2. Overview • Who we are We briefly introduce UMBC and our research groups: ebiquity, dawn, diadic • What motivates our research agenda We sketch a DoD relevant CBMANET scenario • What we offer CBMANET We identify two key enablers for the CBMANET vision • What we have already done We cite papers with recent research results we will use to realize the CBMANET scenario

3. ebiquity@umbc • Part of the University of Maryland,Baltimore County, one of three re-search campuses in the UM System • “Building intelligent systems in open, heterogeneous, dynamic, distributed environments” mobile & pervasive computing, security/trust/privacy, semantic web, RFID, multiagent systems, advanced databases & high performance computing • Core faculty: Tim Finin, Anupam Joshi, Yelena Yesha • Colleagues: Krishna Sivalingam (DAWN), Hillol Kargupta (DIADIC) Representing Networking, Distributed Systems, Databases, Data Mining • Students: ~10 PhD, ~10 MS, ~4 undergrads in ebiquity, similar numbers in DAWN and DIADIC • Partners and Funders: DARPA (DAML, Trauma Pod), NSF, NASA, NIST, … IBM, HP, Fujitsu, LMCO, Rockwell Collins …

4. CBMAET Scenario UAVs and recon/intel assets sendvideo surveillance data to troops. CBMANET recognizes it as MPEG 2 video that is critical to the mission per policy and… (1) Finds a delay jitter satisfying multicast tree for feed using distributed state information computed from measurements and adapts to sensed environment changes by adjusting algorithm parameters. (2) Maintains multicast tree proactively between UAVs and MCS & Battalion TACP in response to mobility and environment, reactively for others

5. CBMAET Scenario (3) Distinguishes between base layerand enhancement frames of the video. (4) Recognizes that UAV and Intel feeds must get to the TACP, MCS and the infantry platoon commander and • Configures to ensure survivability with some visual degradation • Finds link/node disjoint paths to critical nodes for base layer packets only via nodes that have behaved well in the past. • Turns on FEC or ARQ on the MAC. (5) Does this even if some individual warfighters no longer will see the feed due to lack of resources. (6) Tracks where it failed and logs/reports this.

6. 2 Key Enablers for CBMANETs (1) Cross Layer Design • Declarative policy systems grounded in open standards • “Symbolic” reasoning to complement “numeric” utility measures • Handling individual preferences vs. global decisions in a principled way – the policy provides “norms” of good behavior • Using Data Semantics • Using what the data is and how important it is (and to whom) in conjunction with network state and policy to make smart “per router, per flow, per packet” decisions. Learn and Evolve. • Modeling environments and measuring parameters • What (low level parameters) influence performance ? How ?

7. 2 Key Enablers for CBMANETs (2) Resource Allocation • Declarative policy systems grounded in open standards • What are the resources, who controls what, who is allowed to make alterations and how, who can provide “overrides”? • Observe deviations from norm to create sanctions/reputations • Modeling resources and environments • How does resource allocation affect performance ? What should be measured ? • Privacy preserving, lightweight, distributed analysis of (sensor) data streams • Should not shuffle data across network to central site to calculate “utility” • Needn’t always share state observations to cooperate in achieving optimality

8. Some Observations • Resource allocation and cross layer design must be coupled to achieve maximum effectiveness • Treat each entity as “semi-autonomous” with beliefs, desires and intentions that will cooperate to maximize some utility measure • New algorithms will flow from their intersection • In some cases, this will essentially be a polyalgorithmic approach • In others, there will be parameter tweaking • Semantic labels will let us “learn” new algorithms as well • Policy as “norms” of behavior • Measure/report/analyze deviation – nodes with a “conscience” • Extensible, standards-based representations for semantics and policy • Generate bit efficient versions (work with W3C on this)

9. First policy forautonomic systems Actionable Policies for Autonomic Systems • We’ve developed Rei as a declarative policylanguage and used it to model and enforcepolicies in ad-hoc systems for • Authorization for services and information • Privacy in pervasive computing and the web • Information flow among agents and devices • Team formation, collaboration and maintenance • Covers permissions, obligations, prohibitions,dispensations and sanctions • Rei is supported by shared domain ontologies, rules and constraints expressed in RDF and OWL. • Rei is used to describe policies for trust and cooperative behavior in ad hoc environments

10. Managing data and services in MANETs • MoGATU/Anamika/SWANs are data and service management modules for MANETs spanning application, transport, network and MAC layers • Functionality based on cross layer constructs, e.g., treating service names or data sources as routing endpoints in route construction • Services/data/state described using OWL and SOUPA • Service invocation and composition, failure recovery • SPJ type data functionality, what is a transaction ? • Devices send queries and requests to peers • Proactively manage MANET peer interactions based on node “intentions”, data type and network state • Each device builds a ring of trust … • Devices measure environment state, react locally and advise global controllers as appropriate. Controllers respond deliberatively

11. Fostering cooperative behavior in MANETs • Each agent recognizes good andbad behavior in their neighbors • Kudos and accusations are signedand shared • Reputations emerge from thecorroborated and unchallenged observations and opinions at multiple layers (PHY, MAC, NW, … App) • Uncorroborated or false reports are noted too! • Agents use local policies, their own observations, and global reputation to make decisions • On communication, services, tasks, grouping, etc.

12. Privacy Preserving Data Analytics for MANETs • Multi-party, distributed, sometimes privacy-sensitive data • Compute global patterns without direct access to the raw unprotected data distributed over a network. • Important requirements: • Provably correct privacy-guarantees • Scalable with respect to the number of data sites • Scalable with respect to the size of the data • Work at UMBC DIADIC Laboratory & Agnik, LLC • Development of distributed privacy-preserving algorithms: • Randomized privacy-preserving representation • K-Ring of privacy • Application systems: PURSUIT system for privacy-preserving cross-domain intrusion detection, funded by DHS

13. Some Prior Work Policy based control in MANETs/Pervasive Computing • L. Kagal et al., "A Policy Language for A Pervasive Computing Environment", 4th IEEE Int. Workshop on Policies for Distributed Systems and Networks, June 2003 • L. Kagal et al., Modeling Communicative Behavior using Permissions and Obligations, in Developments in Agent Communication, Dignum et al. eds., Jan. 2005 • A. Patwardhan et al., Enforcing Policies in Pervasive Environments, Int. Conf. on Mobile and Ubiquitous Systems: Networking and Services, Aug. 2004 • M. Cornwell et al., A Policy Based Collaboration Infrastructure for P2P Networking, 12th Int. Conf. on Telecommunication Systems, Modeling and Analysis, July 2004

14. More Prior Work Cross Layer Service/Data/Network/MAC integration for MANETs/ Sensors • D. Chakraborty et al., Integrating Service Discovery with Routing and Session Management for Ad hoc Networks, Ad Hoc Networks Journal, Elsevier, 2006. • D. Chakraborty et al., Towards Distributed Service Discovery in Pervasive Computing Environments, IEEE Transactions on Mobile Computing, July 2004 • F. Perich et al., On Data Management in Pervasive Computing Environments, IEEE TDKE, May 2004 • S. Avancha et al., Ontology-driven Adaptive Sensor Networks, MobiQuitous 2004, Aug. 2004 • J. Ding, K. Sivalingamand B. Li, Design and Analysis of an Integrated MAC and Routing Protocol Framework for Wireless Sensor Networks, Int. Journal on Ad Hoc & Sensor Wireless Networks, Mar. 2005. • S. Lindsey, C. Raghavendra and K. Sivalingam, Data Gathering Algorithms in Sensor Networks using Energy Metrics, IEEE Transactions on Parallel and Distributed Systems, v13n9, pp. 924-935, Sep. 2002. • K. Ravichandran and K. Sivalingam, “Secure Localization in Sensor Networks”, in Security in Sensor Networks, (Yang Xiao, ed.), CRC Press, 2006.

15. Still More Prior Work Distributed Observation/Decision making in MANETs • A. Patwardhan et al., Active Collaborations for Trustworthy Data Management in Ad Hoc Networks, 2nd IEEE Int. Conf. on Mobile Ad-Hoc and Sensor Systems, Nov. 2005 • A. Patwardhan et al., Secure Routing and Intrusion Detection in Ad Hoc Networks, 3rd Int. Conf. on Pervasive Computing and Communications, March 2005 • F. Perich et al., In Reputation We Believe: Query Processing in Mobile Ad-Hoc Networks,Int. Conf. on Mobile and Ubiquitous Systems: Networking and Services, Aug. 2004 • H. Kargupta et al., Random Data Perturbation Techniques and Privacy Preserving Data Mining, Knowledge and Information Systems Journal, v7n4, 2004. (2003 ICDM Conference Best Paper Award) • K. Liu, H. Kargupta, and J. Ryan, Multiplicative Noise, Random Projection, and Privacy Preserving Data Mining from Distributed Multi-Party Data, IEEE TDKE, (in press).

16. http://ebiquity.umbc.edu/finin@umbc.edu joshi@umbc.edu yeyesha@umbc.edu http://dawn.cs.umbc.edu/ krishna@umbc.edu http://www.cs.umbc.edu/~hillol/Kargupta/diadic.html hillol@umbc.edu