CamNets: Coverage, Networking and Storage Problems in Multimedia Sensor Networks

1. CamNets: Coverage, Networking and Storage Problems in Multimedia Sensor Networks Nael B. Abu-Ghazaleh State University of New York at Binghamton and Carnegie Mellon University, Qatar nael@cs.binghamton.edu

2. Talk Outline • Introduction • Overview of past work • Current Active Research • Camera Networks • Camera coverage • Networking for data delivery and coordination • Storage and Indexing • Future directions

3. Wireless Networks Mesh networks Wireless Local Area Networks Sensor networks

4. Sensor Networks • What is a sensor network? • Sensing • Microsensors • Constraints, Problems, and Design Goals

5. Applications

6. Applications • Interface between Physical and Digital Worlds • Many applications • Military • Target tracking/Reconnaissance • Weather prediction for operational planning • Battlefield monitoring • Industry: industrial monitoring, fault-detection… • Civilian: traffic, medical… • Scientific: eco-monitoring, seismic sensors, plume tracking…

7. Microsensors for in-situ sensing • Small • Limited resources • Battery powered • Embedded processor, e.g., 8bit processor • Memory: KB—MB range • Radio: Kbps – Mbps, tens of meters • Storage (none to a few Mbits)

8. Mica2 Mote Chipcorn CC1000 Radio Transciever Max 38Kbps - Lossy transmission Flash Memory 128KB – 512KB 128KB Instruction EEPROM UART 4KB Data RAM Atmega 128L microprocessor 7.3827MHz UART, ADC 51 pin expansion connector

9. Properties • Wireless • Easy to deploy: ad hoc deployment • Most power-consuming: transmiting 1 bit ≈ executing 1000 instructions • Distributed, multi-hop • Closer to phenomena • Improved opportunity for LOS • radio signal is proportional to 1/r4 • Centralized apporach do not scale • Spatial multiplexing • Collaborative • Each sensor has a limited view in terms of location and sensor type • Sensors are battery powered • In-network processing to reduce power consumption and data redundancy

10. Typical Scenario Deploy Wake/Diagnosis Disseminate Self-Organize

11. Sensor Network Systems

12. Ghost of Research Past

13. Design Space and Infrastructure Tradeoffs • We defined the design space for sensor networks • Studied infrastructure and deployment alternatives • Identified congestion and its impact on sensor networks • New congestion management solutions • …including non-uniform information dissemination

14. Routing • Real-Time Routing based on Just-in-Time-Scheduling • Stateless Routing Protocols • Explain Anomalies in Virtual Coordinate Systems • Developed solutions that addressed them • Aligned Virtual Coordinates • Delivery guaranteed routing • Hybrid geographical/virtual routing protocols

15. Sensor Network Storage • Collaborative storage to reduce space and load balance • Resolution Ordered Storage for space reclamation • Interval summaries for indexing and coordination • RESTORE testbed

16. Localization and Security • Securing Localization Systems • Localization for Mobile Nodes: the self-tracking problem • Trusted routing • Defeating Timing and Space/Time Analysis attacks

17. Applications and Programmability • Testbed for chemical/biological attack monitoring • Camera Networks Testbed • Filesystem abstraction for sensor networks • Virtualizing sensor networks

18. Ghost of Research Present

19. General Areas of Interest Modeling Simulation Network testbed Robotic testbed Applications Characterization Performance Security

20. Wireless Interference • Nodes interfere with each other • Effects • Lower throughput, Longer delays • Application performance • Our work • Understand and characterize interference • Design interference-mitigating protocols A C B

21. Example 1: Two-flow problems • Only 2 links • What are different ways in which they interact? • How often do they occur? • How does it affect throughput and delay? A B A B D C C D C D A B

22. Example 2: Application of interactions Interaction Engineering • Goal: Avoid harmful interactions • Approach: • Detect interactions dynamically • Adapt parameters to overcome harmful interactions A B A B C D C D

23. Routing • Transmit packets from source to destination • Link quality, scheduling and application-specific traffic. • Our work: Study the optimal routing problem and heuristic protocols. Congested!!

24. Example 2: Interference-aware routing Goal: Find routes that are aware of interference. Approaches: • Multi-objective optimization • Network-flow problems • Approximate heuristic protocols.

25. Testbeds State-of-the-art wireless devices • Soekris boards, Software-Defined Radios Current research projects: • Real-time models • Scheduling, routing • Efficient protocol development • Power control, rate-control, routing • Robotic projects • Camera-Nets • Localization

26. Example 3: Mesh Network Monitoring tool Distributed measurement protocols • Network Topology, Link Quality, ... • Detect interactions Framework to build higher level protocols.

27. Introduction • A smart camera network is a network of autonomous and cooperative camera nodes. • Traditional Camera Networks:

28. Why are they interesting? • Many applications • Military: sensitive areas • Homeland security: suspicious activities, aftermath • Disaster recovery: help rescue operations • Habitat monitoring: capture scientific information such as behavioral/migration patterns of animals • Road traffic monitoring: detect and report traffic violations

29. Motivation • Problems with traditional networks: • Simple capture-and-stream nature: • needs human to monitor and control cameras. • Fixed and costly infrastructure: • high-end cameras, wired connectivity. • An expectation from a smart camera network: • autonomously capture most useful information from the deployment region.

30. Major Problems in Camera Networks • Computer vision related problems • Camera calibration • Target detection and identification • Event classification and clustering • Systems related problems • Camera Coverage • Network: Quality of Service for data delivery • Network: Coordination • Storage and indexing

31. Coverage Maximization Problem How to configure cameras’ FoVs to maximize the total number of targets covered? • Assuming all targets are equally important. • Camera Configuration Parameters • Pan: horizontal adjustment • Tilt: vertical adjustment • Zoom: coverage range adjustment • Camera Field-of-View (FoV): • Represented by angle and depth of view R

32. Coverage Maximization Problem • Assumptions • Discrete pans • Boolean coverage model • No occlusions

33. Solution Approach Why not a greedy approach? C2 C3 C1

34. Contributions • Integer Linear Programming based formulation • Centralized heuristic • Semi-centralized approach for scalability

35. ILP Formulation Subject to:

36. Centralized Approach for Solving ILP • Each camera sends state information to a central node • State information:<Camera Id, Target Id, Target location> • Central node computes optimal orientations (pans) for each camera and sends them back. • The optimization problem is NP-hard!

37. Centralized Force-directed Approach (CFA) • Approach: Iteratively choose camera-pan pair with highest force (Fik) Approach: Iteratively choose camera-pan pair with highest force (Fik) Example: • M: set of targets • N: set of cameras • P: set of pans F=1 F=0.5 F=0.5

38. Centralized Force-directed Approach (CFA) Algorithm:

39. Centralized Force-directed Approach (CFA) C2 Counter Example: P1 P2 C3 P1 P1 P2 P2 C1 P2 P1 Force Matrix C4

40. Scalable Semi-centralized Approach • Centralized approaches are not scalable • Exponential computations for optimal solution • Large response delay • Hierarchical Approach • Address scalability by spatially decomposing camera nodes into multiple partitions. • Key Idea: • take advantage of physical separation among cameras, at a possible expense of coverage gain

41. Spatial Partitioning Approach • Single Linkage Approach (SLA) • Bottom-up clustering approach • Start by treating each camera as a cluster • Merge two clusters if the smallest distance (d) between any two nodes is smaller than threshold. • Keep increasing the threshold to merge more clusters, forming a hierarchy. • Modifications in SLA: • Termination condition for merging: d > 2*Rsensing • Maximum cluster size (Smax) R R

42. Performance Evaluation • Simulations using QualNet network simulator • Parameters: • FoV Rmax = 100 meters; Rmin = 0 meters • FoV Angle = 45° • Terrain 1000x1000 meters • Benchmarks: • Centralized Greedy Approach (CGA) [Abouzeid’06] • Distributed Greedy Approach (DGA) [Abouzeid’06] • Pure Greedy Approach (Greedy)

43. Study of varying number of targets # Cameras = 50 Random Clustered Percent Coverage:Ratio of covered to coverable objects

44. Study of varying number of cameras # Targets = 100. E2E delay:Worst-case delay to receive response.

45. Scalable Coverage for Static Targets Study of impact of Smax #Cameras=50; #Targets=100; Terrain: 500x500m

46. Coverage for Mobile Targets • Problem: • How to maximize the total mobile targets tracked? • Approach: • How to compute the camera configurations? • Optimal, CFA, Hierarchical • How often to compute the optimal solution? • Locally: local collaboration approach • Globally: periodic recalibration • Collaboratively: on-demand recalibration • Hybrids

47. Coverage for Mobile Targets Comparison of different policies and their combinations Params: N = 20; Mobility: pedestrian mobility parameters

48. Conclusion & Future Work • Focused on the coverage maximization problem • Proposed three solution approaches: • ILP based formulation • Centralized heuristic: CFA • Semi-centralized approach: Hierarchical • Semi-centralized approach can reap benefits of centralized and distributed approaches • Future Work: • Extend formulation for tilt and zoom • Model obstacles in the formulation • Propose approach for mobile targets case

49. Ghost of Research Future

50. Future Directions • Immediate Future • Camera Networks • Software Defined Radios • Measurement based protocols • Getting into • Cyber physical systems –Smart cities • Environmental Observatory Networks • Augmented with mobile sensing and personal sensing