CMPE 259

1. CMPE 259 Sensor Networks Katia Obraczka Winter 2005 Deployment, Organization, Localization

2. Announcements • Homework due on 02.14. • Submission: e-mail to katia, cintia, kumarv@soe. • Plain text or pdf. • Final project presentations. • March 15th. from 4-7pm. • Venkatesh Rajendra’s MAC presentation. • Wed, Feb 16th. • Homework 2. • Exam.

3. Node localization

4. Node Localization • For some sensor network applications, exact location is critical. • Tracking. • Monitoring. • For most applications, having location information enhance value of information. • Also needed in geographic routing.

5. How to determine node location? • May be trivially available if: • Satellite based GPS is feasible and available on all nodes. • All nodes are hand-placed and pre-configured with location coordinates. • Otherwise it is quite challenging (even with a fraction of known reference/beacon nodes). • Typically, one assumes some nodes have position information (e.g., through GPS), but not all.

6. Basic Localization Approaches • Proximity: • Near/far. • Connectivity information. • Lateration/ranging (based on distance estimate): • Received signal strength. • Time difference of arrival. • Time of arrival. • Angulation. • Location service.

7. Ad Hoc Positioning System

8. APS • Provides (approximate) location information for all nodes given location information from subset of nodes. • Positioning mechanism requirements: • Distributed. • Energy-efficient. • Minimize communication and processing. • Robustness. • In the face of partitions.

9. APS basics • Employ same principles as GPS for computing positions. • I.e., triangulation. • Landmarks: nodes that know their position. • Distance to landmarks propagate hop-by-hop. • Distance vector approach. • Once node has distance to 3 landmarks, it can compute its position.

10. Hop-by-hop propagation • DV-hop. • DV-distance. • Euclidean.

11. DV-Hop • Nodes get distance in hops to landmarks. • Landmarks compute distance of a hop. • Landmarks get distance to other landmarks in hops. • Landmarks know euclidean distance to other landmarks. • Landmark broadcasts hop distance. • Controlled flooding. • Once nodes gets and forwards hop distance, it will drop subsequent ones. • Nodes use triangulation to compute their position based on the position of the landmarks.

12. DV-distance • Distances measured using received signal strength.

13. Euclidean propagation • Euclidean distance to landmark is propagated. • Node needs at least 2 neighbors with known estimates to landmark. • Also need distance from node to these neighbors and distance between neighbors.

14. Evaluation • Simulations using ns-2. • 100 nodes. • 2 topologies: isotropic and anisotropic. • Metrics: • Location error. • Coverage. • Overhead. • Use of APS-estimated locations in routing.

15. Results • From paper… • DV-based perform relatively well with low overhead. • Euclidean-based exhibits better accuracy at the expense of signaling.

16. Organization

17. Tracking • Given a sensor network, use the sensors to determine the motion of one or more targets • Typically requires more cooperation among entities than other examples we have seen • Compare: “is there an elephant out there?” vs. “where has that particular elephant been?”

18. Tracking challenges • Data dissemination and storage • Resource allocation and control • Operating under uncertainty • Real-time constraints • Data fusion (measurement interpretation) • Multiple target disambiguation • Track modeling, continuity and prediction • Target identification and classification

19. Tracking domains • Appropriate strategy depends on the sensors’ capabilities, domain goals and environment • Requires multiple measurements? • Bounded communication? • Target movement characteristics? • No single solution for all problems • For example… • Limited bandwidth encourages local processing • Limited sensors requires cooperation

20. Why not centralized? • Scale! • Data processing combinatorics • Resource bottleneck (communication, processing) • Single point of failure • Ignores benefits of locality

21. Why not (fully) distributed? (i.e. everyone tracks) • Redundant information and computation • Can increase uncertainty • Lack of unified view • High communication costs • (exception: overhearing [Fitzpatrick 2003])

22. Organization-based tracking • Use structure, roles to control data and action flow • Can be static, or dynamically evolved • [Brooks 2003]: Spontaneous coalition formation • [Horling 2003]: Partitions, mediated clustering • [Li 2002]: Hierarchical information fusion • [Yadgar 2003]: Hierarchical teams • [Wang 2003]: Roles and group formation • [Zhao 2002]: Geographic groups

23. Using and Maintaining Organization in a Large-Scale Sensor Network

24. Problem Domain • Fixed doppler radars • Requires multiple, coordinated measurements • Multiple targets • Shared 8-channel RF communication

25. Sensor Characteristics • Hardware • Fixed location, orientation • Three 120° radar heads • Agent controller • Doppler radar • Amplitude and frequency data • One (asynchronous) measurement at a time

26. Organizational Control • Use organization to address scaling issues • Environment is partitioned • Constrains information propagation • Reduces information load • Exploits locality • Agents take on one or more roles • Limits sources of information • Facilitates data retrieval • Other techniques also built into negotiation protocol and individual role behaviors

27. Typical Node Layout • Nodes are arranged or scattered, and have varied orientations. • One agent is assigned to each node.

28. Partitioning of nodes • The environment is first partitioned into sectors. • Sector managers are then assigned.

29. Sector manager • Generate scanning plans. • Assign track managers. • Keep local sensor information.

30. Distribution of scan schedule • Sector members send their capabilities to their managers. • Each manager then generates and disseminates a scan schedule.

31. Track Manager Selection • Nodes in the scan schedule perform scanning actions. • Detections reported to manager, and a track manager selected.

32. Track manager • Organizes tracking task. • Discovers sensors capable of tracking target. • Determines track schedule. • When to perform scan. • Fidelity, timeliness, etc.

33. Managing limited resources • Track manager discovers and coordinates with tracking nodes. • New tracking tasks may conflict with existing tasks at the node.

34. Data fusion (track generation) • Tracking data sent to an agent which performs the fusion. • Results sent back to track manager for path prediction.

35. Sector size • Sector manager load. • Smaller sector –› smaller manager directory. • Larger sector –› better sector coverage. • Track manager actions. • Smaller sector –› fewer update messages. • Larger sector –› fewer directory queries. • Depends on sensor density, sensor range, target speed, etc. • Empirical evaluation of how sector size affects performance.

36. Experimental setup • Radsim simulator • 36 sensors • 1-36 equal sized sectors • 4 mobile targets • 10 runs per configuration • Hypothesis: sector size of 6-10 agents is best

37. Communication characteristics • Larger sectors with more agents leads to less messaging overall.

38. Load disparity • Large sectors increase SM comm. Load. • Greater disparity in activity load.

39. Domain metrics • Communication distance increases with larger sectors • Track migration triggered by boundaries • …but better accuracy. • More measurements due to lower control overhead

40. What’s best? • This would vary, depending on sensor and environmental characteristics • Find inflection point in graphs’ intersection • Empirical evidence supports sector size from 5-10 sensors

41. Conclusions • Specific results are domain-specific. • However, this demonstrates that organizational controls can affect performance.

42. Deployment

43. Deployment • Depend application. • Two main classes: • Structured placement. • Random deployment. • In both classes the two main goals are network connectivity and sensor coverage. • Costs have to do primarily with equipment and energy.

44. Network connectivity R • Idealized* geometric model for wireless links: perfect connectivity within radio range R. • Network graph G formed by nodes as vertices and these links as edges. • Basic notion of connectivity: there exists at least one multihop path between any pair of nodes in the network; could be generalized to k-connectivity, existence of Hamiltonian cycle, etc. *Caveat: Perhaps good for preliminary analysis, but known to be unrealistic

45. Random deployment • E.g., scattered from an aircraft/robot, mixed into concrete. • Issues of average density and range settings are important. • Connectivity issues can be explored using the Theory of Random Graphs and Percolation Theory.

46. Random graphs • Bernoulli Random Graphs G(n,p): edge between any pair of the n nodes independently with probability p. • Geometric Random Graphs G(n,R): n nodes placed with a uniform random distribution in a finite region; edge between any pair of nodes within range R.

47. Sensor coverage • Application specific. • Some possible notions of coverage: • Density of placement (average/max distance between nodes). • Percentage of desired (known a-priori) measurement points covered. • Percentage of the operational area that is covered with “k” sensors: k-coverage. • Others?

48. Sensor Placement for Effective Coverage

49. Goals • Minimum number of sensors for adequate coverage. • Adequate coverage: every grid point is covered with minimum confidence level.

50. Sensor placement • Greedy approach. • Place one sensor at a time. • Algorithm terminates when: • No more sensors or • Sufficient coverage achieved.