Organization-Based Tracking

Tracking Given a sensor network, use the sensors to determine the motion of one or more targets Canonical domain for DSNs - much of what we have seen so far is applicable data routing, query propagation, wireless protocols 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?”

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

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

Why Not Centralized? Scale! Data processing combinatorics Resource bottleneck (communication, processing) Single point of failure Ignores benefits of locality

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])

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

Distributed Target Classification and Tracking in Sensor Networks Richard Brooks, Parameswaran Ramanathan, & Akbar Sayeed

Problem Domain Single target Fixed, acoustic sensors Requires multiple measurements Limited ad-hoc wireless network Track and classify target (classification, which uses a supervised learning technique, is not discussed here)

Location-Centric Tracking Control and data flow at each node: Initialization: disseminate sensor information Receive candidates: describe approaching targets Local detections: gather measurements Merge detections: form track, compare candidates Determine confidence: estimate uncertainty Estimate track: predict future target location Transmit track: notify relevant sensors

Location-Centric Tracking “Closest point of approach” (CPA) measurements Target detection causes cell formation Cells formed around the target’s estimated location Intended to include relevant sensors Manager is selected Node with greatest signal strength Manager collects local CPA’s Linear regression over CPA node locations CPA Time Magnitude

Location-Centric-Tracking Estimated location compared to prior tracks Projections from candidate tracks Cell created for track in new area Size is a function of target velocity Track information propagated to cell Tracking repeats…

Location-Centric Advantages Avoids combinatorial explosion of track association Centralized: n targets, n candidate locations = n2 Distributed: 1 target, n candidate locations = n Reduces communication costs (multi-hop ad hoc) Saves energy

Results No Filtering Kalman Lateral Inhibition RMS Comm 18.1 14,512 (?) 8.9 254,552 11.3 21,792

Using and Maintaining Organization in a Large-Scale Sensor Network Bryan Horling, Roger Mailler, Mark Sims and Victor Lesser Multi-Agent Systems Lab University of Massachusetts

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

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

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

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

Partitioning of Nodes The environment is first partitioned into sectors. Sector managers are then assigned.

Competition for Sensor Agents Sector members send their capabilities to their managers. Each manager then generates and disseminates a scan schedule.

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

Managing Conflicted Resources Track manager discovers and coordinates with tracking nodes. New tracking tasks may conflict with existing tasks at the node.

Data Fusion (Track Generation) Tracking data sent to an agent which performs the fusion. Results sent back to track manager for path prediction.

Protocol Usage Map Protocols Sector Manager Tracking Manager DrA DrQ DrR TB RR TD PTC RB PC DA TBU ES Sector Manager Tracking Manager Scanning Agent Tracking Agent

Sector Size This one parameter affects many things… 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 Communication distance, agent activity, RMS error, message type counts Empirical evaluation of varying this parameter Sector manager - smaller sectors mean it has less directory service information to receive and store. Larger sectors provide better coverage for scan schedule generation, and provide more candidates for track manager selection Track manager - Smaller sectors mean fewer incoming messages describing close proximity targets, since there are fewer targets covered by the sectors, larger sectors allow it to get needed directory information with fewer queries, since information is more centralized.

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

Communication Characteristics Larger sectors with more agents leads to less messaging overall Less tracking control Fewer directory queries More sectors to query More tracking data Smaller sectors have more overall messaging, because track managers have to keep more sector managers up to date, and require more directory messages to get the relevant data. The second graph breaks this down, showing that tracking control and directory service messages go down as there are more agents per sector, while result messages increase because of this reduced control overhead.

Load Disparity Large sectors increase SM comm. load More messages to handle Greater disparity - SM is a “hotspot” Greater disparity in activity load Average action totals are constant Large sectors mean each sector manager has more sensors to manage, so it has more information processing (sensor location information, scan scheduling, new target detections, etc.) This leads to “hotspots” in the environment, where individual agents have a greater load than their peers. We see the same behavior in activities, caused by similar reasons. Notice that the average number of actions is constant, and it is only the deviation which grows, indicating that particular agents are shouldering more than their fair share of the load.

Domain Metrics Communication distance increases with larger sectors Track migration triggered by boundaries …but better RMS error More measurements due to lower control overhead Track manager migration (really the only factor which affects communication distance), is initiated when a target moves outside of the track manager’s home sector. Thus, with larger sectors, migration happens less frequently, and the average distance of communication is higher. Smaller sectors impose greater control overhead (more track migration, more update messages to send, it takes longer to get directory information). This results in fewer measurements being taken, because the track manager is distracted with these other tasks. This results in a higher RMS error with smaller sectors.

What’s Best? Find inflection point in graphs’ intersection Empirical evidence supports sector size from 5-10 sensors This would vary, depending on sensor and environmental characteristics This supports our initial hypothesis of 6-8 sensors per sector.

Conclusions Specific results are domain-specific However, this demonstrates that organizational controls can affect performance General notions of locality, information bottlenecks, organizational control overhead