Introduction • Sittler, in 1964, gave a formal description of the multiple-target tracking (MTT) problem . • Traditional target tracking systems are based on powerful sensor nodes, capable of detecting and locating targets in a large range. • Nowadays, tracking methods use large-scale wireless sensor networks.
Introduction • Multiple-Target Tracking (MTT):Varying number of targets arise in the field at random locations and at random times. • The movement of each target follows an arbitrary but continuous path, and it persists for a random amount of time before disappearing in the field. • The target locations are sampled at random intervals. • The goal of the MTT problem is to find the moving path for each target in the field.
Introduction • Large-scale target tracking wireless multisensor system has several advantages: (1) Better geometric fidelity; (2) Quick deployment (3) Robustness and accuracy
Challenges and Difficulties • Collaborative communication and computation • Limited processing power • Tight budget on energy source
Two Components for Target Tracking • The method that determines the current location of the target. It involves localization as well as the tracing of the path that the moving target takes. • Algorithms and network protocols that enable collaborative information processing among multiple sensor nodes.
Information-drivendynamic sensor collaboration • F. Zhao, J. Shin, and J. Reich, Information-driven dynamic sensor collaboration for tracking applications, IEEE Signal Proces. Mag. (March 2002). • The participants for collaboration in a sensor network were determined by dynamically optimizing the information utility of data for a given cost of computation and communication. • The metrics used to determine the participant nodes (who should sense and whom the information must be passed to) are(1) detection quality(2) track quality(3) scalability(4) survivability(5) resource usage
Information-driven dynamic sensor collaboration • Summary:The algorithm described is power-efficient in terms of bandwidth. • The selection of sensors is a local decision. Thus, if the first leader is incorrectly elected, it could have a cascading effect and overall accuracy could suffer. • It is also computationally heavy on leader nodes. • This approach is applied to tracking a single object only.
Tracking Using Binary Sensors • Binary sensors are so called because they typically detect one bit of information. • This one bit could be used to represent indicate whether the target is(1) within the sensor range or(2) moving away from or toward the sensor.
Centralized Tracking Using Binary Sensors • J. Aslam, Z. Butler, V. Crespi, G. Cybenko, and D. Rus, Tracking a moving object with a binary sensor network, Proc. ACM Int. Conf. Embedded Networked Sensor Systems (SenSys), 2003. • Each sensor node detects one bit of information, namely, whether an object is approaching or moving away from it. This bit is forwarded to the basestation along with the node id. • Each sensor performs a detection. If the probability of presence is greater than the probability of absence, also called the likelihood ratio, the detection result is positive.
Distributed Tracking Using Binary Sensors • K. Mechitov, S. Sundresh, Y. Kwon, and G. Agha, Cooperative Tracing with Binary-Detection Sensor Networks, Technical report UIUCDCS-R-2003-2379, Computer Science Dept., Univ. Illinois at Urbaba — Champaign, 2003. • It is assumed that nodes know their locations and that their clocks are synchronized. • The density of sensor nodes should be high enough for sensing ranges of several sensors to overlap for this algorithm to work • Sensors should be capable of differentiating the target from the environment.
Distributed Tracking Using Binary Sensors • Sensors determine whether the object is within their detection range. • Assuming that sensors are uniformly distributed in the environment, a sensor with range R will(1) always detect an object at a distance of less than or equal to (R - e) from it, (2) sometimes detect objects that lie at a distance ranging between (R – e) and (R + e)(3) never detect any object outside the range of (R + e), where e = 0.1R but could be user-defined.
Distributed Tracking Using Binary Sensors • The trajectory of the object is linearly approximated to a sequence of line segments along which the object moves with constant speed. • For each point in time, the object’s estimated position is computed as a weighted average of the detecting node locations. • The weights assigned are proportional to a function of the duration for which the target is within range of a sensor. • The target will remain within range of sensors closer to the target path for a longer period. A line fitting algorithm (least-squares regression) is executed on the resulting set of points. • The object path is predicted by extrapolating the target trajectory to enable asynchronous wakeup of nodes along that path.
Distributed Tracking Using Binary Sensors • Different weighting schemes: • Assigning equal weights to all readings. • Heuristic: wi = ln(1+ ti), where ti is the duration for which the sensor heard the object.
Distributed Tracking Using Binary Sensors • The first scheme yields the most imprecise results, namely, a higher rate of error between actual target path and its sensed path. • The second scheme has a lower error rate and gives a better approximation of the object trajectory. • The third scheme is the most precise method but requires estimation of the velocity of the object, which is too costly in terms of the communication costs required to make the estimate. • Hence the second approach is the most appropriate.
Distributed Tracking Using Binary Sensors • The line fitting computation requires collection of all position estimates at a centralized location for processing. • To minimize latency and bandwidth usage, some nodes are designated as gateways to outside networks with more computing resources. • The sensor network is logically organized into trees rooted at each gateway, and each node collects data from its children and sends it up to the nearest or least busy gateway.
Distributed Tracking Using Binary Sensors • This algorithm works very well for time-critical applications. • It can continuously refine the path estimated using old data in conjunction with new data. • Also, as it is a collaborative approach, it provides more accurate results than do those based on a single sensor detection of objects and is less computationally expensive on one particular node.
Distributed Tracking Using Binary Sensors • The protocol indicates that the higher the node density, the better the estimate on the trajectory. • There exists a tradeoff between power usage in terms of active sensor nodes detecting the object and the preciseness of the estimation. • A denser network should not necessarily mean turning on all nodes that are near the object, but only a certain number required to make an acceptable estimate. • There is no way of detecting multiple targets.
Distributed Group Management for Track Initiation andMaintenance in Target Localization Applications • J. Liu, J. Liu, J. Reich, P. Cheung, and F. Zhao, Distributed group management for track initiation and maintenance in target localization applications, Proc. Int. Workshop on Information Processing in Sensor Networks (IPSN), 2003. • A cluster-based distributed tracking scheme. • The sensor network is logically partitioned into local collaborative groups. Each group is responsible for providing information on a target and tracking it. • Sensors that are nearest to the target form a group. As the target moves, the local region must move with it • Hence, groups are dynamic with nodes dropping out and others joining in.
Distributed Group Management for Track Initiation andMaintenance in Target Localization Applications • A leader-based tracking algorithm similar to the one described by Zhao et al.  is used. • All sensor nodes that record a detection greater than the threshold form a collaborative group and elect a leader. • At any time t, each group has a unique leader who knows the geographic region of the collaboration yet does not need to know the exact members of the group. • The leader measures and updates its estimate of the target location, called the ‘‘belief state.’’ • On the basis of the new information, the leader selects the most informative sensor and sends it the updated information. • This sensor then becomes the leader at time t + x, where x is the communication delay.
Distributed Group Management for Track Initiation andMaintenance in Target Localization Applications • The leader suppresses other nodes from further detection, thereby limiting power dissipation and also preventing creation of multiple tracks for the same target. • The leader node initializes the belief state and kicks off the tracking algorithm.
Distributed Group Management for Track Initiation andMaintenance in Target Localization Applications • Group Formation and Leader Election • Group formation is based on geographic nearness to the target and is done as follows. • Initially all nodes are in sensing mode. Once a detection is made at a node, it sends the likelihood ratio to all other nodes within twice its detection range: 2R. • Each node that detects the target checks and compares all detection messages received within a time period of tcomm, where tcomm is set to a value that is greater than the time taken for the detection messages to reach their respective destination yet less than the time required for the target to move. • The group leader is elected according to the timestamp of the detection message. • A sensor node declares itself the leader if its message is timestamped earlier than all other messages or if an identically timestamped message has a lower likelihood ratio.
Distributed Group Management for Track Initiation andMaintenance in Target Localization Applications • Group Management: • As the target moves, groups have to be broken and reformed with new members dynamically. Group management is therefore a key aspect of the algorithm. The selected leader initializes the belief state p(x0|z0) as a uniform disk of radius R centered at its own location. The belief state gets refined with each successive measurement. • This area will contain the target with high probability. • Different algorithms are used to compute the suppression region, defined as the region in which all sensor nodes will form part of the group. • In this case a regression region containing all the sensor nodes that detect the target with a probability greater than a specified threshold is identified. • A margin R is added to this region to compute the suppression region. • Hence in the initial case the suppression region will be a concentric circle of radius 2R centered at the leader.
Distributed Group Management for Track Initiation andMaintenance in Target Localization Applications • As the target moves, nodes that were previously not detecting may begin detecting the target. • This can cause multiple tracks for a single target to exist, in other words, contention. • SUPPRESSION messages are used to claim group membership and to achieve single-node tracking. • The leader sends a SUPPRESS message to all other nodes in the collaborative region to tell them to stop sensing and join the group. • The leader also sends UNSUPRESS messages only to those nodes that now are no longer part of the region.
Distributed Group Management for Track Initiation andMaintenance in Target Localization Applications • Each node can be in any one of the following four states : 1. Detecting — this node is not a part of any group and periodically checks for possible targets. 2. Leader—this node performs the sensing and updates the track and the group. 3. Idle — this node belongs to a collaborative group but is not performing any sensing. It is waiting passively for a possible hand off from the leader. 4. Waiting for timeout — intermediate states waiting for potential detections to arrive from other nodes.
Distributed Group Management for Track Initiation andMaintenance in Target Localization Applications • Another message type used by the algorithm is the HANDOFF message. • HANDOFF messages are used to hand off the leadership to another node. • Each HANDOFF message comprises of (1) the belief state, (2) the sender ID, (3) the receiver ID, (4) a flag indicating successful or lost track, (5) a timestamp • This scheme assumes that all sensor nodes are time-synchronized and are aware of their one-hop neighborhood. • It is assumed that the routing protocol used will limit the propagation of detection messages to the specified region (in order to avoid flooding the network).
Distributed Group Management for Track Initiation andMaintenance in Target Localization Applications • It is clear that time synchronization is a major prerequisite for this approach to work. • Consider the case where a few nodes miss some detection messages because they did not arrive within the tcomm window; then multiple groups will be formed for tracking the same object. • Since these tracks correspond to the same target, they may collide; hence a merge mechanism for redundant paths is required.
Distributed Group Management for Track Initiation andMaintenance in Target Localization Applications • Distributed Track Maintenance • The algorithm can handle multiple target tracking since each target is tracked by a single group at any point of time, and the sensor network consists of many such groups. • Multiple tracking is easy if tracks are far apart and the collaborative regions are nonoverlapping. • However, multiple tracks, whether for the same object or different objects, could collide. We therefore need a mechanism to handle just such a scenario and perform track maintenance accordingly. • Each track is assigned a unique ID, for example, in terms of the timestamp of the track initiation. All messages originating from that group are tagged with the ID. • Each node can now keep track of its multiple membership. A node that belongs to more than one group and is not a leader in any group would require as many UNSUPPRESSION messages as SUPPRESSION messages in order to free it.
Distributed Group Management for Track Initiation andMaintenance in Target Localization Applications • If a leader node receives a SUPPRESSION message with an ID different from its own, this implies that a group collision has taken place. In such a case the algorithm supports group merging, and one track should be dropped on the basis of the timestamp of the SUPPRESSION messages. • Each leader compares the timestamp in the newly received SUPPRESSION (tsuppression) message with its own (tleader), and the older one is retained on the assumption that the belief state of the older track would be more refined and hence more reliable. • Therefore, if tsuppression < tleader, the leader drops its own track and relays the new SUPPRESSION message to its group and then relinguishes leadership. Hence the two groups merge into one, and the new group leader is now the collective group’s leader. • If tsuppression >= tleader, then the leader’s track survives.
Distributed Group Management for Track Initiation andMaintenance in Target Localization Applications • This algorithm works well for merging multiple tracks corresponding to the same target. If two targets come very close to each other, the two groups merge into one group and track the two targets as a single virtual target. Once the targets
Tracking Tree Management • W. Zhang and G. Cao, Dctc: Dynamic convoy tree-based collaboration for target tracking in sensor networks, IEEE Trans. Wireless Commun. 11(5) (Sept. 2004). • W. Zhang and G. Cao, Optimizing tree recon.guration for mobile target tracking in sensor networks, Proc. IEEE InfoCom., 2004. • A dynamic convoy tree-based collaboration (DCTC) framework has been proposed. • The convoy tree includes sensor nodes around the detected target, and the tree progressively adapts itself to add more nodes and prune some nodes as the target moves.
Tracking Tree Management • When the target first enters the surveillance zone, active (not in sleeping mode) sensor nodes that are close to the target will detect the target. • These nodes will collaborate with each other to select a root and construct an initial convoy tree. • Relying on the convoy tree, the information about the target generated from all the on-tree nodes will be gathered to the root node, which will process the gathered information and generate a more accurate report on the location and direction of movement of the target.
Tracking Tree Management • As the target moves, some nodes lying upstream of the moving path will drift farther away from the target and will be pruned from the convoy tree. • On the other hand, some free nodes lying on the projected moving path will soon need to join the collaborative tracking. Since they normally are under power saving mode, it is necessary to wake them up before the target actually arrives. • As the tree further adapts itself according to the movement of the target, the root will be too far away from the target, which introduces the need to relocate a new root and reconfigure the convoy tree accordingly.
Tracking Tree Management • If the moving target’s trail is known a priori and each node has knowledge about the global network topology, it is possible for the tracking nodes to agree on an optimal convoy tree structure. • An algorithm that optimizes the energy consumption for data gathering along the convoy tree is discussed. • However, in the real scenario, this global information may not be available.
Tracking Tree Management • Construction of Initial Tree: • When a target first enters the surveillance zone, the sensor nodes that can detect the target can collaborate to construct the initial convoy tree. • First, a root node should be elected among the initial nodes, which is the election phase of the initialization process. • The root election is based on the heuristic that the root is the closest to the target, namely, the geometric center of the nodes in the tree.
Tracking Tree Management • If a node does not receive any election message with (dj, idj) that is smaller than (di, idi), it becomes a root candidate. • Otherwise, it gives up and selects the neighbor with the smallest (dj, idj) to be its parent. • It is possible that multiple root candidates will come up. Thus, the second phase is needed by letting the candidate i flooding a winner(di,idi) message to other nodes in the initial convoy tree. • When a root candidate i receives a winner(dj,idj) message with smaller (di, idi) values, it gives up the candidacy. It will further attach itself into the tree rooted at the candidate with the smallest (di, idi):
Tracking Tree Management • Tree Expansion and Pruning • For each time interval, the root of the convoy tree adds some nodes and removes some nodes according to the target’s movement. • To identify which nodes are to be added and removed, a prediction-based method has been discussed . • It is assumed that the location of the target in the next time interval can be predicted given the estimated moving speed of the target. • If the target moving direction does not change frequently, the chance of correctly predicting the target’s future position is high.
Tracking Tree Management • Tree Reconfiguration: • With the movement of the target, the nodes that participate in the tracking change continuously. • When the target moves farther away, more and more nodes drift farther from the root node. • Thus, the root should be replaced by a node closer to the target, and the convoy tree needs to be reconfigured accordingly. • This can be triggered by a simple heuristic, that if the distance between the current target location and the root becomes larger than a threshold, the tree needs to be reconfigured. • The reconfiguration threshold can be set as dm + vt , where dm is a parameter specifying the minimum distance that triggers the reconfiguration, vt is the velocity of the target at time t, and is a parameter specifying the impact of the velocity.
Tracking Tree Management • After the reconfiguration is triggered, a sequential scheme can be used. It is based on the grid structure, which is normally used for the power saving mode for the free nodes . • In order to save power for the free nodes, the network is divided into grids. • At each time, only one node is selected to be the grid head and remains active continuously. Other nodes will be in power saving mode. The grid size is smaller than the transmission range so that the grid heads can form a connected topology.
Tracking Tree Management • Suppose that the newly selected root is at the grid g0. • The reconfiguration procedure starts by the new root broadcasting the RECONF message to the nodes in its grid. • The new root also needs to send the RECONF message to the heads of the neighboring grids (namely, g1,g2,g3,g4). • Thereafter, the nodes in grid g0 can all set their parent as the new root. Also the nodes in the neighboring four grids will be informed by their grid head, and can adjust their parent by the information provided by the new root. • The heads of grids g5,g6,g7,g8 may not be able to receive the RECONF message directly from the new root at g0. They should be informed by the heads in grids g1,g2,g3,g4. • A repetitive procedure can further adjust the tree structure in those four corner grids.
Deployment Optimization for Target Tracking • An important issue in designing a target tracking sensor network is the placement of sensors within the surveillance zone. • First, the sensors should fully cover the surveillance zone. In most cases, when a target is detected, a single sensor node is enough for pinpointing the location of the target; thus, it should be ensured that every point in the zone is covered by at least k sensor nodes. • Huang and Tseng , show how each node can check if the local area in its sensing range satisfies the k-overage condition. • Further, the placement of the sensor nodes can also affect the way how target localization is conducted. • This issue is discussed elsewhere in the literature [4,25].
Deployment Optimization for Target Tracking • Chakrabarty et al.  have studied the sensor placement issues for target tracking analytically. • The paper provides a modified problem model for target localization, based on a grid manner discretization of the space. • In some applications or systems, it is sensible to find the gridpoint closest to the target’s estimated location, instead of pinpointing the exact coordinates of the target. • In such a problem model, an optimized placement of sensors will satisfy the requirement that every gridpoint in the sensor field be covered by a unique subset of sensors.