A Distributed Clustering Algorithm for Target Tracking in Vehicular Ad-Hoc Networks

1. A Distributed Clustering Algorithm for Target Tracking in Vehicular Ad-Hoc Networks Dr. Khalil El-Khatib, Dr. Richard Pazzi, SanazKhakpour

2. Table of Contents • Introduction • Related Work • Algorithm Features • Algorithm Overview • Algorithm Description • Conclusion

3. Introduction • VANETs are network of autonomous mobile nodes that communicate with each other without any fixed infrastructure. • VANETs are large-scale networks and dividing the network into smaller clusters in such dynamic environment is an advantageous technic.

4. Related Work • MANET and WSN clustering algorithms do not work properly in VANET environment. • The most important challenge in clustering algorithms that most of the protocols are trying to solve are: • Optimal cluster management in VANET’s highly dynamic environment. • Increasing cluster stability (MDMAC, SBCA) • Prevention of frequent cluster changes • Increasing cluster head availability (SBCA) • Increasing cluster lifetime by using appropriate mobility metrics (DCA, MDCAM, DMAC, SBCA, MOBIC, …)

5. Special features of the proposed algorithm • A cluster-based target tracking algorithm • high cluster head and cluster lifetime • robust and stable clusters • low delay and overhead for electing new cluster head in lost CH scenarios • distributed cluster head selection mechanism

6. Table of Contents • Introduction • Related Work • Algorithm Features • Algorithm Overview • Algorithm Description • Conclusion

7. Assumptions and Definitions • The proposed clustering algorithm was designed for vehicle tracking in VANETs. • This algorithm assumes that vehicles have front and rear cameras and can detect visual features of a target. • A central entity such as a police station is seeking help to find a specific target. This entity is called Control Centre (CC) and is a node located in multi-hop communication distance from target.

8. Tracking Failure Probability Metric (TFP) • Assumptions: • All vehicles are aware of their location and velocity by using a GPS device. • The location of a target is unknown; But can be calculated by visual processing. • To calculate TFP between a vehicle A and the target vehicle T at time t, we need to have the distance between node A and T and their speed vector at that time. • We define a value called Valid Distance Range (VDR), which is used to normalize the distance between any node and target.

9. Tracking Failure Probability Metric (TFP) • The normalized distance is calculated as follow: • (1) = • By using velocity vectors of vehicles we can differentiate between nodes moving in the same direction and nodes moving in opposite direction. is defined as: • (2) = • We use a value called Valid Velocity Range (VVR) in order to normalize the value of velocity vectors.

10. Tracking Failure Probability Metric (TFP) • V and V Are normalized velocity vectors of vehicle A and target T respectively. • (3) = (4)= • Two values α and β are defined as Distance and speed Efficiency Factors. These values are coefficients of distance and velocity to control efficiency of these metrics of each vehicle. • The TFP of node A at time t is calculated as in the following formula. The lower TFP indicates higher priority to become cluster head. • (5) = 100 * ( + β

11. Table of Contents • Introduction • Related Work • Algorithm Features • Algorithm Overview • Algorithm Description • Conclusion

12. Table of Contents • Introduction • Related Work • Algorithm Features • Algorithm Overview • Algorithm Description • Control Centre Functions • Initialization Phase • Cluster Management Phase: • Cluster Head Functions • Cluster Members Functions • Tracking Phase • Conclusion

13. Control Centre (CC) • CC broadcasts a “Target Tracking Request Message” (TTRM) to the entire network with target vehicle’s visual information. • When CC receives “Response Message” from any vehicle that has located the target, it will stop broadcasting. • At any point later, if the CC stops receiving any information from the cluster head regarding the specified target (after a pre-defined time interval) it will assume the cluster no longer exists and it will start broadcasting target’s information again in the network.

14. Initialization Phase • Any vehicle that receives a TTRM message from the Control Center (CC) and which can detect the target responds to CC and starts the initialization process. • OBNs start broadcasting “Request Messages” to their neighbors and receive “Response Messages” from them. OBNs also check the TDV field of the response messages. • OBNs calculate their TFP. This value displays which vehicle has closer movement pattern to target and is more appropriate to be the cluster head.

15. Initialization Phase • Cluster members are divided into 2 groups: level-1 (OBNs) and Level-2 (NNs). • Member nodes are connected to cluster instead of cluster head. • Initialization phase might be repeated only if there is not any cluster members available and the cluster is destroyed. • The purpose of our design is to avoid switching to Initialization Phase from Cluster Maintenance phase. • After this phase the initial cluster is created and the cluster head is selected.

16. Cluster Maintenance Phase(Cluster Head Functions) • CH is responsible of managing the cluster by sending request messages at every time intervals to find new cluster members. • the cluster head calculates its own TFP every, and compares it with other values in the neighbour list to check if it is still a valid CH. If not it will send a “Resign Message”. • A “Safe Threshold” is defined because the TFPs are changing so quickly and we do not want to change CH so frequently. • vehicles moving in opposite direction of the target are not supposed to join cluster because these nodes are unstable cluster members and will decrease cluster’s stability.

17. Cluster Maintenance Phase(Cluster Members Functions) • OBNs calculate their TFP every time interval and send it in RPM to their neighbors. Also, OBNs store the TFP of other nodes in their neighbor list. • If member nodes receive a RSM they are responsible to find a node with lowest TFP value in their neighbor list and select it as CH. • Also, if a member node does not receive any kind of message after a defined time interval, it assumes to be out of cluster borders and will try to send its information directly to CC (if possible).

18. Tracking Phase • Tracking includes taking continuous video of target and sending video data and location information of target to CC during specified time intervals. • CMs send their data to CH and CH is responsible to inform CC about target’s location. • In case CM goes out of cluster boundaries (and does not have access to CH), it should send the latest information to CC.

19. Table of Contents • Introduction • Related Work • Algorithm Features • Algorithm Overview • Algorithm Description • Conclusion

20. Conclusion • Introduced algorithm aims to improve cluster performance by making stable and long living cluster. • The stability of this algorithm is mostly because of adding candidate cluster members which are highly probable of detecting target in near future. • The TFP value is used as an evaluation value to compare movement pattern of vehicles with target. • The idea of distributed cluster head selection is introduced by use of TFP.