A prediction-based protocol for online target tracking in VSNs

In this paper, we propose the use of a prediction approach for real-time tracking in VSNs. Our solution anticipates the target’s future direction and concentrates the tracking process in specific zones to enhance the tracking precision. Furthermore, it optimizes the network load by concentrating tracking data in the predicted zones and thereby lightens the overhead on the rest of the network. To achieve this, the prediction module calculates probabilities of undertaking each direction at intersections on the basis of three parameters: road type, checkpoints avoidance and direction trends. We have conducted a stimulative study considering two types of movement profile : known and unknown profile respectively. Simulation results show a prediction success rate above 60% and up to 85% for the known profile scenarios. In addition, the performance comparison with a previous solution revealed a significant decrease in terms of network overhead, reaching a network load of 3080,622 bits/s using the prediction module as compared to 5006,88 bits for the previous protocol.