Fault‐Tolerant Prediction‐Based Scheme for Target Tracking in Wireless Sensor Networks

International audienceFault-tolerance is an important function in target tracking application using wireless sensor networks. We propose in this paper, an efficient fault-tolerant approach for target tracking that prevents the loss of the target. Instead of using a single prediction mechanism, our approach uses a multi-level incremental prediction technique that adjusts the prediction precision of the target movement. The responsible node of target detection uses multiple historical information pieces to calculate multi-level predictions which have different precision levels according to the number of information pieces used. Thanks to our parametric prediction model, our approach increases the prediction success rate and decreases the target loss frequency compared to basic approaches that use simple prediction models

A Survey on Quality of Service Metrics Using Cross Layer Optimization Technique

Wireless Sensor Network (WSN) is group of sensor nodes connected in a network to monitor the physical situations and collect the information to communicate to sink node. The low-cost feature of WSN is a significant advantage over the conventional networks. The successful implementation of WSN requires to adapt a strict QoS metrics. Each application demands a specific QoS such as throughput, latency, reliability bandwidth etc. The cross-layer algorithm optimizes the overall performance. The efficient design of routing algorithm and cross layer optimization technique improves the performance of WSNs. This paper depicts the comparison of various optimization techniques on WSNs with their strengths and limitations. The cross-layer optimization techniques of previously proposed methods highlighting its merits and demerits to develop optimized cross layer technique