Target Tracking Algorithm in Wireless Sensor Networks with Optimum Power Consumption Using Quantized Observation

Consensus-based methods are the most commonly used tracking methods in wireless sensor networks due to high error tolerance, precision tracking and scalability. But these methods, due to the high telecommunication overhead, do not have suitable energy efficiency and bandwidth in networks. The proposed tracking algorithm reduces the number of contributing sensors and the network interchange information overhead using dynamic clustering (based on the Cramer-Rao lower bound), and the adaptive quantization of the observations,. On the other hand, the algorithm uses a combination of Multi-lateration method and particle filtering to track targets based on the quantized information. This has led to a decrease in the accuracy of sent observations by 50% (4 bits). as a result, the tracking error is only 10% higher than the algorithm in which no quantization is used.