Resource aware adaptive binary quantizer design for target tracking in wireless sensor networks

In this thesis, we design a resource aware adaptive binary quantizer for tracking a moving target in a Wireless Sensor Network (WSN). Due to stringent WSN resources, such as node energy or communication bandwidth, rather than transmitting the analog sensor measurements, sensors first preprocess their measurements and then send binary quantized versions of their measurements either directly (Single-hop transmission) or via cluster heads (2-hop transmission) to the Fusion Center (FC). Firstly, at each time step of tracking, the local decision thresholds of sensors are obtained optimally and dynamically as a result of a Multiobjective Optimization Problem (MOP). The considered MOP jointly minimizes the estimation error and number of sensor transmitting to FC under Single-hop links. Secondly, while considering energy depletion in hardware of sensors during transmission, we also formulate MOP to minimize the total energy consumption of the WSN under Single-hop and 2-hop links. As well as MOP, we also prefer Minimum Transmission Energy Path (MTEP) based transmission where sensors' observations follow less energy required path which can be either Single-hop or 2-hop path to reach the FC. Numerical results show that significant savings in both total energy consumption of WSN and the average number of sensors transmitting to the FC are provided while keeping good target tracking performance. Finally, we propose a proportional Time Division Multiple Access (TDMA) based medium access control (MAC) approach where the time allocated to each sensor to transmit its binary decision to the FC becomes related with the value of its measurement while considering wireless channel impairments under Single-hop links. Numerical results show that proportional time allocation provides better estimation performance as compared to equal time allocation.