An Adaptive Sampling Algorithm for Target Tracking in Underwater Wireless Sensor Networks

Abstract

Target tracking is an important application of underwater wireless sensor networks (UWSNs). Due to the energy constraint and energy imbalanced dissipation of underwater nodes, it is a challenge to maximize the energy efficiency and balance energy consumption simultaneously. In this paper, we propose an adaptive sampling algorithm for target tracking in UWSNs to address this issue. First, for maximizing the energy efficiency, we design an adaptive sampling interval adjustment (ASIA) method using a two-input-single-output fuzzy logic controller. In this method, the sampling interval is adaptively adjusted to make the actually uncertainty equal to the uncertainty threshold, which minimizes the sampling frequency and then reduces the energy consumption of information exchange. Second, for balancing the energy consumption, we develop a dynamic uncertainty threshold adjustment (DUTA) method using a single-input-single-output fuzzy logic controller. According to the residual energy of network nodes, the DUTA method dynamically adjusts the uncertainty threshold in the ASIA method, which changes the sampling frequency for avoiding premature death of nodes. Finally, the simulations show that, compared to the existing adaptive sampling algorithm, the proposed algorithm not only saves about 36% of energy but also alleviates the imbalance of energy consumption in different parts of the tracking area.