Resource Allocation Strategy for UWSNs Based on Hybrid Acoustic and MI Communication

The traditional underwater wireless sensor networks (UWSNs) based on acoustic communication (AC) have low transmission rate and high latency. In this paper, a resource allocation strategy for UWSNs based on hybrid AC and magnetic induction (MI) communication is investigated to reduce the total system energy consumption. Specifically, the sensor nodes (SNs) are distributed in seawater at different depths. All the SNs are divided into different clusters and each cluster has a cluster head (CH). First, the SNs in each cluster transmit the data to the CH via MI communication with omnidirectional coils. Then, each CH forwards the aggregated data to the surface base station by AC. The transmitting power and the bandwidth are jointly optimized to minimize the total system energy consumption under the constraints of the transmitting power, bandwidth, transmission rate, and transmission time. The suboptimal solution is obtained by using a social learning particle swarm optimization (SL-PSO) algorithm. Simulation results show that the SL-PSO algorithm has better global search capability and higher convergence speed than other methods. Furthermore, when the number of particles is in a moderate range, the SL-PSO algorithm has lower system energy consumption.