Learning automaton-based energy-efficient and fault-tolerant topology evolution algorithm for underwater acoustic sensor network

Abstract

With the development of Marine Internet of Things, underwater acoustic sensor network (UASN) has become a research focus. However, the energy of UASN nodes is limited, and the complex underwater environment is easy to cause the failure of UASN nodes, which will affect the normal operation of UASN. To address this issue, in this paper, an UASN energy-efficient and fault-tolerant topology is generated to prolong the node lifetime and improve the fault tolerance for node failure. Initially, the learning automaton is introduced and improved to optimize the transmission power of each node. Based on the optimized power of nodes, the node lifetime considering node load is analyzed. Then the preferential growth mechanism is improved to present the energy-efficient and fault-tolerant topology evolution algorithm. Consequently, the energy-efficient and fault-tolerant UASN topology is generated. In the simulation experiments, the selection criteria of parameters about transmission power and node load under different actual requirements is obtained, and the performance of generated topology is validated. Simulation results show that the fault tolerance and energy efficiency of generated topology in this paper outperforms the TCEB and PG-OSTCG topologies.