Abstract
Aimed at the limited energy of nodes in underwater wireless sensor networks (UWSNs) and the heavy load of cluster heads in clustering routing algorithms, this paper proposes a dynamic layered dual-cluster routing algorithm based on Krill Herd optimization in UWSNs. Cluster size is first decided by the distance between the cluster head nodes and sink node, and a dynamic layered mechanism is established to avoid the repeated selection of the same cluster head nodes. Using Krill Herd optimization algorithm selects the optimal and second optimal cluster heads, and its Lagrange model directs nodes to a high likelihood area. It ultimately realizes the functions of data collection and data transition. The simulation results show that the proposed algorithm can effectively decrease cluster energy consumption, balance the network energy consumption, and prolong the network lifetime.
Highlights
Underwater wireless sensor networks (UWSNs) are network monitoring systems which consist of sensor nodes
As a computing technology that can quickly and efficiently solve complex problems, swarm intelligence has been applied to UWSNs clustering routing algorithm [6]
To improve the utilization of network energy, this paper proposes dynamic layered dual-cluster heads routing algorithm based on Krill Herd (KH) optimization in UWSNs
Summary
Underwater wireless sensor networks (UWSNs) are network monitoring systems which consist of sensor nodes. There is no doubt that the UWSNs become a popular research topic today [1] Sensor nodes use their own battery to provide limited energy. As a computing technology that can quickly and efficiently solve complex problems, swarm intelligence has been applied to UWSNs clustering routing algorithm [6]. Kong et al [8] developed an energy-aware routing algorithm based on cat swarm optimization (CSO). Xie et al [9] introduced a new dual-cluster heads clustering routing algorithm based on PSO (DC-PSO). To improve the utilization of network energy, this paper proposes dynamic layered dual-cluster heads routing algorithm based on Krill Herd (KH) optimization in UWSNs. When the distance between sink nodes is minimal, a huge amount of energy is consumed. It solves the problem that the cluster head nodes are under heavy load, and effectively prolongs the survival time of cluster nodes
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