Abstract
Clustering is an effective method for reducing energy consumption in wireless sensor networks (WSNs). In a multihop clustered network scenario, each sensor node transmits data to its own cluster head (CH), and the CH aggregates the data from its member nodes and forwards it to the base station (BS) via other CHs. However, the “hot spot” problem is prone to occur in clustered WSNs because CHs closer to the BS have heavier intercluster forwarding tasks. To address this problem, this paper proposes an unequal clustering algorithm based on interval type-2 TSK fuzzy logic theory (UCT2TSK). The relative distance to the BS (RDB), residual energy (RE), and node density (ND) are considered as the inputs of an interval type-2 fuzzy logic system (FLS). Through fuzzy reasoning, outputs are acquired that can be used to optimize the CHs and determine the cluster sizes. Simulation results verify that UCT2TSK can effectively balance energy consumption and enhance energy efficiency because it has better performance in network lifetime and network throughput than other classical and recent clustering algorithms.
Highlights
A typical wireless sensor network (WSN) is composed of large numbers of inexpensive microsensor nodes that can cooperatively perceive, process, and transmit the information of interest in the monitoring area
Inspired by the unequal clustering structure and the type2 fuzzy logic system (FLS) and to achieve higher energy efficiency, we propose an unequal clustering algorithm based on interval type-2 TSK (Takagi-Sugeno-Kang) fuzzy logic theory (UCT2TSK) in this paper to solve the hot spot problem and prolong the network lifetime
At the beginning of each round, each node inputs its relative distance to the BS (RDB), residual energy (RE), and node density (ND) (the ND of the first round is set artificially, and the subsequent round ND is obtained according to equation (6)) into the interval type-2 TSK FLS, and the system outputs the competition radius (Cr) and the level of nodes selected as cluster head (CH) (Rank), as described in Lines 6-7 of algorithm 1
Summary
A typical wireless sensor network (WSN) is composed of large numbers of inexpensive microsensor nodes that can cooperatively perceive, process, and transmit the information of interest in the monitoring area. To achieve high energy efficiency and improve the scalability of the network, sensor nodes can be divided into small clusters. The sensor node close to the BS becomes the hot spot and dies prematurely [12] To solve this problem, unequal clustering algorithms are usually introduced in which the network is divided into clusters with unequal sizes. Inspired by the unequal clustering structure and the type FLS and to achieve higher energy efficiency, we propose an unequal clustering algorithm based on interval type-2 TSK (Takagi-Sugeno-Kang) fuzzy logic theory (UCT2TSK) in this paper to solve the hot spot problem and prolong the network lifetime.
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