Abstract
In order to reduce the energy consumption and enhance the robustness of wireless sensor network (WSN), this paper proposes a hierarchical clustering routing algorithm based on fuzzy mathematics (HCRAFM). To make a comprehensive analysis of WSN, it is also necessary to detect the robustness of the network. Facing the multiple random variables, the traditional robustness detection models assume that all nodes have the same weight, making it impossible to quantify the analysis indices or obtain accurate results. Thus, the fuzzy mathematics theory was introduced to the WSN robustness detection, forming a fuzzy comprehensive evaluation method. The simulation results show that the HCRAFM strikes a load balance between WSN nodes, extends the life cycle of each node, and prolongs the service life of the network. In addition, the proposed algorithm is proved to have sound robustness and strong applicability.
Highlights
In wireless sensor network (WSN), the routing algorithm is responsible for setting up the path and mechanism of data transmission, realizing the dynamical update of network topology, and maintaining the information of network routing [1]
In light of the above, this paper proposes a hierarchical clustering routing algorithm based on fuzzy mathematics (HCRAFM) for WSN
The proposed algorithm first divides the network into several layers, selects multiple cluster heads using the fuzzy algorithm based on node energy, density and centrality in each layer
Summary
In wireless sensor network (WSN), the routing algorithm is responsible for setting up the path and mechanism of data transmission, realizing the dynamical update of network topology, and maintaining the information of network routing [1]. The drawback can be resolved by integrating multi-hop routing and the hybrid energy efficient distributed (HEED) algorithm Considering both the primary and secondary parameters, the HEED can select cluster heads with uniform communication load and balance the load in the network by selecting cluster heads with uniform load. To solve the above problems, some non-uniform clustering methods have been invented, namely the energy-efficient uneven clustering (EEUC) algorithm, the distributed energy-balanced unequal clustering routing protocol (DEBUC), and the uneven clustering routing protocol based on dynamic partition (UCDP) These methods reduce data forwarding tasks, shorten the communication radius of near-BS clusters, thereby improving the load balance of the whole network. It is wise to adopt multi-hop routing for cluster head communication These moves are likely to further balance node load and slash the energy consumption of the network. The proposed algorithm can balance network load and reduce energy consumption without pushing up the computing complexity
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