Affiliations: [a] School of Information Technology and Intelligent Manufacturing, Shanghai Xingjian College, Shanghai, China
| [b] State Key Laboratory of Rail Transit Engineering Informatization (FSDI), Xi’an, China
| [c] College of Artificial Intelligence Applications, Shanghai Urban Construction Vocational College, Shanghai, China
Correspondence:
[*]
Corresponding author. Jun Wang, State Key Laboratory of Rail Transit Engineering Informatization (FSDI), Xi’an, China. E-mail: [email protected].
Abstract: Selecting appropriate Cluster Heads (CHs) can significantly enhance the lifetime of the wireless sensor networks (WSNs). Fuzzy logic is an effective approach for CH election. However, existing fuzzy-logic-based CH election methods usually require a large number of fuzzy rules, making the CH election procedure inefficiency. In this study, a data-driven CH election method is proposed based on a compact set of fuzzy rules, which are learned by group sparse Takagi-Sugeno-Kang (GS-TSK) fuzzy system. Specifically, five linguistic variables were first used as features to describe the status of sensor nodes. After that, a compact set of fuzzy rules were learned by GS-TSK, and they were then used to predict the chance of each sensor node becoming a CH. Based on the selected CHs, the clusters are generated. Simulation results show that the GS-TSK can select CHs with fewer rules more accurately. Besides, by using the proposed DD-FLC, an average improvement of WSN was shown in terms of first node dead (FND), 10% of nodes dead (10PND), quarter of nodes dead (QND), half of nodes dead (HND).
