An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.

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Energy harvesting (EH) is a promising and critical technology to mitigate the dilemma between the limited battery capacity and the increasing energy consumption in the Internet of everything. However, the current EH system suffers from energyinformation cross threats, facing the overlapping vulnerability of energy deprivation and private information leakage. Although some existing works touch on the security of energy and information in EH, they treat these two issues independently, without collaborative and intelligent protection cross the energy side and information side. To address the above challenge, this paper proposes a joint protection framework of energy security and information privacy for EH with an incentive federated learning approach. First, we design a federated learning-based malicious energy user detection method according to energy status and behaviors to provide energy security protection. Secondly, a differential privacy-empowered information preservation scheme is devised, where sensitive information is protected by the customized demand-based noise. Thirdly, a non-cooperative gameenabled incentive mechanism is established to encourage EH nodes to participate in the joint energy-information protection system. The proposed incentive mechanism derives the optimal energy-information security strategy for EH nodes and achieves a tradeoff between the protection of energy security and information privacy. Evaluation results have verified the effectiveness of our proposed joint protection mechanism.

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