The current dispensation of big data analytics requires innovative ways of data capturing and transmission. One of the innovative approaches is the use of a sensor device. However, the challenge with a sensor network is how to balance the energy load of wireless sensor networks, which can be achieved by selecting sensor nodes with an adequate amount of energy from a cluster. The clustering technique is one of the approaches to solve this challenge because it optimizes energy in order to increase the lifetime of the sensor network. In this article, a novel bio-inspired clustering algorithm was proposed for a heterogeneous energy environment. The proposed algorithm (referred to as DEEC-KSA) was integrated with a distributed energy-efficient clustering algorithm to ensure efficient energy optimization and was evaluated through simulation and compared with benchmarked clustering algorithms. During the simulation, the dynamic nature of the proposed DEEC-KSA was observed using different parameters, which were expressed in percentages as 0.1%, 4.5%, 11.3%, and 34% while the percentage of the parameter for comparative algorithms was 10%. The simulation result showed that the performance of DEEC-KSA is efficient among the comparative clustering algorithms for energy optimization in terms of stability period, network lifetime, and network throughput. In addition, the proposed DEEC-KSA has the optimal time (in seconds) to send a higher number of packets to the base station successfully. The advantage of the proposed bio-inspired technique is that it utilizes random encircling and half-life period to quickly adapt to different rounds of iteration and jumps out of any local optimum that might not lead to an ideal cluster formation and better network performance.

中文翻译：

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无线传感器网络上高级物联网分析边缘计算的智能能源优化

当前大数据分析的分配需要创新的数据捕获和传输方式。其中一种创新方法是使用传感器设备。然而，传感器网络面临的挑战是如何平衡无线传感器网络的能量负载，这可以通过从集群中选择具有足够能量的传感器节点来实现。聚类技术是解决这一挑战的方法之一，因为它优化了能量以增加传感器网络的寿命。在本文中，针对异构能源环境提出了一种新颖的仿生聚类算法。所提出的算法（简称DEEC-KSA）与分布式节能聚类算法相结合，以确保高效的能源优化，并通过仿真评估并与基准聚类算法进行比较。在模拟过程中，使用不同的参数观察所提出的 DEEC-KSA 的动态特性，以百分比表示为 0.1%、4.5%、11.3% 和 34%，而比较算法的参数百分比为 10%。仿真结果表明，DEEC-KSA 的性能在能量优化的比较聚类算法中在稳定期、网络寿命和网络吞吐量方面是有效的。此外，提议的 DEEC-KSA 具有向基站成功发送更多数据包的最佳时间（以秒为单位）。