Designing green IoT communication by adaptive spotted hyena tunicate swarm optimization-based cluster head selection,Transactions on Emerging Telecommunications Technologies

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Designing green IoT communication by adaptive spotted hyena tunicate swarm optimization-based cluster head selection
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2022-07-14 , DOI: 10.1002/ett.4595
Anupam Das ₁

Affiliation

The Internet of Things (IoT) has completely transformed the digital as well as virtual worlds of interconnected objects. In today's environment, the IoT describes a critical enabler for a broader variety of applications. Energy is at the center of the smart IoT application that allows the sensors to function. The sensors' ability to perform efficiently is hampered by rapid energy loss. To avoid the rapid loss of energy from sensors in the IoT, and energy-effective method is necessary. Because heuristic approaches are not ideal for these issues and may turn into NP-hard problems, meta-heuristic techniques are mainly successful in solving various problems with near-optimal solutions. This paper intends to develop a novel clustering protocol in Green IoT using a well-performing novel hybrid adaptive meta-heuristic algorithm. Here, an energy-efficient clustering is performed by the hybrid spotted hyena optimization (SHO) and tunicate swarm algorithm (TSA) referred to as adaptive spotted hyena tunicate swarm optimization (ASHTSO) by deriving a multi-objective function with “distance, energy, delay, security, and QoS”. The simulation findings show that the suggested technique outperforms existing algorithms significantly.

中文翻译：

基于自适应斑鬣狗被囊群优化的簇头选择设计绿色物联网通信

物联网 (IoT) 彻底改变了互连对象的数字世界和虚拟世界。在当今的环境中，物联网描述了更广泛应用的关键推动力。能源是智能物联网应用的核心，它允许传感器发挥作用。快速能量损失阻碍了传感器高效执行的能力。为了避免物联网中传感器的能量快速损失，节能方法是必要的。由于启发式方法对于这些问题并不理想，并且可能会变成 NP 难题，因此元启发式技术主要成功地解决了具有接近最优解决方案的各种问题。本文旨在使用性能良好的新型混合自适应元启发式算法在绿色物联网中开发一种新型集群协议。这里，由混合斑点鬣狗优化 (SHO) 和被囊群算法 (TSA) 执行节能聚类，称为自适应斑点鬣狗被囊群优化 (ASHTSO)，通过导出具有“距离、能量、延迟、安全性和 QoS”。模拟结果表明，建议的技术明显优于现有算法。

更新日期：2022-07-14

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