Energy-Efficient Routing for Internet of Things Using Combination of Meta-Heuristic Algorithms in Viral Pandemics

Emerging viral illnesses like COVID-19, MERS, and SARS are a major concern for public health and have piqued the interest of scientists. Rarely, the Internet of Things transmits vital signs like a patient's temperature, pulse, blood pressure, and oxygen saturation levels. On a regular basis, the medical center receives reports from the patient's body that use little power. It could be challenging to transmit information to data centers due to the uneven power consumption of nodes. In order to decrease device power consumption and ensure efficient interaction, a reliable routing protocol is required. When it comes to routing techniques, clustering is among the best for lowering power consumption and increasing system longevity. In this work, a whale optimization approach and a harmony search strategy are developed to identify intermediate and cluster head nodes required for routing, respectively, following the NP-Hard clustering format. NS-3 simulator outcomes showed that the offered method defeats the regular algorithm in the factors of lifetime, energy consumption, and the number of active and inactive nodes in the system. In comparison to conventional clustering methods, A decrease in system power consumption and a shortening of latency are both accomplished by the proposed method.