LAUSR

The complex and heterogeneous ecosystem of the Internet of Things (IoT) makes it difficult to achieve energy-efficient routing because of the power, memory, and processing constraints of smart motes. Recently, metaheuristic-based routing is preferred by researchers for energy-efficient transmission in IoT. Existing literature divulge their studies to apply metaheuristic directly in IoT by ignoring the principles that account for the overall performance and the global optimum solution. Also, there is no comprehensive study that addresses the issues, principles, and significance of metaheuristic routing in terms of hybridization, objectivity, and applicability in IoT. Being enthused by the aforementioned issues, a detailed taxonomy of metaheuristic-based routing in IoT is presented in this study. A detailed taxonomy is abstracted into an energy-efficient routing framework to provide solutions to the main adversaries encountered during evaluation. The theoretical framework dwells upon two predictive models: i) Metaheuristic-based selection of potential node for energy efficiency in an IoT. ii) Introduction of metaheuristic principles to avoid convergence issues during evaluation. The comprehensive study confers the routing vulnerabilities, energy saving mechanisms, and the pros and cons of metaheuristic in context to IoT. The key research challenges in metaheuristic based routing and future directions to curb with same are thoroughly provided. Further, a smart manufacturing-based case study is demonstrated to generate the fitness criteria for process scheduling problems to gain energy efficiency. The proposed framework provides a benchmarking solution for the ongoing metaheuristic adversaries that have been undercoated in literature to prove the superiority of an algorithm.