MWaOA: A Bio-Inspired Metaheuristic Algorithm for Resource Allocation in Internet of Things

Recently, the Internet of Things (IoT) technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices. Furthermore, the IoT plays a key role in multiple domains, including industrial automation, smart homes, and intelligent transportation systems. However, an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness. To address these issue, this research proposes a Modified Walrus Optimization Algorithm (MWaOA) for effective resource management in smart IoT systems. In the proposed MWaOA, a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability. During resource allocation, the MWaOA prevents early convergence, which aids in achieving a better balance between the exploration and exploitation phases during optimization. Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34% and minimizes the response time by 6% to 33% across different service arrival rates. Compared to traditional optimization algorithms, MWaOA reduces energy consumption by 5% to 30% and minimizes the response time by 4% to 28% across different simulation epochs. The proposed MWaOA provides adaptive and robust resource allocation, thereby minimizing transmission cost while considering network constraints and real-time performance parameters.