Multihop Routing for IoT-Based Digital Twin: Novel Metaheuristic Approaches

This article addresses the challenge of optimizing multihop routing in Internet of Things (IoT)-based digital twin systems, referred to as the MOUNTAIN problem. Multihop routing is inherently complex due to the need to balance energy consumption and communication reliability across multiple nodes, especially in dynamic and large-scale IoT networks. In MOUNTAIN, multiple IoT devices in the physical network (PN) frequently transmit data to the digital network twin (DNT), managed by a central server. Given the limited energy resources of IoT devices, our approach considers both energy efficiency and communication reliability. We formulate the MOUNTAIN problem as an optimization task aimed at reducing overall energy consumption while maintaining robust data transmission. Moreover, we address the problem with both single task optimization and multitask optimization and propose two corresponding evolution-based metaheuristics that utilize well-designed solution representations and genetic operators to obtain near-optimal solutions to the problem. Among them, the proposed single-task evolutionary algorithm (STEA) solves each problem instance independently, while the proposed multitask evolutionary algorithm (MTEA) solves multiple instances at the same time to take advantage of exchanging useful solution information during parallel solution searches. Extensive experiments on synthetic datasets demonstrate that our proposed algorithms significantly outperform existing methods, reducing energy consumption and improving network stability. This research contributes to the development of sustainable and efficient IoT infrastructures, which are essential for the operational demands of digital twin applications.