EALLR: Energy-Aware Low-Latency Routing Data Driven Model in Mobile Edge Computing
Xuemin Zhang, Delin Hou, Zenggang Xiong, Yanchao Liu, Shihui Wang, Yuan Li
Abstract
With the rapid development of the Mobile Edge Computing (MEC) and the Internet of Things (IoT), intelligent interconnectivity and data exchange between devices have become feasible, bringing about the need for efficient energy management and complex network communication strategies. This paper proposes an Energy-Aware Low-Latency Routing Data Driven Model in Mobile Edge Computing (EALLR). This model introduces edge nodes that can perform computation and data processing close to the data source, thereby reducing latency and bandwidth consumption, optimizing data transmission paths, and ensuring that messages reach the target nodes quickly and efficiently. It overcomes the challenges posed by the social attributes of nodes, the diversity of network topology, and energy constraints. EALLR utilizes constraints on social strength between nodes, energy status, packet size, and message Time-To-Live (TTL) to optimize routing decisions. By optimizing the selection of routes with minimal energy consumption and adjusting the transmission strategy based on the size of the data packet, the algorithm enhances network energy efficiency and overall transmission efficiency. Simulation tests have proven that EALLR significantly reduces transmission delay while improving message delivery rates, especially in resource-limited and topologically complex network environments, demonstrating superior performance.