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Energy-efficient cache content placement strategy for electric Internet of Things.

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Title: Energy-efficient cache content placement strategy for electric Internet of Things.
Authors: Xia, Yuanyi1 (AUTHOR) xiayuanyi@yeah.net, Teng, Changzhi1 (AUTHOR) tengcz@126.com, Zeng, Zeng1 (AUTHOR) zengz@js.sgcc.com.cn, He, Muxin1 (AUTHOR) mxhejsepc@163.com, Shen, Yingying2 (AUTHOR) 1223013629@njupt.edu.cn
Source: Wireless Networks (10220038). Jun2026, Vol. 32 Issue 3, p1569-1579. 11p.
Subjects: Energy consumption, Cache memory, Genetic algorithms, Edge computing, Cyber physical systems
Abstract: The surge in electric grid applications imposes severe burdens on electric Internet of Things (eIoT) networks. In mobile edge computing (MEC), edge caching reduces latency and energy consumption by storing frequently requested content at the network edge. Nevertheless, achieving energy-efficient cache placement in eIoT remains challenging due to eIoT's heterogeneity. In particular, most existing works focus on isolated SBS caching and overlook the hierarchical collaboration between SBSs and MBSs in content delivery. To address this gap, we propose an effective cache content placement strategy. Specifically, we develop an integrated energy consumption model for hierarchical eIoT networks that explicitly accounts for caching energy at SBSs and MBS multicast transmission overhead, a critical cost factor overlooked in existing single-tier caching approaches. An optimization problem is subsequently formulated with the objective of minimizing overall system energy consumption. Given that this problem is a complex, multi-dimensional, and multi-option knapsack problem, we propose a content placement matrix optimization algorithm inspired by genetic algorithms. The proposed method effectively combines random exploration with systematic refinement to iteratively improve the content placement strategy. Simulation results demonstrate that our algorithm significantly outperforms existing benchmarks, reducing total system energy consumption, and establishes a new benchmark for energy-efficient content placement in eIoT systems. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:The surge in electric grid applications imposes severe burdens on electric Internet of Things (eIoT) networks. In mobile edge computing (MEC), edge caching reduces latency and energy consumption by storing frequently requested content at the network edge. Nevertheless, achieving energy-efficient cache placement in eIoT remains challenging due to eIoT's heterogeneity. In particular, most existing works focus on isolated SBS caching and overlook the hierarchical collaboration between SBSs and MBSs in content delivery. To address this gap, we propose an effective cache content placement strategy. Specifically, we develop an integrated energy consumption model for hierarchical eIoT networks that explicitly accounts for caching energy at SBSs and MBS multicast transmission overhead, a critical cost factor overlooked in existing single-tier caching approaches. An optimization problem is subsequently formulated with the objective of minimizing overall system energy consumption. Given that this problem is a complex, multi-dimensional, and multi-option knapsack problem, we propose a content placement matrix optimization algorithm inspired by genetic algorithms. The proposed method effectively combines random exploration with systematic refinement to iteratively improve the content placement strategy. Simulation results demonstrate that our algorithm significantly outperforms existing benchmarks, reducing total system energy consumption, and establishes a new benchmark for energy-efficient content placement in eIoT systems. [ABSTRACT FROM AUTHOR]
ISSN:10220038
DOI:10.1007/s11276-026-04138-y