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<p>With the growing demand for computation-intensive and latency-critical tasks in intelligent mining, the computing capabilities of terminal devices are becoming increasingly inadequate. Consequently, task offloading has emerged as a vital mechanism. However, existing approaches often depend on centralized resource allocation algorithms, which tend to produce suboptimal assignment decisions. As a result, tasks are frequently offloaded to inappropriate edge servers that become unstable under heavy upload traffic, leading to higher latency and increased energy consumption. To effectively assess system performance, we introduce the Overall Utility Value (OUV), which balances system delay and energy usage. In this paper, we present an edge computing task-offloading framework tailored for mining scenarios, which comprises a central control unit, distributed service nodes, and a large number of terminal devices. By leveraging the environmental awareness of the service nodes, we present a Cooperative Communication and Sensing Task Offloading Scheme (CCTS) designed to minimize both system latency (SL) and system energy consumption (SEC) through optimized task allocation and wireless bandwidth ratios. To tackle this optimization problem, we develop an Improved Gray Wolf Optimization algorithm integrated with a Feasibility Checking Algorithm (IGWO-FCA). Simulation results demonstrate that the IGWO-FCA achieves the lowest OUV, validating its effectiveness.</p>

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Task offloading and resource allocation for cooperative communication and sensing in edge computing for mine

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月15日

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  • Wanbo Zheng,
  • Qiuping Yang,
  • Kerong Chen,
  • Siqi Li,
  • Yunni Xia,
  • Kunyin Guo

摘要

With the growing demand for computation-intensive and latency-critical tasks in intelligent mining, the computing capabilities of terminal devices are becoming increasingly inadequate. Consequently, task offloading has emerged as a vital mechanism. However, existing approaches often depend on centralized resource allocation algorithms, which tend to produce suboptimal assignment decisions. As a result, tasks are frequently offloaded to inappropriate edge servers that become unstable under heavy upload traffic, leading to higher latency and increased energy consumption. To effectively assess system performance, we introduce the Overall Utility Value (OUV), which balances system delay and energy usage. In this paper, we present an edge computing task-offloading framework tailored for mining scenarios, which comprises a central control unit, distributed service nodes, and a large number of terminal devices. By leveraging the environmental awareness of the service nodes, we present a Cooperative Communication and Sensing Task Offloading Scheme (CCTS) designed to minimize both system latency (SL) and system energy consumption (SEC) through optimized task allocation and wireless bandwidth ratios. To tackle this optimization problem, we develop an Improved Gray Wolf Optimization algorithm integrated with a Feasibility Checking Algorithm (IGWO-FCA). Simulation results demonstrate that the IGWO-FCA achieves the lowest OUV, validating its effectiveness.