Design of intelligent operation inspection platform based on the multi-agent system for live line measurement of substation

Xuefeng, Ning; Guo, Yufeng

doi:10.1088/1742-6596/2401/1/012088

Cited by 1 publication

(2 citation statements)

References 13 publications

(1 reference statement)

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“…Machine learning-based task allocation [28] DQN-D EC [54] IoT [38] MARL IoT [42] ACO IoT, 5G [51] FLOM-Opt [53] Q-Learning IoT, IoV [57] MA IoT [58] Heuristic, RL EC Quality of service task allocation [26] EC [32] MMAS EC [48] QT EC, IoT Resource-aware task allocation [76] MAPPO MEC [62] MDP EC [14] MAP VEC [75] JTORA VEC [72] JTORA MEC [74] JTORA MEC [15] DNF [16] MARL MEC [21] JTORA NOMA-MEC [37] EC [39] ELB [40] Knapsack MCS [70] EC, 5G…”

Section: Proposed Task Allocation Optimization (Rq2) Applied Network ...mentioning

confidence: 99%

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Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature

Patsias,

Amanatidis,

Karampatzakis

et al. 2023

Future Internet

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Task allocation in edge computing refers to the process of distributing tasks among the various nodes in an edge computing network. The main challenges in task allocation include determining the optimal location for each task based on the requirements such as processing power, storage, and network bandwidth, and adapting to the dynamic nature of the network. Different approaches for task allocation include centralized, decentralized, hybrid, and machine learning algorithms. Each approach has its strengths and weaknesses and the choice of approach will depend on the specific requirements of the application. In more detail, the selection of the most optimal task allocation methods depends on the edge computing architecture and configuration type, like mobile edge computing (MEC), cloud-edge, fog computing, peer-to-peer edge computing, etc. Thus, task allocation in edge computing is a complex, diverse, and challenging problem that requires a balance of trade-offs between multiple conflicting objectives such as energy efficiency, data privacy, security, latency, and quality of service (QoS). Recently, an increased number of research studies have emerged regarding the performance evaluation and optimization of task allocation on edge devices. While several survey articles have described the current state-of-the-art task allocation methods, this work focuses on comparing and contrasting different task allocation methods, optimization algorithms, as well as the network types that are most frequently used in edge computing systems.

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Section: Proposed Task Allocation Optimization (Rq2) Applied Network ...mentioning

confidence: 99%

“…Moreover, Xuefeng et al [62] addressed the drawbacks of the aforementioned current technologies. The paper created a platform for intelligent operation inspection that relies on a multi-agent system and is used for the live line measurement of substation equipment.…”

Section: Resource-aware Task Allocationmentioning

confidence: 99%