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Decentralized Network Topology Design for Task Offloading in Mobile Edge Computing
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The rise of delay-sensitive yet computing-intensive Internet of Things (IoT) applications poses challenges due to the limited processing power of IoT devices. Mobile Edge Computing (MEC) offers a promising solution to address these challenges by placing computing servers close to end users. Despite extensive research on MEC, optimizing network topology to improve computational efficiency remains underexplored. Recognizing the critical role of network topology, we introduce a novel decentralized network topology design strategy for task offloading (DNTD-TO) that jointly considers topology design and task allocation. Inspired by communication and sensor networks, DNTD-TO efficiently constructs three-layered network structures for task offloading and generates optimal task allocations for these structures. Comparisons with existing topology design methods demonstrate the promising performance of our approach.
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Cited by 1 Pith paper
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Knowledge-Guided Attention-Inspired Learning for Task Offloading in Vehicle Edge Computing
KATO uses a knowledge-guided attention-style encoder to select roadside units and an iterative algorithm to allocate tasks, achieving near-optimal offloading times at low computational cost in simulations.
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