Computation Offloading Scheme with D2D for MEC-enabled Cellular Networks

Tong, Minglei; Wang, Xiaoxiang; Wang, Yulong; Lan, Yanwen

doi:10.1109/icccworkshops49972.2020.9209940

Cited by 4 publications

(1 citation statement)

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“…In these systems, the tasks of each device can be offloaded to an MEC-BS or a nearby D2D device. Tong et al [33] considered indivisible tasks, balanced the number of mobile devices to be offloaded and the energy consumption of task offloading, and used D2D technology to reduce the backhaul load of the BSs. Both works discussed D2D communication in terms of a single BS; however, in the case of large-scale task offloading, D2D communication is not always feasible because device resources are inherently limited.…”

Section: Related Workmentioning

confidence: 99%

Energy-Aware Cloud-Edge Collaborative Task Offloading with Adjustable Base Station Radii in Smart Cities

Zhang

2022

Mathematics

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In smart cities, the computing power and battery life of terminal devices (TDs) can be effectively enhanced by offloading tasks to nearby base stations (BSs) with richer resources. With the goal of TDs being fully served and achieving low-carbon energy savings for the system, this paper investigates task offloading in cloud-edge collaborative heterogeneous scenarios with multiple BSs and TDs. According to the proportional relationship between the energy and coverage radii of BSs, a complete coverage task offloading model with adjustable BS radii is proposed. The task offloading problem is formulated as an integer linear program with multidimensional resource constraints to minimize the sum of energy consumption of BS coverage, offloading tasks to BSs and the cloud data center (CC). Since this task offloading problem is NP-hard, two approximate algorithms with polynomial time complexity are designed based on the greedy strategy of seeking the most energy-effective disk and the primal–dual method of constructing primal feasible solutions according to dual feasible solutions. Experimental results show that both the greedy and primal–dual algorithms can achieve good approximation performance, but each of them has its own advantages due to different design principles. The former is superior in execution time and energy consumption, while the latter has advantages in balancing loads among BSs and alleviating core network bandwidth pressure.

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Section: Related Workmentioning

confidence: 99%