Task Execution Cost Minimization-Based Joint Computation Offloading and Resource Allocation for Cellular D2D MEC Systems

Chai, Rong; Lin, Junliang; Chen, Minglong; Chen, Qianbin

doi:10.1109/jsyst.2019.2921115

Cited by 63 publications

(32 citation statements)

References 35 publications

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“…By exploiting the inequalities ( 9), (10) and constraint (7b), p 12 ≤ min {A 1 , p 14 } is obtained consequently. Since the objective value of (7a) increases with p 1 , we further have p * 1 = min {A 1 , p 14 }.…”

Section: B Optimal Solutionmentioning

confidence: 99%

Resource Allocation for Enhancing Offloading Security in NOMA-Enabled MEC Networks

Wang

Zhou

et al. 2021

IEEE Systems Journal

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This letter studies an uplink non-orthogonal multiple access (NOMA) enabled mobile-edge computing (MEC) network. Specifically, we focus on the practical design of secure offloading without knowing the eavesdropper's channel state information. The aim is to maximize the minimum antieavesdropping ability (AEA) for uplink NOMA users subject to the worst-case secrecy rate requirements and limited transmission power budgets. The formulated problem is non-convex and difficult to be solved directly. In order to tackle this issue, we propose an efficient iterative algorithm by jointly designing the secrecy rate, local computing bits and power allocation. The local computing bits and the power allocation are derived in closed form. Numerical results are provided to demonstrate the efficiency of our proposed scheme in terms of AEA.

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Section: B Optimal Solutionmentioning

confidence: 99%

Resource Allocation for Enhancing Offloading Security in NOMA-Enabled MEC Networks

Wang

Zhou

et al. 2021

IEEE Systems Journal

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“…Note that, in addition to performance on communications, UAV coalitions focus the collaboration among UAVs and the performance of missions executions. As an effective technology to reduce the latency and energy consumption of mobile devices, MEC has been studied in many papers, which mainly optimized network delay [16], [18], [35], [36], energy consumption [17], [37], and the weighted sum of delay and energy consumption [20], [38], [39], [43]. Actually, UAVs' flight energy consumption is much larger than energy of transmission and computation.…”

Section: Related Workmentioning

confidence: 99%

“…The computing resource of leaders and members are 12GHz and 3GHz respectively. The data size and the number of CPU cycles required to process 1-bit of data obey the uniform distribution in [20,100] MB and [100,500] cycles. The time constraint is set as 4s.…”

Section: A Simulation Setupmentioning

confidence: 99%

Joint Computation Offloading, Channel Access and Scheduling Optimization in UAV Swarms: A Game-Theoretic Learning Approach

Chen

Wang

et al. 2021

IEEE Open J. Comput. Soc.

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Coalition-based unmanned aerial vehicle (UAV) swarms have been widely used in urgent missions. To fasten the completion, mobile edge computing (MEC) has been introduced into UAV networks where coalition leaders act as servers to help members with data computing. This paper investigates a relative delay optimization in MEC-assisted UAV swarms. Considering that the scheduling methods have great impact on the delay, some theoretical analysis are made and a scheduling method based on shortest effective job first (SEJF) is proposed. Based on the coupled relationship between scheduling and resource allocation, the computation offloading and channel access problem are then jointly optimized. To solve the problem in distributed UAV networks, the optimization problem is formulated as an offloading game. It is proved that the game is an exact potential game (EPG) and it has at least one pure strategy Nash Equilibrium (PNE). To reach the PNE, a distributed offloading algorithm based on concurrent best-better response (CBBR) is designed. Finally, the simulations show that the performance of the proposed CBBR algorithm is better than traditional algorithms. Compared with other scheduling methods, the proposed scheduling method based on SEJF reduces the delay by up to 30%.

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“…Step 3: If the perfect matching is not found, modify the value of the feasible top marks lx(α) and ly(β) to increase the number of feasible edges, the detailed operations can be found in [35].…”

Section: Definitionmentioning

confidence: 99%

An Optimal Guidance Strategy for Moving-Target Interception by a Multirotor Unmanned Aerial Vehicle Swarm

et al. 2020

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To solve the problem of intercepting a moving target by a multirotor unmanned aerial vehicle (UAV) swarm, an optimal guidance strategy is proposed. The proposed guidance law is based on the integration of the classic pure pursuit guidance law and Kuhn-Munkres (KM) optimal matching algorithm, and virtual force potential functions are used to avoid collision. The proposed optimal guidance strategy is demonstrated by simulation experiments. The simulation results indicate that with the proposed optimal guidance strategy, a UAV swarm can intercept a moving target while maintaining the predetermined formation, and during the formation flight, the collisions between UAVs or the target can be avoided. Through a comparative experiment, the proposed optimal matching algorithm is proven to significantly reduce the average per-sampling-period total flight distance of all the UAVs and accelerate the interception process, and the formation completion degree is improved. INDEX TERMS Optimal matching; Unmanned aerial vehicles; Pursuit algorithms; Three-dimensional guidance law; Target interception; Collision avoidance. Xi WANG received his B.E. degree in electrical engineering in 2011 and MA.Eng. degree in control science and engineering in 2014, from Hunan University of Science and Technology. He is currently a doctoral student in the School of Automation, Central South University, China. His research areas are unmanned aerial vehicles, swarm intelligence, and related applications.

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Task Execution Cost Minimization-Based Joint Computation Offloading and Resource Allocation for Cellular D2D MEC Systems

Cited by 63 publications

References 35 publications

Resource Allocation for Enhancing Offloading Security in NOMA-Enabled MEC Networks

Resource Allocation for Enhancing Offloading Security in NOMA-Enabled MEC Networks

Joint Computation Offloading, Channel Access and Scheduling Optimization in UAV Swarms: A Game-Theoretic Learning Approach

An Optimal Guidance Strategy for Moving-Target Interception by a Multirotor Unmanned Aerial Vehicle Swarm

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