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Since the flight of Unmanned Aerial Vehicle (UAV) greatly consumes the limited on-board energy and affects the ability to afford computation services, it is urgent and challenging to minimize UAV’s endurance time while satisfying varieties of constraints from the UAVs and users, such as energy budgets, cache capacities, computation resources, and latency requirements. In this paper, the UAVs are endowed with the capability to temporarily buffer some offloaded tasks and process them later, then adopt cooperative computation and cache scheduling to minimize their total endurance time. Following this, the formulated problem is a mix-integer nonlinear programming problem, which can be further transformed into three more tractable subproblems: 1) PO, offloading decision and scheduling strategy for computing & caching; 2) PH, hovering trajectory design; 3) PC, computation resource allocation, and then iteratively tackled in a sequence. Due to the high complexity of solving PO, the low-complexity algorithm based on the K-means clustering and Penalty Method is proposed to reduce complexity and facilitate the processing procedure, which can save about 91.04%-189.36% running time and ONLY sacrifice 8.05%-22.08% of the total endurance time. Simulation results show that cooperative computation and cache scheduling has a great effect on saving endurance time.

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged to provide data processing and caching in the infrastructure-less areas. However, the limited battery capacity of UAV constrains its endurance time, and makes energy efficiency one of the top priorities in implementing UAV-enabled MEC architecture. In this backdrop, we aim to minimize the UAV’s energy consumption by jointly optimizing its trajectory and resource allocation, and task decision and bits scheduling of users considering fairness. The problem is formulated as a mix-integer nonlinear programming problem with strongly coupled variants, and further transformed into three more tractable subproblems: 1) Trajectory optimization PT, 2) Task Decision and Bits Scheduling PS, 3) Resource allocation PR. Then, we propose an iterative algorithm to deal with them in a sequence, and further design a penalty methodbased algorithm to reduce computation complexity when the branch-and-bound (B&B) algorithm incurs a high complexity to solve PS. Simulation results demonstrate that our proposed algorithm can efficiently reduce the energy consumption of UAV, and help save 17.7% -54.6% and 78.9% -91.9% energy compared with Equal Resource Allocation and Random Resource Allocation. Moreover, it reduces more than 88% running time and achieves relatively satisfactory performance compared with B&B.

Mobile Edge Computing (MEC) is a promising architecture to reduce the energy consumption of mobile devices and provide satisfactory quality-of-service to time-sensitive services. How to jointly optimize task offloading and resource allocation to minimize the energy consumption subject to the latency requirement remains an open problem, which motivates this paper. When the latency constraint is taken into account, the optimization variables, including offloading ratio, transmission power, and subcarrier and computing resource allocation, are strongly coupled. To address this issue, we first decompose the original problem into three subproblems named as offloading ratio selection, transmission power optimization, and subcarrier and computing resource allocation. Then, we propose an iterative algorithm to deal with them in a sequence. To be specific, we derive the closed-form solution of offloading ratios, employ the equivalent parametric convex programming to obtain the optimal power allocation policy, and deal with subcarrier and computing resource allocation by the primal-dual method. Simulation results demonstrate that the proposed algorithm can save 20%–40% energy compared with the reference schemes, and can converge to local optimal solutions.

To minimize the average latency of users to complete tasks, this letter jointly optimizes offloading decision, computation and bandwidth resource allocation for Mobile Edge Computing (MEC) networks via partial offloading. Since the considered problem is strongly non-convex with coupled variables, we decompose the original problem into two subproblems: (1) offloading decision; (2) communication bandwidth and computation resources allocation, and further employ Block Coordinate Descent (BCD) method to tackle them sequentially with linear computation complexity. Simulation results demonstrate that the proposed algorithm can achieve better performance on the average latency, and converge speedily.

Since the initial publication of Kelly et al .’s seminal paper on resource allocation in wired networks, many studies based on the cross-layer design philosophy have been conducted in both wired and wireless networks. The Lagrangian duality technique has been widely adopted to solve the cross-layer optimization problem, but it is slowly convergent and sensitive to the iterative step size, especially for large networks. In this paper, a joint congestion control and power allocation second-order algorithm (JCCPA) is proposed, in which the joint optimization problem is modeled as a network utility maximization (NUM) framework, and the primal-dual interior-point method is used to solve the NUM problem. The JCCPA updates the primal variables and dual variables simultaneously according to their Newton directions; as a result,resulting in a faster convergence speed is reached. Moreover, the matrix-splitting technology is utilized to decompose the computation of the Hessian matrix and its inverse into different nodes and links so that the distributed update of source rate and node power can be implemented. The simulation results demonstrate that the JCCPA not only significantly improves energy efficiency but also has a faster convergence speed and is insensitive to step size.

Mobile edge computing (MEC) provides users with a high quality experience (QoE) by placing servers with rich services close to the end users. Compared with local computing, MEC can contribute to energy saving, but results in increased communication latency. In this paper, we jointly optimize task offloading and resource allocation to minimize the energy consumption in an orthogonal frequency division multiple access (OFDMA)-based MEC networks, where the time-sensitive tasks can be processed at both local users and MEC server via partial offloading. Since the optimization variables of the problem are strongly coupled, we first decompose the original problem into two subproblems named as offloading selection (PO), and subcarriers and computing resource allocation (PS), and then propose an iterative algorithm to deal with them in a sequence. To be specific, we derive the closed-form solution for PO, and deal with PS by an alternating way in the dual domain due to its NP-hardness. Simulation results demonstrate, IEEE GLOBECOM 2020. arXiv admin note: substantial text overlap with arXiv:2003.12719

Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV are rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ($$\mathbf {P}_{\mathbf {T}}$$ P T ), resource allocation at UAV ($$\mathbf {P}_{\mathbf {R}}$$ P R ) and offloading decisions at IoT devices ($$\mathbf {P}_{\mathbf {O}}$$ P O ) and then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

In this paper, we investigate offloading scheme and resource allocation strategy for Orthogonal Frequency-Division Multiple Access (OFDMA) based multi-access edge computing (MEC) network to minimize the total system energy consumption. Partial data offloading is studied where mobile date can be computed at both local devices and the edge cloud with the consideration of time-sensitive tasks for users. Since the NP-hardness of the considered optimization problem, we propose an iterative algorithm to decide the proportion of data to offload and design the resource allocation strategy in a sequence. Simulation results show that the proposed algorithm achieves better performance than the reference schemes.