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 (
 $$\mathbf {P}_{\mathbf {T}}$$
 ), resource allocation at UAV (
 $$\mathbf {P}_{\mathbf {R}}$$
 ) and offloading decisions at IoT devices (
 $$\mathbf {P}_{\mathbf {O}}$$
 ) 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.

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Collaborative offloading for UAV-enabled time-sensitive MEC networks

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.

Energy-Aware Task Offloading and Resource Allocation for Time-Sensitive Services in Mobile Edge Computing Systems

Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication networks. These networks have an avenue for generating a considerable amount of heterogeneous data by the expanding number of Internet of Things (IoT) devices in smart environments. However, storing and processing massive data with limited computational capability and energy availability at local nodes in the IoT network has been a significant difficulty, mainly when deploying Artificial Intelligence (AI) techniques to extract discriminatory information from the massive amount of data for different tasks.Therefore, Mobile Edge Computing (MEC) has evolved as a promising computing paradigm leveraged with efficient technology to improve the quality of services of edge devices and network performance better than cloud computing networks, addressing challenging problems of latency and computation-intensive offloading in a UAV-assisted framework. This paper provides a comprehensive review of intelligent UAV computing technology to enable 6G networks over smart environments. We highlight the utility of UAV computing and the critical role of Federated Learning (FL) in meeting the challenges related to energy, security, task offloading, and latency of IoT data in smart environments. We present the reader with an insight into UAV computing, advantages, applications, and challenges that can provide helpful guidance for future research.

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Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

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.

Latency-Aware Offloading for Mobile Edge Computing Networks

In this paper, we exploit energy-efficiency and QoS-awareness for receiver-arbitrated and sender-predicted MAC Protocol (EQAP-MAC) in wireless sensor network. EQAP-MAC improves the transmission concurrency by separating control stream from data stream; minimizes energy consumption by sender by predicting receiver’s wake-up time; supports priority-based QoS through priority-based data flow selected, arbitrated and interrupted mechanisms; reduces transmission interference between the nodes by interleaving neighbor nodes’ wake-up time; improves system throughput and reduces the data stream transmitting delay by pipelining mechanism and optimizing send sequence. We have implemented EQAP-MAC on TinyOS and evaluated its performance on testbed. The result shows EQAP-MAC achieves higher concurrent performance and priority-based QoS guarantee with lower energy consumption compared with RI-MAC and X-AMC.

Utilizing Energy Efficiency and QoS awareness for Receiver-Arbitrated and Sender-Predicted MAC Protocol in Wireless Sensor Network

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

Energy Minimization for Mobile Edge Computing Networks with Time-Sensitive Constraints

Mobile edge computing (MEC) provides users with a high quality of 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 ($\mathrm{P}_{\mathrm{O}}$), and subcarriers and computing resource allocation ($\mathrm{P}_{\mathrm{S}}$), and then propose an iterative algorithm to deal with them in a sequence. To be specific, we derive the closed-form solution for $\mathrm{P}_{\mathrm{O}}$, and deal with $\mathrm{p}_{\mathrm{s}}$ by an alternating way in the dual domain due to its NP-hardness. Simulation results demonstrate that the proposed algorithm outperforms the existing schemes.

Mi Wu

Papers

Energy Minimization for Mobile Edge Computing Networks with Time-Sensitive Constraints

Energy Minimization for Mobile Edge Computing Networks with Time-Sensitive Constraints