Mobile Edge Computing

This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists

Full-duplex UAV relay positioning for vehicular networks

Abstract. The unmanned aerial vehicles (UAVs) can be deployed as aerial base stations or wireless relays to enhance the coverage and guarantee the quality of service (QoS) of wireless networks. In this thesis, the positioning of a full-duplex (FD) UAV as a relay to provide coverage for an FD vehicular network is investigated. This problem is solved using two different methods. In both of the methods, the problem is formulated using a predefined set of locations for the UAV. Then this problem is solved for different configurations of the ground users and an optimal location is selected for the UAV to operate at. In the first approach, given the position of the vehicular users on the ground, a novel algorithm is proposed to find a location for the UAV to satisfy the QoS requirements of the vehicles in the network. The positioning problem is formulated as an $\mathcal{l}_0$ minimization which is non-combinatorial and NP-hard, and finding a globally optimal solution for this problem has exponential complexity. Therefore, the $\mathcal{l}_0$-norm is approximated by the $\mathcal{l}_1$-norm. Simulation results show that by locating the UAV using the proposed algorithm the overall performance of the network increases. In the second approach, the UAV positioning problem is solved using an MAB framework. In this case, a simple scenario where only one source node is communicating with the relay to transmit its message to the base station is considered. Given the location of the source node and the predefined locations of the UAV, the MAB algorithm can successfully identify the optimal location for the UAV so the system achieves the maximum possible sum rate. The Greedy, ϵ-Greedy, and upper confidence bound (UCB) algorithms are used to solve the problem. The comparison of these algorithms based on their regret values reveals that the UCB algorithm outperforms the performance of the other algorithms. Simulation results show that the UCB algorithm can successfully identify the optimal location for the UAV to maximize the sum rate of the communication links

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Energy-aware Graph Job Allocation in Software Defined Air-Ground Integrated Vehicular Networks

The software defined air-ground integrated vehicular (SD-AGV) networks have emerged as a promising paradigm, which realize the flexible on-ground resource sharing to support innovative applications for UAVs with heavy computational overhead. In this paper, we investigate a vehicular cloud-assisted graph job allocation problem in SD-AGV networks, where the computation-intensive jobs carried by UAVs, and the vehicular cloud are modeled as graphs. To map each component of the graph jobs to a feasible vehicle, while achieving the trade-off among minimizing UAVs' job completion time, energy consumption, and the data exchange cost among vehicles, we formulate the problem as a mixed-integer non-linear programming problem, which is Np-hard. Moreover, the constraint associated with preserving job structures poses addressing the subgraph isomorphism problem, that further complicates the algorithm design. Motivated by which, we propose an efficient decoupled approach by separating the template (feasible mappings between components and vehicles) searching from the transmission power allocation. For the former, we present an efficient algorithm of searching for all the subgraph isomorphisms with low computation complexity. For the latter, we introduce a power allocation algorithm by applying convex optimization techniques. Extensive simulations demonstrate that the proposed approach outperforms the benchmark methods considering various problem sizes.Comment: 14 pages, 7 figure