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Mobile-Edge Computing (MEC) is an emerging paradigm that provides a capillary distribution of cloud computing capabilities to the edge of the wireless access network, enabling rich services and applications in close proximity to the end users. In this article, a MEC enabled multi-cell wireless network is considered where each Base Station (BS) is equipped with a MEC server that can assist mobile users in executing computation-intensive tasks via task offloading. The problem of Joint Task Offloading and Resource Allocation (JTORA) is studied in order to maximize the users' task offloading gains, which is measured by the reduction in task completion time and energy consumption. The considered problem is formulated as a Mixed Integer Non-linear Program (MINLP) that involves jointly optimizing the task offloading decision, uplink transmission power of mobile users, and computing resource allocation at the MEC servers. Due to the NP-hardness of this problem, solving for optimal solution is difficult and impractical for a large-scale network. To overcome this drawback, our approach is to decompose the original problem into (i) a Resource Allocation (RA) problem with fixed task offloading decision and (ii) a Task Offloading (TO) problem that optimizes the optimal-value function corresponding to the RA problem. We address the RA problem using convex and quasi-convex optimization techniques, and propose a novel heuristic algorithm to the TO problem that achieves a suboptimal solution in polynomial time. Numerical simulation results show that our algorithm performs closely to the optimal solution and that it significantly improves the users' offloading utility over traditional approaches. Index TermsMobile edge computing; computation offloading; multi-server resource allocation; distributed systems.The authors are with the

Mobile Edge Computing (MEC) is an emerging paradigm that provides computing, storage, and networking resources within the edge of the mobile Radio Access Network (RAN). MEC servers are deployed on generic computing platform within the RAN and allow for delay-sensitive and context-aware applications to be executed in close proximity to the end users. This paradigm alleviates the backhaul and core network and is crucial for enabling low-latency, highbandwidth, and agile mobile services. This article envisages a real-time, context-aware collaboration framework that lies at the edge of the RAN, comprising MEC servers and mobile devices, and that amalgamates the heterogeneous resources at the edge. Specifically, we introduce and study three representative use-cases ranging from mobile-edge orchestration, collaborative caching and processing, and multi-layer interference cancellation. We demonstrate the promising benefits of the proposed approaches in facilitating the evolution to 5G networks. Finally, we discuss the key technical challenges and open-research issues that need to be addressed in order to make an efficient integration of MEC into 5G ecosystem.

Abstract-Ocean bottom sensor nodes can be used for oceanographic data collection, pollution monitoring, offshore exploration and tactical surveillance applications. Moreover, Unmanned or Autonomous Underwater Vehicles (UUVs, AUVs), equipped with sensors, will find application in exploration of natural undersea resources and gathering of scientific data in collaborative monitoring missions. Underwater acoustic networking is the enabling technology for these applications. Underwater Networks consist of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area.In this paper, several fundamental key aspects of underwater acoustic communications are investigated. Different architectures for two-dimensional and three-dimensional underwater sensor networks are discussed, and the underwater channel is characterized. The main challenges for the development of efficient networking solutions posed by the underwater environment are detailed at all layers of the protocol stack. Furthermore, open research issues are discussed and possible solution approaches are outlined.

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