关键词： Mobile edge computing  

摘要： The mobile edge computing framework offers the opportunity to reduce the energy that devices must expend to complete computational tasks. The extent of that energy reduction depends on the nature of the tasks, and on the choice of the multiple access scheme. In this paper, we first address the uplink communication resource allocation for offloading systems that exploit the full capabilities of the multiple access channel (FullMA). For indivisible tasks we provide a closed-form optimal solution of the energy minimization problem when a given set of users with different latency constraints are offloading, and a tailored greedy search algorithm for finding a good set of offloading users. For divisible tasks we develop a low-complexity algorithm to find a stationary solution. To highlight the impact of the choice of multiple access scheme, we also consider the TDMA scheme, which, in general, cannot exploit the full capabilities of the channel, and we develop low-complexity optimal resource allocation algorithms for indivisible and divisible tasks under that scheme. The energy reduction facilitated by FullMA is illustrated in our numerical experiments. Further, those results show that the proposed algorithms outperform existing algorithms in terms of energy consumption and computational cost. Copyright © 2018, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Recently, along with the rapid development of mobile communication technology, edge computing theory and techniques have been attracting more and more attentions from global researchers and engineers, which can significantly bridge the capacity of cloud and requirement of devices by the network edges, and thus can accelerate the content deliveries and improve the quality of mobile services. In order to bring more intelligence to the edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with the mobile edge systems, for optimizing the mobile edge computing, caching and communication. And thus, we design the "In-Edge AI" framework in order to intelligently utilize the collaboration among devices and edge nodes to exchange the learning parameters for a better training and inference of the models, and thus to carry out dynamic system-level optimization and application-level enhancement while reducing the unnecessary system communication load. "In-Edge AI" is evaluated and proved to have near-optimal performance but relatively low overhead of learning, while the system is cognitive and adaptive to the mobile communication systems. Finally, we discuss several related challenges and opportunities for unveiling a promising upcoming future of "In-Edge AI". Copyright © 2018, The Authors. All rights reserved.

关键词： Delay   Energy efficiency   Hybrid energy harvesting   Internet of things   Lyapunov optimization   Mobile edge computing  

摘要： Mobile Edge Computing (MEC) has evolved into a promising technology that can relieve computing pressure on wireless devices (WDs) in the Internet of Things (IoT) by offloading computation tasks to the MEC server. Resource management and allocation are challenging because of the unpredictability of task arrival, wireless channel status and energy consumption. To address such a challenge, in this paper, we provide an energy-efficient joint resource management and allocation (ECM-RMA) policy to reduce time-averaged energy consumption in a multi-user multi-task MEC system with hybrid energy harvested WDs. We first formulate the time-averaged energy consumption minimization problem while the MEC system satisfied both the data queue stability constraint and energy queue stability constraint. To solve the stochastic optimization problem, we turn the problem into two deterministic sub-problems, which can be easily solved by convex optimization technique and linear programming technique. Correspondingly, we propose the ECM-RMA algorithm that does not require priori knowledge of stochastic processes such as channel states, data arrivals and green energy harvesting. Most importantly, the proposed algorithm achieves the energy consumption-delay trade-off as [O(1/V), O(V)]. V, as a non-negative weight, which can effectively control the energy consumption-delay performance. Finally, simulation results verify the correctness of the theoretical analysis and the effectiveness of the proposed algorithm. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Mobile edge computing  

摘要： Mobile edge computing is a new computing paradigm, which pushes cloud computing capabilities away from the centralized cloud to the network edge. However, with the sinking of computing capabilities, the new challenge incurred by user mobility arises: Since end-users typically move erratically, the services should be dynamically migrated among multiple edges to maintain the service performance, i.e., user-perceived latency. Tackling this problem is non-trivial since frequent service migration would greatly increase the operational cost. To address this challenge in terms of the performance-cost trade-off, in this paper we study the mobile edge service performance optimization problem under long-term cost budget constraint. To address user mobility which is typically unpredictable, we apply Lyapunov optimization to decompose the long-term optimization problem into a series of real-time optimization problems which do not require a priori knowledge such as user mobility. As the decomposed problem is NP-hard, we first design an approximation algorithm based on Markov approximation to seek a nearoptimal solution. To make our solution scalable and amenable to future 5G application scenario with large-scale user devices, we further propose a distributed approximation scheme with greatly reduced time complexity, based on the technique of best response update. Rigorous theoretical analysis and extensive evaluations demonstrate the efficacy of the proposed centralized and distributed schemes. Copyright © 2018, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Achieving an end-to-end low-latency for computations offloading, in Mobile Edge Computing (MEC) systems, is still a critical design problem. This is because the offloading of computational tasks via the MEC servers entails the use of uplink (UL) and downlink (DL) radio links that are usually assumed to be coupled to a single base station (BS). However, for heterogeneous networks, a new architectural paradigm whereby UL and DL are not associated with the same BS is proposed and seen to provide gains in network throughput due to the improved UL performance. Motivated by such gains, and by using typical results from stochastic geometry, we formulate the offloading latency for the MEC-based scheme with decoupled UL/DL association, or decoupled access, and compare its performance to the conventional coupled access scheme. Despite the backhaul delay necessary for the communication between the two serving BSs in UL and DL, the offloading scheme with decoupled access is still capable of providing a fairly lower offloading latency compared to the conventional offloading scheme with coupled access. Copyright © 2018, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： This paper studies a wireless powered mobile edge computing (MEC) system, where a dedicated energy transmitter (ET) uses the radio-frequency (RF) signal enabled wireless power transfer (WPT) to charge wireless devices for sustainable computation. In such a system, we present a new user cooperation approach to improve the computation performance of active devices, in which surrounding idle devices are enabled as helpers to use their opportunistically harvested wireless energy from the ET to help remotely execute active users' computation tasks. In particular, we consider a basic scenario with one user (with computation tasks to execute) and multiple helpers, in which the user can partition the computation tasks into various parts for local execution and computation offloading to helpers, respectively. Both the user and helpers are subject to the so-called energy neutrality constraints, such that their energy consumption does not exceed the respective energy harvested from the ET. Under this setup and considering a frequency division multiple access (FDMA) based computation offloading protocol, we maximize the computation rate (i.e., the number of computation bits over a particular time block) of the user, by jointly optimizing the transmit energy beamforming at the ET, as well as the communication and computation resource allocations at both the user and helpers. By leveraging the Lagrange duality method, we present the optimal solution to this problem in a semi-closed form. Numerical results show that the proposed wireless powered user cooperative computation design significantly improves the computation rate at the user, as compared to conventional schemes without such cooperation. Copyright © 2018, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： This paper investigates an uplink non-orthogonal multiple access (NOMA)-based mobile-edge computing (MEC) network. Our objective is to minimize the total energy consumption of all users including transmission energy and local computation energy subject to computation latency and cloud computation capacity *** first prove that the total energy minimization problem is a convex problem, and it is optimal to transmit with maximal time. Then, we accordingly proposed an iterative algorithm with low complexity, where closed-form solutions are obtained in each step. The proposed algorithm is successfully shown to be globally optimal. Numerical results show that the proposed algorithm achieves better performance than the conventional methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Mobile edge caching enables content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case where the model noise is zero-mean, a ridge regression based online algorithm with positive perturbation is proposed. Regret analysis indicates that the proposed algorithm asymptotically approaches the optimal caching strategy in the long run. When the noise structure is unknown, an H1 filter based online algorithm is further proposed by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases, and hence are robust to the time-varying user demands. The underlying causes of estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments on real world dataset are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving content hit rate that is comparable to that via the hindsight optimal strategy. Copyright © 2018, The Authors. All rights reserved.

关键词： Cloud computing   mobile edge computing   computation offloading   Stackelberg game   pricing scheme  

摘要： The mobile edge computation offloading (MECO) system has emerged as a promising technology to address several problematic issues in cloud computing such as data throughput, capacity, and security. In this letter, we consider a single-cloud-multi-user MECO system with orthogonal frequency-division multiple access and propose an optimal pricing scheme to consider each mobile user's (MU) need for resources. The proposed model was formulated as a single-leader-multi-user Stackelberg game model, and the Stackelberg equilibrium was provided to optimize the utility of the MUs and the edge cloud. Here, the utility of the MU implies the energy efficiency and payment for using the computation capacity of the edge cloud, and the utility of the edge cloud implies the revenue for serving computation offloading. The optimal strategies of the MUs are uniquely given by closed-form expressions, and that of the edge cloud is given by gradient descent methods. Some numerical examples are provided to verify our approach.

关键词： Tiered mobile wireless sensor networks   Mobile edge computing   Spatio-temporal Top-k query   Privacy   Integrity  

摘要： TMWSNs (Tiered Mobile Wireless Sensor Networks), which can be seen as a novel paradigm of mobile edge computing, attracts more and more attentions because of their high scalability, good robustness, and perfect deployment flexibility. However, TMWSNs are facing security threat because their key nodes, namely the storage nodes, are much more prone to be captured by the adversaries and become malicious. With the storage nodes captured, all the data stored on them are disclosed to the adversaries, and the data processing at the storage nodes is no longer secure either. In this paper, we focus on the privacy and-integrity preservation for fine-grained spatio-temporal Top-k queries in TMWSNs, and propose two different schemes which are named as SSSTQl (Scheme 1 for Securing Spatio-temporal Top-k Queries) and SSSTQ2 (Scheme 2 for Securing Spatio-temporal Top-k Queries) respectively. SSSTQl is much fit for scenarios where the mobile sensor nodes in TMWSNs only move within their own cells, while SSSTQ2 can be exploited in the case that the mobile sensor nodes move freely in the whole network deployment field with a little sacrifice of efficiency and security. We illustrate their performances on energy efficiency and security through both extensive simulations and theory analysis. (C) 2018 Elsevier B.V. All rights reserved.