关键词： multi-access   mobile edge computing   computational power allocation   optimization   Blockchain   NONORTHOGONAL MULTIPLE-ACCESS   NETWORKS   CLOUD   BITCOIN   5G  

摘要： Blockchain has emerged as a decentralized and trustable ledger for recording and storing digital transactions. The mining process of Blockchain, however, incurs a heavy computational workload for miners to solve the proof-of-work puzzle (i.e., a series of the hashing computation), which is prohibitive from the perspective of the mobile terminals (MTs). The advanced multi-access mobile edge computing (MEC), which enables the MTs to offload part of the computational workloads (for solving the proof-of-work) to the nearby edge-servers (ESs), provides a promising approach to address this issue. By offloading the computational workloads via multi-access MEC, the MTs can effectively increase their successful probabilities when participating in the mining game and gain the consequent reward (i.e., winning the bitcoin). However, as a compensation to the ESs which provide the computational resources to the MTs, the MTs need to pay the ESs for the corresponding resource-acquisition costs. Thus, to investigate the trade-off between obtaining the computational resources from the ESs (for solving the proof-of-work) and paying for the consequent cost, we formulate an optimization problem in which the MTs determine their acquired computational resources from different ESs, with the objective of maximizing the MTs' social net-reward in the mining process while keeping the fairness among the MTs. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose efficient distributed algorithms for the MTs to individually determine their optimal computational resources acquired from different ESs. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our proposed multi-access MEC for Blockchain.

关键词： Auction   mobile edge computing   network economics   resource allocation   CELLULAR NETWORKS   EFFICIENT  

摘要： Mobile edge computing (MEC) yields significant paradigm shift in industrial Internet of things (IIoT), by bringing resource-rich data center near to the lightweight IIoT mobile devices (MDs). In MEC, resource allocation and network economics need to be jointly addressed to maximize system efficiency and incentivize price-driven agents, whereas this joint problem is under the locality constraints, i.e., an edge server can only serve multiple IIoT MDs in the vicinity constrained by its limited computing resource. In this paper, we investigate the joint problem of network economics and resource allocation in MEC where IIoT MDs request offloading with claimed bids and edge servers provide their limited computing service with ask prices. Particularly, we propose two double auction schemes with dynamic pricing in MEC, namely a breakeven-based double auction (BDA) and a more efficient dynamic pricing based double auction (DPDA), to determine the matched pairs between IIoT MDs and edge servers, as well as the pricing mechanisms for high system efficiency, under the locality constraints. Through theoretical analysis, both algorithms are proved to be budget-balanced, individual profit, system efficient, and truthful. Extensive simulations have been conducted to evaluate the performance of the proposed algorithms and the simulation results indicate that the proposed DPDA and BDA can significantly improve the system efficiency of MEC in IIoT.

关键词： Connected vehicle network   Internet of Things   delay-tolerant data   mobile edge computing   software-defined networking   smart city   OPPORTUNISTIC SPECTRUM ACCESS   AD HOC NETWORKS   RESOURCE-ALLOCATION   WIRELESS NETWORKS   POMDP APPROACH   INTERNET   TRANSMISSION   FRAMEWORK   DESIGN   THINGS  

摘要： With the explosion in the number of connected devices and Internet of Things (IoT) services in smart city, the challenges to meet the demands from both data traffic delivery and information processing are increasingly prominent. Meanwhile, the connected vehicle networks have become an essential part in smart city, bringing massive data traffic as well as significant communication, caching, and computing resources. As the two typical services types in smart city, delay-tolerant and delay-sensitive traffic requires very different quality of service (QoS)/quality of experience (QoE), and could be delivered through the routes with different features to meet their QoS/QoE requirements with the lowest costs. In this paper, we propose a novel vehicle network architecture in the smart city scenario, mitigating the network congestion with the joint optimization of networking, caching, and computing resources. Cloud computing at the data centers as well as mobile edge computing at the evolved node Bs and on-board units are taken as the paradigms to provide caching and computing resources. The programmable control principle originated from the software-defined networking paradigm has been introduced into this architecture to facilitate the system optimization and resource integration. With the careful modeling of the services, the vehicle mobility, and the system state, a joint resource management scheme is proposed and formulated as a partially observable Markov decision process to minimize the system cost, which consists of both network overhead and execution time of computing tasks. Extensive simulation results with different system parameters reveal that the proposed scheme could significantly improve the system performance compared to the existing schemes.

关键词： Cloud computing   mobile edge computing   computation offloading   Stackelberg game   pricing scheme   RESOURCE-ALLOCATION   RADIO  

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   ANONYMOUS MUTUAL AUTHENTICATION   WIRELESS   VERIFICATION   SCHEME  

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.

关键词： Fog computing   mobile edge cloud (MEC)   mobile edge computing   service model   survey   system architecture   SECURITY  

摘要： Mobile edge cloud (MEC) is a model for enabling on-demand elastic access to, or an interaction with a shared pool of reconfigurable computing resources such as servers, storage, peer devices, applications, and services, at the edge of the wireless network in close proximity to mobile users. It overcomes some obstacles of traditional central clouds by offering wireless network information and local context awareness as well as low latency and bandwidth conservation. This paper presents a comprehensive survey of MEC systems, including the concept, architectures, and technical enablers. First, the MEC applications are explored and classified based on different criteria, the service models and deployment scenarios are reviewed and categorized, and the factors influencing the MEC system design are discussed. Then, the architectures and designs of MEC systems are surveyed, and the technical issues, existing solutions, and approaches are presented. The open challenges and future research directions of MEC are further discussed.

关键词： Mobile edge computing   cloudlet   scalability   resource management   virtualization   transmission power control   particle swarm optimization   OPTIMIZATION   MANAGEMENT   NETWORKS   RADIO  

摘要： Mobile devices have several restrictions due to design choices that guarantee their mobility. A way of surpassing such limitations is to utilize cloud servers called cloudlets on the edge of the network through Mobile Edge Computing. However, as the number of clients and devices grows, the service must also increase its scalability in order to guarantee a latency limit and quality threshold. This can be achieved by deploying and activating more cloudlets, but this solution is expensive due to the cost of the physical servers. The best choice is to optimize the resources of the cloudlets through an intelligent choice of configuration that lowers delay and raises scalability. Thus, in this paper we propose an algorithm that utilizes Virtual Machine Migration and Transmission Power Control, together with a mathematical model of delay in Mobile Edge Computing and a heuristic algorithm called Particle Swarm Optimization, to balance the workload between cloudlets and consequently maximize cost-effectiveness. Our proposal is the first to consider simultaneously communication, computation, and migration in our assumed scale and, due to that, manages to outperform other conventional methods in terms of number of serviced users.

关键词： Mobile Edge Computing   Mobile cloudlets   Load balancing   Extensive game model  

摘要： Mobile Edge computing (MEC) and caching are new forms of computing architecture which brings network functions to the network edge or physical proximity of the users. In MEC, the mobile cloudlets are smart phones which offer computational services to other smart phones available in the physical proximity. Selection of the device for offloading computationally intensive tasks is a very important criterion for end-users Quality of Service. This work focuses on optimal selection of devices which receives offloaded computational tasks. This paper presents a non-cooperative extensive game model where players maximize their pay-offs which leads to minimization of response time. Further, we make our proposed model to implicitly depend upon the battery life of the computational task receivers. The game model achieves Nash Equilibrium by using backward induction technique. This work also takes care of the device availability by clustering the previous availability data. Finally, we evaluate the performance of the proposed model upon response time, end-users utility and memory utilizations. We also see that the proposed work out performs the different schemes which we used for comparisons. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Virtualized network function   Mobile edge computing   Random walk   Boundary value problem   COUPLED PROCESSORS   NETWORKS  

摘要： In this paper, we analyse a system composed of two data centres with limited capacity in terms of computing (namely virtual cores). When one request for a single core is blocked at the first data centre, this request is forwarded to the second one. To protect the requests originally assigned to the second data centre, a trunk reservation policy is introduced (i.e., a redirected request is accepted only if the number of free cores at the second data centre is greater than a given threshold). After rescaling the system when there are many cores in both data centres and high request arrival rates, we are led to analyse a random walk in the quarter plane, which has the particularity of having nonconstant reflecting conditions on one boundary of the quarter plane. Computing the stationary distribution of the present random walk requires the determination of three unknown functions, one polynomial and two infinite generating functions. We show that the coefficients of the polynomial are solutions to a linear system. After numerically solving this linear system, we are able to compute the two other unknown functions and the blocking probabilities at both data centres. Numerical experiments are eventually performed to estimate the gain achieved by the trunk reservation policy. (C) 2018 Elsevier B.V. All rights reserved.

关键词： mobile edge computing   Internet of Things   hybrid energy harvesting   Lyapunov optimization   energy efficiency   delay   NETWORKS   THINGS   CLOUD  

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.