关键词： Edge computing   Internet of things (IoT)   Mobile edge computing   Modules placement   Resources management  

摘要： Using mobile and Internet of Things (IoT) applications is becoming very popular and obtained researchers’ interest and commercial investment, in order to fulfill future vision and the requirements for smart cities. These applications have common demands such as fast response, distributed nature, and awareness of service location. However, these requirements’ nature cannot be satisfied by central systems services that reside in the clouds. Therefore, edge computing paradigm has emerged to satisfy such demands, by providing an extension for cloud resources at the network edge, and consequently, they become closer to end-user devices. In this paper, exploiting edge resources is studied;therefore, a cooperative-hierarchical approach for executing the pre-partitioned applications’ modules between edges resources is proposed, in order to reduce traffic between the network core and the cloud, where this proposed approach has a polynomial-time complexity. Furthermore, edge computing increases the efficiency of providing services, and improves end-user experience. To validate our proposed cooperative-hierarchical approach for modules placement between edge nodes’ resources, iFogSim toolkit is used. The obtained simulation results show that the proposed approach reduces network’s load and the total delay compared to a baseline approach for modules’ placement, moreover, it increases the network’s overall throughput. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved.

关键词： Mobile edge computing   cognitive radio   underlay spectrum sharing   physical-layer security   wireless power transfer  

摘要： Wireless power transfer (WPT) and cognitive radio (CR) are two promising techniques in designing mobile-edge computing (MEC) systems. In this paper, we study a robust secure wireless powered multiple-input single-output (MISO) cognitive MEC system, which integrates several techniques: physical-layer security, WPT, CR, underlay spectrum sharing and MEC. Three optimization problems are formulated to minimize the total transmission power (TTP) of the primary transmitter (PT) and the secondary base station (SBS) under perfect channel state information (CSI) model, bounded CSI error model and the probabilistic CSI error model, respectively. The formulated problems are nonconvex and hard to solve. Three two-phase iterative optimization algorithms combined with Lagrangian dual, semidefinite relaxation (SDR), S-Procedure and Bernstein-type inequalities are proposed to jointly optimize the beamforming vectors of the PT and the SBS, the central processing unit (CPU) frequency and the transmit power of the MD. Simulation results are provided to verify the effectiveness of the proposed algorithms.

关键词： Array signal processing   mobile edge computing   IoT   smart city  

摘要： To support the application of IoT and smart city, high data-rate wireless transmission is required. To meet the demand of high data-rate, the techniques of multiple antennas and mobile edge computing (MEC) networks have been proposed in order to enhance the data transmission rate significantly. However, there still exist lots of challenges array signal processing assisted MEC networks. In this paper, we propose an intelligent framework of offloading strategy for MEC networks assisted by array signal processing, where one user with multiple antennas has some computational tasks. These tasks can be computed by the user itself which however has limited computational capability, or computed by the near-by computational access points (CAPs) which has a powerful computational capability at the cost of wireless transmission. We consider the system cost by jointly taking into account the computational price, the energy consumption and the latency. By minimizing the system cost, we propose an intelligent offloading strategy based on ant colony optimization (ACO) algorithm, where the ants randomly visit the CAPs in order to obtain the final results. To further enhance the MEC network performance, the array signal processing is utilized at the user, where either the maximum ratio transmission (MRT) or selection combining (SC) is used to assist the data transmission from the user to CAPs. Simulation results with MRT and SC are finally demonstrated to verify the effectiveness of the proposed ACO-based offloading strategy and array signal processing schemes.

关键词： Task analysis   Neural networks   Approximation algorithms   Computational modeling   Energy consumption   Computer architecture   Servers   Mobile edge computing   multidimensional multiple knapsack problem   multi-pointer networks   reinforcement learning  

摘要： Computation offloading is an efficient approach to reduce the energy consumption of a mobile device (MD). In this paper, we consider the multi-user offloading problem for mobile edge computing (MEC) in a multi-server environment. Its aim is to minimize the total energy consumption of MDs. This problem has been proven to be NP-hard. We formulate the problem as a multidimensional multiple knapsack (MMKP) problem with constraints, and propose a neural network architecture called Multi-Pointer networks (Mptr-Net) to solve the problem. We train Mptr-Net based on the reinforcement learning method, and design an algorithm to search for feasible solutions that meet the constraints. The simulation results show that the probability of a Mptr-Net obtaining an optimal solution can exceed 98%, which is approximately 25% more than that of a baseline heuristic algorithm. Additionally, the time needed to solve the problem by our neural network is stable compared with that of a mathematical programming solver named or-tools.

关键词： Cloud computing   Servers   Edge computing   Mobile handsets   5G mobile communication   Task analysis   Internet of Things   Mobile edge computing   cloudlet   scalability   communication   computation resource management   ML   artificial intelligence  

摘要： Mobile Edge Computing (MEC) is considered an essential future service for the implementation of 5G networks and the Internet of Things, as it is the best method of delivering computation and communication resources to mobile devices. It is based on the connection of the users to servers located on the edge of the network, which is especially relevant for real-time applications that demand minimal latency. In order to guarantee a resource-efficient MEC (which, for example, could mean improved Quality of Service for users or lower costs for service providers), it is important to consider certain aspects of the service model, such as where to offload the tasks generated by the devices, how many resources to allocate to each user (specially in the wired or wireless device-server communication) and how to handle inter-server communication. However, in the MEC scenarios with many and varied users, servers and applications, these problems are characterized by parameters with exceedingly high levels of dimensionality, resulting in too much data to be processed and complicating the task of finding efficient configurations. This will be particularly troublesome when 5G networks and Internet of Things roll out, with their massive amounts of devices. To address this concern, the best solution is to utilize Machine Learning (ML) algorithms, which enable the computer to draw conclusions and make predictions based on existing data without human supervision, leading to quick near-optimal solutions even in problems with high dimensionality. Indeed, in scenarios with too much data and too many parameters, ML algorithms are often the only feasible alternative. In this paper, a comprehensive survey on the use of ML in MEC systems is provided, offering an insight into the current progress of this research area. Furthermore, helpful guidance is supplied by pointing out which MEC challenges can be solved by ML solutions, what are the current trending algorithms in frontier ML research and

关键词： Mobile edge computing   cognitive radio   physical-layer security   wireless power transfer   MISO  

摘要： The finite battery capacity, limited spectrum resource and the transmission security are three challenges in designing mobile edge computing (MEC) systems. In this paper, a framework for wireless powered secure multiple-input single-output (MISO) cognitive-based MEC-enabled networks is proposed, which integrates several technologies: physical-layer security, cooperative relaying, cognitive radio (CR), wireless power transfer (WPT) and MEC. Two optimization problems are formulated to maximize the number of computation bits (NCB) of the mobile device (MD) and minimize the total transmission power (TTP) of the primary transmitter (PT) and the SBS, respectively. The formulated problems are non-convex and hard to solve. Two two-phase methods with block coordinate decent (BCD) and Lagrangian dual decomposition methods are proposed to jointly design the beamforming vector of the PT, beamforming matrix of the secondary base station (SBS), the central processing unit (CPU) frequency, the transmit power, the number of the offloaded bits and the offloading time of the MD. Simulation results are presented to show the effectiveness of the proposed methods.

关键词： Mobile handsets   Resource management   Task analysis   Central Processing Unit   Performance evaluation   Edge computing   Blockchain   mobile edge computing   deep reinforcement learning  

摘要： Mobile edge computing (MEC) raises the issue of resisting selfish edge attackers that use less computation resources than promised to process offloading tasks or provide faked computation results. In this paper, we present a blockchain based trust mechanism to help MEC address selfish edge attacks and faked service record attacks. This mechanism evaluates the computational performance of the edge devices and broadcasts such information to the neighboring edge devices and mobile devices. By building a reputation assignment method for the edge devices, the edge reputation system chooses the miner of the blockchain, which applies the joint Proof-of-Work and Proof-of-Stake consensus protocol to append a block recording the new service reputations onto the MEC blockchain. We propose a reinforcement learning (RL) based edge central processing unit (CPU) allocation algorithm without knowing the mobile service generation model and the network model in the dynamic edge computing process and a deep RL version to further improve the computational performance. The security performance is analyzed and the performance bound of the edge utility is provided. Experimental results show that this framework suppresses the selfish edge attacks, decreases the response latency and saves the energy compared with a benchmark MEC scheme.

关键词： Computation offloading   Mobile edge computing   Processing time optimization   Simulated annealing  

摘要： This article describes a simulated annealing based offloading decision with processing time, energy consumption and resource constraints in a Mobile Edge Computing Node. Edge computing mostly deals with mobile devices subject to constraints. Especially because of their limited processing capacity and the availability of their battery, these devices have to offload some of their heavy tasks, which require many calculations. We consider a single mobile device with a list of heavy tasks that can be offloadable. The formulated optimization problem takes into account both the dedicated energy capacity and the total execution time. We proposed a heuristic solution schema. To evaluate our solution, we performed a set of simulation experiments. The results obtained in terms of processing time and energy consumption are very encouraging. © 2020, Kohat University of Science and Technology.

关键词： NOMA   cognitive radio   mobile edge computing   resource allocation  

摘要： In this paper, we first employ non-orthogonal multiple access (NOMA) in a cognitive radio (CR) based mobile edge computing (MEC) network to reduce the system delay. In particular, each secondary user has two computation tasks which can be offloaded to the MEC servers via NOMA, while the transmissions of different secondary users are orthogonal by exploiting different spectrum bands. Then, we investigate the system delay minimization problem by jointly optimizing the offloading policy, the transmit power for offloading, the computing capabilities for local secondary users and MEC servers. Since the formulated problem is non-convex and difficult to be solved straightforwardly, we first analyze its characteristics and decompose it into four sub-problems, which can be solved independently. After that, we propose a low-complexity algorithm to obtain the sub-optimal solution. Simulation results prove the superiority of the proposed scheme in terms of the system delay compared with the equal allocation scheme. Our proposed scheme can achieve a sub-optimal solution which is very close to the optimal solution with low complexity compared with the Branch and Bound method.

关键词： Mobile edge computing   massive multiple input multiple output   jamming attack   eavesdropping   energy consumption  

摘要： Under the double threat of malicious user jamming attacks and eavesdropping, mobile edge computing (MEC) systems will have some information security issues, such as uplink offloading is hindered, user information leakage, and low system energy utilization. To address this problem, we propose to use physical layer security technology to prevent eavesdropping, and use massive multiple input multiple output (mMIMO) technology to reduce interference and improve system energy efficiency. In addition, under the conditions of limited delay, the user's total energy consumption is minimized by jointly optimizing the user's offloading data bits, transmission power, and offloading rate. For this non-convex multi-objective optimization problem, which can be decomposed into multiple sub-optimal sub-problems. First, the Lagrange multiplier method is used to obtain a closed expression of the user's offloading data bits, and then the iterative algorithm is used to obtain the user's optimal transmit power and maximum offloading rate. The simulation results show that, under the premise of ensuring information security, the system's total energy efficiency of antennas (M = 128) is improved 30% compared with antennas (M = 4) when the amount of data reaches 5 x 10(5) bits.