关键词： Servers   NOMA   Task analysis   Energy consumption   Optimization   Delays   Resource management   Mobile edge computing   NOMA   server cooperation   full-duplex   joint computation and communication resources optimization  

摘要： Mobile edge computing (MEC) is fast becoming a key communication technique by enabling mobile users to offload their computation tasks to the edge servers. However, the computation resource of each MEC server is limited which may lead to a worse offloading experience of dense edge users. Besides, the communication and computation resources are usually unevenly distributed among different MEC servers which affect the computational efficiency of the MEC network. In this paper, we propose a non-orthogonal multiple access (NOMA) assisted MEC system with two near-far edge servers performing cooperative communication, i.e., the edge user employs NOMA to offload partial computation workloads to a nearer MEC server and a farther MEC server, then the nearer server decodes and forwards the farther server's task data by full-duplex relaying mode. Based on the above system model, we formulate an optimization problem of the total system energy consumption minimization by jointly optimizing the local CPU frequency, the power allocation for the user and nearer MEC server, the system time assignment and the task partition. Due to the optimization problem is non-convex, a joint communication and computation resource iterative optimization (JCCRIO) algorithm based on approximation and alternation is designed. Firstly, the local CPU frequency is optimized so as to transform the original problem into a simplified equivalent form. In this way, then the simplified minimization problem can be solved iteratively in two steps. Finally, we obtain the closed-form solutions to the optimization variables at each step. Numerical results show that the proposed NOMA-assisted cooperative MEC scheme is more effective against the terms of energy consumption reduction than comparable schemes.

关键词： Wireless fidelity   Computational modeling   Edge computing   Data models   Cloud computing   Adaptation models   Tools   Mobile edge computing   data offloading   traffic prediction   security   deep learning  

摘要： Mobile edge computing (MEC) becomes popular as it offers cloud services and functionalities to the edge devices, to enhance the quality of service (QoS) of end-users by offloading their computationally intensive tasks. At the same time, the rise in the number of internet of things (IoT) objectives poses considerable cybersecurity issues owing to the latest rise in the existence of attacks. Presently, the development of deep learning and hardware technologies offers a way to detect the present traffic condition, data offloading, and cyber-attacks in edge networks. The utilization of DL models finds helpful in several domains in which the MEC provides the decisive beneficiary of the approach for traffic prediction and attack detection since a large quantity of data generated by IoT devices enables deep models to learn better than shallow approaches. In this view, this paper presents a new DL based traffic prediction with a data offloading mechanism with cyber-attack detection (DLTPDO-CD) technique. The proposed model involves three major processes traffic prediction, data offloading, and attack detection. Initially, bidirectional long short term memory (BiLSTM) based traffic prediction to enable the proficient data offloading process. Then, the adaptive sampling cross entropy (ASCE) technique is executed to maximize the network throughput by making decisions related to offloading users to the WiFi system. Finally, a deep belief network (DBN) optimized by a barnacles mating optimizer (BMO) algorithm called BMO-DBN is applied as a detection tool for cyberattacks in MEC. Extensive simulation is carried out to ensure the proficient performance of the DLTPDO-CD model. The experimental outcome stated the superiority of the presented model over the compared methods under different dimensions.

关键词： Task analysis   Servers   Energy consumption   Edge computing   Genetic algorithms   Heuristic algorithms   Resource management   Probability preferred   workload   offloading   genetic algorithm   mobile edge computing  

摘要： Mobile edge computing (MEC) can provide computation and storage capabilities via edge servers which are closer to user devices (UDs). The MEC offloading system can be viewed as a system where each UD is covered by single or multiple edge servers. Existing works prefer a posterior design when task offloads, which can lead to increased workloads. To investigate the task offloading of edge computing in multi-coverage scenario and to reduce the workload during task offloading, a probability preferred priori offloading mechanism with joint optimization of offloading proportion and transmission power is presented in this paper. We first set up an expectation value which is determined by the offloading probability of heterogeneous edge servers, and then we form a utility function to balance the delay performance and energy consumption. Next, a distributed PRiori Offloading Mechanism with joint Offloading proportion and Transmission (PROMOT) power algorithm based on Genetic Algorithm (GA) is proposed to maximize the utility of UD. Finally, simulation results verify the superiority of our proposed scheme as compared with other popular methods.

关键词： Servers   Planning   Resource management   Edge computing   Optimization   Virtual machining   Task analysis   Mobile edge computing   edge server planning   uncertain programming   learning-based framework   resource optimization  

摘要： Mobile Edge Computing (MEC) architecture is composed of geographically distributed edge servers, in which computing capabilities are provisioned at the boundary of the network, which is in close proximity to the end users to provide network services with low latency. The planning of MEC edge servers at appropriate locations is the fundamental first step towards the deployment of the MEC system. In the literature, edge servers planning is based on deterministic resource requirements. This assumption largely neglects the pragmatic complexities imposed by the real dynamic world, in which base station (BS) resource demands are stochastic variables with arbitrary pattern. In view of this fact, we formulate the MEC planning problem as a joint optimization problem of MEC edge servers placement and resource allocation with uncertain BS demands through an uncertain programming formulation. Due to the complexity of this joint-uncertain problem, a learning based framework is utilized to practically solve this problem, and the relevance of applying this mechanism in practical usage with sampled arbitrary BS demands data is also discussed. Finally, we conducted intensive real-data driven simulations to evaluate the performance of our proposed mechanism. The results show the effectiveness of our approach with arbitrary BS demands.

关键词： Edge computing   Probability   Device-to-device communication   Throughput   Bipartite graph   Base stations   Internet   Mobile edge computing   capacity   time-varying   degree of files   transmission distance  

摘要： Capacity is a fundamental metric for mobile edge computing scenarios, where the system state plays an important role. Previous studies have mostly been based on the premise that the system state is stable. In reality, the network is dynamic and the system state changes with time. In this paper, we study the capacity of a mobile edge system in which users continuously join or leave the coverage of base station. We first change the problem of maximum network capacity into a minimum transmission distance problem. We observe that both the probability of the files being requested and the distance of the files transmission are related to the degree of files, i.e., the number of users who are interested in a file and request it with a certain probability. Then, we evaluate the degree of files in a time-varying situation, and calculate the probability of the files being requested and the transmission distance according to the degree of files. Finally, we calculate the capacity of the network under time-varying conditions. In the experimental section, we analyze the degree of files, the optimal copies number, and the change in network capacity over time. In addition, we compare the capacity in our system with classic studies. The experimental results verify the superiority of the proposed method.

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： edge computing   WSN   mobile edge computing   intelligent agriculture  

摘要： Agricultural informatisation has been an extraordinary aspect in the field of agriculture due to advancement in communication technologies. Many studies have been conducted to assist production, such as Wireless Sensor Network (WSN) and Cloud Computing. However, few studies have been focused on the severe requirements of delay and energy consumption of the mobile node. In order to improve network performance, edge computing is introduced into our system architecture for intelligent agriculture. Mobile Edge Computing (MEC) is a distributed computing architecture to offload some tasks to the edge of core network. We adopt a hierarchical structure made of three layers: physical perception layer, information service layer and intelligent application layer. In addition, we further confirm an offloading model for intelligent agriculture. In this article, the state-of-the-art development in intelligent agriculture and current approach of its key technology are discussed. Their potential opportunities and challenges in proposed architecture are presented as well.

关键词： Mobile edge computing   massive MIMO   resource allocation   power control  

摘要： This paper designs a single-cell multi-user massive MIMO-MEC network. In order to ensure the fairness of users, a joint pilot transmission, data transmission and resource allocation model during the computation execution process with the goal of minimizing the maximum offload computing delay for all users is constructed. The resulted problem is non-convex and non-linear optimization, thus difficult to be solved optimally. To tackle this challenge, an improved fruit fly optimization algorithm (FOA) based on the external penalty function steepest descent algorithm (IFOA-PFSA) is proposed. The point obtained by the steepest descent algorithm based on the external penalty function has been employed as the initial point of the fruit fly optimization algorithm, which can greatly reduce the population size and the maximum number of iterations in the random search process of the traditional fruit fly optimization algorithm, reducing the algorithm complexity. Simulation results show that the proposed algorithm IFOA-PFSA has a smaller delay than the traditional FOA (TFOA) algorithm. The complexity of the proposed algorithm is also lower than the TFOA algorithm.

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： 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.