关键词： Pricing   mobile cloud computing   mobile edge computing  

摘要： With offloading the tasks that mobile users (MUs) running in their mobile devices (MDs) to the data centers of remote public clouds, mobile cloud computing (MCC) can greatly improve the computing capacity and prolong the battery life of MDs. However, the data centers of remote public cloud are generally far from the MUs, thus long delay will be caused due to the transmission from the base station to the public clouds over the Internet. Mobile edge computing (MEC) is recognized as a promising technique to augment the computation capabilities of MDs and shorten the transmission delay. Nevertheless, compared with the traditional MCC and MEC generally has a limited number of cloud resources. Therefore, making a choice on offloading task to the MCC or MEC is a challenging issue for each MU. In this paper, we investigate service selection in a mobile cloud architecture, in which MUs select cloud services from two cloud service providers (CSPs), i.e., public cloud service provider (PSP) and an edge cloud service provider (ESP).We use M/M/infinity queue and M/M/1 queue to model PSP and ESP, respectively. We analyze the interaction of the two CSPs and MUs by adopting Stackelberg game, in which PSP and ESP set the prices first, and then the MUs decide to select cloud services based on performances and prices. In particular, we study the relationship between PSP and ESP in the simultaneous-play game (SPG) scenario, in which they compete to set prices of their cloud services simultaneously. Our numerical results show that MUs prefer to select service from the edge cloud if the number of tasks they run is small. In another hand, more tasks will be offloaded to the remote public cloud if the number of tasks they run becomes large.

关键词： Cost optimization   low latency   mobile edge computing   proximity detection   time-aware road networks   time distance  

摘要： Given a set of moving objects as well as their friend relationships, a time-aware road network, and a time threshold per friend pair, the proximity detection problem in time-aware road networks is to find each pair of moving objects such that the time distance (defined as the shortest time needed for two moving objects to meet each other) between them is within the given threshold. The problem of proximity detection is often encountered in autonomous driving and traffic safety related applications, which require low-latency, real time proximity detection with relatively low communication cost. However, (i) most existing proximity detection solutions focus on the Euclidean space which cannot be used in road network space, (ii) the solutions for road networks focus on static road networks and do not consider time distance and thus cannot be applied in time-aware road networks, and (iii) there are no works aiming to simultaneously reduce the communication cost, the communication latency, and computational cost. Motivated by these, we first design a low-latency proximity detection architecture based on Mobile Edge Computing (MEC) with the purpose of achieving low communication latency, then propose a proximity detection method including a client-side algorithm and a server-side algorithm, aiming at reducing the communication cost, and subsequently propose server-side computational cost optimization techniques to reduce the computational cost. Experimental results show that our MEC enhanced proximity detection architecture, our proximity detection method, and the server-side computational cost optimization techniques can reduce the communication latency, the communication cost, and the computational cost effectively.

关键词： Mobile edge computing   cognitive radio   wireless power transfer   cooperative relay   semi-closed form  

摘要： Traditional mobile edge computing (MEC) methods always assume that the wireless devices (WDs) can offload their data to the base stations (BSs) or the access points (APs) at any time, which are not practical due to the tension between a large number of the WDs and the limited spectrum resources. In this paper, a framework for MEC-enabled cognitive radio (CR) networks is proposed, which integrates three technologies: MEC, CR, and wireless power transfer (WPT). To obtain the spectrum for offloading, cooperative relaying is considered. Optimization problems are formulated to study the upper bound of the energy efficiency (EE) of the WD and to maximize the practical EE in both partial offloading and local computing scenarios, which are non-convex and intractable. In order to tackle these problems, a two-phase method is proposed. The transmit power, the time for energy harvesting (EH) and MEC, and the central processing unit (CPU) frequency of the WD are jointly optimized. Semi-closed-form solutions are obtained in partial offloading scenario by using fractional programming theory, Lagrangian dual decomposition, and successive pseudo-convex approximation (SPCA) methods. Closed-form solutions are obtained for local computing scenarios. The simulation results show the effects of the different parameters on the system performance.

关键词： Underwater sensor network   mobile edge computing   data collection   mobile elements  

摘要： With the rapid developments in edge devices and wireless technologies, the underwater wireless sensor networks (UWSNs) are in the process of vigorous development. In UWSNs, the traditional multi-hop data collection methods have some disadvantages such as high power consumption, severe unbalance in power consumption, and so on. In recent years, mobile edge elements (such as an autonomous underwater vehicle, AUV) are widely used in underwater data collection to solve energy consumption imbalance problems. However, the existing methods do not fully consider the efficient mobile edge computing and the real mobility model of AUV in the underwater environment. In this paper, we propose a data collection scheme based on a mobility model of mobile edge elements under water. In this model, the mobility direction and velocity are fully considered, which are close to the mobility characteristic of AUVs in the stable 3D environment. By using computing, storage, and mobility abilities of AUVs, a target selection algorithm is designed to calculate the mobility path of data collection for AUV. The theoretical analysis and experimental results show that the proposed method improves the efficiency of data collection, reduces the power consumption of nodes, and extends the network lifetime.

关键词： Computing Capabilities   Hybrid Firefly Algorithm   Mobile edge computing   Mobile Switching Center  

摘要： Mobile edge computing (MEC) is a recent technology that brings the computing resources too closer to the network edge with reduced response rate in computationally intensive applications. With increasing mobile devices with limited computing and wireless capabilities, the response time of MSC deteriorates. Therefore it is necessary to jointly manage the computing and wireless resources in order to avoid deterioration due to limited capabilities. Also, the joint optimisation techniques limits its capability due to computational complexity and incompatible devices. To avoid all these limitations, in this paper, we propose a joint optimisation model that suitably optimizes the cloud and wireless resources in an optimal way, thereby reducing its response time. A game base theoretical model is developed to maintain the equilibrium between the resources and computational capabilities. To improve the resources allocation capability, we use the Bees Swarm Optimisation (BSO) model for better allocation of resources in MEC. The performance is compared with other existing methods to test the effectiveness of the model. © 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved.

关键词： Full-duplex   mobile edge computing   offload   simultaneous wireless information and power transfer   group iterative optimization  

摘要： In view of the growing contradiction between the intensive computation demands and the resource limitations of mobile users, mobile edge computing (MEC) and simultaneous wireless information and power transfer (SWIPT) have emerged as new paradigms towards 5G communication. However, coordinating the communication and computation between users and edge servers proves to be challenging for MEC. In this paper, we propose a novel multi-user full-duplex (FD) communication system that combines MEC and SWIPT technology in order to take the advantage of high-speed mobile computing and long-lasting self-sustainability. Through MEC technology, users are able to calculate local computation tasks using their batteries, and can offload partial computation tasks to the base station (BS) to reduce their energy shortage. Moreover, users can refill their batteries while receiving the computation result sent by the BS, thus benefiting from SWIPT technology. The FD mode can potentially increase the system performance by allowing the simultaneous transmitting and receiving of computation tasks. Our work aims to minimize the energy consumption of the system, while formulating resource allocation as a joint non-linear optimization problem. We decouple the original non-convex problem into two subproblems and solve them using a proposed algorithm that applies group iterative optimization. Numerical results prove that the proposed algorithm is superior to other two comparison schemes and can significantly reduce the system energy consumption and the latency.

关键词： Ultra-dense network   resource allocation   caching   mobile edge computing   stochastic geometry   stochastic optimization   mean field game  

摘要： Driven by the explosive data traffic and new quality of service requirement of mobile users, the communication industry has been experiencing a new evolution by means of network infrastructure densification. With the increase of the density as well as the variety of access points (APs), the network benefits from proximal transmissions and increased spatial reuse of system resources, thus introducing a new paradigm named ultra-dense networks (UDNs). Since the limited available resources are shared by ubiquitous APs in UDNs, the demand for efficient resource allocation schemes becomes even more compelling. However, the large scale of UDNs impedes the exploration of effective resource allocation approaches particularly on the computational complexity and significance overhead or feedback. In this paper, we provide a survey-style introduction to resource allocation approaches in UDNs. Specifically, we first present some common scenarios of UDNs with the relevant special issues. Second, we provide a taxonomy to classify the resource allocation methods in the existing literatures. Then, to alleviate the main difficulties of UDNs, some prevailing and feasible solutions are elaborated. Next, we present some emerging technologies thriving UDNs with special RA features discussed. Additionally, the challenges and open research directions are outlined in this field.

关键词： Mobile edge computing   computation offloading   transmission power  

摘要： With the exploding growth of smart devices and application of 5G communications, there are more and more computation-intensive tasks which are delay-sensitive. Mobile edge computing (MEC) is a new paradigm that provides rich computing resources in close proximity to mobile users. However, how to make the decision of whether offloading or not as well as control the transmission power jointly remains a challenge. In this paper, we first study the joint multiuser offloading and transmission power control optimization problem in a multi-channel wireless interference scenario. We formulate the joint optimization problem into a system-wide computation overhead minimization problem as a mixed integer non-linear program. We then propose an efficient semi-distributed algorithm consisting of two subalgorithms of offloading scheme and transmission power control. The numerical simulation results demonstrate that the proposed algorithm can effectively reduce system-wide computation overhead and increase the number of beneficial edge computing users, compared with all local or edge computing and fixed transmission power schemes.

关键词： Mobile edge computing   offloading decision   node clustering   optimal strategy   Nash equilibrium  

摘要： Mobile edge computing is a new cloud computing paradigm that utilizes small-sized edge clouds to provide real-time services to users. These mobile edge clouds (MECs) are located near users, thereby enabling users to seamlessly access applications that are running on MECs and to easily access MECs. Terminal devices can transfer tasks to MEC servers nearby to improve the quality of computing. In this paper, we study multi-user computation offloading problem for mobile-edge computing in a multichannel wireless interference environment. Then, we analyze the overhead of each mobile devices, and we propose strategies for task scheduling and offloading in a multi-user MEC system. For reducing the energy consumption, we propose a server partitioning algorithm that is based on clustering. We formulate the task offloading decision problem as a multi-user game, which always has a Nash equilibrium. The simulation results demonstrate that our scheme outperforms the traditional offloading strategy in terms of energy consumption.

关键词： Mobile edge computing   non-orthogonal multiple access   partial offloading   physical layer security   computation efficiency   sequential fractional programming  

摘要： In this paper, a computation efficiency maximization problem is studied in a multi-user non-orthogonal multiple access (NOMA) enabled mobile edge computing (MEC) networks. Physical layer security is used to prevent multi-user computing tasks from eavesdropping. The secure computation efficiency optimization problem is formulated by optimizing the transmission power and central processing unit (CPU) frequency jointly in the NOMA-enabled MEC networks. The problem is non-concave and challenging to solve, and a low complexity sequential fractional programming is proposed to solve this problem. The numerical results are provided to verify that the proposed scheme is superior to the traditional scheme in terms of the secure computation efficiency and our designed iteration algorithm's efficiency is verified.