关键词： Mobile Edge Computing   energy harvesting devices   offloading   integer linear programming   Internet of things  

摘要： Conserving energy resources and enhancing computation capability have been the key design challenges in the era of the Internet of Things (IoT). The recent development of energy harvesting (EH) and Mobile Edge Computing (MEC) technologies have been recognized as promising techniques for tackling such challenges. Computation offloading enables executing the heavy computation workloads at the powerful MEC servers. Hence, the quality of computation experience, for example, the execution latency, could be significantly improved. In a situation where mobile devices can move arbitrarily and having multi servers for offloading, computation offloading strategies are facing new challenges. The competition of resource allocation and server selection becomes high in such environments. In this paper, an optimized computation offloading algorithm that is based on integer linear optimization is proposed. The algorithm allows choosing the execution mode among local execution, offloading execution, and task dropping for each mobile device. The proposed system is based on an improved computing strategy that is also energy efficient. Mobile devices, including energy harvesting (EH) devices, are considered for simulation purposes. Simulation results illustrate that the energy level starts from0.979%and gradually decreases to0.87%. Therefore, the proposed algorithm can trade-off the energy of computational offloading tasks efficiently.

关键词： mobile edge computing   resource allocation   task priority   Lyapunov stability  

摘要： With the emergence of increasingly computing-intensive and delay-sensitive tasks, the processing of computing tasks on cloud servers cannot meet the current needs any longer. The emergence of mobile edge computing (MEC) technology and the popularity of 5G applications can solve these demands. Offloading tasks to the MEC server reduces the energy consumption of local devices, and also has a lower latency than offloading to the cloud server. In this paper, an MEC-edge cloud server collaborative system model with energy harvesting technology is designed to minimize the processing delay of computing tasks by allocating computing resources. We propose an optimal integer linear programming (OILP) algorithm with two steps. Firstly, we propose a Lyapunov stability optimization algorithm based on task priority. With the constraints of local mobile device power stability, the divide-and-conquer idea is used for solving the target values of the processing tasks locally, and the MEC and edge cloud servers separately. Therefore, the objective problem is transformed into an integer linear programming problem, and then an integer linear programming algorithm based on CPU utilization optimization is proposed to obtain a resource allocation scheme. Simulation results show that the proposed OILP algorithm can further reduce the delay, improve the CPU's utilization of the MEC server, and reduce the number of the tasks that cannot be processed under the condition of the energy stability of the local device.

关键词： Energy consumption   Servers   Object detection   Energy resolution   Image resolution   Batteries   Wireless communication   Augmented reality   energy efficiency   mobile edge computing   object detection   optimization  

摘要： This letter proposes a novel resolution and power control scheme for energy-efficient mobile augmented reality (MAR) services through computation offloading to mobile edge computing (MEC). For this purpose, analytical models for the energy consumption of a MAR device (MD), detection accuracy of MAR objects, and service latency are rigorously formulated. Considering their tradeoff in MAR with MEC, a theoretical framework for the computation offloading problem of the MD that jointly optimizes the transmission power and frame resolution is designed. The proposed scheme significantly reduces energy consumption by 73.64% with an accuracy loss of 1.6% while guaranteeing the required latency constraint.

关键词： unmanned aerial vehicle   mobile edge computing   offloading   energy efficiency  

摘要： The data volume is exploding due to various newly-developing applications that call for stringent communication requirements towards 5th generation wireless systems. Fortunately, mobile edge computing makes it possible to relieve the heavy computation pressure of ground users and decrease the latency and energy consumption. What is more, the unmanned aerial vehicle has the advantages of agility and easy deployment, which gives the unmanned aerial vehicle enabled mobile edge computing system opportunities to fly towards areas with communication demand, such as hotspot areas. However, the limited endurance time of unmanned aerial vehicle affects the performance of mobile edge computing services, which results in the incomplete mobile edge computing services under the time limit. Consequently, this paper concerns the energy-efficient scheme design of the unmanned aerial vehicle while providing high-quality offloading services for ground users, particularly in the regions where the ground communication infrastructures are overloaded or damaged after natural disasters. Firstly, the model of energy-efficient design of the unmanned aerial vehicle is set up taking the constraints of the energy limitation of the unmanned aerial vehicle, the data causality, and the speed of the unmanned aerial vehicle into account. Subsequently, aiming at maximizing the energy efficiency of the unmanned aerial vehicle in the unmanned aerial vehicle enabled mobile edge computing system, the bits allocation in each time slot and the trajectory of the unmanned aerial vehicle are jointly optimized. Secondly, a successive convex approximation based alternating algorithm is brought forward to deal with the non-convex energy efficiency maximization problem. Finally, it is proved that the proposed energy efficient scheme design of the unmanned aerial vehicle is superior to other benchmark schemes by the simulation results. Besides, how the performance of proposed scheme design change under different

关键词： Routing   Bandwidth   Approximation algorithms   Heuristic algorithms   IEEE transactions   Cloud computing   Edge computing   Service placement   routing   joint optimization   mobile edge computing  

摘要： The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network edge, in proximity to the end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be pre-stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in dense MEC networks with multidimensional constraints. We show that this problem generalizes several well-known placement and routing problems and propose an algorithm that achieves close-to-optimal performance using a randomized rounding technique. Evaluation results demonstrate that our approach can effectively utilize available storage, computation, and communication resources to maximize the number of requests served by low-latency edge cloud servers.

关键词： Global positioning system (GPS) devices   mobile edge computing   resource-constrained   trajectory mapping   trajectory compression  

摘要： Vehicles can be easily tracked due to the proliferation of vehicle-mounted global positioning system (GPS) devices. V Tracer is a cost-effective mobile system for online trajectory compression and tracing vehicles, taking the streaming GPS data as inputs. Online trajectory compression, which seeks a concise and (near) spatial-lossless data representation before revealing the next vehicle's GPS position, is gradually becoming a promising way to alleviate burdens such as communication bandwidth, storing, and cloud computing. In general, an accurate online mapmatcher is a prerequisite. This two-phase approach is nontrivial because we need to overcome the essential contradiction caused by the resource-constrained GPS devices and the heavy computation tasks. V Tracer meets the challenge by leveraging the idea of mobile edge computing. More specifically, we offload the heavy computation tasks to the nearby smartphones of drivers (i.e., smartphones play the role of cloudlets), which are almost idle during driving. More importantly, they have relatively more powerful computing capacity. We have implemented V Tracer on the Android platform and evaluate it based on a real driving trace dataset generated in the city of Chongqing, China. Experimental results demonstrate thatV Tracer achieves the excellent performance in terms of matching accuracy, compression ratio, and it also costs the acceptable memory, energy, and app size.

关键词： Blockchain   Mobile handsets   Servers   Resource management   Task analysis   Energy consumption   Optimization   Mobile edge computing   blockchain   energy consumption   delay   time to finality  

摘要： The application of blockchain to mobile edge computing (MEC) systems has attracted great interests. However, the design and optimization of blockchain and MEC in most existing works are done separately, which will result in sub-optimal performance. In this paper, we propose a joint optimization framework for blockchain-enabled MEC systems to achieve the optimal trade-off between the performance of the MEC system and the performance of the blockchain system. Specifically, both MEC and blockchain are considered as services in the framework, where energy consumption and delay/time to finality (DTF) are the performance metrics for the MEC system and the blockchain system, respectively. We formulate an optimization problem to achieve the optimal trade-off through jointly optimizing user association, data rate allocation, block producer scheduling, and computational resource allocation. To solve the problem, we decouple the optimization variables for efficient algorithm design. In addition, we develop an iterative algorithm for user association and data rate allocation and a bisection algorithm for computing resource allocation. Simulation results show the convergence of the proposed algorithms, and the proposed scheme can achieve the optimal trade-off between energy consumption and DTF.

关键词： DVB   digital video broadcasting   HbbTV   hybrid broadcast broadband   MEC   mobile edge computing   MNO   mobile network operator   NFV   network function virtualization   RAN   radio access network   SDN   software defined network   VLC   visible light communication  

摘要： Fifth-Generation (5G) mobile networks are expected to perform according to the stringent performance targets assigned by standardization committees. Therefore, significant changes are proposed to the network infrastructure to achieve the expected performance levels. Network Function Virtualization, cloud computing and Software Defined Networks are some of the main technologies being utilised to ensure network design, with optimum performance and efficient resource utilization. The aforementioned technologies are shifting the network architecture into service-based rather device-based architecture. In this regard, we introduce Media Casting as a service (MCaaS) and IoRL-Cache service (IoRL-C). The former is a service that proposed as an integration solution between the broadcasting industry and Mobile Network Operators, while the latter proposed as a solution for improving IoRL small-cells caching efficiency. IoRL is an emerging 5G small-cell for indoor environment, which utilises mmWave and Visible Light Communications (VLC) as access technologies, while exploiting Software Defined Networking (SDN) and Network Function Virtualisation (NFV) technologies to offer flexible and intelligent services to its clients. In this paper, we introduce MCaaS and IoRL-C services, we perform simulation work for testing IoRL-C as a proof of concept for both services. The network was simulated using OMNeT++ simulation tool, and we validated the services efficiency by comparing their performance with traditional deployments. We have also examined the cache service performance at different link lengths, and found out that IoRL-C is able to support IoRL small-cells with more than 50+Km separation distances.

关键词： Delays   Computational modeling   Downlink   Graphical models   Distribution functions   Analytical models   Cellular networks   Mobile edge computing   MEC   stochastic geometry   local delay   VANET  

摘要： Mobile edge computing (MEC) is a promising computing paradigm for improving the quality of service (QoS) of a vehicular ad hoc network (VANET). The local delay is one of the major components in the delay for a node to transmit a data packet to another node. This paper studies the modeling and analysis of the downlink local delay in an MEC-based highway road scenario. A theoretical model is built based on stochastic geometry for analyzing the average downlink local delay for a vehicle driving on a highway road to receive a data packet from an edge node along the highway road. In deriving the model, the spatial distributions of vehicle nodes and edge nodes are respectively modeled as an independent one-dimensional (1-D) homogeneous Poisson point process (PPP). A nearest-transmitter model is used to determine the edge node for transmitting a computing result to the corresponding service requesting vehicle node. Moreover, a carrier sense multiple access (CSMA) mechanism is considered for channel access. The derived theoretical model is validated through simulation results and the impacts of major parameters on the average downlink local delay are investigated based on the derived model. The built model provides an insight into the deployment of edge nodes in order to reduce the downlink local delay and provide timely network services.

关键词： vehicular network   mobile edge computing   software-defined networking   re-source allocation  

摘要： As an important application sce-nario of 5G,the vehicular network has a huge amount of computing data,which brings chal-lenges to the scarce network *** edge computing(MEC)sinks cloud services to the edge of network,which reduces the de-lay jitter caused by remote cloud ***-defined networking(SDN)is an emerging network paradigm with the features of logic centralized control and *** this paper,we construct an SDN-assist-ed MEC network architecture for the vehicular *** introducing SDN controller,the efficiency and flexibility of vehicular network are improved,and the network state can be perceived from the global *** fur-ther reduce the system overhead,the problem of vehicle to everything(V2X)offloading and resource allocation is proposed,where the op-timal offloading decision,transmission power control,subchannels assignment,and comput-ing resource allocation scheme are *** optimization problem is transformed into three stages because of the heterogeneity of the off-loaded tasks and the NP-hard property of the ***,the analytic hierarchy pro-cess is used to select initial offloading node,then stateless Q-learning is adopted to allocate transmission power,subchannels and comput-ing *** addition,the offloading decision is modeled as a potential game,and the Nash equilibrium is proved by the potential function ***,the numerical results show that the proposed mechanism can effectively reduce the system overhead and achieve better results compared with others' algorithms.