关键词： Energy efficiency   Internet of Things   mobile edge computing   offloading  

摘要： The rapid development of the Internet of Things gives rise to the emergence of delay-sensitive and computation-intensive applications. Due to the inherent long delay of cloud computing and the limited resources at end devices, mobile edge computing is considered a promising approach to meet the stringent delay requirement of such demanding applications. To handle the massive connection of the Internet of Things, the 5G network is shifting toward heterogenous architecture, where each end device can access more than one edge server (e.g., base stations and access points). In the presence of multiple edge servers, this paper investigates the interesting problem of how to exploit the heterogenous computation resources at the network edge to achieve the best energy efficiency among multiple end devices while satisfying their delay requirements. We study a computation offloading management problem by jointly considering the heterogeneous computation resources, latency requirements, power consumption at end devices, and channel states. The formulated energy minimization problem falls into the category of mixed-integer and nonlinear program. To solve it efficiently, we decompose the original problem into two subproblems and propose an iterative solution framework to solve for transmission power allocation strategy and computation offloading scheme. Through simulation results, we show that the proposed solution is competitive when compared with the optimal solution. Moreover, we leverage the optimal solutions to analyze the impact of computation resource distribution on energy consumption and computation offloading decision.

关键词： Mobile edge computing   intelligent and connected vehicle   upper confidence bound algorithm   Markov model   Q-learning  

摘要： With the rise of intelligent and connected vehicles (ICVs), new vehicle applications continue to emerge, while the computing capability of vehicles remains limited. Mobile edge computing (MEC) is considered to be the most effective technique for mitigating vehicle computing pressure, with computation offloading being a key technology for MEC. To solve the problem of excessive task processing delay and energy consumption due to the vehicle-limited computing power in the vehicular network, we consider the tasks and the characteristics of MEC, and divide the tasks into indivisible tasks and divisible tasks according to the size of data (that is, whether it affects functionality after segmentation). Then, two computation offloading algorithms are proposed named binary offloading and partial offloading separately. The binary offloading unloads the task to the mobile edge computing server as a whole and selects only an optimal offloading site;thus, an improved upper confidence bound algorithm is adopted. The partial offloading divides the complex tasks with large data volumes through time slots processed by different MEC servers, and uses the Q-learning algorithm to find the most effective offloading strategy. The simulation results show that the total cost of delay and energy consumption of the binary offloading algorithm is lower when processing computationally intensive tasks. When addressing divisible and complex tasks, the partial offloading algorithm improves the real-time performance of the tasks significantly and conserves the energy of the vehicle terminal.

关键词： Task analysis   Delays   Cloud computing   Edge computing   Resource management   Servers   Computational modeling   5G and beyond   computation offloading   mobile edge computing   fog computing   resource allocation   offloading decision  

摘要： With the emergence of delay-sensitive task completion, computational offloading becomes increasingly desirable due to the end-users limitations in performing computation-intense applications. Interestingly, fog computing enables computational offloading for the end-users towards delay-sensitive task provisioning. In this paper, we study the computational offloading for the multiple tasks with various delay requirements for the end-users, initiated one task at a time in end-user side. In our scenario, the end-user offloads the task data to its primary fog node. However, due to the limited computing resources in fog nodes compared to the remote cloud server, it becomes a challenging issue to entirely process the task data at the primary fog node within the delay deadline imposed by the applications initialized by the end-users. In fact, the primary fog node is mainly responsible for deciding the amount of task data to be offloaded to the secondary fog node and/or remote cloud. Moreover, the computational resource allocation in term of CPU cycles to process each bit of the task data at fog node and transmission resource allocation between a fog node to the remote cloud are also important factors to be considered. We have formulated the above problem as a Quadratically Constraint Quadratic Programming (QCQP) and provided a solution. Our extensive simulation results demonstrate the effectiveness of the proposed offloading scheme under different delay deadlines and traffic intensity levels.

关键词： Cooperative caching   mobile edge computing   multi-bitrate video transcoding   adaptive bitrate streaming   multicarrier   NOMA   wireless network virtualization   cache refreshment  

摘要： Cooperative video caching and transcoding in mobile edge computing (MEC) networks is a new paradigm for future wireless networks, e.g., 5G and beyond-5G, to reduce scarce and expensive backhaul resource usage by prefetching video files within radio access networks (RANs). Integration of this technique with other advent technologies, such as wireless network virtualization and multicarrier non-orthogonal multiple access (MC-NOMA), provides more flexible video delivery opportunities, which leads to enhancements both for the network's revenue and for the end-users' service experience. In this regard, we propose a two-phase RAF for a parallel cooperative joint multi-bitrate video caching and transcoding in heterogeneous virtualized MEC (HV-MEC) networks. In the cache placement phase, we propose novel proactive delivery-aware cache placement strategies (DACPSs) by jointly allocating physical and radio resources based on the network stochastic information to exploit flexible delivery opportunities. Then, for the delivery phase, we propose a delivery policy based on user requests and network channel conditions. The optimization problems corresponding to both phases aim to maximize the total revenue of network slices, i.e., virtual networks. Both problems are non-convex and suffer from high-computational complexities. For each phase, we show how the problem can be solved efficiently. We also propose a low-complexity RAF, in which the complexity of the delivery algorithm is significantly reduced. A delivery-aware cache refreshment strategy (DACRS) in the delivery phase is also proposed to tackle the dynamical changes of network stochastic information. The extensive numerical assessments demonstrate a performance improvement of up to 30% for our proposed DACPSs and DACRS over traditional approaches.

关键词： Accident detection   fog computing   mobile edge computing   cloud computing   Internet of Things   emergency alerts   disaster management system  

摘要： Emergencies, by definition, are unpredictable and rapid response is a key requirement in emergency management. Globally, a significant number of deaths occur each year, caused by excessive delays in rescue activities. Vehicles embedded with sophisticated technologies, along with roads equipped with advanced infrastructure, can play a vital role in the timely identification and notification of roadside incidents. However, such infrastructure and technologically-rich vehicles are rarely available in less developed countries. Hence, in such countries, low-cost solutions are required to address the issue. Systems based on the Internet of Things (IoT) have begun to be used to detect and report roadside incidents. The majority of the systems designed for this purpose involve the use of the cloud to compute, manage, and store information. However, the centralization and remoteness of cloud resources can result in an increased delay that raises serious concerns about its feasibility in emergency situations;in life-threatening situations, all delays should be minimized where feasible. To address the problem of latency, fog computing has emerged as a middleware paradigm that brings the cloud-like resources closer to end devices. In light of this, the research proposed here leverages the advantages of sophisticated features of smartphones and fog computing to propose and develop a low-cost and delay-aware accident detection and response system, which we term Emergency Response and Disaster Management System (ERDMS). An Android application is developed that utilizes smartphone sensors for the detection of incidents. When an accident is detected, a plan of action is devised. Initially, a nearby hospital is located using the Global Positioning System (GPS). The emergency department of the hospital is notified about the accident that directs an ambulance to the accident site. In addition, the family contacts of the victim are also informed about the accident. All the required comp

关键词： Average power consumption   average response time   computation offloading   cost-performance ratio   mobile edge computing   Nash equilibrium   non-cooperative game   queueing model  

摘要： There are two fundamental purposes in mobile edge computing, i.e., performance enhancement and cost reduction. By offloading computation tasks to a mobile edge cloud (MEC), a user equipment (UE), also called mobile user, mobile subscriber, or mobile device, can possibly reduce its average response time, which is the main performance measure, and can possibly reduce its average power consumption. Optimizing both performance and cost may be conflicting requirements. In this paper, we optimize the cost performance ratio (CPR), i.e., the power-time product, which combines performance (average response time) and cost (average power consumption) into one quantity. A unique feature in mobile edge computing is the competitiveness of mobile users, who are selfish in competing for resources in a mobile edge cloud. We take a game theoretic approach to the stabilization of a competitive mobile edge computing environment. The main contributions of the paper are summarized as follows. 1) We consider a mobile edge computing environment with multiple UEs and a single MEC. We establish an M/G/1 queueing model for the UEs and an M/G/m queueing model for the MEC. The UEs are entirely heterogeneous in terms of task characteristics, computation and communication speeds, and power consumption models for both computation and communication. 2) We analytically derive the average response time and the average power consumption of each UE and the MEC, so that cost-performance ratio optimization can be studied mathematically and rigorously. 3) We establish a non-cooperative game framework to systematically study the stabilization of a competitive mobile edge computing environment. Our framework includes a set of seven non-cooperative games among the UEs and the MEC, each attempts to minimize its payoff function, i.e., its cost-performance ratio. These games are different in terms of the number of variables to play and which variables to play. 4) We develop efficient algorithms for each player

关键词： Offloading strategy   resource allocation   cloud radio access network   mobile edge computing   spectrum efficiency  

摘要： The cloud radio access network (C-RAN) with mobile edge computing (MEC) structure which consists of a baseband unit (BBU) pool integrating with an MEC server and several remote radio heads (RRHs) beside the mobile terminals can help users with computational resource-intensive tasks and bring extra profits to network operator at the same time. This paper presents a novel task-aware C-RAN with MEC structure and formulates a profit maximization problem by jointly optimizing offloading strategy, radio and computational resources allocation under the constraints of offloading latency, fronthaul capacity along with limited bandwidth and computational resource. To solve this NP-hard optimization problem in a distributed and efficient way, we propose a spectrum efficiency (SE)-based joint optimization for offloading and resource allocation (SJOORA) scheme which decomposes the original problem into two sub-problems. A SE-based offloading strategy is proposed with confirmed resource allocation, and on the other hand, bandwidth and computational resource allocation problem is solved by using a Lagrangian multiplier method with predetermined offloading strategy. Finally, by solving these two sub-problems iteratively, a suboptimal solution is obtained for the original problem. The simulation results show that the proposed SJOORA scheme can effectively increase the profit of network operator with relative lower complexity.

关键词： Data offloading   energy aware   mobile edge computing   deep reinforcement learning  

摘要： A large number of connected sensors and devices in Internet of Things (IoT) can generate large amounts of computing data and increase massive energy consumption. Real-time states monitoring and data processing of IoT nodes are of great significance, but the processing power of IoT devices is limited. Using the emerging mobile edge computing (MEC), IoT devices can offload computing tasks to an MEC server associated with small or macro base stations. Moreover, the use of renewable energy harvesting capabilities in base stations or IoT nodes may reduce energy consumption. As wireless channel conditions vary with time and the arrival rates of renewable energy, computing tasks are stochastic, and data offloading and renewable energy aware for IoT devices under a dynamic and unknown environment are major challenges. In this work, we design a data offloading and renewable energy aware model considering an MEC server performing multiple stochastic computing tasks and involving time-varied wireless channels. To optimize data transmission delay, energy consumption, and bandwidth allocation jointly, and to avoid the curse of dimensionality caused by the complexity of the action space, we propose a joint optimization method for data offloading, renewable energy aware, and bandwidth allocation for IoT devices based on deep reinforcement learning (JODRBRL), which can handle the continuous action space. JODRBRL can minimize the total system cost(including data buffer delay cost, energy consumption cost, and bandwidth cost) and obtain an efficient solution by adaptively learning from the dynamic IoT environment. The numerical results demonstrate that JODRBRL can effectively learn the optimal policy, which outperforms Dueling DQN, Double DQN (DDQN), and greedy policy in stochastic environments.

关键词： Mobile edge computing   edge cloud deployment   offloading decision   greedy algorithm  

摘要： The mobile edge computing (MEC) technology can provide mobile users (MU) with high reliability and low time-delay computing and communication services. The imbalanced edge cloud deployment can better adapt to the non-uniform spatial-time distribution of tasks and reduce the deployment cost of edge cloud servers. For multi-user and multi-task offloading decision based on the imbalanced edge cloud, a new offloading cost criteria, based on the tradeoff among time delay-energy consumption-cost, is designed to quantify the user experience of task offloading and to be the optimization target of offloading decision. Both the optimization problems of minimizing the sum offloading costs for all MUs (efficiency-based) and minimizing the maximal offloading cost per MU (fairness-based) are discussed. Efficiency-based offloading decision algorithm [centralized greedy algorithm (CGA) and modified greedy algorithm (MGA)] and fairness-based offloading decision algorithm [fairness-based greedy algorithm (FGA)] are proposed, respectively, and the performance bounds of the algorithm are analyzed. The simulation results show that the offloading cost of the MGA is lower than the CGA, the efficiency of resource utilization of the CGA is higher than that of the FGA, and the fairness of the FGA is stronger than that of the CGA.

关键词： Mobile edge computing   content caching   content popularity   live streaming  

摘要： Mobile Edge Computing Caching System (MECCS) realizes low-latency and high-bandwidth content access and enables seamless 4K Ultra High Definition (UHD) video streaming by caching content in advance at edge-servers of a cellular network. The objective of MECCS is to maximize cache hit by caching highly popular video content while utilizing the storage capacity efficiently in edge-servers. Most of existing caching schemes estimate the popularity of each content based on content request history in off-line or on-line manners, considering the characteristics of Video-on-Demand (VoD) content which has long-term time-varying popularity. However, since live streaming follows Short-term Time-Varying (STV) characteristics, estimating popularity based on content request history do not guarantee acceptable performance on cache hit for live streaming. In this paper, we propose a request model to estimate the popularity distribution considering STV characteristics. Also, we propose a STV request model-based chunk caching scheme to cache highly popular content and enhance cache hit in multiple live channels, utilizing the storage capacity of collaborative edge-servers efficiently. Experimental results show that the proposed scheme outperforms existing schemes regarding cache hit and backhaul traffic.