关键词： Opportunistic mobile social networks   mobile edge computing   routing-forwarding algorithm   mobile device similarity   fuzzy reasoning system  

摘要： The arrival of 5G is accompanied by massive data transmission between mobile devices (MDs) and huge transmission energy consumption in the wireless networks. Therefore, how message applications select the appropriate relay MDs to complete the efficient data transmission process in the opportunistic mobile social networks (OMSNs)? At present, designing an efficient routing-forwarding algorithm is extremely challenging. Some routing-forwarding algorithms choose appropriate nodes as relay nodes based on the similarity between nodes, but most existing routing-forwarding algorithms only consider a few similar factors and even completely ignore the importance of movement similarity in data transmission of the node. In particular, existing routing-forwarding algorithms will bring extra energy consumption to the nodes in the wireless networks, and excessive energy consumption will further affect the delay and data transmission efficiency. In order to solve the problems in the existing strategies, we apply the mobile edge computing (MEC) to OMSNs, and we propose the fuzzy reasoning routing-forwarding (FRRF) algorithm that integrates the movement and social similarity in the MEC-based OMSNs. In detail, the fuzzy evaluation of the movement and social similarity is integrated to determine the transmission priority value between MDs, and finally, the transmission priority between MDs is compared to make transmission decision. Through simulation experiment and comparison with other algorithms, the correctness of the theoretical analysis and the efficiency of the FRRF algorithm in energy consumption, delay, and transmission efficiency are verified.

关键词： Mobile edge computing   multi-access edge computing   mobile edge computing video service   mobile cloud computing   virtual reality streaming   video streaming  

摘要： Recently, new services using virtual reality (VR)/augmented reality (AR) have appeared and then exploded in entertainment fields like video games and multimedia contents. In order to efficiently provide these services to users, an infrastructure for mobile cloud computing with powerful computing capabilities is widely utilized. However, existing mobile cloud system utilizes a cloud server located at a relatively long distance, so that there are problems that a user is not effectively provided with personalized immersive multimedia service. So, this paper proposes the home VR streaming system that can provide fast content access time and high immersiveness by using mobile edge computing (MEC).

关键词： Mobile edge computing   energy harvesting   device-to-device (D2D) communication   computation offloading  

摘要： Mobile edge computing (MEC) is envisioned as a new paradigm by integrating the mobile computing functionality into 5G wireless networks, aiming at empowering communication networks with low-latency services. In general, mobile devices have finite battery lifetime (e.g., machine-type devices) and the energy harvesting is advocated to provide perpetual energy supply for achieving sustainable operation, which is very important for facilitating sustainable computing in future applications. In this paper, we propose a wireless powered MEC network architecture that employs device-to-device (D2D) communications underlaying heterogeneous networks (HetNets) to enable the computational tasks offloading to resource-rich edge servers. A dynamic offloading decision is made to execute the computation tasks. Then, we focus on the energy-efficient offloading scheme, and joint offloading and user association scheme. From the illustrative results, we provide insights for the design of this new network architecture. Furthermore, several open research topics are discussed.

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： Information-centric network   Internet of Things   mobile edge computing   joint optimization   deep Q-learning  

摘要： With the rapid development of the Internet of Things (IoT) network, the IoT devices need to perform the artificial intelligence (AI) model to make decisions according to the specific service requirement under a dynamic environment. However, the generation and usage of AI model typically requires a huge amount of communication, computing, and caching resource. Thus, the construction of the network and the scheduling of the limited network resources to realize the rapid generation and propagation of AI models are critical. Therefore, we propose a software-defined Information Centric-Internet of Things (IC-IoT) architecture to bring caching and computing capabilities to the IoT network. Based on the proposed IC-IoT architecture, we design a joint resource scheduling scheme to uniformly manage the computing and caching resources. The objective is to maximize the reward which consists not only short-term reward but also long-term reward brought by caching popular AI models. The resource scheduling problem is formulated into a multi-dimensional optimization problem. A new deep Q-learning method is proposed due to the complexity and high dimension of this problem. The simulation results verify the effectiveness of the software-defined IC-IoT architecture and the joint resource allocation strategy.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) provides cloud-computing services for mobile devices to offload intensive computation tasks to the physically proximal MEC servers. In this paper, we consider a multiserver system where a single mobile device asks for computation offloading to multiple nearby servers. We formulate this offloading problem as the joint optimization of computation task assignment and CPU frequency scaling, in order to minimize a tradeoff between task execution time and mobile energy consumption. The resulting optimization problem is combinatorial in essence, and the optimal solution generally can only be obtained by exhaustive search with extremely high complexity. Leveraging the Markov approximation technique, we propose a light-weight algorithm that can provably converge to a bounded near-optimal solution. The simulation results show that the proposed algorithm is able to generate near-optimal solutions and outperform other benchmark algorithms.

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   Mobile switching center   Reinforcement learning   Resources and computing capabilities  

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 Mobile Switching Center (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 optimisationtechniques 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. An equilibrium model is presented that aims to reduce the trade-offs between the resources and computing capabilities. A state-reward model is developed to maintain the equilibrium between the resources and computational capabilities. To improve the resources allocation capability, we use the reinforcement learning 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： Smart cities   street cleaning   garbage detection   deep learning   mobile edge computing  

摘要： During the process of smart city construction, city managers always spend a lot of energy and money for cleaning street garbage due to the random appearances of street garbage. Consequently, visual street cleanliness assessment is particularly important. However, the existing assessment approaches have some clear disadvantages, such as the collection of street garbage information is not automated and street cleanliness information is not real-time. To address these disadvantages, this paper proposes a novel urban street cleanliness assessment approach using mobile edge computing and deep learning. First, the high-resolution cameras installed on vehicles collect the street images. Mobile edge servers are used to store and extract street image information temporarily. Second, these processed street data is transmitted to the cloud data center for analysis through city networks. At the same time, Faster Region-Convolutional Neural Network (Faster R-CNN) is used to identify the street garbage categories and count the number of garbage. Finally, the results are incorporated into the street cleanliness calculation framework to ultimately visualize the street cleanliness levels, which provides convenience for city managers to arrange clean-up personnel effectively. The overall approach is illustrated and visualized using the street images of Jiangning District in Nanjing, China. The practical application shows the feasibility and usability of the approach.

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.