关键词： Age-of-information   mobile edge computing   computation-intensive  

摘要： In status update scenarios, the freshness of information is measured in terms of age-of-information (AoI), which essentially reflects the timeliness for real-time applications to transmit status update messages to a remote controller. For some applications, computational expensive and time consuming data processing is inevitable for status information of messages to be displayed. Mobile edge servers are equipped with adequate computation resources and they are placed close to users. Thus, mobile edge computing (MEC) can be a promising technology to reduce AoI for computation-intensive messages. In this paper, we study the AoI for computation-intensive messages with MEC, and consider three computing schemes: local computing, remote computing at the MEC server, and partial computing, i.e., some part of computing tasks are performed locally, and the rest is executed at the MEC server. Zero-wait policy is adopted in all three schemes. Specifically, in local computing, a new message is generated immediately after the previous one is revealed by computing. While in remote computing and partial computing, a new message is generated once the previous one is received by the remote MEC server. With infinite queue size and exponentially distributed transmission time, closed-form average AoI for exponentially distributed computing time is derived for the three computing schemes. For deterministic computing time, the average AoI is analyzed numerically. Simulation results show that by carefully partitioning the computing tasks, the average AoI in partial computing is the smallest compared to local computing and remote computing. The results also indicate numerically the conditions on which remote computing attains smaller average AoI compared with local computing.

关键词： Mobile edge computing   cell-free massive MIMO   stochastic geometry   energy consumption   queueing theory  

摘要： Mobile edge computing (MEC) has been introduced to provide additional computing capabilities at network edges in order to improve performance of latency critical applications. In this paper, we consider the cell-free (CF) massive MIMO framework with implementing MEC functionalities. We consider multiple types of users with different average time requirements for computing/processing the tasks, and consider access points (APs) with MEC servers and a central server (CS) with the cloud computing capability. After deriving successful communication and computing probabilities using stochastic geometry and queueing theory, we present the successful edge computing probability (SECP) for a target computation latency. Through numerical results, we also analyze the impact of the AP coverage and the offloading probability to the CS on the SECP. It is observed that the optimal probability of offloading to the CS in terms of the SECP decreases with the AP coverage. Finally, we numerically characterize the minimum required energy consumption for guaranteeing a desired level of SECP. It is observed that for any desired level of SECP, it is more energy efficient to have larger number of APs as compared to having more number of antennas at each AP with smaller AP density.

关键词： Mobile edge computing   MEC   stochastic geometry   local delay   VANET  

摘要： Mobile edge computing (MEC) is a new computing paradigm for addressing users' ever-increasing demand for quality of service (QoS) in vehicular ad hoc networks (VANETs). The local delay is a performance metric that has a big impact on the QoS provisioned to users. This paper studies the analysis of the local delay in an MEC-based VANET based on stochastic geometry. An analytical model is first derived to analyze the average uplink local delay of a vehicle on a highway for sending a packet to an edge node along the highway. In deriving the model, the spatial distributions of vehicles and edge nodes are modeled as an independent one-dimensional (1-D) homogeneous Poisson point process (PPP). The edge node for accommodating the service request of a vehicle node is determined based on a nearest-receiver model. Moreover, a carrier sense multiple access (CSMA) mechanism is considered for channel access in the derived local delay model. The derived analytical model is validated through simulation results and the impacts of major parameters on the average uplink local delay are investigated. The derived model can be used to provide a basis for parameter settings in the deployment of edge nodes.

关键词： Enterprise multimedia security   Resource allocation   Lyapunov optimization   Mobile edge computing  

摘要： Mobile edge computing (MEC) is a promising computing model and has gained remarkable popularity, as it deploys the resources (e.g., computation, network, and storage) to the evolved NodeB (eNB) to provide enormous benefits such as low delay and energy consumption. More and more enterprises construct their edge computing platforms to store multimedia contents (i.e., video, audio, photos, and text data) for the user equipment (UE). However, both the eNB and UEs will experience serious security attacks when transmitting or receiving multimedia data via the wireless network. Existing MEC studies mainly focus on task offloading and performance improvement without considering the enterprise multimedia security problem. This paper proposes a security and performance-aware resource allocation (Spara) algorithm for enterprise multimedia in MEC environment. More specifically, we first build the architecture of enterprise multimedia security for sending the data requests to UEs, which mainly consists of computing and bandwidth resource allocation. Then, we formulate the stochastic data transmission problem to minimize the delay and energy consumption of UEs subject to the security guarantee. To achieve this goal, two queues, namely front-end queue and back-end queue, are used for each UE, and the Lyapunov optimization technique is applied to determine how to allocate the computing and bandwidth resources. Rigorous theoretical analysis shows that Spara algorithm meets the [O(1/V), O(V)] energy-delay tradeoff. Extensive simulation experiments validate this analysis result and the effectiveness of Spara algorithm.

关键词： Mobile edge computing   buffer-aided relay networks   task computation offloading   convex optimization  

摘要： This paper studies buffer-aided relay-assisted Mobile Edge Computing (MEC) architecture as the fundamental system in hierarchical multi-hop MEC. We propose a stochastic offloading scheme where a buffer-aided relay, equipped with a computing server, randomly decides to process the tasks of a source node by its own server, or to forward them to the server of the next node in the hierarchy. We analyze the proposed scheme and formulate a problem to minimize the average response time in the system. We derive the feasible set and prove that the problem is a convex optimization problem which can be solved based on the one-dimensional search methods. Simulation results confirm the analytical results and demonstrate the effectiveness of the proposed scheme.

关键词： Adaptive streaming   DASH   Deep learning   Mobile edge computing   Adaptive algorithm  

摘要： Adaptive streaming has recently emerged as a technology enabling high-quality streaming at various bitrates. One of the video streaming challenges remains in research topic nowadays that is choosing optimal segment base on network characteristics and streaming devices, such as network bandwidth, latency, the computational capacities of devices. Researchers have proposed many algorithms to overcome such issues within their predefined conditions. However, those proposed methods do not perform efficiently in the heterogeneous network today. Consequently, in this article, we present research on a context-aware adaptive algorithm for ambient intelligence dynamic adaptive employing mobile edge computing (MEC). Specifically, we apply deep learning in the adaptive algorithm which is installed at the MEC to assist clients in choosing the optimal streaming segments as well as reduce network latency. Furthermore, we apply the multilayer perceptron classifier with data obtained from various experiments of adaptive streaming algorithms then combine them in a general algorithm. In the analysis, we use network simulator NS3 as a tool to carry out the verification of our proposed method. As a result, the proposed research reduces network latency as well as improve quality streaming compared to existing approaches.

关键词： Cellular networks   Computation capacity   Device-to-device communications   Internet of Things   Mobile edge computing  

摘要： Recently, network computing, this is a kind of cloud computing suggested to torrent wireless services and cloud hosting (IoT) frameworks. Instead of sending data to remotely distributed domains, snugly-located network associations are required. Amid computation, a neural network is built in this project by exploring the disparity in user interest, which exploits the transition of sustainability-based cooperation game to Split subscribers in an adaptive direction in countless fog societies. By interpreting the majors of user pertinence in the communication part and using multidimensional network status dimensions such as the finest cooperative user, social and physical attributes, including local centrality and interconnection scale, can be reasonably evaluated based on users. Consequently, the correct inter-community and intra-community D2D cooperative communication mechanisms are designed to significantly simplify the use of internet services. Computational findings suggest that the actual mechanism exploits the professional relationship between customer and consumer comprehensively and efficiently enhances the computer system's operational efficiency at relatively low postponement concentrations. © 2020 IJSTR.

关键词： Millimeter wave   hybrid beamforming   mobile edge computing   resource allocation   distributed implementation  

摘要： Mobile edge computing (MEC) has been identified as a key technique of next-generation wireless networks, which supports cloud computing along with other compelling service capabilities at the network & x2019;s edge with the objective of reducing the system delay. As one of the prospective candidates for new spectrum in next-generation networks, millimeter wave (mmWave) communications has been gaining significant attention as a benefit of its high rate. Hence we conceive a joint hybrid beamforming and resource allocation algorithm for mmWave MEC. Explicitly, we jointly optimize the analog beamforming vectors at the users, the analog and digital beamforming matrices at the base station (BS), the computation task offloading ratios and resource allocation at the MEC server for minimizing the maximum system delay subject to the affordable communication and computing budget. We conceive a powerful algorithm for solving this challenging nonconvex optimization problem with coupled constraints based on the penalty dual decomposition (PDD) technique. The proposed algorithm can be implemented in a parallel and distributed fashion. Our numerical results demonstrate the superiority of the proposed algorithm by quantifying the benefits of intrinsically amalgamating MEC with mmWave communications.

关键词： Adaptive bitrate streaming   video transcoding   mobile edge computing   wireless networks   deep reinforcement learning  

摘要： Adaptive bitrate (ABR) streaming has been used in wireless networks to deal with the time-varying wireless channels. Traditionally, wireless video is fetched from remote Internet server. However, wireless video streaming from Internet server faces challenges such as congestion and long latency. ABR streaming and video transcoding at the radio access network (RAN) edge have shown the potential to overcome such problem and provided better video streaming service. In the paper, we consider joint computation and communication for ABR streaming based on mobile edge computing (MEC) under time-varying wireless channels. We propose a joint video transcoding and quality adaptation framework for ABR streaming by enabling RAN with computing capability. By modeling the wireless channel as a finite state Markov channel, we formulate the optimization problem as a stochastic optimization problem of joint computational resource assignment and video quality adaptation for maximizing the average reward, which is defined as the tradeoff between user perceived quality of experience and the cost of performing transcoding at edge server. By using deep reinforcement learning (DRL) algorithm, we develop an automatic algorithm to perform the computational resource assignment and video quality adaptation without any prior knowledge of channel statistics. Simulation results using Tensorflow show the effectiveness of the designed MEC-enabled ABR streaming system and DRL algorithm.

关键词： Mobile edge computing   D2D communications   partial offloading   latency   resource allocation  

摘要： We consider Device-to-Device (D2D)-enabled mobile edge computing offloading scenario, where a device can partially offload its computation task to the edge server or exploit the computation resources of proximal devices. Keeping in view the millisecond-scale latency requirement in 5G service scenarios and the spectrum scarcity, we focus on minimizing the sum of task execution latency of all the devices in a shared spectrum with interference. In particular, we provide an integrated framework for partial offloading and interference management using orthogonal frequency-division multiple access (OFDMA) scheme. Accordingly, we formulate total latency minimization as a mixed integer nonlinear programming (MINLP) problem by considering desired energy consumption, partial offloading, and resource allocation constraints. We use decomposition approach to solve our problem and propose a novel scheme named Joint Partial Offloading and Resource Allocation (JPORA). With aim to reduce the task execution latency, JPORA iteratively adjusts data segmentation and solves the underlying problem of quality of service (QoS)-aware communication resource allocation to the cellular links, and interference-aware communication resource allocation to D2D links. Extensive evaluation results demonstrate that JPORA achieves the lowest latency as compared to the other baseline schemes, meanwhile limiting the local energy consumption of user devices.