关键词： VANETs   SDN-based handover   Mobile edge computing   Cellular   IEEE 802   11p hybrid vehicular networks  

摘要： With the arrival of 5G, the wireless network will be provided with abundant spectrum resources, massive data transmissions and low latency communications, which makes Vehicle-to-Everything applications possible. However, VANETs always accompany with frequent network topology changes due to the highly mobile feature of vehicles. As a result, the network performance will be affected by the frequent handover. In this paper, a seamless handover scheme is proposed where the Software-Defined Networking (SDN) and Mobile Edge Computing (MEC) technologies are employed to adapt to the dynamic topology change in VANETs. The introduction of SDN provides a global view of network topology and centralized control, which enables a stable transmission layer connection when a handover takes place, so that the upper layer performance is not influenced by the network changes. By employing MEC server, the data are cached in advance before a handover happens, so that the vehicle can restore normal communication faster. In order to confirm the superiority of our proposal, computer simulations are conducted from different aspects. The results show that our proposal can significantly improve the network performance when a handover happens.

关键词： smart city   Internet of Things   mobile edge computing   potential game   Q-learning  

摘要： With the development of global urbanization, the Internet of Things (IoT) and smart cities are becoming hot research topics. As an emerging model, edge computing can play an important role in smart cities because of its low latency and good performance. IoT devices can reduce time consumption with the help of a mobile edge computing (MEC) server. However, if too many IoT devices simultaneously choose to offload the computation tasks to the MEC server via the limited wireless channel, it may lead to the channel congestion, thus increasing time overhead. Facing a large number of IoT devices in smart cities, the centralized resource allocation algorithm needs a lot of signaling exchange, resulting in low efficiency. To solve the problem, this paper studies the joint policy of communication and computing of IoT devices in edge computing through game theory, and proposes distributed Q-learning algorithms with two learning policies. Simulation results show that the algorithm can converge quickly with a balanced solution.

关键词： Handover   Task analysis   Servers   Uplink   Computational modeling   Trajectory   Mobile edge computing   offloading   random mobility   vehicular networks   handover  

摘要： We study the mobility-aware computation offloading design for mobile edge computing (MEC) based vehicular wireless networks which considers random mobility of the vehicle and its potential handover during the offloading process. The computation offloading decision is made by comparing the average total costs due to the local and remote computation task executions at the vehicle and the MEC servers, respectively. Toward this end, we propose an analytical model to determine the average total cost due to remote task execution. We evaluate the performance of the proposed design by comparing its achievable cost with that due to an average mobility-based approximation. Simulation results show a cost reduction of up to 17% compared to the baseline.

关键词： Mobile edge computing   resource allocation   software defined cellular networks   task offloading   wireless networks  

摘要： The rapid growth of the internet usage and the distributed computing resources of edge devices create a necessity to have a reasonable controller to ensure efficient utilization of distributed computing resources in mobile edge computing (MEC). We envision the future MEC services, where quality of experience (QoE) of the services is further enhanced by software defined networks (SDNs) capabilities to reduce the application-level response time without service disruptions. SDN, which is not proposed specifically for edge computing, can in fact serve as an enabler to lower the complexity barriers involved and let the real potential of edge computing be achieved. In this paper, we investigate the task offloading and resource allocation problem in wireless MEC aiming to minimize the delay while saving the battery power of user device simultaneously. However, it is challenging to obtain an optimal policy in such a dynamic task offloading system. Learning from experience plays a vital role in time variant dynamic systems where reinforcement learning (RL) takes a long term goal into consideration besides immediate reward, which is very important for a dynamic environment. A novel software defined edge cloudlet (SDEC) based RL optimization framework is proposed to tackle the task offloading and resource allocation in wireless MEC. Specifically, Q-learning and cooperative Q-learning based reinforcement learning schemes are proposed for the intractable problem. Simulation results show that the proposed scheme achieves 31.39% and 62.10% reduction on the sum delay compared to other benchmark methods such as traditional Q-learning with a random algorithm and Q-learning with epsilon greedy.

关键词： FiWi   mobile edge computing   vehicular edge computing networks   SDN   load balancing  

摘要： Recently, the rapid advance of vehicular networks has led to the emergence of diverse delay-sensitive vehicular applications such as automatic driving, auto navigation. Note that existing resource-constrained vehicles cannot adequately meet these demands on low / ultra-low latency. By offloading parts of the vehicles' compute-intensive tasks to the edge servers in proximity, mobile edge computing is envisioned as a promising paradigm, giving rise to the vehicular edge computing networks (VECNs). However, most existing works on task offloading in VECNs did not take the load balancing of the computation resources at the edge servers into account. To address these issues and given the high dynamics of vehicular networks, we introduce fiber-wireless (FiWi) technology to enhance VECNs, due to its advantages on centralized network management and supporting multiple communication techniques. Aiming to minimize the processing delay of the vehicles' computation tasks, we propose a software-defined networking (SDN) based load-balancing task offloading scheme in FiWi enhanced VECNs, where SDN is introduced to provide supports for the centralized network and vehicle information management. Extensive analysis and numerical results corroborate that our proposed load-balancing scheme can achieve superior performance on processing delay reduction by utilizing the edge servers' computation resources more efficiently.

关键词： Mobile edge computing  

摘要： Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. Resource allocation strategies for maximizing the computation efficiency are critically important. In this paper, computation efficiency maximization problems are formulated in wireless-powered MEC networks under both partial and binary computation offloading modes. A practical non-linear energy harvesting model is considered. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) are considered and evaluated for offloading. The energy harvesting time, the local computing frequency, and the offloading time and power are jointly optimized to maximize the computation efficiency under the max-min fairness criterion. Two iterative algorithms and two alternative optimization algorithms are respectively proposed to address the non-convex problems formulated in this paper. Simulation results show that the proposed resource allocation schemes outperform the benchmark schemes in terms of user fairness. Moreover, a tradeoff is elucidated between the achievable computation efficiency and the total number of computed bits. Furthermore, simulation results demonstrate that the partial computation offloading mode outperforms the binary computation offloading mode and NOMA outperforms TDMA in terms of computation efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： 5G   LSTM   Mobile edge computing   Task offloading   Unmanned axial vehicle  

摘要： Mobile edge computing (MEC) network provides near-users computing and communication functions and has become a potential 5G evolutionary architecture. In order to overcome the shortcomings of the existing MEC network in fixed base stations and limited computing resources, unmanned anal vehicle (UAV) is introduced as a relay edge computing node and UAV-enabled MEC networks are proposed. However, UAVs have limited energy. Thus, energy consumption would be an optimal target during the information interaction. Therefore, an energy efficiency optimization algorithm based on a three-layer computation offloading strategy is proposed in this paper by combining the UAV position optimization algorithm and the LSTM-based task prediction algorithm. The experiments show that the computation offloading strategy of the UAV-enabled MEC network can be dynamically programmed with the proposed algorithm and architecture, according to the required delay, UAV height, and data size in order to effectively reduce the energy consumption of the UAV.

关键词： Mobile edge computing  

摘要： With the advent of the modern mobile traffic, e.g., online gaming, augmented reality delivery and etc., a novel bidirectional computation task model where the input data of each task consists of two parts, one generated at the mobile device in real-time and the other originated from the Internet proactively, is emerging as an important use case of 5G. In this paper, for ease of analytical analysis, we consider the homogeneous bidirectional computation task model in a mobile edge network which consists of one mobile edge computing (MEC) server and one mobile device, both enabled with computing and caching capabilities. Each task can be served via three mechanisms, i.e., local computing with local caching, local computing without local caching and computing at the MEC server. To minimize the average bandwidth, we formulate the joint caching and computing optimization problem under the latency, cache size and average power constraints. We derive the closed-form expressions for the optimal policy and the minimum bandwidth. The tradeoff among communications, computing and caching is illustrated both analytically and numerically, which provides insightful guideline for the network designers. Copyright © 2020, The Authors. All rights reserved.

关键词： edge cloud   IoT   LPWAN   mobile edge computing   network control  

摘要： Recently, low-power wide area network (LPWAN) has attracted attention as a wireless network for long-range Internet of Things (IoT) services. IoT devices in an LPWAN are managed by a network server in the cloud. Various data are delivered from the IoT devices to the network server, and the role of the centralized network server in the cloud has become important for efficient data transfer over an LPWAN. However, control by the network server concentrates the control load on the network server and is vulnerable in terms of response time and bandwidth utilization. Therefore, this paper takes the approach of moving the control of the LPWAN to the edge cloud, which provides computing and storage environments at base stations. An LPWAN gateway is then integrated with the edge cloud, and LPWAN data are cached at the edge cloud in the gateway for LPWAN control. LPWAN control such as report interval control for data, transmission power control, and data aggregation is applied to improve the efficiency of data transmission. Network control is performed by learning using cached data in the edge cloud. Compared with the existing LPWAN control approach, the proposed approach exhibits improved performance for IoT data transmission. The simulation results show the proposed approach's efficiency.

关键词： Tactile Internet   mobile edge computing   haptric feedback   evolved packet system   data forwarding element   software defined network   network function virtualization  

摘要： In today’s communication era, we are speedily moving from various generations of mobile communication technologies to Next Generation mobile communication. Moving from 3G, 4G and 5G, one to the next generation, it includes in-built improvements in various characteristics. The world is collecting traditional information this is huge and providing the same to every corner of the world that creates huge pressure on the core networking and backhaul resources. This paper focuses on combining 5G technologies with mobile edge computing for robotic telesurgery. A huge amount of data is required to continuously transfer over the high-speed network for this process. This process demands high network bandwidth, minimal information loss, minimal delay and real-time response for precise surgery. As of today, useful and cost-effective solutions for medical domain telerobotic surgeries are not preferred yet may be due to the performance limitations of existing communication technologies like 4G. Hence, realtime medical video transmission using 5G enabled tactile (T5ET) internet technology environment is experimented in this research, with a focus on QoS parameters like jitter control, throughput, and delay. Promising results are observed in the evaluation and discussed here which will prominently contribute to the medical domain.