关键词： 5G   mobile edge computing   V2X communication   cooperative perception   intelligent transport systems  

摘要： Vehicle-to-everything (V2X) communications enable real-time information exchange between vehicles and infrastructure, which extends the perception range of vehicles beyond the limits of on-board sensors and, thus, facilitating the realisation of cooperative, connected, and automated mobility (CCAM) services that will improve road safety and traffic efficiency. In the context of CCAM, the successful deployments of cooperative intelligent transport system (C-ITS) use cases, with the integration of advanced wireless communication technologies, are effectively leading to make transport safer and more efficient. However, the evaluation of multi-vendor and multi-protocol based CCAM service architectures can become challenging and complex. Additionally, conducting on-demand field trials of such architectures with real vehicles involved is prohibitively expensive and time-consuming. In order to overcome these obstacles, in this paper, we present the development of a standards-compliant experimental vehicular on-board unit (OBU) that supports the integration of multiple V2X protocols from different vendors to communicate with heterogeneous cloud-based services that are offered by several original equipment manufacturers (OEMs). We experimentally demonstrate the functionalities of the OBU in a real-world deployment of a cooperative collision avoidance service infrastructure that is based on edge and cloud servers. In addition, we measure end-to-end application-level latencies of multi-protocol supported V2X information flows to show the effectiveness of interoperability in V2X communications between different vehicle OEMs.

关键词： Task analysis   Uplink   Satellites   Servers   Batteries   Wireless communication   Interference cancellation   Full-duplex   mobile edge computing   satellite Internet of Thing   simultaneous wireless information and power transfer  

摘要： Satellite communication is an important technology for the coverage of open country, and plays a vital role of link channel in remote Internet of Things (IoT). However, various kinds of IoT terminals may suffer from the limited battery capacity and computing capability. Therefore, this article proposes a new multiuser IoT system design, in which the satellite link is used to provide communication service for access points (AP), and allow terminals to download and upload data through APs. Then, the AP will exploit simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) technologies to alleviate the deficiencies mentioned above. Moreover, the AP will equip with the full duplex (FD) and multi-input multi-output (MIMO) technologies to further improve the spectrum efficiency. Besides, a hybrid energy storage is assumed in the AP, i.e., the energy may come from power grid or renewable energy. Taking into account all issues above, we optimize the uplink achievable rate through jointly optimizing the CPU frequency, computation tasks, terminal transmitting power and the ratio of MEC task. In order to solve this optimization problem, we first decouple it into two sub-problems. For the first one, we can obtain the closed-form solution of CPU frequency. For the second one, we continue to decompose it into two non-convex problems, and then the iterative active-set method is used to solve these two problems. Numerical simulations demonstrate the effectiveness of the proposed design.

关键词： Task analysis   Radio access technologies   Resource management   Optimization   Protocols   Adaptive systems   Servers   Mobile edge computing   multi-RAT   max-min fairness   resource allocation  

摘要： Recently, mobile edge computing (MEC) has been recognized as an emerging paradigm to meet the ever-increasing computation demands. Besides, multiple radio access technology (multi-RAT) has been proposed to enhance the network throughput and service reliability. However, the two technologies have been evolving independently. In this paper, we will develop an integrated multi-RAT and MEC network framework, which enables smart devices (SDs) to offload computation tasks over multiple RAT links in parallel. Considering the resource-limited nature as well as the time-varying property of the proposed network, we then focus on investigating long-term fairness guarantee mechanisms to facilitate fair resource sharing/allocation between SDs. Specifically, we formulate a max-min stochastic optimization problem with the objective of maximizing the minimum long-term time-average offloading utility. An adaptive task splitting and resource allocation algorithm is proposed based on the Lyapunov optimization technology, which jointly optimizes the SD task splitting and uplink transmit power allocation, the RAT subcarrier allocation, and the MEC computation frequency allocation. The adaptive algorithm can accommodate to the time-varying network dynamics without requiring their distribution information. Moreover, the proposed algorithm is shown to have polynomial computation complexity and be asymptotically optimal by rigorous analysis. Simulation results verify the theoretical analysis and show that the Jain's fairness index of the proposed algorithm converges to 1 as the number of time-slots grows, which outperforms the benchmark algorithms.

关键词： Task analysis   Servers   Resource management   Wireless communication   Optimization   Bandwidth   Wireless sensor networks   Mobile edge computing   resource management   resource supply   resource demand   min-max fairness   convex optimization  

摘要： This letter focuses on fairness-aware resource management in a multi-user and multi-server mobile edge computing (MEC) network, where the resource supply and demand are jointly considered for resource allocation and task assignment, respectively. In particular, we aim to minimize the maximum task execution latency of all users subject to task and resource constraints. Although the optimization problem includes power, spectrum, hashrate, and task variables and is nonconvex in its primal form, it can be equivalently transformed to a more tractable programming. Then, a low-complexity iteration based algorithm is proposed to find the global optimum of the primal problem since only a convex feasibility problem is tackled in each iteration. Simulation results in typical scenarios show that the proposed resource management strategy can reduce the maximum task execution latency of users by more than 15% comparing with the available baseline approaches.

关键词： Task analysis   Servers   NOMA   Resource management   Collaboration   Energy consumption   Silicon carbide   Multiple radio access   mobile edge computing   nonorthogonal multiple access   resource allocation   task offloading  

摘要： Multi-access mobile edge computing (MEC) allows each user to offload partial task bits to multiple MEC servers via multi-access radio transmission, enabling the collaboration of edge computing. By leveraging the advantage of nonorthogonal multiple access (NOMA) in improving the transmission efficiency, it is expected to effectively reduce the energy consumption of users in multi-access MEC with the aid of NOMA. However, considering the co-channel interference of NOMA and computation collaboration among MEC servers, the joint task offloading and resource allocation is a challenging problem. In this article, with the objective to minimize the weighted sum energy of users, we first propose optimal task offloading and resource allocation algorithms for the special single-user (OTORA-SU) and general multi-user (OTORA-MU) cases, by exploiting the convex and layered structure of the formulated problem. Interestingly, it is found that the single user always preferentially offloads task bits to the MEC servers with better channel gains, regardless of the computation capacity of each MEC server. Then, a low-complexity algorithm (LTORA-MU) is proposed for the general multi-user case, which converges fast and achieves near-optimal performances. Considering the channel estimation error, the impacts of imperfect channel state information (CSI) and successive interference cancellation (SIC) are also investigated. Simulation results demonstrate that for both perfect and imperfect CSI and SIC, 1) the proposed OTORA-SU and LTORA-MU outperform the local computing, full offloading, FDMA-based offloading and non-collaborative MEC schemes;2) as the number of MEC servers grows, the ratio of offloading task bits increases while the energy consumption is decreased.

关键词： Task analysis   Bayes methods   Games   Resource management   Machine learning   Protocols   Bayesian methods   blockchains   deep learning   game theory   incomplete information   machine learning   mobile edge computing   partially-observable Markov decision process   reinforcement learning   resource management  

摘要： We present a novel game-theoretic, Bayesian reinforce-ment learning (RL) and deep learning (DL) framework to represent interactions of miners in public and consortium blockchains with mobile edge computing (MEC). Within the framework, we formulate a stochastic game played by miners under incomplete information. Each miner can offload its block operations to one of the base stations (BSs) equipped with the MEC server. The miners select their offloading BSs and block processing rates simultaneously and independently, without informing other miners about their actions. As such, no miner knows the past and current actions of others and, hence, constructs its belief about these actions. Accordingly, we devise a Bayesian RL algorithm based on the partially-observable Markov decision process for miner's decision making that allows each miner to dynamically adjust its strategy and update its beliefs through repeated interactions with each other and with the mobile environment. We also propose a novel unsupervised Bayesian deep learning algorithm where the uncertainties about unobservable states are approximated with Bayesian neural networks. We show that the proposed Bayesian RL and DL algorithms converge to the stable states where the miners' actions and beliefs form the perfect Bayesian equilibrium (PBE) and myopic PBE, respectively.

关键词： Fiber-Wireless access network   mobile edge computing   energy efficiency   two-layer Stackelberg game  

摘要： Carbon footprints of both computation and communications infrastructures attract growing public attention, raising concerns about global warming and motivating research efforts in developing energy-efficient solutions. Since Fiber-Wireless (FiWi) access networks have succeeded in interconnecting our digitalized world and mobile/multi-access edge computing (MEC) further removes unnecessary data transmission from optical backbone networks, MEC enhanced FiWi emerges as the win-win architecture for resource-hungry and delay-sensitive mobile apps. However, the assumption of unlimited and free resources offered by FiWi infrastructure is impractical, and energy-efficient offloading mechanisms for MEC enhanced FiWi still remain underexplored, conflicting with globally advocated green communications. This motivates us to design the Two-layer Stackelberg Game based Offloading (TSGO) strategy, including the FiWi layer and mobile edge computation offloading (MeCo) layer subgames capturing bandwidth allocation and offloading decision respectively. Then the backward induction and an iterative algorithm are exploited to acquire the Nash equilibrium of the two-layer Stackelberg game. Furthermore, we propose three energy efficiency benchmarks from both user-side and system-wide perspectives to evaluate mechanisms proposed for, but not limited to, FiWi, MEC, or a combination thereof. Simulation results validate the proposed TSGO strategy, which echoes the profound notion of green communications.

关键词： mobile edge computing   low-latency network   prototype   experiment  

摘要： The Internet of Things (IoT) with its support for cyber-physical systems (CPS) will provide many latency-sensitive services that require very fast responses from network services. Mobile edge computing (MEC), one of the distributed computing models, is a promising component of the low-latency network architecture. In network architectures with MEC, mobile devices will offload heavy computing tasks to edge servers. There exist numbers of researches about low-latency network architecture with MEC. However, none of the existing researches simultaneously satisfy the followings: (1) guarantee the latency of computing tasks and (2) implement a real system. In this paper, we designed and implemented an MEC based network architecture that guarantees the latency of offloading tasks. More specifically, we first estimate the total latency including computing and communication ones at the centralized node called orchestrator. If the estimated value exceeds the latency requirement, the task will be rejected. We then evaluated its performance in terms of the blocking probability of the tasks. To analyze the results, we compared the performance between obtained from experiments and simulations. Based on the comparisons, we clarified that the computing latency estimation accuracy is a significant factor for this system.

关键词： Mobile edge computing   computation offloading   energy harvesting   game theory   queueing theory   Karush-Kuhn-Tucker condition  

摘要： Energy harvesting aided mobile edge computing (MEC) has gained much attention for its widespread application in the computation-intensive, latency-sensitive and energy-hungry scenario. Computation offloading, which leverages powerful MEC servers (MEC-ss) to augment the computing capability of less powerful mobile devices (MDs), is intrinsically a distributed computing over heterogeneous MEC networks. In this article, computation offloading from multi-MD to multi-MEC-s in heterogeneous MEC systems with energy harvesting is investigated from a game theoretic perspective. The objective is to minimize the average response time of an MD that consists of communication time, waiting time and processing time. M/G/1 queueing models are established for MDs' computation generation and MEC-ss' computation task receiving. The interference among MDs, the randomness in computation task generation, harvested energy arrival, wireless channel state, queueing at the MEC-s, and the power budget constraint of an MD are taken into consideration. A noncooperative computation offloading game is formulated. The action is a vector that denotes the amount of computation tasks offloaded to all MEC-ss (the element value can be zero) and local process. We give the definition and existence analysis of the Nash equilibrium (NE). Furthermore, we reconstruct the optimization problem of an MD. A 2-step decomposition is presented and performed. Thereby, we arrive at a one-dimensional search problem and a greatly shrunken sub-problem. The sub-problem is nonconvex, but its Karush-Kuhn-Tucker (KKT) conditions have finite solutions. We can obtain the optimal solution of the sub-problem by seeking the finite solutions. Thereafter, a distributive NE-orienting iterated best-response algorithm is designed. Simulations are carried out to illustrate the convergence performance and effectiveness of the proposed algorithm.

关键词： Differential Evolution   Mobile Edge Computing   Machine Learning   Computing Offloading   Context-aware  

摘要： With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.