关键词： Servers   Task analysis   Resource management   Delays   Computational modeling   Minimization   Smart glasses   Mobile edge computing   partial program offloading   program partitioning   resource allocation  

摘要： This paper considers a practical mobile edge computing (MEC) system, where edge server does not pre-install the program required to perform user offloaded computing tasks. A partial program offloading (PPO) scheme is proposed, which can divide a user program into two parts, where the first part is executed by the user itself and the second part is transferred to an edge server for remote execution. However, the execution of the latter part requires the results of the previous part (called intermediate result) as the input. We aim to minimize the overall time consumption of a multi-server MEC system to complete all user offloaded tasks. It is modeled as a mixed integer nonlinear programming (MINLP) problem which considers user-and-server association, program partitioning, and communication resource allocation in a joint manner. An effective algorithm is developed to solve the problem by exploiting its structural features. First, the task completion time of a single server is minimized given the computing workload and available resource. Then, the working time of the edge servers are balanced by updating user-and-server association and communication resource allocation. Numerical results show that significant performance improvement can be achieved by the proposed scheme.

关键词： Computational modeling   Task analysis   Optimization   Energy consumption   Costs   Connected vehicles   Resource management   Mobile edge computing   multitier roadside computing   system stability   vehicular communication  

摘要： Owing to the heterogeneity and massiveness of data generated by connected vehicles, multitier roadside computing (MRC) plays a key role in an intelligent transportation system (ITS). MRC provides a localized cloudization capability in close proximity to the connected vehicles. Because the massive data correspondingly necessitate a high computing energy consumption, stable workload processing with respect to energy efficiency is a crucial problem of MRC. To address this problem, we propose an energy-efficient workload (E2W) scheduling algorithm for flexibly handling the random offloading traffic from connected vehicles. In our E2W algorithm, an MRC is transformed into a multitier queuing system, where the workload arrived from the vehicles and the computing capability of the roadside units are considered to be arrival and departure processes, respectively. The departure rate that closely impinges on the computing energy consumption is supervised using the Lyapunov drift-plus-penalty policy to achieve efficient energy reduction while maintaining service satisfaction. In addition, the deterministic upper bound of the Lyapunov optimization provides the MRC system with stable operation. Simulation results demonstrate that the E2W algorithm outperforms existing optimization strategies in terms of energy efficiency and system stability.

关键词： Mobile edge computing   Machine learning   Deep learning   Big data analytics   Bio-inspired computing   Deep forest   Ant colony optimization   Ant lion optimization  

摘要： Today, incorporating advanced machine learning techniques into intrusion detection systems (IDSs) plays a crucial role in securing mobile edge computing systems. However, the mobility demands of our modern society require more advanced IDSs to make a good trade-off between coping with the rapid growth of traffic data and responding to attacks. Thus, in this paper, we propose a lightweight distributed IDS that exploits the advantages of centralized platforms to train and learn from large amounts of data. We investigate the benefits of two promising bioinspired optimization algorithms, namely Ant Lion Optimization and Ant Colony Optimization, to find the optimal data representation for the classification process. We use Deep Forest as a classifier to detect intrusive actions more robustly and generate as few false positives as possible. The experiment results show that the proposed approach can enhance the reliability of lightweight intrusion detection systems in terms of accuracy and execution time.

关键词： mobile edge computing   unmanned aerial vehicles   computation offloading   Stackelberg game  

摘要： This paper studies an efficient computing resource offloading mechanism for UAV-enabled edge computing. According to the interests of three different roles: base station, UAV, and user, we comprehensively consider the factors such as time delay, operation, and transmission energy consumption in a multi-layer game to improve the overall system performance. Firstly, we construct a Stackelberg multi-layer game model to get the appropriate resource pricing and computing offload allocation strategies through iterations. Base stations and UAVs are the leaders, and users are the followers. Then, we analyze the equilibrium states of the Stackelberg game and prove that the equilibrium state of the game exists and is unique. Finally, the algorithm's feasibility is verified by simulation, and compared with the benchmark strategy, the Stackelberg game algorithm (SGA) has certain superiority and robustness.

关键词： Mobile edge computing   Computation offloading   Task scheduling   Object-oriented applications   UAV  

摘要： The high mobility and maneuverability of unmanned aerial vehicles (UAVs) enable them to act as temporary base stations (BSs) in extreme environments, expanding the computing capacities of terminals for intelligent applications, most of which are object-oriented ones. Computation offloading in a UAV-based edge-cloud environment is an excellent way to improve the performance of these object-oriented intelligent applications. In contrast, the computation-intensive tasks are offloaded to the cloud, and the data-intensive ones are offloaded to the edge. Though computation offloading over the cloud, edge, and terminals has been broadly studied, existing researches primarily establish scheduling algorithms on program high-level abstraction without consideration of challenges from program structures. We focus on task scheduling for offloading object-oriented applications while considering the 'encapsulation' characteristic. We proposed a time-driven offloading strategy based on a particle swarm optimization algorithm employing the genetic algorithm operators with floating encoding (PGFE). This strategy introduces a genetic algorithm's randomly two-point crossover and mutation operator to avoid converging on local optima effectively. The simulation results show that our strategy can reduce the average execution time of object-oriented applications by 11.78-48.02%, compared with other classic algorithms in a UAV-based edge-cloud environment.

关键词： unmanned aerial vehicle   mobile edge computing   stackelberg game  

摘要： When an unmanned aerial vehicle (UAV) performs tasks such as power patrol inspection, water quality detection, field scientific observation, etc., due to the limitations of the computing capacity and battery power, it cannot complete the tasks efficiently. Therefore, an effective method is to deploy edge servers near the UAV. The UAV can offload some of the computationally intensive and real-time tasks to edge servers. In this paper, a mobile edge computing offloading strategy based on reinforcement learning is proposed. Firstly, the Stackelberg game model is introduced to model the UAV and edge nodes in the network, and the utility function is used to calculate the maximization of offloading revenue. Secondly, as the problem is a mixed-integer non-linear programming (MINLP) problem, we introduce the multi-agent deep deterministic policy gradient (MADDPG) to solve it. Finally, the effects of the number of UAVs and the summation of computing resources on the total revenue of the UAVs were simulated through simulation experiments. The experimental results show that compared with other algorithms, the algorithm proposed in this paper can more effectively improve the total benefit of UAVs.

关键词： Broadcast scheduling   Mobile edge computing   Mobile P2P streaming network   News broadcast applications  

摘要： The enhanced mobile broadband (eMBB) of 5G drives the News-Broadcast-on-Demand (NBoD) service to be delivered to mobile users in high quality video streams. In NBoD, each user receives a cascade of news clips chosen from a set of prestored news video clips according to the user's preference or subscription policy. While in some dense population areas, the high volume of video traffic generated may congest the data path from the backbone news servers to the mobile users connected to the radio access networks (RAN) under the same vicinity. As a result, users may experience poor viewing quality. To solve this problem, the mobile peer-to-peer streaming (MPPS) overlay network provides a promising solution. It conducts stream sharing in the RAN to reduce the backbone traffic. Unfortunately, the sharing efficiency of MPPS is deteriorated by the sparely distributed requests and mobile users' limited cache buffer space. In this regard, we introduce a novel mobileedge-computing (MEC) server at the mobile cloud edge, which purposely clusters requests of the same video clip to exploit the stream sharing opportunity in the RAN. The MEC server consists of two parts: a news directory manager (NDM) and a broadcast-channel scheduling manager (BSM). NDM takes the users' requests and compiling customized news playlists. BSM schedules the news-clip play times with the objective of maximizing the number of peers participating stream sharing. For BSM, two scheduling algorithms are investigated, termed the clip-wise scheduling (CS) for the short-term-stream-sharing optimization with a lower computational cost and the session-wise scheduling (SS) for the long-term-stream-sharing optimization with a higher computational cost. Through packet-level simulation, we evaluate these two schemes with respect to the parameters of user cache buffer size, startup delay tolerance and system load. The results reveal that with a small (9-s) cache buffer, CS and SS can achieve 70.1% similar to 76% and 74%

关键词： Task analysis   Resource management   Pricing   Cloud computing   Computational modeling   Mobile applications   Edge computing   Pricing mechanisms   resource allocation   market equilibrium   microeconomic theory   mobile edge computing  

摘要： Mobile edge computing (MEC) can significantly improve the performance of mobile applications by leveraging nearby servers as the edge cloud to provide task offloading execution service for a smart mobile device (SMD) through wireless access points (APs). However, the edge cloud and AP will not provide free services. Their radio frequency resources and computing resource are limited but the service requests from various mobile devices could be massive. The goal of this article is to provide a pricing mechanism to efficiently allocate limited resources in the MEC system according to the budget of SMDs. To this end, we first present a market model of MEC resources that can give a real insight into the incentives for resource sharing at network edges. In the model, computation, and radio resources can be traded between resource suppliers (AP and edge cloud) and buyers (SMDs). Furthermore, we employ the microeconomic theory to get an optimal budget allocation strategy for the SMD to maximize its utility within a limited budget. Moreover, we propose an Equilibrium Price Finding (EPF) algorithm to find the equilibrium price of the MEC system, maximizing the whole system utility and leading to optimal resource allocation. Finally, simulation results show that, compared with state-of-the-art resource allocation methods, our optimal budget allocation algorithm can find budget allocation strategy more effectively and our equilibrium price finding algorithm can achieve market equilibrium to optimally allocate computation and radio resources in the MEC system.

关键词： Task offloading   mobile edge computing   Internet of Vehicles   task classification   offloading nodes selection  

摘要： The Mobile Edge Computing (MEC)-based task offloading in the Internet of Vehicles (IoV) scenario, which transfers computational tasks to mobile edge nodes and fixed edge nodes with available computing resources, has attracted interest in recent years. The MEC-based task offloading can achieve low latency and low operational cost under the tasks delay constraints. However, most existing research generally focuses on how to divide and migrate these tasks to the other devices. This research ignores delay constraints and offloading node selection for different tasks. In this article, we design the MEC-enabled IoV architecture, in which all vehicles and MEC servers act as offloading nodes. Mobile offloading nodes (i.e., vehicles) and fixed offloading nodes (i.e., MEC servers) provide low latency offloading services cooperatively through roadside units. Then we propose the task offloading scheme that considers task classification and offloading nodes selection (TO-TCONS). Our goal is to minimize the total execution time of tasks. In TO-TCONS Scheme, we divide the task offloading into the same region offloading mode and cross-region offloading mode, which is based on the delay constraints of tasks and the travel time of the target vehicle. Moreover, we propose the mobile offloading nodes selection strategy to select offloading nodes for each task, which evaluates offloading candidates for each task based on computing resources and transmission rates. Simulation results demonstrate that TO-"ICONS Scheme is indeed capable of reducing total latency of tasks execution under the delay constraints in MEC-enabled IoV.

关键词： Autonomous aerial vehicles   Task analysis   Servers   Security   Resource management   Optimization   Energy consumption   Intelligent omni-surface   mobile edge computing   physical layer security   secrecy energy efficiency   unmanned aerial vehicle  

摘要： Different from conventional reflecting-only metasurfaces, intelligent omni-surfaces (IOSs) are capable of reflecting and transmitting the received signals simultaneously. As such, users located at the both sides of IOSs can be served efficiently. In this paper, a novel IOS-enhanced aerial secure offloading system is proposed in the presence of multiple ground eavesdroppers. Specifically, the IOS is adopted to prevent information leakage, improve legitimate reception quality and expand the security deployment range of unmanned aerial vehicles (UAVs). To maximize the secrecy energy efficiency (SEE) of the considered system, a non-convex resource allocation problem is formulated by allocating computing frequency, determining offloading strategy, controlling transmit power, designing phase shifts, and deploying UAV locations. The formulated problem is non-trivial and challenging to be solved directly due to the highly coupled variables. To tackle this issue, the alternating optimization technique is invoked to decouple the original one into five subproblems, and then the computing and communication settings are alternatively optimized using a low complexity iterative algorithm. Finally, simulation results demonstrate that: i) In terms of the SEE performance, our IOS-enhanced aerial secure offloading system significantly outperforms the benchmark schemes;ii) Compared with reflecting-only surfaces, IOSs can take full advantage of the flexible deployment ability of UAVs, such that their secure offloading space can be considerably expanded.