关键词： Steganography   Mobile Edge Computing   Covert channel   HTTP protocol   D2D communications  

摘要： Recently, due to unexpected conditions introduced by the COVID-19 outbreak, collaborative tools are widely adopted in almost all sectors of our daily lifestyle. Almost all tools rely mainly on the World Wide Web technologies that, in turn, are built upon the HTTP protocol. The HTTP protocol is considered as the "bricks" of all kind of communications among people/devices that exchange messages with different purposes and meanings. Unfortunately, it is widely used to track and monitor people when using the Internet. This paper exploits the HTTP protocol and try to reverse this negative aspect by designing and implementing a way to help users (and devices) to not disclose too much information when collaborating each other even in an unfriendly environment. A novel steganographic protocol is proposed by using the HTTP "control" messages. The proposed protocol can be easily adopted by devices communicating in a MEC (Mobile Edge Computing) environment where it is important to guarantee the integrity and the confidentiality of all communications, especially messages that give "instructions" to devices or in device-to-device communications. The proposed protocol allows to avoid using complex and computationally demanding cryptographic protocols that are very difficult to be used in devices with limited resources.

关键词： Internet of Things   Edge computing   Authentication   Access control   Mobile handsets   Control systems   Computer architecture   Internet of things   mobile edge computing   multi-access authentication   network anomaly detection   software-defined networking  

摘要： Due to the blooming of Internet of Things (IoT), heterogeneous IoT mobile devices emerge to connect the network infrastructure. Traditional mobile access system faces several challenges arising from these IoT devices: 1) centralized controllers are distant from the end devices, 2) inefficient access control of heterogeneous IoT devices, and 3) insufficient authentication and monitoring for IoT devices. In order to tackle the challenges from IoT devices on mobile access control and scalable access monitoring, we present LEDGE, an agile and secured software-defined edge computing system for resilient access management of mobile IoT. In a nutshell, our LEDGE is a synergy of an efficient location authentication method to secure communication between each IoT mobile device and access point (AP) pair, an optimal AP assignment scheme to satisfy IoT flow requests, a Personal AP protocol for scalable access, and a deep learning model for anomaly detection. We prototype our system, and realistic testbed experiments demonstrate that LEDGE could achieve promising results in mobile IoT.

关键词： utility maximization   lyapunov optimization   task offloading   mobile edge computing  

摘要： In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and *** the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation ***,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high ***,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,***,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.

关键词： Cloud computing   Delays   Image edge detection   Base stations   Edge computing   Heuristic algorithms   Microservice   mobile edge computing   coordination   delay   migration  

摘要： As an emerging service architecture, microservice enables decomposition of a monolithic web service into a set of independent lightweight services which can be executed independently. With mobile edge computing, microservices can be further deployed in edge clouds dynamically, launched quickly, and migrated across edge clouds easily, providing better services for users in proximity. However, the user mobility can result in frequent switch of nearby edge clouds, which increases the service delay when users move away from their serving edge clouds. To address this issue, this article investigates microservice coordination among edge clouds to enable seamless and real-time responses to service requests from mobile users. The objective of this work is to devise the optimal microservice coordination scheme which can reduce the overall service delay with low costs. To this end, we first propose a dynamic programming-based offline microservice coordination algorithm, that can achieve the globally optimal performance. However, the offline algorithm heavily relies on the availability of the prior information such as computation request arrivals, time-varying channel conditions and edge cloud's computation capabilities required, which is hard to be obtained. Therefore, we reformulate the microservice coordination problem using Markov decision process framework and then propose a reinforcement learning-based online microservice coordination algorithm to learn the optimal strategy. Theoretical analysis proves that the offline algorithm can find the optimal solution while the online algorithm can achieve near-optimal performance. Furthermore, based on two real-world datasets, i.e., the Telecom's base station dataset and Taxi Track dataset from Shanghai, experiments are conducted. The experimental results demonstrate that the proposed online algorithm outperforms existing algorithms in terms of service delay and migration costs, and the achieved performance is close to the optima

关键词： mobile edge computing   Wireless Power Transmission (WPT)   task offloading   joint optimization   resource allocation  

摘要： The combination of mobile edge computing (MEC) and wireless power transfer (WPT) is recognized as a promising technology to solve the problem of limited battery capacities and insufficient computation capabilities of mobile devices. This technology can transfer energy to users by radio frequency (RF) in wireless powered mobile edge computing. The user converts the harvested energy, stores it in the battery, and utilizes the harvested energy to execute corresponding local computing and offloading tasks. This paper adopts the Frequency Division Multiple Access (FDMA) technique to achieve task offloading from multiple mobile devices to the MEC server simultaneously. Our objective is to study multiuser dynamic joint optimization of computation and wireless resource allocation under multiple time blocks to solve the problem of maximizing residual energy. To this end, we formalize it as a nonconvex problem that jointly optimizes the number of offloaded bits, energy harvesting time, and transmission bandwidth. We adopt convex optimization technology, combine with Karush-Kuhn-Tucker (KKT) conditions, and finally transform the problem into a univariate constrained convex optimization problem. Furthermore, to solve the problem, we propose a combined method of Bisection method and sequential unconstrained minimization based on Reformulation-Linearization Technique (RLT). Numerical results demonstrate that the performance of our joint optimization method outperforms other benchmark schemes for the residual energy maximization problem. Besides, the algorithm can maximize the residual energy, reduce the computation complexity, and improve computation efficiency.

关键词： Mobile edge computing   Offloading   Precedence constraints   Resource utilization  

摘要： Efficient task scheduling improves offloading performance in mobile edge computing (MEC) environment. The jobs offloaded by different users would have different dependent tasks with diverse resource demands at different times. Meanwhile, due to the heterogeneity of edge servers configurations in MEC, offloaded jobs may frequently have placement constraints, restricting them to run on a particular class of edge servers meeting specific software running settings. This spatio-temporal information gives the opportunity to improve the resource utilization of the computing system. In this paper, we study the scheduling method for the jobs consisting of dependent tasks offloaded by different users in MEC. A new task offloading scheduler, Horae, is proposed to not only improve the resource utilization of MEC environment but also guarantees to select the edge server which could satisfy placement constraints for each offloaded task. Concretely, considering the fact that each job would experience slack time as a result of competing for limited resource with other jobs in MEC, Horae minimizes the sum of all slack time values of all the jobs while guaranteeing placement constraints, and therefore improve the resource utilization of the system. Horae was validated for its feasibility and efficiency by means of extensive experiments, which are presented in this paper.

关键词： Task analysis   Mobile handsets   Servers   Computational modeling   Bandwidth   Data models   Internet   Bidirectional data computation   mobile edge computing   wireless caching   bandwidth minimization  

摘要： A novel bidirectional computation task model has emerged as an important use case of 5G. For example, interactive AR/VR gaming service needs to render the live scene by jointly computing user features such as 3D positions and video data generated from the Internet. In this article, we consider the bidirectional computation task model, where each task is served via three mechanisms, i.e., local computing with local caching, local computing without local caching, and computing at the mobile edge computing server. To minimize the average bandwidth, we formulate the joint caching and computing optimization problem under the latency, cache size and average power constraints. In the homogeneous scenario, we derive the optimal policy and analytical expression for the minimum bandwidth. In the heterogeneous scenario, to reduce the computation complexity of the NP-hard problem, we relax some constraints of the original problem and propose a Lagrangian relaxation (LR) suboptimal solution, which may be infeasible. We then reformulate the original problem as an auxiliary problem based on the LR solution and solve this via Concave-Convex Procedure (CCCP), which outputs feasible local optimal solution. Simulation has shown that LR-based algorithms outperform the baselines including greedy and CCCP algorithms in the bandwidth performance and time efficiency.

关键词： Computation offloading   energy consumption   execution time   fog computing   heuristic algorithm   mobile edge computing   mobile user   performance evaluation  

摘要： The investigation in this article makes the following important contributions to combinatorial optimization of computation offloading in fog computing. First, we rigorously define the two problems of optimal computation offloading with energy constraint and optimal computation offloading with time constraint. We do this in such a way that between execution time and energy consumption, we can fix one and minimize the other. We prove that our optimization problems are NP-hard, even for very special cases. Second, we develop a unique and effective approach for solving the proposed combinatorial optimization problems, namely, a two-stage method. In the first stage, we generate a computation offloading strategy. In the second stage, we decide the computation speed and the communication speeds. This method is applicable to both optimization problems. Third, we use a simple yet efficient greedy method to produce a computation offloading strategy by taking all aspects into consideration, including the properties of the communication channels, the power consumption models of computation and communication, the tasks already assigned and allocated, and the characteristics of the current task being considered. Fourth, we experimentally evaluate the performance of our heuristic algorithms. We observe that while various heuristics do exhibit noticeably different performance, there can be a single and simple heuristic that can perform very well. Furthermore, the method of compound algorithm can be applied to obtain slightly improved performance. Fifth, we emphasize that our problems and algorithms can be easily extended to study combined performance and cost optimization (such as cost-performance ratio and weighted cost-performance sum optimization) and to accommodate more realistic and complicated fog computing environments (such as preloaded mobile edge servers and multiple users) with little extra effort. To the best of our knowledge, there has been no similar study in the exis

关键词： Multi-agent deep reinforcement learning   MADDPG   mobile edge computing   UAV   trajectory control  

摘要： An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground. We aim to jointly optimize the geographical fairness among all the UEs, the fairness of each UAV' UE-load and the overall energy consumption of UEs. The above optimization problem includes both integer and continues variables and it is challenging to solve. To address the above problem, a multi-agent deep reinforcement learning based trajectory control algorithm is proposed for managing the trajectory of each UAV independently, where the popular Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is applied. Given the UAVs' trajectories, a low-complexity approach is introduced for optimizing the offloading decisions of UEs. We show that our proposed solution has considerable performance over other traditional algorithms, both in terms of the fairness for serving UEs, fairness of UE-load at each UAV and energy consumption for all the UEs.

关键词： Delay effects   Energy consumption   Resource management   Task analysis   Precoding   MIMO communication   Servers   Computation offloading   mobile edge computing   MU-MIMO   precoding   resource allocation  

摘要： Mobile edge computing is considered as a promising method to release the computation burden of mobile devices (MDs) by transferring the computation tasks to the nearby edge server. In this paper, we address the computation offloading problem by jointly optimizing offloading-decision making, multi-user multiple input and multiple output (MU-MIMO) precoding and computation resource allocation. The optimization problem is formulated as the minimization of the weighted sum of energy consumption and time delay of MDs, which is a mixed-integer non-linear programming problem. Due to the complexity of offloading time delay, we consider two special cases namely, the lower bound and upper bound of offloading time delay for the original problem, and exploit semidefinite relaxation and rounding methods to obtain the offloading decisions. Specially, we adopt the quadratic transform based fractional programming and the weighted minimum mean square error methods to solve the MU-MIMO precoding design problem for the two cases of offloading time delay, respectively. Simulation results confirm the effectiveness of the proposed method, and show that the application of multi-antenna MU-MIMO communication into MEC can sufficently reduce the energy consumption and time delay during computation offloading.