关键词： Mobile edge computing   Resource allocation   Task offloading   Double weighted support vector transfer regression   Flow direction algorithm   Smart city  

摘要： Mobile edge computing (MEC) technology is gaining more attention in smart cities due to its powerful computation capability. However, there arise complications related to security and privacy while transmitting and processing raw data to other cloud or MEC servers. This makes the users unwilling to update their private information on the cloud servers. To tackle this issue, we proposed a novel approach for optimal task scheduling and resource allocation processes in this paper. The proposed 'double-weighted support vector transfer regression based flow direction (DSTR-FD) approach' resolves the issues of resource management of edge servers and makes optimal task offloading decisions with minimized energy consumption. Here, the model parameters such as weight functions, regularization parameters, and kernel parameters of the DSTR network are tuned using the flow direction (FD) algorithm. The proposed method thus provides better data privacy without sharing the original data with other servers along with minimizing the utilization of energy in the Internet of Things (IoT). The efficiency of the proposed DSTR-FD approach is evaluated by comparing its results with other states of art methods. The simulation experiments illustrate that the proposed DSTR-FD approach effectively minimizes the energy utilization of all IoT devices.

关键词： Mobile edge computing   multi-agent system   task offloading   cooperative edge computing   deep reinforcement learning  

摘要： Driven by the prevalence of the computation-intensive and delay-intensive mobile applications, Mobile Edge Computing (MEC) is emerging as a promising solution. Traditional task offloading methods usually rely on centralized decision making, which inevitably involves a high computational complexity and a large state space. However, the MEC is a typical distributed system, where the edge servers are geographically separated, and independently perform the computing tasks. This fact inspires us to conceive a distributed cooperative task offloading system, where each edge server makes its own decision on how to allocate local computing resources and how to migrate tasks among the edge servers. To characterize diverse task requirements, we divide the arrival tasks into different priorities according to the tolerance time, which enables to dynamically schedule the local computing resources for reducing the task timeout. In order to coordinate the independent decision makings of geographically separate edge servers, we propose a priority driven cooperative task offloading algorithm based on multi-agent deep reinforcement learning, where the decision making of each edge server not only depends on its own state but also on the shared global information. We further develop a Variational Recurrent Neural Network (VRNN) based global state sharing model which significantly reduces the communication overhead among edge servers. The performance evaluation conducted on a movement trajectories dataset of mobile devices verifies that the proposed algorithm can reduce the task consumption time and improve the edge computing resources utilization.

关键词： Containers   Servers   Cloud computing   Virtualization   Resource management   Kernel   Linux   Cloud-RAN   mobile edge computing   containers   resource management  

摘要： Leveraging virtualization technology, Cloud-RAN deploys multiple virtual Base Band Units (vBBUs) along with collocated applications on the same Mobile Edge Computing (MEC) server. However, the performance of real-time (RT) applications such as the vBBU could potentially be impacted by sharing computing resources with collocated workloads. To address this challenge, this paper presents a dynamic CPU sharing mechanism, specifically designed for containerized virtualization in MEC servers, that hosts both RT and non-RT general-purpose applications. Initially, the CPU sharing problem in MEC servers is formulated as a Mixed-Integer Programming (MIP). Then, we present an algorithmic solution that breaks down the MIP into simpler subproblems that are then solved using efficient, constant factor heuristics. We assessed the performance of this mechanism against instances of a commercial solver. Further, via a small-scale testbed, we assessed various CPU sharing mechanisms and their effectiveness in reducing the impact of CPU sharing on RT application processing performance. Our findings indicate that our CPU sharing mechanism reduces the worst-case execution time by more than 150% compared to the default host RT-Kernel approach. This evidence is strengthened when evaluating this mechanism within Cloud-RAN, in which vBBUs share resources with collocated applications on a MEC server. Using our CPU sharing approach, the vBBU's scheduling latency decreases by up to 21% in comparison with the host RT-Kernel.

关键词： Double deep Q network   Partial offloading   Karush-Kuhn-Tucker   Mobile edge computing   Wireless node  

摘要： The 5G Wireless Environments have huge data transmission;therefore, there is an increase in the requests for computational tasks from Intelligent Wireless Mobile Nodes. This computational capability leads to high reliability and low latency in a 5G network. Mobile edge computing (MEC) allows end systems with constrained computing capacity to handle computationally demanding tasks and offer accurate alternatives. The MEC server's physical position is nearer to WN than other servers, which satisfies the demands for low latency and excellent dependability. To overcome the issues of existing work, such as low latency, offloading and task scheduling, the proposed method provides efficient results. In this work for job scheduling, Multi-agent Collaborative Deep Reinforcement Learning based Scheduling Algorithm with a double deep Q network (DQN) is used in the MEC system. To minimize the total Latency in Wireless Nodes, it uses Karush-Kuhn-Tucker (KKT) approach. This approach provides the optimum solutions to the partial and complete offloading tasks. The double deep Q network (DQN) reduces energy consumption and offers better convergence Between the Wireless Nodes. Compared to traditional algorithms like DeMDRL and BiDRL, the proposed MDRL-DDQN demonstrates superior performance.

关键词： Mobile edge computing  

摘要： Smart mobility is emerging, addressing heterogeneous scenarios with high impact on technology infrastructures, solutions, and people. Safety and availability are mandatory, forcing the design of new reliable services for localization, health monitoring of the user, maintenance of vehicle, and protection of the environment. This paper proposes a container-based microservice approach to the edge computing in IoT smart mobility scenarios. Since smart mobility backends must manage a large heterogeneity of applications, the proposed approach is promising with respect to the classical solutions (based on "monolithic hardware+software" devices), from the point of view of flexibility, upgradability, security, scalability, and reliability. A demo use case, based on industry-grade hardware and Docker, has been realized and multiple implementations of the same services have been executed in parallel, showing strong independence between them. Moreover, average delays of less than 10 ms are obtained, confirming the usability in several smart mobility (and smart city) applications. © 2023, Ada-Europe. All rights reserved.

关键词： unmanned aerial vehicle   federated learning   trusted execution environment   assisted learning   mobile edge computing  

摘要： In the face of increasing concerns around privacy and security in the use of unmanned aerial vehicles (UAVs) for mobile edge computing (MEC), this study proposes a novel approach to secure UAV-assisted federated learning. This research integrates a trusted execution environment (TEE) into UAV-assisted federated learning and proposes a robust aggregation algorithm based on cosine distance, denoted as CosAvg. This study further designs and evaluates a TEE-based federated learning model, comparing its resource overhead with other secure aggregation frameworks, like homomorphic encryption (HE) and differential privacy (DP). Experimental results indicate a significant reduction in resource overhead for TEE against DP and HE. Moreover, the proposed CosAvg algorithm demonstrated superior robustness against adversarial scenarios, maintaining high accuracy in the presence of malicious clients. The integration of TEE and the CosAvg algorithm provides a secure and robust solution for UAV-assisted federated learning, effectively defending both gradient inversion attacks and byzantine attacks.

关键词： Autonomous vehicular network   Data security and privacy   Mobile edge computing   User authentication   User quality of experience   Quality of service  

摘要： Cloud computing has widely been used for applications that require huge computational and data storage resources. Unfortunately, with the advent of new technologies such as fifth generation of cellular networks that provide new applications like IoT, cloud computing presents many limits among which the End-To-End (E2E) latency is the main challenge. These applications generally degrade scenarios that require low latency. Mobile Edge Computing (MEC) has been proposed to solve this issue. MEC brings computing and storage resources from cloud data center to edge data center, closer to end-user equipment to reduce the E2E latency for request processing. However, MEC is vulnerable to security, data privacy, and authentication that affect the end-user Quality of Experience (QoE). It is therefore fundamental that these challenges are addressed to avoid poor user experience due to the lack of security or data privacy. In this paper, we propose a hybrid cryptographic system that uses the symmetric and asymmetric cryptographic systems, to improve data security, privacy, and user authentication in a MEC-based network. We show that our proposed scheme is secured by validating it with the Automated Validation of Internet Security Protocol and Application tool. Simulation results show that our solution consumes less computing resources.

关键词： space-air-ground integrated   mobile edge computing   UAV deployment   device access   computation offloading  

摘要： Computation-intensive applications offloading is challenging, especially in the designated regions where communication infrastructure is absent or compromised. In this paper, we present a Space-Air-Ground integrated Mobile Edge Computing (SAGiMEC) system for these regions to provide quality computational services, where the unmanned aerial vehicles (UAVs) act as in-fight edge servers to provide low-latency edge computing and the satellite provides resident cloud computing. A joint optimization problem is formulated considering UAV deployment, ground device (GD) access, and computation offloading to minimize the system average response latency. To cope with the problem's complexity, we propose a Particle Swarm Optimization (PSO) and Greedy Strategy (GS)-based algorithm (PSO & GS) to obtain an approximate optimal solution. Extensive simulations validate the convergence of the proposed algorithm. Numerical results show that the proposed approach has excellent convergence, and the system average response latency is about 0.65x-0.85x that of the benchmark algorithm.

关键词： Context-aware   Task scheduling   Mobile edge computing   Time series data prediction   Cloud-edge collaborative architecture  

摘要： Context-aware time-series data prediction is crucial and ubiquitous in cloud-edge collaborative storage in Mobile Edge Computing. Most existing studies aim to reduce energy consumption or improve performance in different aspects by computation offloading among the MEC nodes. In the MEC scenario, the mobile edge devices generate and send time series data at a high frequency. The edge nodes will process the collected data to support real-time tasks and periodically migrate the compressed local data to Cloud Data Center(CDC). However, when it comes to reality, it is crucial to locate the data and decide the task execution strategy. In this paper, we first design a management system to manage the metadata and the data collected from mobile edge devices. Based on the management system, we propose a context-aware approach to task scheduling for time-series data prediction in MEC, using the task contexts to locate the data sources, generate the task execution strategy, and choose the targets of result forwarding. For the data prediction, we adopted the LSTM model to predict future time-series data considering the performance. We evaluate the feasibility and effectiveness of the proposed approach on an Automatic Identification System (AIS) dataset. The results illustrate that the proposed approach can effectively schedule the context-aware time-series data prediction tasks in MEC.

关键词： Mobile edge computing  

摘要： The digital twin edge network (DITEN) aims to integrate mobile edge computing (MEC) and digital twin (DT) to provide real-time system configuration and flexible resource allocation for the sixth-generation network. This paper investigates an intelligent reflecting surface (IRS)-aided multi-tier hybrid computing system that can achieve mutual benefits for DT and MEC in the DITEN. For the first time, this paper presents the opportunity to realize the network-wide convergence of DT and MEC. In the considered system, specifically, over-the-air computation (AirComp) is employed to monitor the status of the DT system, while MEC is performed with the assistance of DT to provide low-latency computing services. Besides, the IRS is utilized to enhance signal transmission and mitigate interference among heterogeneous nodes. We propose a framework for designing the hybrid computing system, aiming to maximize the sum computation rate under communication and computation resources constraints. To tackle the non-convex optimization problem, alternative optimization and successive convex approximation techniques are leveraged to decouple variables and then transform the problem into a more tractable form. Simulation results verify the effectiveness of the proposed algorithm and demonstrate the IRS can significantly improve the system performance with appropriate phase shift configurations. Moreover, the results indicate that the DT assisted MEC system can precisely achieve the balance between local computing and task offloading since real-time system status can be obtained with the help of DT. Copyright © 2023, The Authors. All rights reserved.