Mobile Edge Computing Environment Research Articles

A mobile edge computing-focused transferable sensitive data identification method based on product quantization

Sensitive data identification represents the initial and crucial step in safeguarding sensitive information. With the ongoing evolution of the industrial internet, including its interconnectivity across various sectors like the electric power industry, the potential for sensitive data to traverse different domains increases, thereby altering the composition of sensitive data. Consequently, traditional approaches reliant on sensitive vocabularies struggle to adequately address the challenges posed by identifying sensitive data in the era of information abundance. Drawing inspiration from advancements in natural language processing within the realm of deep learning, we propose a transferable Sensitive Data Identification method based on Product Quantization, named PQ-SDI. This innovative approach harnesses both the composition and contextual cues within textual data to accurately pinpoint sensitive information within the context of Mobile Edge Computing (MEC). Notably, PQ-SDI exhibits proficiency not only within a singular domain but also demonstrates adaptability to new domains following training on heterogeneous datasets. Moreover, the method autonomously identifies sensitive data throughout the entire process, eliminating the necessity for human upkeep of sensitive vocabularies. Extensive experimentation with the PQ-SDI model across four real-world datasets, resulting in performance improvements ranging from 2% to 5% over the baseline model and achieves an accuracy of up to 94.41%. In cross-domain trials, PQ-SDI achieved comparable accuracy to training and identification within the same domain. Furthermore, our experiments showcased the product quantization technique significantly reduces the parameter size by tens of times for the subsequent sensitive data identification phase, particularly beneficial for resource-constrained environments characteristic of MEC scenarios. This inherent advantage not only bolsters sensitive data protection but also mitigates the risk of data leakage during transmission, thus enhancing overall security measures in MEC environments.

MEC-enabled resource allocation in Internet of Vehicles

Mobile edge computing (MEC), a promising technology, is widely used in the context of the Internet of Vehicles (IoV) owing to its robust computing capabilities and proximity to mobile devices. Leveraging its advantageous features, MEC facilitates lower latency and more efficient computing services for intelligent vehicles compared to preceding technologies. The tasks inherent to IoV exhibit varying sensitivities to latency, necessitating a nuanced approach to processing. Therefore, this study aims to develop a sophisticated solution in the form of a joint task sequencing and resource allocation (JTSARA) algorithm based on deep reinforcement learning. The primary objective is to adeptly manage both computing and communication resources within the MEC environment, thereby ensuring the fulfillment of performance requirements of tasks. Initially, tasks are categorized into distinct levels according to their latency tolerance. Subsequent to this classification, a method is formulated to optimize task sequencing, improving the efficiency of computational processing. Furthermore, we tackle the intricate challenge of resource allocation by transforming it into a Markov decision process, employing deep reinforcement learning for resolution. The feasibility and effectiveness of the proposed scheme are demonstrated through comprehensive simulation experiments, affirming its potential for enhancing the performance of IoV systems.

Hybrid Energy-Efficient Task Offloading Algorithm (HEETA): A Framework for Optimizing Edge Computing Offloading Decisions

In the evolving realm of Mobile Edge Computing (MEC), efficient task offloading remains pivotal. This paper introduces the Hybrid Energy-Efficient Task Offloading Algorithm (HEETA) to address the deficiencies of the current Joint Optimization Task Offloading Strategy based on Particle Swarm Optimization (JOBPSO). Drawing from a broad dataset encompassing diverse MEC operational variables, HEETA exhibits exemplary performance metrics, with a notable mean fitness of approximately 0.99984 and a minimal standard deviation of 0.000116. Such metrics not only reflect HEETA's robustness but also its adaptability across multifaceted MEC parameters. Furthermore, its dynamic nature facilitates adaptability to variables including task numbers, computational capacity, and latency constraints, resulting in marked improvements in energy efficiency. Quantitative evaluations, as evidenced by a performance matrix, position HEETA's global best fitness values between approximately 0.9998 and 0.9999. While HEETA signifies a monumental step in enhancing energy efficiency, prolonging device longevity, and optimizing overall MEC system performance, the research acknowledges potential limitations, emphasizing the imperatives of accurate modeling and subsequent validations within distinct MEC environments.

Blockchain Based Decentralized and Proactive Caching Strategy in Mobile Edge Computing Environment.

In the mobile edge computing (MEC) environment, the edge caching can provide the timely data response service for the intelligent scenarios. However, due to the limited storage capacity of edge nodes and the malicious node behavior, the question of how to select the cached contents and realize the decentralized security data caching faces challenges. In this paper, a blockchain-based decentralized and proactive caching strategy is proposed in an MEC environment to address this problem. The novelty is that the blockchain was adopted in an MEC environment with a proactive caching strategy based on node utility, and the corresponding optimization problem was built. The blockchain was adopted to build a secure and reliable service environment. The employed methodology is that the optimal caching strategy was achieved based on the linear relaxation technology and the interior point method. Additionally, in a content caching system, there is a trade-off between cache space and node utility, and the caching strategy was proposed to solve this problem. There was also a trade-off between the consensus process delay of blockchain and the caching latency of content. An offline consensus authentication method was adopted to reduce the influence of the consensus process delay on the content caching. The key finding was that the proposed algorithm can reduce latency and can ensure the security data caching in an IoT environment. Finally, the simulation experiment showed that the proposed algorithm can achieve up to 49.32%, 43.11%, and 34.85% improvements on the cache hit rate, the average content response latency, and the average system utility, respectively, compared to the random content caching algorithm, and it achieved up to 9.67%, 8.11%, and 5.95% increases, successively, compared to the greedy content caching algorithm.

Offloading Decision and Resource Allocation in Mobile Edge Computing for Cost and Latency Efficiencies in Real-Time IoT

Internet of Things (IoT) devices can integrate with applications requiring intensive contextual data processing, intelligent vehicle control, healthcare remote sensing, VR, data mining, traffic management, and interactive applications. However, there are computationally intensive tasks that need to be completed quickly within the time constraints of IoT devices. To address this challenge, researchers have proposed computation offloading, where computing tasks are sent to edge servers instead of being executed locally on user devices. This approach involves using edge servers located near users in cellular network base stations, and also known as Mobile Edge Computing (MEC). The goal is to offload tasks to edge servers, optimizing both latency and energy consumption. The main objective of this paper mentioned in the summary is to design an algorithm for time- and energy-optimized task offloading decision-making in MEC environments. Therefore, we developed a Lagrange Duality Resource Optimization Algorithm (LDROA) to optimize for both decision offloading and resource allocation for tasks, whether to locally execute or offload to an edge server. The LDROA technique produces superior simulation outcomes in terms of task offloading, with improved performance in computation latency and cost usage compared to conventional methods like Random Offloading, Load Balancing, and the Greedy Latency Offloading scheme.

A Secured Intrusion Detection System for Mobile Edge Computing

With the proliferation of mobile devices and the increasing demand for low-latency and high-throughput applications, mobile edge computing (MEC) has emerged as a promising paradigm to offload computational tasks to the network edge. However, the dynamic and resource-constrained nature of MEC environments introduces new challenges, particularly in the realm of security. In this context, intrusion detection becomes crucial to safeguard the integrity and confidentiality of sensitive data processed at the edge. This paper presents a novel Secured Edge Computing Intrusion Detection System (SEC-IDS) tailored for MEC environments. The proposed SEC-IDS framework integrates both signature-based and anomaly-based detection mechanisms to enhance the accuracy and adaptability of intrusion detection. Leveraging edge computing resources, the framework distributes detection tasks closer to the data source, thereby reducing latency and improving real-time responsiveness. To validate the effectiveness of the proposed SEC-IDS framework, extensive experiments were conducted in a simulated MEC environment. The results demonstrate superior detection rates compared to traditional centralized approaches, highlighting the efficiency and scalability of the proposed solution. Furthermore, the framework exhibits resilience to resource constraints commonly encountered in edge computing environments.

Securing Mobile Edge Computing Using Hybrid Deep Learning Method

In recent years, Mobile Edge Computing (MEC) has revolutionized the landscape of the telecommunication industry by offering low-latency, high-bandwidth, and real-time processing. With this advancement comes a broad range of security challenges, the most prominent of which is Distributed Denial of Service (DDoS) attacks, which threaten the availability and performance of MEC’s services. In most cases, Intrusion Detection Systems (IDSs), a security tool that monitors networks and systems for suspicious activity and notify administrators in real time of potential cyber threats, have relied on shallow Machine Learning (ML) models that are limited in their abilities to identify and mitigate DDoS attacks. This article highlights the drawbacks of current IDS solutions, primarily their reliance on shallow ML techniques, and proposes a novel hybrid Autoencoder–Multi-Layer Perceptron (AE–MLP) model for intrusion detection as a solution against DDoS attacks in the MEC environment. The proposed hybrid AE–MLP model leverages autoencoders’ feature extraction capabilities to capture intricate patterns and anomalies within network traffic data. This extracted knowledge is then fed into a Multi-Layer Perceptron (MLP) network, enabling deep learning techniques to further analyze and classify potential threats. By integrating both AE and MLP, the hybrid model achieves higher accuracy and robustness in identifying DDoS attacks while minimizing false positives. As a result of extensive experiments using the recently released NF-UQ-NIDS-V2 dataset, which contains a wide range of DDoS attacks, our results demonstrate that the proposed hybrid AE–MLP model achieves a high accuracy of 99.98%. Based on the results, the hybrid approach performs better than several similar techniques.

Digital Twin-Assisted, SFC-Enabled Service Provisioning in Mobile Edge Computing

Mobile Edge Computing (MEC) has been identified as a desirable computing paradigm that provides efficient and effective services for various applications, while meeting stringent service delay requirements. Orthogonal to the MEC computing paradigm, Network Function Virtualization (NFV) technology is another enabling technology that provides the network resource management with great flexibility and scalability, where the instances of Virtual Network Functions (VNFs) are deployed in edge servers as Service Function Chains (SFCs) for SFC-enabled services. Although reliable service provisioning in MEC environments is fundamentally important, the deployed VNF instances usually are not reliable, which can be affected by their software implementation, their execution duration, the workload among edge servers, and so on. Empowered by digital twin techniques, the states of VNF instances can be maintained by their digital twins in a real-time manner and their reliability can be accurately predicted through their digital twins. In this paper, we study digital twin-assisted, SFC-enabled reliable service provisioning in MEC networks by exploiting the dynamics of VNF instance reliability. We concentrate on two novel optimization problems of reliable service provisioning: the service cost minimization problem, and the dynamic service admission maximization problem. We first show their NP-hardness. We then formulate an Integer Linear Program (ILP) solution, and devise an approximation algorithm with a constant approximation ratio for the service cost minimization problem. We thirdly provide an ILP solution to the offline version of the dynamic service admission maximization problem. Built upon this offline ILP solution, we also develop an online algorithm with a provable competitive ratio for the problem, by adopting the primal-dual dynamic updating technique. We finally evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms outperform their comparison benchmarks, and improve the performance of their comparison counterparts by no less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10.2 \%$</tex-math></inline-formula> .

A pipeline approach for privacy preservation against poisoning attacks in a Mobile Edge Computing environment

Mobile Edge Computing (MEC) has become increasingly popular due to its ability to provide real-time data processing and improved user experience. However, it also introduces potential security vulnerabilities, as malicious actors could exploit users’ position data stored in the MEC server. To address this challenge, it is essential to develop privacy-preserving schemes that are resistant to malicious activities, such as poisoning attacks. Previous works have not considered the evaluation of such schemes against certain types of poisoning attacks while ensuring sufficient utility, privacy, and scalability during the generation process of poisoning locations. Indeed, meeting the needs of the user or customers, keeping data secure and confidential, and having capacity to accommodate changes in demand are essential for any application or system to operate efficiently and securely. To address this limitation, we present a new pipeline approach for a privacy-preserving scheme against poisoning attacks in the MEC environment. We evaluate this method with a type of poisoning attack that ensures sufficient utility, privacy, and scalability during the generation process and assess its effectiveness in providing security and privacy. Our approach is evaluated on two data sets and the results are compared with three baseline approaches. The results show that our approach is more effective in detecting poisoning locations than the existing works without affecting the QoS metrics (bandwidth, resources consumption, latency and the storage) with should be consider when evaluating MEC method.

Safe-Learning-Based Location-Privacy-Preserved Task Offloading in Mobile Edge Computing

Mobile edge computing (MEC) integration with 5G/6G technologies is an essential direction in mobile communications and computing. However, it is crucial to be aware of the potential privacy implications of task offloading in MEC scenarios, specifically the leakage of user location information. To address this issue, this paper proposes a location-privacy-preserved task offloading (LPTO) scheme based on safe reinforcement learning to balance computational cost and privacy protection. This scheme uses the differential privacy technique to perturb the user’s actual location to achieve location privacy protection. We model the privacy-preserving location perturbation problem as a Markov decision process (MDP), and we develop a safe deep Q-network (DQN)-based LPTO (SDLPTO) scheme to select the offloading policy and location perturbation policy dynamically. This approach effectively mitigates the selection of high-risk state–action pairs by conducting a risk assessment for each state–action pair. Simulation results show that the proposed SDLPTO scheme has a lower computational cost and location privacy leakage than the benchmarks. These results highlight the significance of our approach in protecting user location privacy while achieving improved performance in MEC environments.

Mobility-aware multi-user service placement and resource allocation in edge computing

The rise of Mobile Edge Computing (MEC) has brought us its enormous potential and value in mobile service applications. By pushing computing and storage resources from the cloud to the network edge, it reduces transmission latency, and supports applications that require low latency, such as virtual reality and video analytics IoT services. However, many existing works only consider the problem of service placement in MEC, and the problem of computing resource allocation has received less attention. This paper focuses on the joint optimization of Service Placement and computational Resource Allocation (SPRA) in the MEC environment, with the goal of minimizing the total cost of service latency, communication latency, and service migration. For offline cases, we propose an optimal algorithm, D-SPA , based on dynamic programming and an improved algorithm, S-SPA, based on state sampling, which effectively alleviates the state explosion problem in D-SPA. In the online case, due to the unpredictability of future environmental information, we propose the online greedy algorithm OGA, and theoretically show the approximate ratio of OGA. Extensive experiments show that our algorithm is more efficient than other baseline algorithms, and it reduces the total cost by 28.6% on average.

Cost-Aware Deployment of Microservices for IoT Applications in Mobile Edge Computing Environment

Cost-Aware Deployment of Microservices for IoT Applications in Mobile Edge Computing Environment

Retracted: Intelligent Classroom Learning Model of College English Based on Data Mining Technology in Mobile Edge Computing Environment

Retracted: Intelligent Classroom Learning Model of College English Based on Data Mining Technology in Mobile Edge Computing Environment

Edge Intelligence for Real-Time IoT Service Trust Prediction

Mobile Edge Computing (MEC)-based Internet of Things (IoT) systems generate trust information in a real-time and distributed manner. Predicting trustworthiness of IoT services in such an MEC environment requires new prediction strategies that cater for the aforementioned characteristics of trust information. More importantly, it is imperative to investigate how the real-time trust information could be effectively integrated into trust prediction strategies in order to capture the ever-evolving nature of trustworthiness of IoT services. In turn, such a strategy allows IoT service consumers to derive more relevant and accurate trust-based decisions. To that end, our work models trust prediction in MEC-based IoT systems as an online regularized finite-sum problem in a distributed MEC environment with a given MEC topology. We then adopt the Online Alternating Direction Method (OADM) to effectively train trust prediction models in parallel over the distributed MEC environment. OADM allows splitting the aforementioned finite-sum problem into multiple sub-problems that correspond to different local MEC environments. These sub-problems can then be solved iteratively within each local MEC environment by using the local trust data therein. This can avoid the movement of data across the core networks of mobile network providers. Experiments on real-world and synthetic datasets demonstrate the effectiveness and scalability of the proposed method.

Survey on Multiple Methods for Task Offloading in Mobile Cloud-Edge Environment

Mobile edge computing (MEC) is an emerging technology that extends cloud computing to the edge of the network. It offloads computing intensive tasks to the edge server to solve the problem of insufficient computing power and resource of the terminal device, meets the low energy consumption and low latency requirements of the mobile user for application task computing, and greatly releases the pressure of the cloud center. Cloud-edge integration architecture has become a trend. How to perform optimal offload scheduling for terminal tasks has always been one of the key issue in the field of MEC research. This paper reviews the research directions and achievements of task offloading technology in today’s cloud-edge environment. First, the development status of MEC environment and task offloading technology are introduced, and the concepts and applications of classic schemes such as heuristic algorithm, metaheuristic algorithm, and reinforcement learning are elaborated. Based on the mainstream solutions in the research literature, two types of task offloading solutions are summarized: traditional task offloading solutions based on algorithm optimization and interactive task offloading solutions based on reinforcement learning. Second, the research literature on the two mainstream directions mentioned above is summarized and organized from aspects such as problem constraints, optimization objectives, and contributions made. Meanwhile, schemes are compared through the classic algorithms and innovations they used. Finally, the current challenges and future research objectives in this field are summarized to provide reference and help for the follow-up research.

GASTO: A Fast Adaptive Graph Learning Framework for Edge Computing Empowered Task Offloading

Mobile edge computing (MEC) has become a research trend that solves effectively computationally intensive and latency-sensitive tasks. MEC environments in the real world are dynamic and uncertain and then the changes of the environments bring challenges to the generalization and robustness of offloading algorithms. In order to solve the above problem, we propose a meta-reinforcement learning task offloading algorithm GASTO based on Graph Neural Network and seq2seq network. Meta-learning can learn the optimal initialization parameter through several gradient descent steps and samples to adapt to new environments more quickly. The task generated in the user equipment is composed of multiple subtasks rather than a single task, and there are dependencies between the subtasks. Therefore, the task on the user equipment is modeled as a Directed Acyclic Graph (DAG). The connection relationship between the subtasks in DAG plays an important role. Drawing on the idea of message passing, Graph Neural Network is applied in DAG to extract the intrinsic correlation between subtasks in GASTO. In addition, Seq2Seq network can reduce the dimension of action space effectively, and the scheduling decisions of all subtasks can be generated simultaneously. Besides, in order to enhance the sampling efficiency of tasks and the robustness of GASTO, the priority of sampling tasks is adjusted dynamically during the training process. The experimental results of four algorithms in different environments show that the proposed algorithm GASTO can quickly adapt to the new environment.

Worker-Centric Model Allocation for Federated Learning in Mobile Edge Computing

Federated Learning (FL) is believed as a promising manner of distributed machine learning for 5G and future 6G networks in the context of mobile edge computing (MEC). From the worker&#x2019;s viewpoint, a problem is that no incentive drives them to participate in FL. Thus, different from the conventional server-centric model allocation methods, we study the worker-centric strategy in this paper. Because communicating with the FL server and training an FL model locally are energy-hungry, a dilemma for each worker is how to make the tradeoff between participating in FL training and the volume restriction of its battery. To address this issue, we formulate a WorkerFirst problem. Its NP-hardness is also proved. Next, we devise a DDQN algorithm and a DQL algorithm to strive for near-optimal decisions for each worker. These two algorithms are proposed by leveraging the deep reinforcement learning framework, while considering the energy consumption, training timespan, and the communication overheads of workers, simultaneously. The benefit of adopting such a DRL-based decision-making framework is that workers can execute the algorithms locally and dynamically adapt to the varying MEC environment. The evaluation results show that the proposed DDQN and DQL algorithms can learn good policies without knowing any prior knowledge of network conditions.

Constraint‐aware and multi‐objective optimization for micro‐service composition in mobile edge computing

AbstractAs a new paradigm of distributed computing, mobile edge computing (MEC) has gained increasing attention due to its ability to expand the capabilities of centralized cloud computing. In MEC environments, a software application typically consists of multiple micro‐services, which can be composed together in a flexible manner to achieve various user requests. However, the composition of micro‐services in MEC is still a challenging research issue arising from three aspects. Firstly, composite micro‐services constructed by ignoring the processing capabilities of different micro‐services may cause waste of edge resources. Secondly, edge servers' limitations in terms of computational power can easily cause service occupancy between composite micro‐services, severely affecting the user experience. Thirdly, in dynamic and unstable mobile environments, different edge users have different sensitivities to request latency, which increases the complexity of micro‐service composition. In order to improve edge resource utilization and user experience on micro‐service invocations, in this paper, we comprehensively consider the above three factors, and we first model the micro‐services composition problem in MEC as a constrained multi‐objective optimization problem. Then, a micro‐service composition optimization method M3C combining graph search and branch‐and‐bound strategy is proposed to find a composition solution set with low energy consumption and high success rate for multiple edge users. Finally, we perform a series of experiments on two widely used datasets. Experimental results show that our proposed approach significantly outperforms the four competing baseline approaches, and that it is sufficiently efficient for practical deployment.

Virtual Machine Migration Strategy Based on Markov Decision and Greedy Algorithm in Edge Computing Environment

In view of the problem of long migration time and low availability of VM (virtual machine) migration in MD (mobile devices) in the MEC (mobile edge computing) environment, this paper designs a MD2N (Mobile Device to Network) model. This model no longer uploads requests to the cloud center, but centers on the edge server, scheduling and selecting migration services through the communication between the servers. And, a virtual machine migration strategy based on Markov decision and greedy algorithm is proposed. Model the edge server based on its geographic location, imitating the traditional cellular network method, and then the improved greedy algorithm is used to dynamically select the best edge device to migrate VM. Simulation and comparison experiments show that the proposed scheme has small migration fluctuations, stable migration status, short migration time, low average time delay, and high virtual machine migration availability after migration.

Secure Smart Healthcare Framework Using Lightweight DNA Sequence and Chaos for Mobile-Edge Computing

Mobile-edge computing (MEC) is a new architecture that provides services to the edge of networks. The software and hardware platforms are positioned at the network edge close to end-users. Emerging developments in MEC can be used for healthcare applications, such as remote patient monitoring, diagnosis, and treatment purposes. The remote access to the data can arise security and privacy issues. Unauthorized access or data leakage can hamper the complete security of the system. This makes the system inconvenient, untrusted, less suitable, and vulnerable. This article is aimed to propose a security framework for the privacy preservation of patient data in a MEC environment where the services are accessed at the network edge. A lightweight cryptographic technique is proposed by including a chaotic map and a DNA sequence of organisms for encryption of electronic health records (EHRs). The identity privacy will be maintained by using anonymous authentication. The framework’s performance is evaluated with memory usage and encryption time and found satisfactory results.