Delay-aware concurrent data management method for IoT collaborative 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.

Cybercrime is significantly growing as the development of internet technology. To mitigate this issue, the law enforcement adopts network surveillance technology to track a suspect and derive the online profile. However, the traditional network surveillance using the single-device tracking method can only acquire part of a suspect’s online activities. With the emergence of different types of devices (e.g., personal computers, mobile phones, and smart wearable devices) in the mobile edge computing (MEC) environment, one suspect can employ multiple devices to launch a cybercrime. In this paper, we investigate a novel cross-device tracking approach which is able to correlate one suspect’s different devices so as to help the law enforcement monitor a suspect’s online activities more comprehensively. Our approach is based on the network traffic analysis of instant messaging (IM) applications, which are typical commercial service providers (CSPs) in the MEC environment. We notice a new habit of using IM applications, that is, one individual logs in the same account on multiple devices. This habit brings about devices’ receiving sync messages, which can be utilized to correlate devices. We choose five popular apps (i.e., WhatsApp, Facebook Messenger, WeChat, QQ, and Skype) to prove our approach’s effectiveness. The experimental results show that our approach can identify IM messages with high F1-scores (e.g., QQ’s PC message is 0.966, and QQ’s phone message is 0.924) and achieve an average correlating accuracy of 89.58% of five apps in an 8-people experiment, with the fastest correlation speed achieved in 100 s.

With the growing prevalence of Internet of Things (IoT) devices and technology, a burgeoning computing paradigm namely mobile edge computing (MEC) is delicately proposed and designed to accommodate the application requirements of IoT scenario. In this paper, we focus on the problems of dynamic task scheduling and resource management in MEC environment, with the specific objective of achieving the optimal revenue earned by edge service providers. While the majority of task scheduling and resource management algorithms are formulated by an integer programming (IP) problem and solved in a dispreferred NP-hard manner, we innovatively investigate the problem structure and identify a favorable property namely totally unimodular constraints. The totally unimodular property further helps to design an equivalent linear programming (LP) problem which can be efficiently and elegantly solved at polynomial computational complexity. In order to evaluate our proposed approach, we conduct simulations based on real-life IoT dataset to verify the effectiveness and efficiency of our approach.

Propelled by recent advances in Mobile Edge Computing (MEC) and the Internet of Things (IoT), the digital twin technique has been envisioned as a de-facto driving force to bridge the virtual and physical worlds through creating digital portrayals of physical objects. In virtue of the flourishing of edge intelligence and abundant IoT data, data-driven modelling facilitates the implementation and maintenance of digital twins, where simulations of physical objects are usually performed based on Deep Neural Networks (DNNs). A significant advantage of adopting digital twins is to enable decisive prediction on the behaviours of objects in near future without waiting for that really happen. To provide accurate predictions, it is vital to keep each digital twin synchronized with its physical object in real-time. However, it is challenging to maintain the real-time synchronization between a digital twin and its physical object due to the dynamics of physical objects and sensing data drift over time, i.e., the live data from a physical object diverge from the model training data of its digital twin. To address this critical issue, continual learning is a promising solution to retrain models of digital twins incrementally. In this paper, we investigate digital twin synchronization issues via continual learning in an MEC environment, with the aim to maximize the total utility gain, i.e., the enhanced model accuracy. We study two novel optimization problems: the static digital twin synchronization problem per time slot and the dynamic digital twin synchronization problem for a finite time horizon. We first formulate an Integer Linear Program (ILP) solution for the static digital twin synchronization problem when the problem size is small; otherwise, we develop a randomized approximation algorithm at the expense of bounded resource violations for it. We also devise a deterministic approximation algorithm with guaranteed performance for a special case of the static digital twin synchronization problem. We thirdly consider the dynamic digital twin synchronization problem by proposing an efficient online algorithm for it. Finally, we evaluate the performance of the proposed algorithms for continuous digital twin synchronization through simulations. Simulation results show that the proposed algorithms are promising, outperforming counterpart benchmarks by no less than 13.2%, in terms of the total utility gain.

Local & Federated Learning at the network edge for efficient predictive analytics

The task offloading and resource allocation strategies aim to provide efficient edge computing services for ground mobile devices in mobile edge computing (MEC) environments. In this paper, we propose a UAV-assisted dynamic deployment and decision-making framework (dDDM) to address the issue of insufficient base station coverage in remote areas or emergency scenarios, which leverages the flexibility and computational capabilities of unmanned aerial vehicle (UAV) to supplement terrestrial cellular networks. We focus on optimizing the UAV trajectory to support and enhance the effectiveness of task offloading and resource allocation strategies under conditions of user mobility and stochastic task arrivals. Therefore, a joint optimization problem is formulated with the objective of minimizing the weighted sum of the total latency of ground mobile devices and the overall energy consumption of UAV. To solve this problem, the original optimization problem is decomposed into two subproblems: UAV trajectory planning, and decision-making for task offloading & computing resource allocation. For the UAV trajectory planning subproblem, we propose an improved particle swarm optimization(PSO) algorithm integrated with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i>-means clustering, i.e., KMPSO algorithm, enabling dynamic UAV deployment in response to user mobility. For the task offloading and resource allocation decision-making, an improved black-winged kite algorithm (IBKA) is developed to determine optimal offloading and allocation strategies. Simulation results demonstrate that the proposed approach outperforms existing benchmark algorithms in terms of both latency and energy consumption.

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

Emerging Internet of Things (IoT) applications share the same characteristics of involving multiple processing components ( i.e. , function modules) and requiring a massive amount of data to be processed with low latency. To meet these needs, edge/fog computing has been proposed for next-generation mobile networks to migrate the computing from the cloud to the edge of the network. Thanks to the development of Network Functions Virtualization (NFV), with which edge computing platform can virtualize function modules and deploy them on any edge devices to provide flexible services on the edge networks. However, such platform would need to deal with complicated function module calling relationship ( i.e. , call graph) of applications and user mobility, and both are not thoroughly considered by existing works of NFV and edge computing. In this paper, based on our previous idea of virtual local-hub (VLH), we propose a complete design of edge computing framework, which applies NFV technology on edge computing environment for IoT applications. To handle the complicated call graphs of IoT applications with better resource utilization, the VLH framework adapts the technologies of container-based virtualization and microservice architecture, which enables remote function module sharing on the edge computing environment. The framework includes the heuristic algorithm for function module allocation with the objective of minimizing total bandwidth consumption. We also present a design of protocols for system operations and mobility handling in the framework. Then we implement the framework on commodity hardware as a testbed. Via simulations under a large-scale environment with practical settings and experiments on the testbed under real-world scenarios, we demonstrate and verify the effectiveness and practicability of the proposed VLH framework for IoT application service provision.

With the rise of latency-sensitive and computationally intensive applications in mobile edge computing (MEC) environments, the computation offloading strategy has been widely studied to meet the low-latency demands of these applications. However, the uncertainty of various tasks and the time-varying conditions of wireless networks make it difficult for mobile devices to make efficient decisions. The existing methods also face the problems of long-delay decisions and user data privacy disclosures. In this paper, we present the FDRT, a federated learning and deep reinforcement learning-based method with two types of agents for computation offload, to minimize the system latency. FDRT uses a multi-agent collaborative computation offloading strategy, namely, DRT. DRT divides the offloading decision into whether to compute tasks locally and whether to offload tasks to MEC servers. The designed DDQN agent considers the task information, its own resources, and the network status conditions of mobile devices, and the designed D3QN agent considers these conditions of all MEC servers in the collaborative cloud-side end MEC system; both jointly learn the optimal decision. FDRT also applies federated learning to reduce communication overhead and optimize the model training of DRT by designing a new parameter aggregation method, while protecting user data privacy. The simulation results showed that DRT effectively reduced the average task execution delay by up to 50% compared with several baselines and state-of-the-art offloading strategies. FRDT also accelerates the convergence rate of multi-agent training and reduces the training time of DRT by 61.7%.

Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment

The efficient migration of virtual machines (VMs) is critical for optimizing resource management, ensuring service continuity, and enhancing resiliency in cloud and edge computing environments, particularly as 6G networks demand higher reliability and lower latency. This study addresses the challenges of dynamically balancing server loads while minimizing downtime and migration costs under stochastic task arrivals and variable processing times. We propose a queuing theory-based model employing continuous-time Markov chains (CTMCs) to capture the interplay between VM migration decisions, server resource constraints, and task processing dynamics. The model incorporates two migration policies—one minimizing projected post-migration server utilization and another prioritizing current utilization—to evaluate their impact on system performance. The numerical results show that the blocking probability for the first VM for Policy 1 is 2.1% times lower than for Policy 2 and the same metric for the second VM is 4.7%. The average server’s resource utilization increased up to 11.96%. The framework’s adaptability to diverse server–VM configurations and stochastic demands demonstrates its applicability to real-world cloud systems. These results highlight predictive resource allocation’s role in dynamic environments. Furthermore, the study lays the groundwork for extending this framework to multi-access edge computing (MEC) environments, which are integral to 6G networks.

This paper presents a dynamic central processing unit (CPU) scheduling algorithm selectionsystem that utilizes machine learning to optimize the process execution within a computer system. Thisstudy evaluated six scheduling algorithms based on key performance indicators such as CPUutilization, turnaround time, waiting time, response time, and throughput. To overcome the limitationsof traditional approaches, the proposed methodology integrates a multilayer neural network (MLNN)to identify patterns and dynamically choose the most appropriate algorithm at runtime. The paperreviews related literature, highlighting efforts to enhance scheduling algorithms through machinelearning and improvements to conventional methods. The methodology includes developing a processdatabase, applying scheduling algorithms, optimizing the outcomes, and training a neural network fordynamic algorithm selection. The results indicate that the proposed approach outperforms existingalgorithms regarding waiting time, turnaround time, throughput, and execution efficiency.Additionally, the methodology offers adaptability to diverse process parameters. The study concludesby underscoring the potential for future advancements, including improvements in algorithmprecision, incorporation of additional process characteristics, exploration of advanced patternrecognition techniques, and integration of security measures for real-world applications such as cloudcomputing, edge computing, and IoT (Internet of things) environments.This paper presents a dynamiccentral processing unit (CPU) scheduling algorithm selection system that utilizes machine learning tooptimize the process execution within a computer system. This study evaluated six schedulingalgorithms based on key performance indicators such as CPU utilization, turnaround time, waitingtime, response time, and throughput. To overcome the limitations of traditional approaches, theproposed methodology integrates a multilayer neural network (MLNN) to identify patterns anddynamically choose the most appropriate algorithm at runtime. The paper reviews related literature,highlighting efforts to enhance scheduling algorithms through machine learning and improvementsto conventional methods. The methodology includes developing a process database, applyingscheduling algorithms, optimizing the outcomes, and training a neural network for dynamic algorithmselection. The results indicate that the proposed approach outperforms existing algorithms regardingwaiting time, turnaround time, throughput, and execution efficiency. Additionally, the methodologyoffers adaptability to diverse process parameters. The study concludes by underscoring the potentialfor future advancements, including improvements in algorithm precision, incorporation of additionalprocess characteristics, exploration of advanced pattern recognition techniques, and integration ofsecurity measures for real-world applications such as cloud computing, edge computing, and IoT(Internet of things) environments.

The rapid growth of the Internet of Things (IoT), cloud computing, Fog computing, mobile edge computing and wireless grids has resulted in the widespread deployment of relatively immature technology. These technologies, which will primarily use 5G wireless communication networks, are becoming popular because they can be deployed quickly with little infrastructure and lends themselves to environments utilizing numerous internet connected devices (ICD). There are, however, many significant challenges faced by security designers, engineers and implementers of these networks in ensuring that the level of security afforded is appropriate. Because of the threat of exploitation, these networks have to be protected by a robust security architecture due to these technologies being plagued with security problems. The authentication of smart ICDs to IoT networks is a critical mechanism for achieving security on these new information system platforms. This article identifies an authentication process required for these ICDs, which will need to prove their identity to authenticate to an IoT fog-mobile edge computing (FMEC) cloud network through a wireless grid authentication process. The purpose of this article is to begin to hypothesize a generic authentication methodology for these FMEC clouds uses in an IoT architecture. The proposed methodology, called wg-IoT, must include the integration of Fog computing, wireless grids and mobile edge computing clouds to create this new IoT architecture. An authentication process developed from the resource sharing protocol (RSP) from a wireless grid is first developed and proposed for the authentication of ICDs. The wireless grid core components must be embedded in IoT devices or sensors depending on their capability to handle five primary functions: management of identification [ID] and presence, permissions management, data transferability, application-programming interface [API] and security.

As a key technology in the 5G era, mobile edge computing (MEC) has developed rapidly in recent years. MEC aims to reduce the service delay of mobile users, while alleviating the processing pressure on the core network. MEC can be regarded as an extension of cloud computing on the user side, which can deploy edge servers and bring computing resources closer to mobile users, and provide more efficient interactions. However, due to the user's dynamic mobility, the distance between the user and the edge server will change dynamically, which may cause fluctuations in Quality of Service. Therefore, when a mobile user moves in the MEC environment, certain approaches are needed to schedule services deployed on the edge server to ensure the user experience. In this article, we model service scheduling in MEC scenarios and propose a delay‐aware and mobility‐aware service management approach based on concise probabilistic methods. This approach has low computational complexity and can effectively reduce service delay and migration costs. Furthermore, we conduct experiments by utilizing multiple realistic datasets and use iFogSim to evaluate the performance of the algorithm. The results show that our proposed approach can optimize the performance on service delay, with 8%–20% improvement and reduce the migration cost by more than 75% compared with baselines during the rush hours.

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.