Edge Of Network Research Articles

An efficient routing protocol to reduce traffic and congestion control in cloud edge networks of wireless sensor networks

SummaryRecently, wireless sensor networks (WSNs) have been used for monitoring, sensing, processing, and communication purposes in real‐time applications. It is employed with a routing protocol that performs an effective data transmission process. However, while transmitting large data, there occurs an over fitting issue, which leads to determining a huge data leakage. Also, the delay is increased with heavy congestion in the network. Hence, a novel method is proposed to diminish the network congestion regarding distributed networks as well as cloud edge computing. Moreover, it diminished the data loss from an overloaded condition. However, the proposed technique controls congestion that resists the traffic in the network through lightweight, ultra‐dense label‐less federation and incorporates adaptive multi‐agent Markov reinforcement learning. Furthermore, a distributed energy‐efficient delay‐aware routing protocol is employed to analyze and regulate congestion control in the network. Also, it varies the network dynamically by adjusting the routing protocol that optimizes the congestion and implements the traffic mechanism. Moreover, the congestion in WSNs overwhelms the nodes and channels distributed in the packets. The evaluation of the proposed method is determined by various metrics such as queuing delay, network lifetime, energy efficiency, throughput, and packet delivery ratio. The experimental results revealed that the proposed method attained an enhanced performance by maximizing energy efficiency and packet delivery ratio by 94% as well as 89% and reducing the delay by 55%, respectively.

Resilient edge predictive analytics by enhancing local models

Abstract In distributed computing environments, the collaboration of nodes for predictive analytics at the network edge plays a crucial role in supporting real-time services. When a node’s service becomes unavailable for various reasons (e.g., service updates, node maintenance, or even node failure), the rest of the available nodes connot efficiently replace its service due to different data and predictive models (e.g., machine learning [ML] models). To address this, we propose decision-making strategies rooted in the statistical signatures of nodes’ data. Specifically, these signatures refer to the unique patterns and behaviors within each node’s data that can be leveraged to predict the suitability of potential surrogate nodes. Recognizing and acting on these statistical nuances ensures a more targeted and efficient response to node failures. Such strategies aim to identify surrogate nodes capable of substituting for failing nodes’ services by building enhanced predictive models. Our resilient framework helps to guide the task requests from failing nodes to the most appropriate surrogate nodes. In this case, the surrogate nodes can use their enhanced models, which can produce equivalent and satisfactory results for the requested tasks. We provide experimental evaluations and comparative assessments with baseline approaches over real datasets. Our results showcase the capability of our framework to maintain the overall performance of predictive analytics under nodes’ failures in edge computing environments.

Probabilistic Task Offloading with Uncertain Processing Times in Device-to-Device Edge Networks

D2D edge computing is a promising solution to address the conflict between limited network capacity and increasing application demands, where mobile devices can offload their tasks to other peer devices/servers for better performance. Task offloading is critical to the performance of D2D edge computing. Most existing works on task offloading assume the task processing time is known or can be accurately estimated. However, the processing time is often uncertain until it is finished. Moreover, the same task can have largely different execution times under different scenarios, which leads to inaccurate offloading decisions and degraded performance. To address this problem, we propose a game-based probabilistic task offloading scheme with an uncertain processing time in D2D edge networks. First, we characterize the uncertainty of the task processing time using a probabilistic model. Second, we incorporate the proposed probabilistic model into an offloading decision game. We also analyze the structural properties of the game and prove that it can reach a Nash equilibrium. We evaluate the proposed work using real-world applications and datasets. The experimental results show that the proposed probabilistic model can accurately characterize the uncertainty of completion time, and the offloading algorithm can effectively improve the overall task completion rate in D2D networks.

From cloud and fog computing to federated-fog computing: A comparative analysis of computational resources in real-time IoT applications based on semantic interoperability

In contemporary computing paradigms, the evolution from cloud computing to fog computing and the recent emergence of federated-fog computing have introduced new challenges pertaining to semantic interoperability, particularly in the context of real-time applications. Fog computing, by shifting computational processes closer to the network edge at the local area network level, aims to mitigate latency and enhance efficiency by minimising data transfers to the cloud. Building upon this, federated-fog computing extends the paradigm by distributing computing resources across diverse organisations and locations, while maintaining centralised management and control. This research article addresses the inherent problematics in achieving semantic interoperability within the evolving architectures of cloud computing, fog computing, and federated-fog computing. Experimental investigations are conducted on a diverse node-based testbed, simulating various end-user devices, to emphasise the critical role of semantic interoperability in facilitating seamless data exchange and integration. Furthermore, the efficacy of federated-fog computing is rigorously evaluated in comparison to traditional fog and cloud computing frameworks. Specifically, the assessment focuses on critical factors such as latency time and computational resource utilisation while processing real-time data streams generated by Internet of Things (IoT) devices. The findings of this study underscore the advantages of federated-fog computing over conventional cloud and fog computing paradigms, particularly in the realm of real-time IoT applications demanding high performance (lowering CPU usage to 20%) and low latency (with picks up to 300ms). The research contributes valuable insights into the optimisation of processing architectures for contemporary computing paradigms, offering implications for the advancement of semantic interoperability in the context of emerging federated-fog computing for IoT applications.

A dynamic pricing scheme for secure offloading and resource allocation based on the internet of vehicles

With the development of sixth-generation (6G) wireless communication technology, billions of vehicles will access the network in the future, and the number of vehicle applications and user data will also increase dramatically. Traditional cloud computing faces serious problems of latency and energy consumption in handling massive data. Mobile edge computing (MEC) has emerged to dramatically improve computing efficiency and reduce energy consumption by placing computing servers at network edge locations close to vehicles. However, the service range of MEC servers is limited and cannot fully satisfy user requirements. In addition, the task offloading process has security risks. The current research focuses on how to reduce the energy consumption and latency overhead of task offloading and neglects the economic cost or data transmission security of vehicles. To solve the above problems, we propose a cooperative security offloading (CSO) scheme for auxiliary vehicles and MEC servers. Firstly, we propose a dynamic pricing mechanism for computing resources by considering the credibility of MEC servers and auxiliary vehicles, the urgency of the task, and the number of users competing for auxiliary vehicles. Secondly, to prevent malicious MEC servers and eavesdroppers from attacking, we employ homomorphic encryption to protect user privacy. Meanwhile, efficient and secure computing services are achieved by optimizing user selection decisions, offloading decisions, and resource allocation decisions. Finally, the optimal decisions are obtained by the dueling DQN-based resource allocation and pricing strategy (DDRP) and the cost-minimizing security offloading (CMSO) algorithm, which minimizes the economic cost of users while maximizing security. Simulation results show that, compared with some existing schemes, the CSO scheme effectively reduces the economic cost of users while ensuring the security of data transmission.

6G Cyber Physical System Based Smart Healthcare Modelling by Mobile Edge Network and Artificial Intelligence

6G Cyber Physical System Based Smart Healthcare Modelling by Mobile Edge Network and Artificial Intelligence

PMRNA: Parameter matching of realtime and non‐realtime applications for resource provisioning in fog‐integrated cloud

SummaryFog computing, an emerging technology, extends Cloud computing services to the network's edge in the proximity of the application request. This extension yields improvement in Bandwidth (BW) utilization, faster responses to Real‐Time (RT) and Internet of Things (IoT) requests, and the provision of the heterogeneous resource services. While extensive work has been conducted on resource allocation for RT and Non‐Real‐Time (NRT) requests separately in Fog as well as Cloud computing, there is limited focus on resource provisioning for mixed RT and NRT requests in the Fog‐integrated Cloud (FiC) environment. Moreover, the majority of the existing provisioning methods primarily consider parameters from the system's perspective, overlooking crucial user aspects such as deadline and request size. To address the gap, this work introduces a resource provisioning method named “Parameter Matching of Realtime and Non‐Realtime Applications (PMRNA),” which considers user parameters and resource information, gathered by the broker. The performance evaluation of the proposed model is done in CloudSim, using various combinations of RT and NRT requests along with diverse Fog and Cloud resource configurations. Evaluation metric includes average Execution Time (ET), average Waiting Time (WT), average Turn Around Time (TAT), and resource utilization. The experimental results demonstrate a significant reduction in both average WT and average TAT for the diverse pool of RT and NRT requests in the FiC compared to the Cloud‐only environment.

Snake swarm optimization‐based deep reinforcement learning for resource allocation in edge computing environment

SummaryMobile edge computing has become popular in the past few years as a means of creating computing resources close to end‐user nodes at the network edge. Nodes—end users—demand work offloading to improve service utilization. Furthermore, when the number of users in mobile edge computing increases, the minimal resources deployed at the edge become a problem. Develop the idea of reinforcement learning using a metaheuristic technique intended to achieve effective resource allocation and resolve offloading issues to handle this issue. The ideal way to manage the implementation of mobile edge computing with a cognitive agent's help is to request compensation for all client necessities. To complete the infrastructure type for the Internet of Things (IoT), the operator information is combined with its distinctive methodology. Neural caching during task execution is provided by reinforcement learning based on snake swarm optimization (SSO). Neural caching during task execution is provided by reinforcement learning based on SSO. In the process of creating the cost mapping table and incentive factor‐based optimal resource allocation, this suggested method is applied to a contract with effective resource allocation among the end manipulators. Using performance metrics like delivery ratio, energy consumption, throughput, and delay, the suggested approach is put into practice and examined. It is also contrasted with traditional methods like Gray Wolf Optimization (GWO) ant colony optimization (ACO) and genetic algorithms (GA).

Neuromorphic computing spiking neural network edge detection model for content based image retrieval

ABSTRACT In contemporary times, content-based image retrieval (CBIR) techniques have gained widespread acceptance as a means for end-users to discern and extract specific image content from vast repositories. However, it is noteworthy that a substantial majority of CBIR studies continue to rely on linear methodologies such as gradient-based and derivative-based edge detection techniques. This research explores the integration of bioinspired Spiking Neural Network (SNN) based edge detection within CBIR. We introduce an innovative, computationally efficient SNN-based approach designed explicitly for CBIR applications, outperforming existing SNN models by reducing computational overhead by 2.5 times. The proposed SNN-based edge detection approach is seamlessly incorporated into three distinct CBIR techniques, each employing conventional edge detection methodologies including Sobel, Canny, and image derivatives. Rigorous experimentation and evaluations are carried out utilizing the Corel-10k dataset and crop weed dataset, a widely recognized and frequently adopted benchmark dataset in the realm of image analysis. Importantly, our findings underscore the enhanced performance of CBIR methodologies integrating the proposed SNN-based edge detection approach, with an average increase in mean precision values exceeding 3%. This study conclusively demonstrated the utility of our proposed methodology in optimizing feature extraction, thereby establishing its pivotal role in advancing edge centric CBIR approaches.

An online approach for cooperative cache updating and forwarding in mobile edge network

An online approach for cooperative cache updating and forwarding in mobile edge network

Contextual Information Based Scheduling for Service Migration in Mobile Edge Computing

Mobile Edge Computing (MEC) is a distributed computing paradigm that delivers processing and data storage capabilities closer to the network edge, which is adjacent to mobile consumers and devices. MEC lowers latency, reduces data transmission times, and improves overall performance for mobile apps by relocating computing resources to the network’s edge. But, due to higher average load and longer elapsed time, modern end devices such as smartphones and tablets cause major load challenges in mobile computing networks. Furthermore, if smartphones cause unpredictable traffic patterns, it becomes impossible to model and forecast the nature of communication. Such confusing traffic figures are caused not just by bursty Internet traffic, but also by multitasking operating systems that allow users to swiftly switch between active apps. Mobility of users and end devices impose a difficult challenge to provide continuous services in mobile computing. In this paper, this issue is addressed using the Contextual Information Based Scheduling (CIBS) technique to optimally allocate resources and provide seamless service to the users. The proposed method is implemented with NS-3, an open-source network simulator that provides a comprehensive set of modules for Mobile Edge Computing (MEC) simulations, including mobility modelling support. The experimental results show that CIBS offers migration time of 97512ms, delay time of 372115ms, execution time of 1061328ms and downtime of 98715ms. The results are compared with the existing Mobility-Aware Joint Task Scheduling (MATS) approach. The obtained results show that CIBS outperforms MATS with regard to migration time, latency, execution time and downtime.

Identifying microbial drivers in biological phenotypes with a Bayesian network regression model.

In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes has an effect on the response (main effects), not just the interactions. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy-to-use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl.

A Novel Federated Learning Scheme for Generative Adversarial Networks

Generative adversarial networks (GANs) have been advancing and gaining tremendous interests from both academia and industry. With the development of wireless technologies, a huge amount of data generated at the network edge provides an unprecedented opportunity to develop GANs applications. However, due to the constraints such as bandwidth, privacy, and legal issues, it is inappropriate to collect and send all data to the cloud or servers for analysis, training, and mining. Thus, deploying and training GANs at the edge becomes a promising alternative solution. The instability of GANs introduced by non-independent and identical data (Non-IID) poses significant challenges to training GANs. To address these challenges, this paper presents a novel federated learning framework for GANs, namely, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> ollaborated g <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> me <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> arallel Learning (CAP). CAP supports parallel training of data and models for GANs, breaking the isolated training among generators that exists in the previous distributed algorithms, and achieving collaborative learning among cloud, edge servers, and devices. Then, to further enhance the ability of CAP-GAN for addressing Non-IID issues, we propose a Mix-Generator module (Mix-G) which divides a generator into the sharing layer and personalizing layer. The Mix-G module extracts the generic and personalization features and improves the performance of CAP-GAN on extremely personalizing datasets. Experimental results and analysis substantiate the usefulness and superiority of our proposed CAP-GAN scheme which can achieve better results in the Non-IID scenarios compared with the state-of-the-art algorithms.

Ensuring Fairness in Edge Networks: A GNN-Based Media Workload Migration Scheme With Fairness Guarantee

Ensuring Fairness in Edge Networks: A GNN-Based Media Workload Migration Scheme With Fairness Guarantee

Fiedler value: The cumulated dynamical contribution value of all edges in a complex network.

Fiedler value, as the minimal real part of (or the minimal) nonzero Laplacian eigenvalue, garners significant attention as a metric for evaluating network topology and its dynamics. In this paper, we address the quantification relation between Fiedler value and each edge in a directed complex network, considering undirected networks as a special case. We propose an approach to measure the dynamical contribution value of each edge. Interestingly, these contribution values can be both positive and negative, which are determined by the left and right Fiedler vectors. Further, we show that the cumulated dynamical contribution value of all edges is exactly the Fiedler value. This provides a promising angle on the Fiedler value in terms of dynamics and network structure. Therefore, the percentage of contribution of each edge to the Fiedler value is quantified. Numerical results reveal that network dynamics is significantly influenced by a small fraction of edges, say, one single directed edge contributes to over 90% of the Fiedler value in the Cat Cerebral Cortex network.

Availability Aware Online Virtual Network Function Backup in Edge Environments

With the rapid advancement of edge computing and network function virtualization, it is promising to provide flexible and low-latency network services at the edge. However, due to the vulnerability of edge services and the volatility of edge computing system states, i.e., service request rates, failure rates, and resource prices, it is challenging to minimize the online service cost while providing the availability guarantee. This paper considers the problem of online virtual network function backup under availability constraints (OVBAC) for cost minimization in edge environments. We formulate the problem based on the characteristics of the volatility system states derived from real-world data and show the hardness of the formulated problem. We use an online backup deployment scheme named Drift-Plus-Penalty (DPP) with provable near-optimal performance for the AVBAC problem. In particular, DPP needs to solve an integer programming problem at the beginning of each time slot. We propose a dynamic programming-based algorithm that can optimally solve the problem in pseudo-polynomial time. Extensive real-world data-driven simulations demonstrate that DPP significantly outperforms popular baselines used in practice.

An Adaptive Route Guidance Model Considering the Effect of Traffic Signals Based on Deep Reinforcement Learning

Navigation or route guidance systems are designed to provide drivers with real-time travel information and the associated recommended routes for their trips. Classical route choice models typically rely on utility theory to represent drivers’ route choice behavior. Such choices, however, may not be optimal from both the individual and the system perspectives. This is simply due to the fact that drivers usually have imperfect knowledge about the time-varying traffic conditions. In this article, we explore and propose a new model-free deep reinforcement learning (DRL) approach to solving the adaptive route guidance problem based on microsimulation. The proposed approach consists of three interconnected algorithms, including a network edge labeling algorithm, a routing plan identification algorithm, and an adaptive route guidance algorithm. Simulation experiments on both a toy network and a real-world network of Suzhou, China, are performed to demonstrate the effectiveness of the proposed approach in terms of guiding a single vehicle as well as multiple vehicles through complex traffic environments. Comparative results confirm that the DRL approach outperforms the traditional shortest path method by further reducing the average travel time in the network.

Bidirectional Service Function Chain Embedding for Interactive Applications in Mobile Edge networks

Bidirectional service function chain (BSFC) consists of multiple virtual network functions (VNFs). Through VNF deployment and link mapping, BSFCs can be embedded into resource-constrained mobile edge networks to provide low-latency network function services to users participating in interactive applications such as multi-player online games. Data from these users are routed through BSFCs to the edge node where the application is located for interaction and then returned to the users through the BSFCs, thus enabling synchronization among multiple users. However, the edge nodes or links have limited computing or bandwidth resources to serve only a fraction of users simultaneously. Therefore, the embedding decisions among different users can affect each other. In this paper, we propose a novel BSFC embedding strategy for interactive applications with the goal of minimizing computing and bandwidth resources while satisfying users' latency requirements. We first model the BSFC embedding problem as an integer nonlinear programming problem. Then, by closely examining the complexity of the problem, we propose a distributed algorithm based on game theory. We theoretically analyze the properties of the proposed algorithm and show that it can obtain a solution with a worst-case performance bound. Finally, extensive experiments show that the proposed algorithm outperforms several existing algorithms.

Predictive Service Provisioning With Online Learning in Wireless Edge Networks

Mobile Edge Computing (MEC) technology can be implemented at cellular base stations, enabling flexible and configurable provisions of services for mobile users to access. Nevertheless, the conventional solutions mainly focus on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">statical</i> service provisioning, which ignores the dynamic nature of the arriving service requests. In this work, we first conduct comprehensive data-driven observations on over 4 million service requests throughout 9,800 base stations. Our key findings suggest that users' demands intrinsically exhibit spatial and temporal patterns, which inevitably lead to performance degradation in statical service provisioning. Motivated by that, we design and implement MobiEdge, a predictive service provisioning system with online learning in wireless edge networks. We propose a graph embedding learning-based model for representation learning, thus to achieve accurate request prediction at different base stations. Then, based on the prediction of incoming service requests, we study the service provisioning reconfiguration problem, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , how to jointly optimize service placement and corresponding request scheduling across dual timescales, under constraints of network resources and the total budget. By leveraging the submodular technique, we transform the research issue into a submodular function maximization problem under the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula> -independence system constraint, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula> is a positive constant related to the ratio of coefficients in constraint conditions. On this basis, we propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1/(1+q)$</tex-math></inline-formula> approximation algorithm with rigorous theoretical analysis on the bounded maximum utility. Extensive trace-driven evaluations are conducted over networks of different scales, and MobiEdge shows remarkable performance enhancements by achieving the accuracy of up to 98% in service prediction and an average utility of 92.9% to the optimal solution in service provisioning.

Task graph offloading via deep reinforcement learning in mobile edge computing

Various mobile applications that comprise dependent tasks are gaining widespread popularity and are increasingly complex. These applications often have low-latency requirements, resulting in a significant surge in demand for computing resources. With the emergence of mobile edge computing (MEC), it becomes the most significant issue to offload the application tasks onto small-scale devices deployed at the edge of the mobile network for obtaining a high-quality user experience. However, since the environment of MEC is dynamic, most existing works focusing on task graph offloading, which rely heavily on expert knowledge or accurate analytical models, fail to fully adapt to such environmental changes, resulting in the reduction of user experience. This paper investigates the task graph offloading in MEC, considering the time-varying computation capabilities of edge computing devices. To adapt to environmental changes, we model the task graph scheduling for computation offloading as a Markov Decision Process (MDP). Then, we design a deep reinforcement learning algorithm (SATA-DRL) to learn the task scheduling strategy from the interaction with the environment, to improve user experience. Extensive simulations validate that SATA-DRL is superior to existing strategies in terms of reducing average makespan and deadline violation.