Fog Nodes Research Articles

Towards intrusion detection in fog environments using generative adversarial network and long short-term memory network

Recently, fog computing has been developed to complement cloud computing, which can provide cloud services at the edge of the network with real-time processing. However, the computational power of fog nodes is limited and this leads to security issues. On the other hand, cyber-attacks have become common with the exponential growth of Internet of Things (IoT) connected devices. This fact necessitates the development of Intrusion Detection Systems (IDSs) in fog environments with the aim of detecting attacks. In this paper, we develop an IDS named GAN-LSTM for fog environments that uses Generative Adversarial Networks (GANs) and Long Short-Term Memory Networks (LSTMs). GAN-LSTM is used to identify anomalies in network traffic to specific types of attacks or non-attacks. In general, GAN-LSTM consists of three components: data preprocessing, generation of real traffic patterns, and sequence analysis of real traffic data. Data preprocessing ensures data quality by removing noise and irrelevant features. The pre-processed data is fed to the GAN to generate real traffic as a baseline for normal behavior. Finally, the LSTM component is applied to detect anomalous anomalies in fog computing. The proposed algorithm was evaluated on public databases and experimental results showed that GAN-LSTM improves the accuracy of attack detection compared to equivalent approaches.

A many-to-many matching with externalities solution for parallel task offloading in IoT networks

The efficient and timely execution of tasks is a fundamental challenge in the realm of future Internet of Things (IoT) networks. To address this challenge, fog devices are often deployed close to end devices to facilitate task processing on behalf of IoT nodes. One strategy for improving task computational delay is to employ parallel task offloading, in which tasks are subdivided into subtasks and sent to different fog devices for execution in parallel. However, allocating computational resources to fog nodes and mapping these resources to IoT subtasks is a key challenge in this area. This work models the parallel task offloading problem as a graph-matching problem and utilizes a many-to-many matching technique to achieve a stable mapping of IoT subtasks to fog node resources. Unfortunately, the proposed solution is subject to the problem of externalities due to the dynamic preference profiling of fog nodes. To address this issue, we employ an iterative algorithm to resolve any blocking pairs that may arise. Our results demonstrate that the proposed technique reduces the task latency by 29% as compared to other matching-based techniques available in the literature.

Multi-Agent Dynamic Fog Service Placement Approach

Fog computing as a paradigm was offered more than a decade ago to solve Cloud Computing issues. Long transmission distances, higher data flow, data loss, latency, and energy consumption lead to providing services at the edge of the network. But, fog devices are known for being mobile and heterogenous. Their resources can be limited, and their availability can be constantly changing. A service placement optimization is needed to meet the QoS requirements. We propose a service placement orchestration, which functions as a multi-agent system. Fog computing services are represented by agents that can both work independently and cooperate. Service placement is being completed by a two-stage optimization method. Our service placement orchestrator is distributed, services are discovered dynamically, resources can be monitored, and communication messages among fog nodes can be signed and encrypted as a solution to the weakness of multi-agent systems due to the lack of monitoring tools and security.

I-Health: SDN-Based Fog Architecture for IIoT Applications in Healthcare.

The Industrial Internet of Things (IIoT) has been introduced in an era of increasingly broad potentials in the medical industry. In recent years, IIoT-based healthcare applications have grown in popularity, with the majority of them relying on Wireless Body Area Network (WBAN) for flexibility. There have been a few recent works that have investigated SDN-based fog architecture for constructing smart healthcare systems. However, the best fog node from the fog layer must be identified and limit the transmission of unnecessary data. To address this issue, the Intelligent Software-defined Fog Architecture (i-Health) is developed in this work. Based on the prior data pattern of each patient, the controller will decide whether to send the data to the fog layer. Furthermore, we introduced the Fog Ranking Service (FRS) and Fog Probing Service (FPS) to select the best fog node. The performance comparison reveals that the proposed i-Health outperforms existing benchmark approaches.

Cloud–Fog Collaborative Computing Based Task Offloading Strategy in Internet of Vehicles

Vehicle terminals in the mobile internet of vehicles are faced with difficulty in the requirements for computation-intensive and delay-sensitive tasks, and vehicle mobility also causes dynamic changes in vehicle-to-vehicle (V2V) communication links, which results in a lower task offloading quality. To solve the above problems, a new task offloading strategy based on cloud–fog collaborative computing is proposed. Firstly, the V2V-assisted task forwarding mechanism is introduced under cloud–fog collaborative computing, and a forwarding vehicles predicting algorithm based on environmental information is designed; then, considering the parallel computing relationship of tasks in each computing node, a task offloading cost model is constructed with the goal of minimizing delay and energy consumption; finally, a multi-strategy improved genetic algorithm (MSI-GA) is proposed to solve the above task offloading optimization problem, which adapts the chaotic sequence to initialize the population, comprehensively considers the influence factors to optimize the adaptive operator, and introduces Gaussian perturbation to enhance the local optimization ability of the algorithm. The simulation experiments show that compared with the existing strategies, the proposed task offloading strategy has the lower task offloading cost for a different number of tasks and fog nodes; additionally, the introduced V2V auxiliary task forwarding mechanism can reduce the forwarding load of fog nodes by cooperative vehicles to forward tasks.

Optimizing resource allocation for cluster D2D-assisted fog computing networks: A three-layer Stackelberg game approach

Resource management in fog computing is a highly challenging issue. Resource constraints of fog devices, the dynamic and diverse nature of workloads such as data-intensive, computationally intensive, and latency-sensitive applications, and the unpredictability of fog computing environments make it more challenging. How to collaboratively schedule edge, and end multi-layer ubiquitous computing resources to break through single node scale and resource bottlenecks to further improve computing performance also needs to be addressed. In this paper, we leverage idle resources on the user side, and study the resource allocation problems in the D2D-assisted fog computing networks. Firstly, we use an improved Louvain algorithm to cluster users and select the D2D device with the highest priority in each cluster as the cluster head to assist fog computing, where cluster heads can act as both servers and relays. Secondly, considering that fog nodes and D2D devices have limited resources and are not obligated to share their resources for free, we propose a price incentive method to construct a three-layer Stackelberg game model that maximizes the benefits of fog nodes and cluster heads while minimizing user costs. Then, we use the Lagrange multiplier method and the Karush–Kuhn–Tucker (KKT) conditions to solve the equilibrium point of the model to achieve our goal. Finally, the simulation results show that our proposed scheme markedly decreases the overall system cost in comparison to the four benchmark schemes. Moreover, in various scenarios the total cost of our scheme consistently remains lower than the other four schemes, affirming the efficiency of the scheme proposed in this paper.

Prioritization-based delay sensitive task offloading in SDN-integrated mobile IoT network

Due to enormous growth of Internet of Things (IoT) in the last decade, the amount of data generated through smart devices is increasing exponentially. Fog computing has emerged as a potential technology to deal such a huge volume of data in which task offloading is the most important aspect which has attracted significant attention. Many research works have been carried out, however, task offloading with latency sensitivity, reliability and result migration over a mobile user environment is still not widely addressed. In this paper, we propose a method for delay-sensitive and fault minimized task offloading for service requests made through a mobile/vehicular end user environment implemented via Software Defined Network (SDN) controllers integrated with the fog layer. This is a novel multi-phased model involving determining the optimal number of SDN controllers, clustering of the fog nodes (FNs) on the basis of SDN proximities, task prioritization and Gravitational Search Algorithm (GSA) based target FN selection. The simulation outcomes of our proposed approach show that there is a reduction in delay by around 23%–30% and around 60%–80% lesser number of tasks unassigned in each round as compared to two base algorithms.

Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection (CCFD) Systems

Increasing global credit card usage has elevated it to a preferred payment method for daily transactions, underscoring its significance in global financial cybersecurity. This paper introduces a credit card fraud detection (CCFD) system that integrates federated learning (FL) with blockchain technology. The experiment employs FL to establish a global learning model on the cloud server, which transmits initial parameters to individual local learning models on fog nodes. With three banks (fog nodes) involved, each bank trains its learning model locally, ensuring data privacy, and subsequently sends back updated parameters to the global learning model. Through the integration of FL and blockchain, our system ensures privacy preservation and data protection. We utilize three machine learning and deep neural network learning algorithms, RF, CNN, and LSTM, alongside deep optimization techniques such as ADAM, SGD, and MSGD. The SMOTE oversampling technique is also employed to balance the dataset before model training. Our proposed framework has demonstrated efficiency and effectiveness in enhancing classification performance and prediction accuracy.

Choice of architecture of video surveillance systems of State Border Guard of Ukraine based on cloud and fog technologies

Modern video surveillance systems have evolved from closed-circuit television systems to the level of complex systems that operate as part of complex integrated systems and solve the tasks of not only recording events in the sectors of view of video surveillance cameras but also analysing the received video information. The increase in the amount of information circulating in modern systems requires the creation of new concepts. In recent years, the attention of scientists has been drawn to distributed information processing technologies. The concept of cloud computing is developing rapidly, and the basic ideas that were laid down in its construction can no longer solve the problems faced by the technology. This has led to the emergence of so-called post-cloud architectures that extend and complement the capabilities of cloud computing. These architectures include Mist, Edge, Fog, etc. The purpose of the article is to study video surveillance systems based on the concept of cloud and fog computing and criteria for assessing their effectiveness. The research was conducted using the methods of analysis and generalisation, modelling, and experimentation. This approach made it possible to conduct a comparative study of video surveillance systems built on two architectures. The results obtained indicate that fog computing technology has the advantage of reducing latency, minimising the need for repeated requests to the cloud by performing calculations at the cloud edge. In a model based on the cloud computing concept, services that use cloud resources lead to an increase in network load. At the same time, fog computing technology allows you to relieve the network load by performing part of the computation by fog nodes. The results of the experimental study show the advantages of fog computing for networks that are sensitive to delays. However, if we consider a video surveillance system with the main task of recording events in the sectors of view of CCTV cameras and the ability to view video information in real time, it is obvious that fog nodes will not provide long-term storage of video information, and delays will not be critical. In the case of a video surveillance system with video analytics functions, fog nodes will be able to perform part of the video analytics algorithms, thus unloading the cloud. Therefore, an urgent task is to study the effectiveness of building video surveillance systems with video analytics functions based on fog architecture.

Learning Framework for Joint Optimal Node Placement and Resource Management in Dynamic Fog Environment

Background: With recent improvements in fog computing, it is now feasible to offer faster response time and better service delivery quality; however, the impending challenge is to place the fog nodes within the environment optimally. A review of existing literature showcases that consideration of joint problems such as fog node placement and resource management are less reported. Irrespective of different available methodologies, it is noted that a learning scheme facilitates better capability to incorporate intelligence in the network device, which can act as an enabling technique for superior operation of fog nodes. Objective: The prime objective of the study is to introduce simplified and novel computational modelling toward the optimal placement of fog nodes with improved resource allocation mechanisms concerning bandwidth Methods: Implemented in Python, the proposed scheme performs predictive operations using the Deep Deterministic Policy Gradient (DDPG) method. Markov modelling is used to frame the model. OpenAI Gym library is used for environment modelling, bridging communication between the environment and the learning agent. Results: Quantitative results indicate that the proposed scheme performs better than existing schemes by approximately 30%. Conclusion: The prime innovative approach introduced is the implementation of a reinforcement learning algorithm with a Markov chain towards enriching the predictive analytical capabilities of the controller system with faster service relaying operations a.

A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks.

Satellite fog computing (SFC) achieves computation, caching, and other functionalities through collaboration among fog nodes. Satellites can provide real-time and reliable satellite-to-ground fusion services by pre-caching content that users may request in advance. However, due to the high-speed mobility of satellites, the complexity of user-access conditions poses a new challenge in selecting optimal caching locations and improving caching efficiency. Motivated by this, in this paper, we propose a real-time caching scheme based on a Double Deep Q-Network (Double DQN). The overarching objective is to enhance the cache hit rate. The simulation results demonstrate that the algorithm proposed in this paper improves the data hit rate by approximately 13% compared to methods without reinforcement learning assistance.

Maximizing UAV fog deployment efficiency for critical rescue operations: A multi-objective optimization approach

In disaster scenarios and high-stakes rescue operations, integrating Unmanned Aerial Vehicles (UAVs) as fog nodes has become crucial. This integration ensures a smooth connection between affected populations and essential health monitoring devices, supporting the Internet of Things (IoT). Integrating UAVs in such environments is inherently challenging, where the primary objectives involve maximizing network connectivity and coverage while extending the networks lifetime through energy-efficient strategies to serve the maximum number of affected individuals. In this paper, we decomposed our problem into two subproblems. Connectivity, coverage subproblem and network lifespan optimization subproblem. For the Connectivity and coverage subproblem, the optimal connectivity and coverage are optimized by strategically deploying UAV fog nodes in a manner that maximizes network coverage, thoughtfully considering the distinctive constraints that emerge in these high-pressure situations where lives are at stake. We shape our UAV fog deployment problem as a multi-objective optimization and introduce a specialized UAV fog deployment algorithm tailored specifically for UAV fog nodes deployed in rescue missions. For the network lifespan subproblem, after determining the optimal connectivity and coverage of UAV nodes and users within the entire network, the network lifespan optimization subproblem is greatly simplified and efficiently solved via a one-dimensional swapping method. After conducting thorough experiments using our proposed architecture across diverse scenarios, our method consistently surpasses existing approaches. It effectively tackles issues like restricted connectivity and potential node failures. This advancement in deployment efficiency notably enhances rescue operations, enabling us to aid the maximum number of affected individuals swiftly during critical and time-pressing situations.

Multi-TA model-based conditional privacy-preserving authentication protocol for fog-enabled VANET

The rapid growth of Vehicular Ad-hoc Networks (VANET), fueled by advancements in the Internet-of-Things, cloud computing, Intelligent Transportation Systems, and fog computing, has led to the introduction of fog node-based VANET to serve resource-constrained devices. In the traditional security models of VANET, due to the use of a centralized trusted authority, there is a chance of single-point-of-failure and service unavailable with the increased service access requests. Also, there was one-to-one communication between each roadside unit and trusted authority. This may increase the system complexity and increase the traffic load. To address these issues, a novel authentication protocol for fog-enabled VANET based on multiple trusted authority model is discussed which reduces the chance of service unavailability and single-point-of-failure as the entire traffic load is distributed among multiple sub-trusted authority. Due to the incorporation of fog node, a group of roadside units can be controlled centrally, where trusted authority does not need to perform individual authentication for each roadside unit. The proposed protocol's security is rigorously examined through both informal and formal security analysis. Additionally, the protocol exhibits enhanced security features, as demonstrated in a performance comparison section, showcasing its ability to meet the security and privacy requirements while incurring relatively low communication and computation and storage costs. Thus, the proposed protocol offers a secure and efficient authentication protocol for fog-enabled VANET.

Reinforcement Learning-Driven Decision-Making for Live Virtual Machine Migration in Fog Computing

Virtualization is an essential mechanism in fog computing that enables elasticity and isolation, which in turn helps achieve resource efficiency. To bring high flexibility in a fog environment, migration of virtual machines from one node to another is required. This can be achieved by live virtual machine migration to reduce downtime and delays. Multiple existing studies have discussed live virtual machine migration in a fog environment. However, these studies have some limitations, such as pre-migrating the virtual machines based on mobility prediction only or based on the load only, which causes an issue of late and early handover. Due to the dynamic nature of fog environments, VM migration decisions require consideration of multiple factors. Hence, there is a need to develop a system that considers multiple factors to decide to migrate a virtual machine or not to solve the issue of early and late handover. This study proposes a novel approach to live virtual machine migration that applies reinforcement learning for decision-making. Experiments show that the proposed approach significantly reduces the latency of time-critical applications. The proposed system, outperforms the existing systems in terms of total average reward. The system outperformed the mobility-only-based system by 97% when tested with two fog nodes and by 80% when tested with sixteen fog nodes in terms of average reward. Further, the proposed system outperforms the load-based system by 50% and 75% when the environment consists of two fog nodes and sixteen fog nodes, respectively. This proved that considering multiple factors in deciding virtual machine migration in a fog environment can be effectively applied in time-critical applications to reduce latency.

Communication efficient federated learning with data offloading in fog-based IoT environment

Federated Learning (FL) has become a popular distributed machine learning technique that preserves privacy of data set generated by the Internet of Things (IoT) devices. However, straggler effect, and communication delay are the two major issues that should be properly taken care during model preparation of any FL process. In this study, we propose a novel and efficient scheme, called Fog Cloud Assisted Federated Learning (FCAFL) in IoT platform that address these issues. Specifically, the proposed scheme is shown to reduce the overall communication delay of the global model preparation in the FL process. It is also able to resolve straggler effect by employing clustering of the IoT devices with an efficient scheme of cluster head (CH) selection based on minimum intra-cluster distance and maximum residual energy. In order to reduce communication delay further, we also propose an efficient scheme of fog node selection using Genetic Algorithm (GA) by formulating the data offloading as a single objective problem with latency as an offloading parameter. There are some existing schemes which address straggler effect and communication delay. However, none of their approaches incorporate explicit clustering technique utilizing residual energy and minimum intra-cluster distance of the CHs to select clients, aiming to address the straggler effect. Additionally, their works do not involve any data offloading as a means to reduce communication delay in the FL process. The simulation results demonstrate better performance of the proposed FCAFL as compared to the traditional FL and existing approaches, namely, Hierarchical Federated Edge Learning (HFEL) and FedFog with respect to communication cost. The FCAFL also outperform traditional FL and HFEL with respect to training and testing accuracy of the produced FL model. The validity of the result is additionally proved through a widely recognized statistical technique, the analysis of variance (ANOVA), followed by a least significant difference (LSD) post-hoc analysis.

An improved hunger game search optimizer based IoT task scheduling in cloud–fog computing

Due to the rapid expansion of Internet of Things (IoT)-related applications, the utilization of cloud services is experiencing significant growth. Although cloud computing has proven its effectiveness in processing and storing data for various applications, it faces challenges in addressing certain requirements, such as the growing need for real-time or latency-sensitive applications and the limitations of network bandwidth. As a result, fog computing is often seen as a supplementary paradigm to cloud computing, providing additional capabilities and benefits to the traditional cloud paradigm, aiming to extend cloud services to edge devices and end-users. However, the limited capabilities of fog nodes require lighter tasks while other tasks that need more processing time are processed in the cloud. In the present research paper, we propose a novel algorithm that is customized for task scheduling within the context of cloud–fog computing on the Internet of Things (IoT) framework. Our approach builds upon the Hunger Game Search algorithm (HGS) as its foundation. To improve the exploitative capabilities of the HGS, our proposed method, called HGSMPA, incorporates the Marine Predator Algorithm (MPA). Through experimental evaluation using various workload traces, we have demonstrated the efficacy of HGSMPA. The findings reveal that HGSMPA surpasses alternative algorithms in terms of reducing energy consumption and minimizing the makespan time. Specifically, The empirical evaluation indicates that HGSMPA can reduce the makespan time by 19.31% for synthetic workloads and by 17.47% for real workloads as compared to similar scheduling algorithms. Moreover, HGSMPA can reduce energy consumption by 14.72% for synthetic workloads and by 17.68% for real workloads as compared to other methods.

Hybrid-grained migration method for load redistribution in heavily-loaded fog nodes

Hybrid-grained migration method for load redistribution in heavily-loaded fog nodes

A hybrid model using JAYA-GA metaheuristics for placement of fog nodes in fog-integrated cloud

A hybrid model using JAYA-GA metaheuristics for placement of fog nodes in fog-integrated cloud

An innovative privacy preservation and security framework with fog nodes in enabled vanet system using hybrid encryption techniques

An innovative privacy preservation and security framework with fog nodes in enabled vanet system using hybrid encryption techniques

ETFC: Energy-efficient and deadline-aware task scheduling in fog computing

The Internet of Things (IoT) is constantly evolving and expanding. However, due to the limited IoT resources, it is intertwined with fog computing to use their resources to compensate for the limitations of IoT resources. On the other hand, fog devices face challenges, such as resource heterogeneity, high distribution, dynamism, and limitations, so an efficient task scheduling approach is needed to deploy fog computing resources effectively and improve the quality of service (QoS). This work mathematically formulates the task scheduling problem to minimize energy consumption and cost and improve QoS by reducing response time and deadline violation times of IoT tasks. Then, it proposes an Energy-efficient and deadline-Aware Task scheduling in Fog Computing (ETFC) method that predicts the traffic of fog nodes by a Support Vector Machine (SVM) and divides them into low-traffic and high-traffic groups. Next, the ETFC method schedules the low-traffic part with an algorithm based on reinforcement learning using the proposed ICLA-SOA, which is an algorithm based on irregular cellular learning automata and schedules the tasks of the high-traffic part with a metaheuristic algorithm using the proposed Non-dominated Sorting Genetic Algorithm (NSGA-III). The simulation results demonstrate that the ETFC method exhibits up to an 84 % enhancement in response time, up to a 33 % reduction in energy consumption, up to a 30 % decrease in costs, and up to a 28 % advancement in meeting task deadlines compared to other methods.