Fog Environment Research Articles

Task Scheduling Optimization in Cloud-Fog-MCS Environment Using Genetic Algorithm and Game Theory

Fog-cloud computing is a promising platform for processing Mobile Crowdsensing (MCS) tasks that come with different requirements. A fog environment is more suitable for processing time-sensitive tasks due to its proximity to the MCS layer. On the other hand. the cloud environment provides powerful resources to handle large tasks. However, due to the heterogeneity of the computing nodes, scheduling MCS tasks in a fog-cloud environment is a challenging issue. This paper presents a non-cooperative game theoretical model for the task scheduling problem of MCS tasks in the fog-cloud environment. Then, the paper presents an improved genetic algorithm to efficiently solve the problem of task scheduling game model with main enhancements including a new strategy to generate a diverse initial population, incorporating the utility function of the game theoretical model with system fitness function, and finally, the paper introduces a new strategy for population sorting and grouping with applying adaptive crossover operator to meet the specific needs of each group. This improves the exploration of the unseen regions of the search space, as well as exploiting the already-found promising solutions, ultimately leading to a faster convergence toward the optimal solution. The experimental results demonstrate that the proposed approach has better performance in terms of reducing the makespan by 26%, decreasing the energy consumption by 32.4%, decreasing total system cost by 28%, and decreasing the degree of imbalance by 21.53% as compared with other scheduling approaches such as Discrete Non-dominated Sorting Genetic Algorithm II (DNSGA-II), Grasshopper Optimization Algorithm (GOA), Grey Wolf Optimization (GWO, Time-Cost Aware Scheduling (TCaS), Moth Flame Optimization (MFO), and Bees Life Algorithm (BLA).

A new authentication scheme for dynamic charging system of electric vehicles in fog environment

AbstractThe dynamic charging system of electric vehicles has great potential for development. Electric vehicles initiate charging requests, and charging stations charge authorized electric vehicles. Fog computing improves the efficiency of request processing. However, open channels can be vulnerable to various attacks by a malicious adversary. Mutual authentication schemes allow users and charging stations to confirm each other. Therefore, numerous authentication and key agreement schemes have been proposed. In 2021, Babu et al. proposed an authentication scheme based on fog servers. Unfortunately, we found that their scheme can not resist FS impersonation attack and replay attack. Hence, we propose an authentication scheme between electric vehicles and fog servers to resolve the security weakness. Our scheme uses lightweight hash functions and XOR operations, which is more suitable for resource‐constrained electric vehicles. We proved our scheme can achieve mutual authentication by using BAN logic, and analyzed that our scheme can resist impersonation, replay, and known session key attacks, ensuring anonymity and untraceability. We finally compare computation cost and communication cost of our scheme with the existing schemes. The result shows that our scheme performs better than others overall. Therefore, our scheme is secure and suitable for dynamic charging systems.

Neural Network-Powered Intrusion Detection in Multi-Cloud and Fog Environments

Neural Network-Powered Intrusion Detection in Multi-Cloud and Fog Environments

An Efficient Sustainable Energy Utilization and Scheduling for Fog Environment Using Glowworm Swarm Optimization

An Efficient Sustainable Energy Utilization and Scheduling for Fog Environment Using Glowworm Swarm Optimization

Multi-objective cost-aware bag-of-tasks scheduling optimization model for IoT applications running on heterogeneous fog environment

Recently introduced by CISCO, the fog computing paradigm extends cloud-based computing services near the Internet of Things (IoT) devices to serve their applications, aiming to increase performance. This new computing paradigm tries to meet the requirements of emerging IoT applications, which mostly are geographically distributed and location-aware, low-cost, and delay-sensitive. One of the frequently applied applications is a bag-of-tasks (BoT) of IoT applications, each of which includes several independent tasks. However, inefficient task scheduling approaches for parallel executions may degrade the delivered quality of service (QoS) to users and also increase the providers’ total cost of ownership (TCO). The reliable execution commensurate with users’ service level agreement (SLA) and affordable services tailored to their budget at the same time is favorable for both users and fog service providers because the highly reliable execution is a reputational metric that increases the users’ trust and adherence. To this end, this paper formulates the task scheduling for the execution of BoT in IoT applications to a multi-objective optimization problem with execution time, costs and reliability perspectives. To measure total costs, both the execution cost and monetary cost models are presented. To measure the resource reliability, the new scheduling failure factor (SFF) metric is also presented, which is a kind of cost parameter. To solve this combinatorial NP-Hard problem, a multi-objective cost-aware discrete grey wolf optimization-based algorithm (MoDGWA) is proposed. To verify the performance of MoDGWA, diverse scenarios were conducted and the performance of the proposed algorithm was compared against other state-of-the-arts. The extensive simulation results prove the average dominance of MoDGWA, and it has 0.55 %, 7.28 %, 10.20 %, and 45.83 % reduction against other state-of-the-art in terms of makespan, ToC, TSFF, and the cost score respectively. Note that, the implementation of the MoDGWA is available on https://github.com/mirsaeid123/Mirsaeid.

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Fog Computing has emerged as a pivotal technology for enabling low-latency, context-aware, and efficient computing at the edge of the network. Effective task scheduling plays a vital role in optimizing the performance of fog computing systems. Traditional task scheduling algorithms, primarily designed for centralized cloud environments, often fail to cater to the dynamic, heterogeneous, and resource-constrained nature of Fog nodes. To overcome these limitations, we introduce a sophisticated machine learning-driven methodology that adapts task allocation to the ever-changing Fog environment's conditions. Our approach amalgamates K-Means clustering algorithm enhanced with fuzzy logic, a robust unsupervised learning technique, to efficiently group Fog nodes based on their resource characteristics and workload patterns. The proposed method combines the clustering capabilities of K-means with the adaptability of fuzzy logic to dynamically allocate tasks to fog nodes. By leveraging machine learning techniques, we demonstrate how tasks can be intelligently allocated to fog nodes, resulting in reducing execution time, response time and network usage. Through extensive experiments, we showcase the effectiveness and adaptability of our proposed approach in dynamic fog environments. Clustering proves to be a time-effective method for identifying groups of jobs per virtual machine (VM) efficiently. To model and evaluate our proposed approach, we have utilized iFogSim. The simulation results affirm the effectiveness of our scheduling technique, showcasing significant enhancements in execution time reduction, minimized network utilization, and improved response time when compared to existing machine learning and non-machine learning based scheduling methods within the iFogSim framework.

Efficient SDN-based Task offloading in fog-assisted cloud environment

A distributed computing model called "fog computing" provides cloud-like services which is closer to end devices, and is rapidly gaining popularity. It offers cloud-like computing including storage capabilities, but with less latency and bandwidth requirements, thereby improving the computation capabilities of IoT devices and mobile nodes. In addition, fog computing offers advantages such as support for context awareness, scalability, dependability, and node mobility. Fog computing is frequently used to offload tasks from end devices' applications, enabling quicker execution utilizing the fog nodes' capabilities. Because of the changing nature of the fog environment, task offloading is challenging and the multiple QoS criteria that depend on the type of application being used. This article proposes an SDN-based offloading technique to optimize the task offloading technique for scheduling and processing activities generated by the Internet of Space Things (IoST) devices. The proposed technique utilizes Software-Defined Networking (SDN) optimization to dynamically manage network resources and to facilitate the deployment and execution of offloaded tasks. To model the system which computes the optimal virtual machines (VM) to be allocated in the fog network in order to actively process the offloaded tasks, the GI/G/r queueing model is utilised. This approach minimizes the delay-sensitive task queue and minimises the necessary number of VMs while minimising the waiting time for the fog layer. The findings of the simulation are used to verify the effectiveness of the proposed model.

Fatigue and Fracture Evaluations in Ti-10V-2Fe-3Al (Ti 10-2-3)

A mechanical assessment was conducted to characterise the near β titanium alloy Ti-10V-2Fe-3Al (Ti 10-2-3), which was heat-treated to provide two strength variants. Low cycle fatigue and crack propagation tests were performed under standard laboratory air plus a salt fog environment. The differences in static strength were also demonstrated under fatigue conditions utilising plain specimens. However, the alloy was essentially insensitive to the test environment when comparing LCF performance in air and salt fog. Salt fog also provided no effect on crack growth behaviour. A double edge notch specimen geometry was employed to measure free initiation and the growth of cracks from a stress-raising feature. The current data now supplement previous studies aimed at expanding the mechanical database for Ti 10-2-3 component design and in-service life predictions.

MicroFog: A framework for scalable placement of microservices-based IoT applications in federated Fog environments

MicroService Architecture (MSA) is gaining rapid popularity for developing large-scale IoT applications for deployment within distributed and resource-constrained Fog computing environments. As a cloud-native application architecture, the true power of microservices comes from their loosely coupled, independently deployable and scalable nature, enabling distributed placement and dynamic composition across federated Fog and Cloud clusters. Thus, it is necessary to develop novel placement algorithms that utilise these microservice characteristics to improve the performance of the applications. However, existing Fog computing frameworks lack support for integrating such placement policies due to their shortcomings in multiple areas, including MSA application placement and deployment across multi-fog multi-cloud environments, dynamic microservice composition across multiple distributed clusters, scalability of the framework to operate within federated environments, support for deploying heterogeneous microservice applications, etc. To this end, we design and implement MicroFog, a Fog computing framework compatible with cloud-native technologies such as Docker, Kubernetes and Istio. MicroFog provides an extensible and configurable control engine that executes placement algorithms and deploys applications across federated Fog environments. Furthermore, MicroFog provides a sufficient abstraction over container orchestration and dynamic microservice composition, thus enabling users to easily incorporate new placement policies and evaluate their performance. The capabilities of the MicroFog framework, such as the scalability and flexibility of the design and deployment architecture of MicroFog and its ability to ensure the deployment and composition of microservices across distributed fog–cloud environments, are validated using multiple use cases. Experiments also demonstrate MicroFog’s ability to integrate and evaluate novel placement policies and load-balancing techniques. To this end, we integrate multiple microservice placement policies to demonstrate MicroFog’s ability to support horizontally scaled placement, service discovery and load balancing of microservices across federated environments, thus reducing the application service response time up to 54%.

Analysis of the effect of optical thickness on polarization in a sea fog stratified environment.

Since there are usually multiple layers present in a real-world sea fog environment, and because previous studies have tended to analyze sea fog as a single layer rather than as refined layered sea fog, this paper splits sea fog into two categories: water fog and salt fog double-layer environments. By adjusting the optical thickness of the two layers of media, we may investigate the issue of the law governing the transmission of polarized light. In this paper, the analysis is mainly carried out through a simulation and experimental tests. The simulation portion is based mostly on the improved layered Monte Carlo approach, which builds a simulation model more appropriate for multilayer non-spherical media by using the accumulation principle to determine the scattering and transmission properties between layers. The tests are conducted by altering the double-layer medium's optical thickness, incoming wavelength, and polarization state, and then getting the polarization information of visible light after transmission through the complicated environment. The findings demonstrate that the optical thickness of the sea fog double-layer media affects polarized light transmission in a non-negligible way. Longer wavelength polarized light may keep polarization information better as the optical thickness increases, and circularly polarized light has polarization-preserving properties that are superior to linearly polarized light. By contrasting the simulation findings with the experimental data, the consistency of the two conclusions is confirmed, and the study offers a helpful resource for the transmission of polarized light in the sea fog environment.

E‐TOALB: An efficient task offloading in IoT‐fog networks

SummarySmart devices are concerned about the processing and computation of tasks due to their tiny nature. They prefer to offload their tasks to the cloud for processing and computation. Due to the huge amount of data being generated by smart devices, the cloud becomes inefficient in terms of huge delay. Thus, Processing tasks in the cloud can add latency and finally needs to be addressed. Thus, fog computing is an alternative to the latency issue. The tasks are offloaded to fog instead of the cloud. In this paper, e‐TOALB (enhanced task offloading and load balancing), a modified and enhanced nature‐inspired and meta‐heuristic ant colony optimization is used to offload tasks in a fog environment. The results obtained by the proposed method are compared with Particle swarm optimization (PSO), round robin (RR), and ant colony optimization. The numerical results clearly show an improvement in average response time and load sharing among all fog nodes. The results of the proposed model produce low response time, low average service time, and low standard deviation. The proposed scheme aims to find the best possible decision for offloading tasks to nearby fog devices and to find an optimal route for offloading with the least communication cost and average service time.

A Hybrid Particle Whale Optimization Algorithm with application to workflow scheduling in cloud–fog environment

Workflow scheduling aims to optimize task allocation and execution in cloud–fog​ computing environments. Finding an optimal algorithm for workflow scheduling poses a significant challenge due to the complexity and variability of tasks and resources involved. Although meta-heuristic algorithms have been proposed to address these challenges, they often suffer from being trapped in local optima, failing to achieve the global optimal solution. Our work used a hybrid approach “Particle Whale Optimization Algorithm” (PWOA) to overcome the above-mentioned problem by combining Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA).PSO’s limitation in high-dimensional search spaces, characterized by slow convergence toward the global optimum, and the population-based WOA algorithm’s limited exploration of search spaces are overcome by the proposed algorithm PWOA. The proposed algorithm PWOA overcomes the limitations of PSO and WOA.The objective of the PWOA algorithm is to minimize the Total Execution Time (TET) and Total Execution Cost (TEC) associated with dependent tasks in a cloud–fog computing environment. To evaluate the performance of the PWOA algorithm, extensive simulations are conducted using test cases (30, 50, 100 and 1000) from five different scientific workflows: Cybershake, Epigenomics, Inspiral, Montage and Sipht. These workflows encompassed varying numbers of assigned tasks. The simulation results demonstrated that the proposed PWOA algorithm outperformed the standard PSO and WOA algorithms to improve TET and TEC across all the diverse workflows. This study also provides a comparative view of the effectiveness of the PWOA algorithm in enhancing workflow scheduling performance.

Consumer-Centric Internet of Medical Things for Cyborg Applications Based on Federated Reinforcement Learning

The Internet of Medical Things (IoMT) is the new digital healthcare application paradigm that offers many healthcare services to users. IoMT-based emerging healthcare applications such as cyborgs, the combination of advanced artificial intelligence (AI) robots, and doctors performing surgical operations remotely from hospitals to patients in their homes. For instance, robot-based knee replacement procedures, and thigh medical care real-time performance monitoring systems are cyborg applications. The paper introduces the multi-agent federated reinforcement learning policy (MFRLP) indicated in mobile and fog agents based on the socket remote procedure call (RPC) paradigm. The goal is to design a consumer-centric cyborg-efficient training testing system that executes the overall application mechanism with minimum delays in the IoMT system. The study develops the RPC based on reinforcement learning and federated learning that adopts dynamic changes in the environment for cyborg applications. As a result, MFRLP minimized the training and testing in the mobile and fog environments by 50%, local processing time by 40%, and processing time by 50% compared to existing machine learning (ML) methods for cyborg applications. The code is publicly available at https://github.com/prayagtiwari/CIoMT

Experimental Characterization of Polarized Light Backscattering in Fog Environments

This paper focuses on the experimental characterization of the polarization behavior of light backscattered through fog. A polarimetric orthogonal state contrast imager and an active, purely polarized white illuminator system are used to evaluate both linear and circular polarization signals. The experiments are carried out in a macro-scale fog chamber under controlled artificial fog conditions. We explore the effect of backscattering in each imaging channel, and the persistence of both polarization signals as a function of meteorological visibility. We confirm the presence of the polarization memory effect with circularly polarized light, and, as a consequence, the maintenance of helicity in backscattering. Moreover, the circular cross-polarized channel is found to be the imaging channel less affected by fog backscattering. These results are useful and should be taken into account when considering active polarimetric imaging techniques for outdoor applications under foggy conditions.

A comprehensive review on trust management approaches in fog computing

Decentralised fog computing can provide real-time interaction, minimize latency, heterogeneity, and provide networking services between edge devices and cloud data centers. One of the biggest challenges in the fog layer network is finding a trustworthy fog node. Trust management encompasses the process of being trustworthy and the act of assessing the reliability of other nodes. It is essential to carry out a comprehensive review using a systematic approach in this field to advance our understanding, address emerging challenges, and foster secure and efficient trust management practices. This research paper considers a comprehensive analysis of high-quality fog computing trust management literature from 2018 to 2022. A variety of distinct approaches have been chosen by fog computing-based trust management and these techniques are classified into three categories: algorithms, challenges, and limitations. Further, it reviews the various trust attacks in fog environments, details the solutions proposed in the current literature, and concludes with a discussion of the open challenges and potential future research directions in fog computing.

AGWO: Cost Aware Task Scheduling in Cloud Fog Environment Using Hybrid Metaheuristic Algorithm

In IoT concepts, efficient approaches like cloud-fog computing are emerging, enhancing system benefits. The performance and output of such frameworks can be greatly improved by optimized scheduling of Internet of Things (IoT) task requests. This study presents a novel technique for scheduling Internet of Things requests in a cloud-fog environment, based on an adaption of ant grey wolf optimisation (AGWO). By combining the operators of ant colony optimisation (ACO) and grey wolf optimisation (GWO), AGWO aims to improve the speed and quality of ACO's solution discovery. The suggested AGWO approach is evaluated using numerous datasets of varying sizes, both synthetic and real-world. The effectiveness of AGWO is further investigated by comparing it to standard metaheuristic methods. The experimental results demonstrate that AGWO is superior to competing strategies in terms of makespan time and cost while resolving the task scheduling problem.

BCSoM: Blockchain-based certificateless aggregate signcryption scheme for Internet of Medical Things

The integration of fog computing and blockchain in the Internet of Medical Things (IoMT) domain has wholly transformed the healthcare industry and e-health services. In IoMT, the patient’s personal health data is shared on an open channel that makes the IoMT system an easy target for attackers. Therefore, confidentiality and authentication are the key security requirements for an IoMT system. Although few works have been proposed to provide secure communication in conventional healthcare infrastructures, limited work has been done to achieve data confidentiality and authentication in blockchain-based IoMT under fog environment. Therefore, this work proposes a blockchain-assisted certificateless aggregate signcryption called BCSoM scheme that achieves data confidentiality and device/patient authentication. In the proposed work, first, IoMT devices generate a signcrypted text and transmit it to an aggregator that generates an aggregated signcrypted text and sends it to a receiver fog server. The receiver fog server verifies the signcrypted text through the blockchain and also performs unsigncryption to retrieve the original message. The proposed scheme is proven secure under the Discrete Logarithm (DL) and Computational Diffe–Hellman (CDH) assumptions. Finally, a thorough performance analysis using the Hyperledger Fabric platform and cryptographic libraries shows that the proposed scheme is computationally more efficient when compared to existing works.

A Framework for QoS Parameters-Based Scheduling for IoT Applications on Fog Environments

A Framework for QoS Parameters-Based Scheduling for IoT Applications on Fog Environments

Characterization of multi-wavelength polarized light transmission in the real sea spray environment

Sea spray particles are a type of non-uniform, non-spherical, non-isotropic, and complex medium, and the study of the transmission characteristics of polarized light in a real sea spray environment can provide reference values in many fields, such as polarization imaging, marine target detection, and LiDAR, which can make up for the vacancy of polarized light transmission in a complex sea spray environment. In this paper, a real sea fog test is carried out in the Qingdao Sea area of China in the horizontal/oblique direction, and a platform for generating and detecting polarized light with multiple tilt angles is constructed by using the active test method, which realizes the test scheme for the characteristics of energy change and polarization state change in the linearly polarized light and circularly polarized light at different visibility levels in sea fog environments. The results show that it is more difficult to deflect the circularly polarized light than linearly polarized light at the same sea spray visibility level. With the increase in the tilt angle, a decrease in the polarization is observed. The polarization of the near-infrared light is always larger than that of the visible light, which indicates that the circularly polarized light has better polarization preservation than the linearly polarized light and the polarization preservation of the near-infrared light is better than that of the visible light.

E-AVOA-TS: Enhanced African vultures optimization algorithm-based task scheduling strategy for fog–cloud computing

In fog computing, inefficient scheduling of user tasks causes more delays. Moreover, how to schedule tasks that need to be offloaded to fog nodes or cloud nodes has not been fully addressed. The task scheduling process needs to be optimized and efficient in order to address the issues of resource utilization, response time, and energy consumption. This paper proposes an Enhanced African Vultures Optimization Algorithm-based Task Scheduling Strategy (E-AVOA-TS) for fog-cloud computing. Through the proposed strategy, each village learns from its neighbors rather than from all of its members. The minimization of makespan, cost, and energy consumption in the proposed algorithm are considered as objective function. To prioritize tasks, the Best Worst Method (BWM) is used to handle the sensitivity of task delays. Latency-sensitive tasks are sent to the fog environment, while latency-tolerant tasks are sent to the cloud. E-AVOA is compared to other state-of-the-art optimizers using classic benchmark functions and ten benchmark tests from CEC-C06. Compared to other competitors, E-AVOA-TS outperforms makespan by 24.2%, cost by 16%, energy consumption by 4.7%, and DST% by 6.2% for large scale tasks. According to the simulation results, makespan shows improvements of 33%, 53%, and 48%, and energy consumption is reduced by 32%, 44%, and 5%, compared with PSG-M, IWC, and DCOHHOTS, respectively.