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Background and Objectives: Today, the increased number of Internet-connected smart devices require powerful computer processing servers such as cloud and fog and necessitate fulfilling requests and services more than ever before. The geographical distance of IoT devices to fog and cloud servers have turned issues such as delay and energy consumption into major challenges. However, fog computing technology has emerged as a promising technology in this field. Methods: In this paper, service/energy management approaches are generally surveyed. Then, we explain our motivation for the systematic literature review procedure (SLR) and how to select the related works. Results: This paper introduces four domains of service management and energy management, including Architecture, Resource Management, Scheduling management, and Service Management. Scheduling management has been used in 38% of the papers. Therefore, they have the highest service management and energy management. Also, Resource Management is the second domain that has been able to attract about 26% of the papers in service management and energy management. Conclusion: About 81% of the fog computing papers simulated their approaches, and the others implemented their schemes using a testbed in the real environment. Furthermore, 30% of the papers presented an architecture or framework for their research, along with their research. In this systematic literature review, papers have been extracted from five valid databases, including IEEE Xplore, Wiley, Science Direct (Elsevier), Springer Link, and Taylor & Francis, from 2013 to 2022. We obtained 1596 papers related to the discussed subject. We filtered them and achieved 47 distinct studies. In the following, we analyze and discuss these studies; then we review the parameters of service quality in the papers, and ultimately, we present the benefits, drawbacks, and innovations of each study. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Load balancing is crucial in distributed systems like fog computing, where efficiency is paramount. Offloading with different approaches is the key to balancing the load in distributed environments. Static offloading (SoA) falls short in heterogeneous networks, necessitating dynamic offloading to reduce latency in time-sensitive tasks. However, prevalent dynamic offloading (PoA) solutions often come with hidden costs that impact sensitive applications, including decision time, networks congested and distance offloading. This paper introduces the Hybrid Offloading (HybOff) algorithm, which substantially enhances load balancing and resource utilization in fog networks, addressing issues in both static and dynamic approaches while leveraging clustering theory. Its goal is to create an uncomplicated low-cost offloading approach that enhances IoT application performance by eliminating the consequences of hidden costs regardless of network size. Experimental results using the iFogSim simulation tool show that HybOff significantly reduces offloading messages, distance, and decision-offloading consequences. It improves load balancing by 97%, surpassing SoA (64%) and PoA (88%). Additionally, it increases system utilization by an average of 50% and enhances system performance 1.6 times and 1.4 times more than SoA and PoA, respectively. In summary, this paper tries to introduce a new offloading approach in load balancing research in fog environments. [ABSTRACT FROM AUTHOR]

Fog computing (FC) is considered one of the smart and effective solutions for service provisioning to the Internet of Everything (IoE) layer. IoE layer means the platforms including homes, vehicles, infrastructures, and alike others. FC supports the IoE layer in a smart and charismatic manner by providing services near the smart devices, from vehicles to other mobilities, and fitting the response and delay time requirements. In the proposed paper, the authors focused on discussing vehicular fog computing (VFC) and how FC supports smart vehicles operating on the IoT layer. In the previous decade, scheduling algorithms are proposed by different scholars such as RTFRS, FCFS, ILP, and others to improve the working and efficacy of VFC. In the proposed paper, an analysis is done on the VFC scheduling algorithms published in the last three years in accordance with areas such as traditional, meta-heuristic, deep reinforcement learning, fuzzy logic, heuristic, and integer linear programming. The analysis is done to examine the areas of scheduling such as task scheduling, resource allocation, and others on which the existing solutions are working. This helps in inspecting the gap across which in future further work can be done. [ABSTRACT FROM AUTHOR]

In the era of continuous development in Internet of Things (IoT) technology, smart services are penetrating various facets of societal life, leading to a growing demand for interconnected devices. Many contemporary devices are no longer mere data producers but also consumers of data. As a result, massive amounts of data are transmitted to the cloud, but the latency generated in edge-to-cloud communication is unacceptable for many tasks. In response to this, this paper introduces a novel contribution—a layered computing network built on the principles of fog computing, accompanied by a newly devised algorithm designed to optimize user tasks and allocate computing resources within rechargeable networks. The proposed algorithm, a synergy of Lyapunov-based, dynamic Long Short-Term Memory (LSTM) networks, and Particle Swarm Optimization (PSO), allows for predictive task allocation. The fog servers dynamically train LSTM networks to effectively forecast the data features of user tasks, facilitating proper unload decisions based on task priorities. In response to the challenge of slower hardware upgrades in edge devices compared to user demands, the algorithm optimizes the utilization of low-power devices and addresses performance limitations. Additionally, this paper considers the unique characteristics of rechargeable networks, where computing nodes acquire energy through charging. Utilizing Lyapunov functions for dynamic resource control enables nodes with abundant resources to maximize their potential, significantly reducing energy consumption and enhancing overall performance. The simulation results demonstrate that our algorithm surpasses traditional methods in terms of energy efficiency and resource allocation optimization. Despite the limitations of prediction accuracy in Fog Servers (FS), the proposed results significantly promote overall performance. The proposed approach improves the efficiency and the user experience of Internet of Things systems in terms of latency and energy consumption. [ABSTRACT FROM AUTHOR]

Modeling IoT applications in distributed computing systems as workflows enables automating their procedure. There are different types of workflow-based applications in the literature. Executing IoT applications using device-to-device (D2D) communications in distributed computing systems especially edge paradigms requiring direct communication between devices in a network with a graph topology. This paper introduces a toolkit for simulating resource management of scientific workflows with different structures in distributed environments with graph topology called WIDESim. The proposed simulator enables dynamic resource management and scheduling. We have validated the performance of WIDESim in comparison to standard simulators, also evaluated its performance in real-world scenarios of distributed computing. The results indicate that WIDESim’s performance is close to existing standard simulators besides its improvements. Additionally, the findings demonstrate the satisfactory performance of the extended features incorporated within WIDESim. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC) is an innovative computing paradigm with an exceptional ability to improve the vehicles' capacity to manage computation-intensive applications with both low latency and energy consumption. Moreover, more and more Artificial Intelligence (AI) technologies are applied in task offloading on the Internet of Vehicles (IoV). Focusing on the problems of computing latency and energy consumption, in this paper, we propose an AI-based Vehicle-to-Everything (V2X) model for tasks and resource offloading model for an IoV network, which ensures reliable low-latency communication, efficient task offloading in the IoV network by using a Software-Defined Vehicular-based FC (SDV-F) architecture. To fit to time-critical data transmission task distribution, the proposed model reduces unnecessary task allocation at the fog computing layer by proposing an AI-based task-allocation algorithm in the IoV layer to implement the task allocation of each vehicle. By applying AI technologies such as reinforcement learning (RL), Markov decision process, and deep learning (DL), the proposed model intelligently makes decision on maximizing resource utilization at the fog layer and minimizing the average end-to-end delay of time-critical IoV applications. The experiment demonstrates the proposed model can efficiently distribute the fog layer tasks while minimizing the delay. [ABSTRACT FROM AUTHOR]

In the era of ubiquitous computing, the challenges imposed by the increasing demand for real-time data processing, security, and energy efficiency call for innovative solutions. The emergence of fog computing has provided a promising paradigm to address these challenges by bringing computational resources closer to data sources. Despite its advantages, the fog computing characteristics pose challenges in heterogeneous environments in terms of resource allocation and management, provisioning, security, and connectivity, among others. This paper introduces COGNIFOG, a novel cognitive fog framework currently under development, which was designed to leverage intelligent, decentralized decision-making processes, machine learning algorithms, and distributed computing principles to enable the autonomous operation, adaptability, and scalability across the IoT–edge–cloud continuum. By integrating cognitive capabilities, COGNIFOG is expected to increase the efficiency and reliability of next-generation computing environments, potentially providing a seamless bridge between the physical and digital worlds. Preliminary experimental results with a limited set of connectivity-related COGNIFOG building blocks show promising improvements in network resource utilization in a real-world-based IoT scenario. Overall, this work paves the way for further developments on the framework, which are aimed at making it more intelligent, resilient, and aligned with the ever-evolving demands of next-generation computing environments. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) and software‐defined networking (SDN) are two relatively recent developments in the field of communication technology that have emerged in response to the growing demand for more efficient, flexible, and dynamic network architectures. As both of these concepts are new, they have received increasing attention from academic or industrial sources to emphasize their potential for integration. This study is aimed at reviewing the literature on SDN for IoT (SDN‐IoT) published from 2014 to 2022 and presenting insights and directions for future research, with a particular focus on cloud, fog, and edge computing. The study collects data from Science Direct, IEEE Explore, and Google Scholar and objectively selects 126 papers and conducts metadata analysis. The study articulates the challenges of managing and orchestrating IoT systems and how SDN can be used to address these challenges by enabling dynamic and flexible network configurations. It delineates not only the function of blockchain (BC) technology in securing and managing IoT networks but also how SDN can be utilized to incorporate BC‐based solutions. Additionally, the potential of SDN for mobile networks is explored, which are increasingly being used to support IoT devices. Finally, this study outlines the issues, challenges, and potential future research directions that may present opportunities for the researchers working in this field, underscoring the demand for more in‐depth investigation and advancement. [ABSTRACT FROM AUTHOR]

Smart agriculture stands as a promising domain for IoT-enabled technologies, with the potential to elevate crop quality, quantity, and operational efficiency. However, implementing a smart agriculture system encounters challenges such as the high latency and bandwidth consumption linked to cloud computing, Internet disconnections in rural locales, and the imperative of cost efficiency for farmers. Addressing these hurdles, this paper advocates a fog-based smart agriculture infrastructure integrating edge computing and LoRa communication. We tackle farmers' prime concern of animal intrusion by presenting a solution leveraging low-cost PIR sensors, cameras, and computer vision to detect intrusions and predict animal locations using an innovative algorithm. Our system detects intrusions pre-emptively, identifies intruders, forecasts their movements, and promptly alerts farmers. Additionally, we compare our proposed strategy with other approaches and measure their power consumptions, demonstrating significant energy savings afforded by our strategy. Experimental results highlight the effectiveness, energy efficiency, and cost-effectiveness of our system compared to state-of-the-art systems. [ABSTRACT FROM AUTHOR]

Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil's U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment. [ABSTRACT FROM AUTHOR]

Large amounts of data are created from sensors in Internet of Things (IoT) services and applications. These data create a challenge in directing these data to the cloud, which needs extreme network bandwidth. Fog computing appears as a modern solution to overcome these challenges, where it can expand the cloud computing model to the boundary of the network, consequently adding a new class of services and applications with high-speed responses compared to the cloud. Cloud and fog computing propose huge amounts of resources for their clients and devices, especially in IoT environments. However, inactive resources and large number of applications and servers in cloud and fog computing data centers waste a huge amount of electricity. This paper will propose a Dynamic Power Provisioning (DPP) system in fog data centers, which consists of a multi-agent system that manages the power consumption for the fog resources in local data centers. The suggested DPP system will be tested by using the CloudSim and iFogsim tools. The outputs show that employing the DPP system in local fog data centers reduced the power consumption for fog resource providers. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) uptake brought a paradigm shift in application deployment. Indeed, IoT applications are not centralized in cloud data centers, but the computation and storage are moved close to the consumers, creating a computing continuum between the edge of the network and the cloud. This paradigm shift is called fog computing, a concept introduced by Cisco in 2012. Scheduling applications in this decentralized, heterogeneous, and resource-constrained environment is challenging. The task scheduling problem in fog computing has been widely explored and addressed using many approaches, from traditional operational research to heuristics and machine learning. This paper aims to analyze the literature on task scheduling in fog computing published in the last five years to classify the criteria used for decision-making and the technique used to solve the task scheduling problem. We propose a taxonomy of task scheduling algorithms, and we identify the research gaps and challenges. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) continues to proliferate, the demand for efficient and secure data processing at the network edge has grown exponentially. Fog computing, a paradigm that extends cloud capabilities to the edge of the network, plays a pivotal role in meeting these requirements. In this context, the reliable and trustworthy forwarding of data is of paramount importance. This paper presents an innovative mechanism designed to ensure the trustworthiness of data forwarding in the context of MQTT (Message Queuing Telemetry Transport), a widely adopted IoT communication protocol. Our proposed mechanism leverages the inherent advantages of MQTT to establish a robust and secure data-forwarding scheme. It integrates fog computing resources seamlessly into the MQTT ecosystem, enhancing data reliability and security. The mechanism employs trust models to evaluate the credibility of IoT devices and fog nodes involved in data forwarding, enabling informed decisions at each stage of the transmission process. Key components of the mechanism include secure communication protocols, authentication mechanisms, and data integrity verification. The proposed secure communication protocols (TLS/SSL, MQTTS, and PKI) and data integrity verification methods (MAC, digital signatures, checksums, and CRC) provide a robust framework for ensuring secure and trustworthy data transmission in IoT systems. These elements collectively contribute to the establishment of a reliable data forwarding pipeline within MQTT. Additionally, the mechanism prioritizes low-latency communication and efficient resource utilization, aligning with the real-time requirements of IoT applications. Through empirical evaluations and simulations, the research demonstrates the effectiveness of our proposed mechanism in improving the trustworthiness of data forwarding, while minimizing overhead, as the experiment was conducted with 15 fog nodes, and the maximum Level of Trust (LoT) score was 0.968, which is very high, with an estimated accuracy of 97.63%. The results indicate that our approach significantly enhances data security and reliability in MQTT-based IoT environments, thereby facilitating the seamless integration of fog computing resources for edge processing. [ABSTRACT FROM AUTHOR]

Sensors are the main components in Cyber-Physical Systems (CPS), which transmit large amounts of physical values and big data to computing platforms for processing. On the other hand, the embedded processors (as edge devices in fog computing) spend most of their time reading the sensor signals as compared with computing time. The impact of sensors on the performance of fog computing is very great, thus, the enhancement of the reading time of sensors will positively affect the performance of fog computing, and solves the CPS challenges such as delay, timed precision, temporal behavior, energy, and cost. In this paper, we propose an algorithm based on the 1st derivative of the sensor signal to generate an adaptive sampling frequency. The proposed algorithm uses an adaptive frequency to capture the sudden and rapid change in sensor signal in the steady state. Finally, we realize and tested it using the Ptolemy II Modeling Environment. [ABSTRACT FROM AUTHOR]

This study aims to structure the implementation of a governmental cloud of things (CoT), edge computing (EC), and fog computing in Iraq in the context of sustainable wireless communication. A base of literature was built that included any challenges, opportunities, and best practices relevant to these innovative technologies to set up the background for this paper. A concept model was created that included core components (cognitive technologies and fog computing), key processes (resource analysis, infrastructure design), and stakeholders (governments, industry, community). A strategic methodology made up of stakeholder involvement, capacity building, and pilot projects was used in the project. Concerning IoT planned deployment and services provision, network infrastructure was put in place to support the devices and a higher level of security measures were recommended. Using scenario hypothesis, MATLAB simulator was employed to simulate data value distribution as well as received power distribution based on different institutions for 12 months. Monitoring and evaluation should be followed to measure performance indicators and effects on this process. Continuously improvement strategies were the highlight of the session which further stimulated innovations. Acquainted projects will be put in the function to extend the range of activities by including additional government agencies, regions, or sectors. Reporting of the collected data and funding will be done with stakeholders to share and pool knowledge. [ABSTRACT FROM AUTHOR]

The quick proliferation of pandemic diseases has been imposing many concerns on the international health infrastructure. To combat pandemic diseases in smart cities, Artificial Intelligence of Things (AIoT) technology, based on the integration of artificial intelligence (AI) with the Internet of Things (IoT), is commonly used to promote efficient control and diagnosis during the outbreak, thereby minimizing possible losses. However, the presence of multi-source institutional data remains one of the major challenges hindering the practical usage of AIoT solutions for pandemic disease diagnosis. This paper presents a novel framework that utilizes multi-site data fusion to boost the accurateness of pandemic disease diagnosis. In particular, we focus on a case study of COVID-19 lesion segmentation, a crucial task for understanding disease progression and optimizing treatment strategies. In this study, we propose a novel multi-decoder segmentation network for efficient segmentation of infections from cross-domain CT scans in smart cities. The multi-decoder segmentation network leverages data from heterogeneous domains and utilizes strong learning representations to accurately segment infections. Performance evaluation of the multi-decoder segmentation network was conducted on three publicly accessible datasets, demonstrating robust results with an average dice score of 89.9% and an average surface dice of 86.87%. To address scalability and latency issues associated with centralized cloud systems, fog computing (FC) emerges as a viable solution. FC brings resources closer to the operator, offering low latency and energy-efficient data management and processing. In this context, we propose a unique FC technique called PANDFOG to deploy the multi-decoder segmentation network on edge nodes for practical and clinical applications of automated COVID-19 pneumonia analysis. The results of this study highlight the efficacy of the multi-decoder segmentation network in accurately segmenting infections from cross-domain CT scans. Moreover, the proposed PANDFOG system demonstrates the practical deployment of the multi-decoder segmentation network on edge nodes, providing real-time access to COVID-19 segmentation findings for improved patient monitoring and clinical decision-making. [ABSTRACT FROM AUTHOR]

Fog computing has evolved as a promising computing paradigm to support the execution of latency-sensitive Internet of Things (IoT) applications. The mobile devices connected to the fog environment are resource constrained and non-stationary. In such environments, offloading mobile user's computational task to nearby fog servers is necessary to satisfy the QoS requirements of time-critical IoT applications. Moreover, the fog servers are also susceptible to numerous attacks which induce security and privacy issues.Offloading computation task to a malicious fog node affects the integrity of users' data. Despite the fact that there are many integrity-preserving strategies for fog environments, the majority of them rely on a reliable central entity that might have a single point of failure. Blockchain is a promising strategy that maintains data integrity in a decentralized manner. The state-of-art blockchain offloading mechnanisms have not considered the mobility during secure offloading process. Besides, it is necessary to ensure QoS constraints of the IoT applications while considering mobility of user devices. Hence, in this paper, Blockchain assisted Mobility-aware Secure Computation Offloading (MSCO) mechanism is proposed to choose the best authorized fog servers for offloading task with minimal computational and energy cost. To address the optimization issue, a hybrid Genetic Algorithm based Particle Swarm Optimization technique is employed. Experimental results demonstrated the significant improvement of MSCO when compared to the existing approaches in terms of on average 11 % improvement of total cost which includes the parameters of latency and energy consumption. [ABSTRACT FROM AUTHOR]

Healthcare data analysis has become essential after epidemic outbreaks. The manual examination of medical images such as X-rays and computed tomography (CT) scans became one of these challenges. This paper introduces a healthcare architecture that tackles the analysis efficiency and accuracy challenges by harnessing artificial intelligence (AI) capabilities. This architecture utilizes fog computing and presents a modified convolutional neural network (CNN) designed specifically for image analysis. Different architectures of CNN layers are thoroughly explored and evaluated to optimize overall performance. To demonstrate the effectiveness of the proposed approach, a dataset of X-ray images is utilized for analysis and evaluation. Comparative assessments are conducted against recent models such as VGG16, VGG19, MobileNet, and related research papers. Notably, the proposed approach achieves an exceptional accuracy rate of 99.88% in classifying normal cases, accompanied by a validation rate of 96.5%, precision and recall rates of 100%, and an F1 score of 100%. These results highlight the immense potential of fog computing and modified CNNs in revolutionizing healthcare image analysis and diagnosis, not only during pandemics but also in the future. By leveraging these technologies, healthcare professionals can improve the efficacy and accuracy of medical image analysis, leading to improved patient care and outcomes. [ABSTRACT FROM AUTHOR]

The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like ChatGPT and Bard and technologies like nanotechnology. This paper introduces the marketing community to a recent computing development: IoT-driven fog computing (FC). Although numerous research studies have been published on FC "smart" applications, none hitherto have been conducted on fog-based smart marketing domains such as recommender systems. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous consumer behavior applications requiring real-time data-driven decision making. This paper provides a conceptual framework for studying the effects of fog computing on consumer behavior, with the goal of stimulating future research by using, as an example, the intersection of FC and RS. Indeed, our conceptualization of the "fog-based recommender systems" opens many novel and challenging avenues for academic research, some of which are highlighted in the later part of this paper. [ABSTRACT FROM AUTHOR]

This study presents a novel web-based learning platform that leverages human-computer intelligent interaction to enhance English communication skills. The platform integrates cutting-edge technologies to create an immersive learning experience, combining natural language processing, speech recognition, and interactive exercises. Learners engage in real-time conversations with virtual tutors, receive personalized feedback, and access a vast repository of educational resources. The platform not only facilitates language acquisition but also encourages self-paced learning, making it a valuable tool for both educators and students. By harnessing the power of artificial intelligence, this web-based platform represents a significant advancement in the realm of English language education. To overcome these issues this paper proposed SVM with an improved satin Bower bird optimization algorithm (SVM-ISBBO). SVM-ISBBO uses fog computing services that minimize the latency and speeds up the process, effectively handling huge wearable devices. In this proposed work SVM-ISBBO monitors the students communication, vocal parameters, blood pressure, etc, and these values are obtained from wearable sensor devices and their notifications are sent back to teachers. Teachers diagnosed the student information and sent back the alert notifications to the students for taking proper medications. All this information is stored in fog-based cloud storage in a secure manner. The accuracy rate of KNN got 78.56%, NB got 81.74%, SVM got 85.15% and the proposed work of SVM-ISBBO got 92.34%. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) technology is progressively maturing because of the growth of private cars and the establishment of intelligent transportation systems. The development of smart cars has, therefore, been followed by a parallel rise in the volume of media and video games in the automobile and a massive increase in the need for processing resources. Smart cars cannot process the enormous quantity of requests created by vehicles because they have limited computing power and must maintain many outstanding jobs in their queues. The distribution of edge servers near the customer side of the highway may also accomplish real-time resource requests, and edge servers can assist with the shortage of computational power. Nevertheless, the substantial amount of energy created while processing is also an issue we must address. A joint task offloading strategy based on mobile edge computing and fog computing (EFTO) was presented in this paper to address this problem. Practically, the position of the processing activity is first discovered by obtaining the computing task's route, which reveals all the task's routing details from the starting point to the desired place. Next, to minimize the time and time expended during offloading and processing, a multi-objective optimization problem is implemented using the task offloading technique F-TORA based on the Takagi–Sugeno fuzzy neural network (T-S FNN). Finally, comparative trials showing a decrease in time consumed and an optimization of energy use compared to alternative offloading techniques prove the effectiveness of EFTO. [ABSTRACT FROM AUTHOR]

Fog computing is today considered a promising candidate to improve the user experience in dynamic on-demand computing services. However, its ubiquitous application would require support for this service in wireless multi-hop mesh systems, where the use of conventional IP-based solutions is challenging. As a complementary solution, in this paper, we consider a Named-Data Networking (NDN) approach to enable fog computing services in autonomous dynamic mesh formations. In particular, we jointly implement two critical mechanisms required to extend the NDN-based fog computing architecture to wireless mesh systems. These are (i) dynamic face management systems and (ii) a learning-based route discovery strategy. The former makes it possible to solve NDN issues related to an inability to operate over a broadcast medium. Also, it improves the data-link layer reliability by enabling unicast communications between mesh nodes. The learning-based forwarding strategy, on the other hand, efficiently reduces the amount of overhead needed to find routes in the dynamically changing mesh networks. Our numerical results show that, for static wireless meshes, our proposal makes it possible to fully benefit from the computing resources sporadically available up to several hops away from the consumer. Additionally, we investigate the impacts of various traffic types and NDN caching capabilities, revealing that the latter result in much better system performance while the popularity of the compute service contributes to additional performance gains. [ABSTRACT FROM AUTHOR]

The Smart Grid (SG) heavily depends on the Advanced Metering Infrastructure (AMI) technology, which has shown its vulnerability to intrusions. To effectively monitor and raise alarms in response to anomalous activities, the Intrusion Detection System (IDS) plays a crucial role. However, existing intrusion detection models are typically trained on cloud servers, which exposes user data to significant privacy risks and extends the time required for intrusion detection. Training a high-quality IDS using Artificial Intelligence (AI) technologies on a single entity becomes particularly challenging when dealing with vast amounts of distributed data across the network. To address these concerns, this paper presents a novel approach: a fog-edge-enabled Support Vector Machine (SVM)-based federated learning (FL) IDS for SGs. FL is an AI technique for training Edge devices. In this system, only learning parameters are shared with the global model, ensuring the utmost data privacy while enabling collaborative learning to develop a high-quality IDS model. The test and validation results obtained from this proposed model demonstrate its superiority over existing methods, achieving an impressive percentage improvement of 4.17% accuracy, 13.19% recall, 9.63% precision, 13.19% F1 score when evaluated using the NSL-KDD dataset. Furthermore, the model performed exceptionally well on the CICIDS2017 dataset, with improved accuracy, precision, recall, and F1 scores reaching 6.03%, 6.03%, 7.57%, and 7.08%, respectively. This novel approach enhances intrusion detection accuracy and safeguards user data and privacy in SG systems, making it a significant advancement in the field. [ABSTRACT FROM AUTHOR]

Due to dynamic changes, applications designed for the Internet of Things (IoT) require new storage resources. Storing IoT data in a fog infrastructure is a common practice to guarantee the IoT application's high performance. Multiple fog nodes spread the data replica to accommodate multiple users and reduce access latency. However, such data replicas are subjected to intentional and accidental corruption. This paper proposes a data integrity verification approach in a distributed fog environment. The proposed work uses fuzzy clustering and consensus mechanism to verify the integrity of multiple replicas stored on distributed fog nodes. Security analysis shows that the proposed approach is highly resistant to replace, replay, and forgery attacks. Performance analysis demonstrates the practicability of the proposed approach in a fog environment. [ABSTRACT FROM AUTHOR]

Smart home and building systems are popular solutions that support maintaining comfort and safety and improve energy efficiency in buildings. However, dynamically developing distributed network technologies, in particular the Internet of Things (IoT), are increasingly entering the above-mentioned application areas of building automation, offering new functional possibilities. The result of these processes is the emergence of many different solutions that combine field-level and information and communications technology (ICT) networks in various configurations and architectures. New paradigms are also emerging, such as edge and fog computing, providing support for local monitoring and control networks in the implementation of advanced functions and algorithms, including machine learning and artificial intelligence mechanisms. This paper collects state-of-the-art information in these areas, providing a systematic review of the literature and case studies with an analysis of selected development trends. The author systematized this information in the context of the potential development of building automation systems. Based on the conclusions of this analysis and discussion, a framework for the development of the Generic IoT paradigm in smart home and building applications has been proposed, along with a strengths, weaknesses, opportunities, and threats (SWOT) analysis of its usability. Future works are proposed as well. [ABSTRACT FROM AUTHOR]

In order to enhance resource allocation in fog computing networks and establish an energy-aware service, this paper proposes a user satisfaction-based energy-saving computation offloading mechanism that jointly optimizes service decision, task offloading ratio, uplink bandwidth resource ratio, and computing resource ratio. Specifically, the proposed mechanism takes user satisfaction as a priority. It constructs a novel satisfaction function that considers the historical energy consumption distribution to capture the user's subjective perception of the service quality. Then, we develop a user satisfaction-based service decision (US-SD) algorithm to select unique service nodes for the users. Furthermore, to minimize the processing energy consumption, a subtask partition and resource allocation-based intelligent computation offloading (SPRA-ICO) algorithm is proposed. In such an algorithm, we design an innovative actor-critic network structure and add noise to the continuous output action to guarantee the randomness of deterministic policy exploration. Meanwhile, the experience replay buffer mechanism and parameter soft update operation are comprehensively employed to reduce the mutual guidance of training samples and improve the function convergence performance. Finally, the simulation results show that compared with other benchmark schemes, the proposed mechanism can realize good convergence speed and user retention rate while effectively mitigating the total energy consumption. [ABSTRACT FROM AUTHOR]

Background and Objectives: With the increase in population in the world along with the decrease in natural resources, agricultural land, and the increase of unpredictable environmental conditions, causes concerns in the field of food supply, which is one of the serious concerns for all countries of the world. Therefore, the agricultural industry has moved towards smart agriculture. Smart agriculture uses the Internet of Things, which uses different types of sensors to collect data (such as temperature, humidity, light, etc.), a communication network to send and receive data, and information systems to manage and analyze data. Smart agriculture deals with a huge amount of data collected from farms, which has fundamental challenges for analysis using old systems such as lack of storage space, and processing delay. The Computational paradigm is a key solution to solve the problems of time delays, security, storage space management, and real-time analysis. Computing paradigms include cloud, fog, and edge computing, which by combining each of them in smart agriculture has caused a great transformation in this industry. The purpose of this article is to provide a comprehensive review of the architecture of computing paradigms in smart agriculture applications. Methods: To achieve the goals of this article, the methodology is divided into two parts: article selection and review of the selected articles. The computational paradigms used in the selected articles are from 2019 to 2022. Each selected paper is then reviewed in detail in terms of categories of computing paradigms, architectures, key points, advantages, and challenges. Results: Computational paradigms have significant advantages. Combining these paradigms in a complementary way covers many challenges. The architecture based on the combination of edge-fog-cloud computing is one of the best architectures combined with smart agriculture. Conclusion: By combining computing paradigms and smart agriculture, the challenges based on traditional and old systems are overcome. Combining these paradigms complement each other's challenges. [ABSTRACT FROM AUTHOR]

Fog computing is a type of distributed computing that makes data storage and computation closer to the network edge. While fog computing offers numerous advantages, it also introduces several challenges, particularly in terms of security. Intrusion Detection System (IDS) plays a crucial role in securing fog computing environments by monitoring network traffic and system activities for signs of malicious behavior. Several techniques can be employed to enhance intrusion detection in fog computing environments. Accordingly, this paper proposes a Shepard Neuro-Fuzzy Network (ShNFN) for intrusion detection in fog computing. Initially, in the cloud layer, the input data are passed to data transformation to transform the unstructured data into structured form. Here, data transformation is done employing the Box-Cox transformation. Following this, the feature selection is done in terms of information gain and symmetric uncertainty process and it is used to create a relationship between two variables. After that, the data are classified by employing the proposed ShNFN. The ShNFN is attained by fusing two networks, such as Cascade Neuro-Fuzzy Network (Cascade NFN) and Shepard Convolutional Neural Networks (ShCNN). After this, the physical process is executed at the endpoint layer. Finally, intrusion detection is accomplished in the fog layer by the proposed ShNFN method. The performance of the intrusion detection using ShNFN is calculated by the metrics of recall, F-measure and precision. The proposed method achieves the values of 93.3%, 92.5% and 94.8% for recall, F-measure, and precision, respectively. [ABSTRACT FROM AUTHOR]

With the emergence of fog computing, new paradigms for data processing and management for IoT devices have been established in the quickly changing world of teaching/learning. This study addresses the complex issues brought about by the infiltration of diverse data sources by investigating novel approaches to strengthen data security and enhance access control mechanisms in fog computing environments. The commonly used cryptographic technique known as CP‐ABE is renowned for providing accurate access control. Unfortunately, current multi‐authority CP‐ABE methods have difficulties when implemented on low‐resource IoT devices. These techniques are not appropriate for resource‐constrained IoT devices since the sizes of the secret key and ciphertext grow in proportion to the number of attributes. In this paper, a novel multi‐authority CP‐ABE approach, called MA‐based CP‐ABE, efficiently tackles these issues by optimizing the length of secret keys and ciphertext. Users' secret keys are always the same size, no matter how many attributes they own. Moreover, MA‐based CP‐ABE ensures that the size of the ciphertext scales linearly with the number of authorities rather than characteristics, which makes it a sensible option for devices with restricted resources. A Generative AI approach has also been integrated along with CP‐ABE to make sure that the IoT data is secure and privacy is maintained. As per the security and experimental analysis, the proposed approach is considered secure and suitable for IoT‐based applications. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has emerged as a cornerstone technology, transforming how we interact with our surroundings. Despite their widespread adoption, IoT devices encounter challenges related to processing capabilities and connectivity, frequently necessitating the delegation of tasks to remote cloud servers. This offloading, essential for enhancing user experience, poses challenges, particularly for latency-sensitive applications. Edge-centric paradigms like fog and mist computing have emerged to address these challenges, bringing computational resources closer to end-users. However, efficiently managing task offloading in dynamic IoT environments remains a complex issue. This paper introduces DIstributed COmputing for MIST (dicomist), a methodology designed to facilitate task offloading in IoT settings. Dicomist utilizes wireless mesh networks to organize mobile nodes, employing clustering and classification techniques. Tasks are treated as consensus problems, enabling distributed computation among selected nodes. Real-world experiments demonstrate dicomist's effectiveness, underscoring its potential to enhance task offloading in IoT environments. [ABSTRACT FROM AUTHOR]

Abstract Load balancing is crucial in distributed systems like fog computing, where efficiency is paramount. Offloading with different approaches is the key to balancing the load in distributed environments. Static offloading (SoA) falls short in heterogeneous networks, necessitating dynamic offloading to reduce latency in time-sensitive tasks. However, prevalent dynamic offloading (PoA) solutions often come with hidden costs that impact sensitive applications, including decision time, networks congested and distance offloading. This paper introduces the Hybrid Offloading (HybOff) algorithm, which substantially enhances load balancing and resource utilization in fog networks, addressing issues in both static and dynamic approaches while leveraging clustering theory. Its goal is to create an uncomplicated low-cost offloading approach that enhances IoT application performance by eliminating the consequences of hidden costs regardless of network size. Experimental results using the iFogSim simulation tool show that HybOff significantly reduces offloading messages, distance, and decision-offloading consequences. It improves load balancing by 97%, surpassing SoA (64%) and PoA (88%). Additionally, it increases system utilization by an average of 50% and enhances system performance 1.6 times and 1.4 times more than SoA and PoA, respectively. In summary, this paper tries to introduce a new offloading approach in load balancing research in fog environments.

This paper presents a monitoring, control, and management system that allows the interoperability of multiple smart gateways in a greenhouse to bolster automatic decision making on water supplied to the crops in real-time. The system consists of several sensor nodes interconnected by Zigbee to measure ambient and soil parameters to control the greenhouse irrigation process. In addition, a web service is included to help the system manager to visualize and operate the greenhouse on a web platform. The system also supports human decision making to cope with undesirable and unexpected environmental events. Smart gateways enable data reading, storage and edge computing using automatic rules to manage actions in the greenhouse, as well as data transmission for centralized processing in cloud services. The service communication system layer is supported by choreography middleware for messaging distribution, which enables agile scalability. This research proposes a hybrid scheme and compares to a centralized scheme. The latency times in the choreography system were analyzed to find a suitable solution adapted to infrastructure deployment features. Numerical results shows that such an adaptive deployment of the irrigation services among edge and fog layers can reduce the computational load on the gateways, improve system response times, and enhance device performance. [ABSTRACT FROM AUTHOR]

The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like ChatGPT and Bard and technologies like nanotechnology. This paper introduces the marketing community to a recent computing development: IoT-driven fog computing (FC). Although numerous research studies have been published on FC “smart” applications, none hitherto have been conducted on fog-based smart marketing domains such as recommender systems. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous consumer behavior applications requiring real-time data-driven decision making. This paper provides a conceptual framework for studying the effects of fog computing on consumer behavior, with the goal of stimulating future research by using, as an example, the intersection of FC and RS. Indeed, our conceptualization of the “fog-based recommender systems” opens many novel and challenging avenues for academic research, some of which are highlighted in the later part of this paper.

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations. [ABSTRACT FROM AUTHOR]

Cloud computing provides each consumer with a large-scale computing tool. Different Cyber Attacks can potentially target cloud computing systems, as most cloud computing systems offer services to many people who are not known to be trustworthy. Therefore, to protect that Virtual Machine from threats, a cloud computing system must incorporate some security monitoring framework. There is a tradeoff between the security level of the security system and the performance of the system in this scenario. If strong security is needed, then the service of stronger security using more rules or patterns is provided, since it needs much more computing resources. A new way of security system is introduced in this work in cloud environments to the VM on account of resources allocated to customers are ease. The main spike of Fog computing is part of the cloud server's work in the ongoing study tells the stepby- step cloud server to change the tremendous measurement of information because the endeavor apps are relocated to the cloud to keep the framework cost. The cloud server is devouring and changing a huge measure of information step by step to reduce complications. The Medical Data Health-Care (MDHC) records are stored in Cloud datacenters and Fog layer based on the guard intensity and the key is provoked for ingress the file. The monitoring center sustains the Activity Log, Risk Table, and Health Records. Cloud computing and Fog computing were combined in this paper to review data movement and safe information about MDHC. [ABSTRACT FROM AUTHOR]

Nowadays, data syncing before switchover and migration are two of the most pressing issues confronting cloud-based architecture. The requirement for a centrally managed IoT-based infrastructure has limited scalability due to security problems with cloud computing. The fundamental factor is that health systems, such as health monitoring, etc., demand computational operations on large amounts of data, which leads to the sensitivity of device latency emerging during these systems. Fog computing is a novel approach to increasing the effectiveness of cloud computing by allowing the use of necessary resources and close to end users. Existing fog computing approaches still have several drawbacks, including the tendency to either overestimate reaction time or consider result correctness, but managing both at once compromises system compatibility. To focus on deep learning algorithms and automated monitoring, FETCH is a proposed framework that connects with edge computing devices. It provides a constructive framework for real-life healthcare systems, such as those treating heart disease and other conditions. The suggested fog-enabled cloud computing system uses FogBus, which exhibits benefits in terms of power consumption, communication bandwidth, oscillation, delay, execution duration, and correctness. [ABSTRACT FROM AUTHOR]

The rapid proliferation of Internet of Things (IoT) devices across various industries has revolutionized the way we interact with technology. However, this widespread adoption has also brought about significant security challenges that must be addressed to ensure the integrity and confidentiality of data transmitted and processed by IoT systems. This survey paper delves into the diverse array of security threats faced by IoT devices and networks, ranging from data breaches and unauthorized access to physical tampering and denial-of-service attacks. By examining the vulnerabilities inherent in IoT ecosystems, we highlight the importance of implementing robust security measures to safeguard sensitive information and ensure the reliable operation of connected devices. Furthermore, we explore cutting-edge technologies such as blockchain, edge computing, and machine learning as potential solutions to enhance the security posture of IoT deployments. Through a comprehensive analysis of existing security frameworks and best practices, this paper aims to provide valuable insights for researchers, practitioners, and policymakers seeking to fortify the resilience of IoT systems in an increasingly interconnected world.

In this paper, we propose the design, operation, and implementation of an Internet of Things-based hybrid structural health monitoring. This innovative system leverages the capabilities of both fog and cloud layers in computing and monitoring. The system architecture consists of leaf nodes deployed on a target structure. These nodes, synchronously, collect acceleration signals from accelerometers attached directly to the structure and transmit the data to an on-site central node via a short-range communication protocol. At the fog layer, the central node, applies damage detection algorithms on the collected data. If a damage is detected, it forwards the acceleration signals to a cloud-based monitoring server using cellular internet connectivity, where more complex algorithms are used to identify and locate the damage. We provide detailed information about the design of the different system nodes, the implementation of damage detection algorithms, and the architecture of the monitoring server. To evaluate the effectiveness of the proposed system, several practical experiments were conducted. The results demonstrate that the hybrid system presented in this paper provides an efficient, reliable and cost-effective approach to damage detection and identification in civil infrastructures.

The proliferation of Internet of Things (IoT) applications poses formidable challenges in managing data processing, privacy, and security. In response, technologies such as Fog Computing (FC), Blockchain (BC), and Federated Learning (FL) have emerged as promising solutions. Combining these technologies can broaden their scope, and impose novel challenges. This paper conducts a Systematic Literature Review (SLR) to investigate their integration within the IoT domain, systematically evaluating the current state-of-the-art by analyzing 40 papers against 38 extraction criteria, encompassing technical characteristics specific to FC, BC, FL, or their integration. The findings offer insights into the advantages, challenges, opportunities, and limitations of this integration, addressing data processing, privacy, and security concerns in IoT. By filling a research gap and directly examining FC, BC, and FL interoperability across architectural layers, this study contributes to knowledge expansion in the field. This paper proposes a novel framework for implementing FL and BC within FC environments for IoT applications, alongside a comprehensive synthesis of existing literature, distinguishing it from previous research efforts. Furthermore, it offers valuable insights into the current landscape, identifies research needs, and proposes future research directions. The framework and literature synthesis provided allow readers to access customized information on FC-BC-FL integration, aiding in designing and implementing robust IoT solutions.

Fog computing is a promising and challenging paradigm that enhances cloud computing by enabling efficient data processing and storage closer to data sources and users. This paper introduces a game-theoretic approach called GTRADPMFC (Game-Theoretic Resource Allocation and Dynamic Pricing Mechanism in Fog Computing) to address resource allocation and dynamic pricing challenges in fog computing environments with limited resources. The proposed model features non-cooperative competition among fog nodes for resources and dynamic pricing mechanisms to encourage efficient resource utilization. Theoretical analysis and simulations demonstrate that GTRADPMFC improves resource efficiency and overall fog computing system performance. Additionally, the paper discusses how to handle situations with insufficient samples and provide flexibility for users unable to meet completion time requirements. GTRADPMFC effectively manages resource allocation by establishing pricing in fog computing, considering potential delays in completion time. This is achieved through research, simulations, convergence analysis, complexity evaluation, and optimization guarantees.