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Preserving customers' expectations and understanding factors affecting their purchasing decisions are crucial in designing effective marketing and advertising strategies. However, constantly and swiftly changing the customers' interests and consumption behaviors make it inevitable to utilize sophisticated tools and approaches based on advanced technologies. Among them, by measuring the customers' physiological and neural signals, studying the customers' cognitive and affective responses to marketing stimuli, neuromarketing provides deep insight into the customers' motivations, preferences, and decisions. Recently, the internet of everything (IoE) has brought many new opportunities to the industry and has attracted the attention of many researchers in recent years. The main objective of this paper is to address how the IoE would empower neuromarketing techniques. Hence, an in-depth understanding of current research issues as well as emerging trends would help meet this goal. In this paper, a comprehensive review has been done by reviewing numerous journal and conference papers from various academic databases (sciencedirect.com, IEEE Xplore, Springer, Elsevier, Wiley, ACM digital library) based on applying various filtrations on specific keywords to identify and categorize the IoE devices in the neuromarketing field. In particular, we discussed the importance and the applications of IoE gadgets and devices, especially wearable medical technologies in eight neuromarketing techniques. Finally, we described critical existing challenges and limitations in neuromarketing and IoE gadgets that researchers have dealt with in this field of research. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an important virtual network that allows remote users to access linked multimedia devices. The development of IoT and its ubiquitous application across various domains of everyday life has led to continuous research efforts. Security is a perceptual concern for researchers involved in IoT as it is a key factor in the acceptance of any innovative technology. Numerous research studies have been conducted concentrating on the level of IoT security on a particular mechanism, on specific applications, or on categorizing vulnerabilities, in order to address a defined situation of securing an IoT network. This present paper aims to comprehensively review potential solutions for securing IoT, between emerging and traditional mechanisms, such as blockchain, machine learning, cryptography, and quantum computing. This study provides a comparative analysis of related papers with their characteristics, pros and cons. Accordingly, it taxonomizes relevant solutions based on their achieved security requirements. Furthermore, the potential benefits and challenges of each of the four mechanisms are discussed. [ABSTRACT FROM AUTHOR]

A blockchain is one distributed ledger system and keeps the high redundancy of ledger copies to make the assurance of network security. However, the continuously duplicated full copies also impose a tremendous amount of demand on some nodes for data storage. The development of blockchain technologies in the Internet of things (IoT) application scenario is hampered by the restricted storage capacities of terminal devices used in the IoT edge computing scenario, which makes it difficult to load the full copy with infinite growth. Our paper suggests a collaborative ledger storing model based on Chord Ring to address the issues with lightweight blockchains in data storage. On-chain blocks are split by Chord Ring structure and stored in various node clusters in a decentralized manner, and off-chain blocks at various levels are provided with proof-of-work consensus asynchronously and archived centrally to the cloud storage on a regular basis. The theoretical and experimental analysis indicates that this model can reduce the data storage redundancy of blockchains while ensuring the high availability of data and the high decentralization of the network. [ABSTRACT FROM AUTHOR]

This paper introduces a sophisticated approach to network security, with a primary emphasis on utilizing deep learning for intrusion detection. In real-world scenarios, the high dimensionality of training data poses challenges for simple deep learning models and can lead to vanishing gradient issues with complex neural networks. Additionally, uploading network traffic data to a central server for training raises privacy concerns. To tackle these issues, the paper introduces a Residual Network (ResNet)-based deep learning model trained using a federated learning approach. The ResNet effectively tackles the vanishing gradient problem, while federated learning enables multiple Internet Service Providers (ISPs) or clients to engage in joint training without sharing their data with third parties. This approach enhances accuracy through collaborative learning while maintaining privacy. Experimental results on the X-IIoTID dataset indicate that the proposed model outperforms conventional deep learning and machine learning methods in terms of accuracy and other metrics used for evaluation. Specifically, the proposed methodology achieved 99.43% accuracy in a centralized environment and 99.16% accuracy in a federated environment. [ABSTRACT FROM AUTHOR]

In recent years, the development of IOTA, a new type of Distributed Ledger (DL) for internet of things (IoT), has gained significant attention. IOTA DL offers key features like scalability, fast and free transactions, making it an optimal choice for IoT devices. However, a major concern with IOTA DL is its reliance on a single coordinator for transaction confirmation. This default coordinator introduces issues of single point of failure and incomplete distribution. To address these limitations, this paper proposes the Multiple Coordinator Selection (MCS) algorithm. MCS aims to overcome the problem by involving multiple coordinators in the consensus process. Four metrics, namely "trust level," "distance from input transactions," "node activity," and "transaction distribution," are defined as properties for coordinator selection. Additionally, a checklist is employed to minimize the probability of collusion within the system. Furthermore, the paper introduces a three-layered architecture based on cloud and fog computing, where the MCS algorithm is implemented. Experimental results demonstrate improved security and distribution of the system, while reducing the chances of collusion and single point of failure. [ABSTRACT FROM AUTHOR]

The intrusion detection system is responsible for revealing different intrusion activities, including the denial of service, man-in-middle, Mirai, Scan, and other types of intrusion activities. It is used in many applications, including the smart home Internet of Things networks, where security risks threaten the privacy of individuals. In this context, many works were proposed for detecting and classifying the different types of attacks. However, many challenges are identified for this type of problem, such as the large amount of data available, the imbalanced nature of the data, and the quality of detection and classification outcomes. This paper aims to address these challenges by proposing an approach that considers a metaheuristic-based random weight neural network to detect intrusion activities and classify the different types and subtypes of activities. The following points summarize the contribution of this paper. First, the automatic tuning of the neural network parameters where the weights, biases, regularization value, the number of neurons, and the type of activation function are optimized by different metaheuristic algorithms to produce high-quality results. Second, the proposed approach adopts a clustering with reduction technique to tackle the challenge of processing large volumes of data. Third, oversampling the dataset is also embedded in the proposed approach to avoid a biased classification of the majority class. The experiments are conducted based on a large dataset with more than half a million instances. The results show that the proposed approach outperforms the other classification approaches in geometric mean (G-Mean) and has promising results. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is increasingly becoming widespread in different areas such as healthcare, transportation, and manufacturing. IoT networks comprise many diverse entities, including smart small devices for capturing sensitive information, which may be attainable targets for malicious parties. Thus security and privacy are of utmost importance. To protect the confidentiality of data handled by IoT devices, conventional cryptographic primitives have generally been used in various IoT security solutions. While these primitives provide just an acceptable level of security, they typically neither preserve privacy nor support advanced functionalities. Also, they overly count on trusted third parties because of some limitations by design. This multidisciplinary survey paper connects the dots and explains how some advanced cryptosystems can achieve ambitious goals. We begin by describing a multi-tiered heterogeneous IoT architecture that supports the cloud, edge, fog, and blockchain technologies and assumptions and capabilities for each layer. We then elucidate advanced encryption primitives, namely wildcarded, break-glass, proxy re-encryption, and registration-based encryption schemes, as well as IoT-friendly cryptographic accumulators. Our paper illustrates how they can augment the features mentioned above while simultaneously satisfying the architectural IoT requirements. We provide comparison tables and diverse IoT-based use cases for each advanced cryptosystem as well as a guideline for selecting the best one in different scenarios and depict how they can be integrated. [ABSTRACT FROM AUTHOR]

Health monitoring systems are improving with the development of the internet of things. This paper proposes a secure architecture consisting of a four-layer internet of things enabled health monitoring system that collects patient data and classifies them into different medical categories. While collecting patient information from their wearable smart sensing devices for computation, the privacy and security of this process are essential. The main motive of this paper is to develop a lightweight and secure communication protocol using blockchain architecture for decentralized IoT networks and classify them into different categories using transfer learning. We propose a framework that uses blockchain for security and incorporates transfer learning to use multiple pre-trained models. The proposed routing technique uses factors like probability, credibility rating, and node energy to route the data to its destination such that the network overhead is reduced and the energy used is minimal. We classify the collected patient information using four different pre-trained convolutional neural network models: ResNet50, VGG19, InceptionV3, and SqueezeNet. We simulate the proposed routing approach and other benchmark schemes on various performance metrics. The results show that the proposed approach gives 92.24% classification accuracy. [ABSTRACT FROM AUTHOR]

The cloud of things (CloudIoT) represents a general system of supporting infrastructure for storing and processing information gathered from smart objects and their communications.There are many resources used to respond to requests in the CloudIoT environment. Therefore, a primary challenge in these systems is resource discovery based on the requests. Discovering and accessing resources, overcoming user constraints, and focusing on dynamic requirements, such as failure nodes, are the most critical issues to be addressed. This paper focuses on several resource discovery mechanisms using the systematic literature review method in the CloudIoT environment. The research aims at analyzing and reviewing studies published from 2016 to 2021 (June) on resource discovery in CloudIoT. The technical classification of resource discovery is based on selected studies by considering architecture, algorithm, middleware, and protocol approaches. These studies are discussed in terms of their main ideas, advantages, and weaknesses. Finally, future research opportunities related to resource discovery in the cloud of things are identified. [ABSTRACT FROM AUTHOR]

While traveling, people need to utilize smart tourism service systems based on the Internet of Things (IoT) to enhance their journeys' convenience. However, existing IoT-based smart tourism service systems still possess certain shortcomings. In light of this, this paper delves into the integration of IoT and machine learning (ML) technologies to improve the design of smart tourism service systems and establish a platform for intelligent tourism services. Simulated datasets are employed to evaluate the performance and functionality of the smart tourism service system. The primary contributions, as inferred from the test results, are as follows: In area 1, the model achieves a congestion recognition rate of 92.6% with an error rate of only 1.5% in congestion judgment. In area 2, congestion is accurately identified 93.5% of the time, with a misjudgment rate of just 1.2%. In area 3, the model achieves a congestion identification rate of 94.2% along with a 1.3% misjudgment rate, effectively identifying congestion situations in scenic spots. When comparing the research and design system with the system based on the support vector machine (SVM) algorithm, the results demonstrate that the smart tourism service system designed in this paper, leveraging IoT technology and ML algorithms, achieves higher prediction accuracy than the SVM-based system. These findings can serve as a valuable guide for designing future smart tourism service systems. [ABSTRACT FROM AUTHOR]

The Internet of Things has increased the demand for data, which has been met through the use of IoT-assisted cloud computing. However, this paradigm introduces new security complexities regarding the exchange of data between entities. In this paper, we investigate a secure strategy for managing IoT data in a cloud-assisted environment, protecting data privacy during data collection, storage, and access. Our method for mitigating the impact of IoT scalability is meticulously devised and supported by empirical evidence. Our productivity model is based on key operations, configurations, and efficiency factors. Our proposed method makes a substantial contribution to both system scalability and user data privacy, surpassing previous scale levels by a significant margin. In particular, our research investigates a secure strategy for managing IoT data in a cloud-assisted environment. We believe that our research will assist in bridging the divide between infrastructure, development, and testing teams, resulting in robust and stable productivity software. Our findings demonstrate the viability and efficacy of our proposed method, which outperforms previous models and previous research. [ABSTRACT FROM AUTHOR]

In this paper, a novel blockchain-based multilevel security and authentication application is proposed to address the problem of counterfeiting in the medicine supply chain. In this blockchain-enabled application, multiple computers owned by different supply chain organizations collaborate in a distributed fashion to establish a trustworthy network. By adopting a resource-efficient Hyperledger Fabric framework, the proposed system aims to enhance transparency, integrity, and traceability in the pharmaceutical supply chain. Hyperledger Fabric blockchain also adds network security, privacy preservation, real-time tracking, and reliability features to the proposed system. Moreover, the proposed system also incorporates a blockchain-based QR code watermarking layer for authentication and verification. A validation module is designed that empowers buyers to validate the identity and history of serialized medicinal products, and buyer identity validation ensures that only legitimate buyers can access the products. Simulation outcomes and performance measurements demonstrate the effectiveness of the proposed system, including location traceability, QR code authentication, and verification. The Caliper tool has been used to measure the performance of the proposed system in terms of execution time, throughput, latency, and resource statistics for up to 100,000 transactions and 20 peers. The highest throughput achieved is 417.5 TPS (transactions per second) with 100,000 transactions and eight peers. The QR code authentication performance is tested under various noisy, cropped, and blurred attacks. We presented simulation outcomes of the implemented supply chain as well as chain code algorithms. The results indicate improved scalability, validation mechanisms, throughput, latency, and resource consumption compared to existing schemes. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) paradigm has emerged to connect the number of devices using the Internet resulting in the deployment of smart cities. Cloud computing has been applied to execute the computational demands of IoT devices by collecting data from the physical environment of a smart city. However, cloud computing could not become a proper choice for latency-sensitive applications because of remote cloud data centers. To overcome this challenge, fog computing has emerged to deal with the inherent limitations of cloud computing environment through provision of computing to the edge of a network. However, resource allocation of IoT service requests among fog nodes is considered as an NP-hard problem, which should be addressed in the fog computing environment. In this paper, an efficient optimization approach based on improved binary particle swarm optimization (IBPSO) algorithm has been provided for resource allocation of IoT requests in the hybrid fog–cloud computing environment. The proposed method aims to reduce the service request latency with ensuring load balancing among fog nodes. The performance of the proposed algorithm has been compared by the binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), binary grey wolf optimization (BGWO)-based, and ranked-based resource allocation methods in terms of latency, missed deadline requests, run time, and load balancing. The results show that the proposed algorithm outperformed with an average of around 11 % , 22 % , 21 % , and 22 % percent in the IBPSO-based method rather than the BGA-based, BPSO-based, BGWO-based, and ranked-based resource allocation methods, respectively. Moreover, the resource allocation based on IBPSO achieved around 11 % , 28 % , 27 % , and 25 % decline in total latency compared to the BGA-based, BPSO-based, BGWO-based, and ranked-based resource allocation methods. Furthermore, the run time of the proposed algorithm could enhance by 45 % , 9 % , and 8 % compared to the BGA-based, BPSO-based, and BGWO-based resource allocation methods. [ABSTRACT FROM AUTHOR]

Power optimization has become a major concern for most digital hardware designers, particularly in early design phases and especially in limited power budget systems (battery-operated hand-held devices, electro-optical pluggable modules, IoT and green energy systems, etc.). Subsequently, early power consumption estimation at design time is crucial for power optimization. The aim of this paper is to present an overview of high-level power estimation techniques currently available along with a comprehensive comparison between different methodologies and their applications on estimated models. When high speed and high performance are key features of a specific embedded system, increase in energy consumption becomes the main hurdle to be tackled while keeping speed/performance v/s power consumption trade-off at a minimum. This paper provides designers, interested in power consumption modeling, with knowledge on how to select best power estimation techniques applied to designated target models. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) is emerging as a prime area of research in the modern era. The significance of IoT in the daily life is increasing due to the increase in objects or things connected to the internet. In this paper, routing protocol for low power and lossy networks (RPL) is examined on the Contiki operating system. This paper used RPL attack framework to simulate three RPL attacks, namely hello-flood, decreased-rank and increased-version. These attacks are simulated in a separate and simultaneous manner. The focus remained on the detection of these attacks through artificial neural network (ANN)-based supervised machine learning approach. The accurate detection of the malicious nodes prevents the network from the severe effects of the attack. The accuracy of the proposed model is computed with hold-out approach and tenfold cross-validation technique. The hyperparameters have been optimized through parameter tuning. The model presented in this paper detected the aforesaid attacks simultaneously as well as individually with 100% accuracy. This work also investigated other performance measures like precision, recall, F1-score and Mathews correlation coefficient (MCC). [ABSTRACT FROM AUTHOR]

The Internet of Things is a rapidly evolving technology in which interconnected computing devices and sensors share data over the network to decipher different problems and deliver new services. For example, IoT is the key enabling technology for smart homes. Smart home technology provides many facilities to users like temperature monitoring, smoke detection, automatic light control, smart locks, etc. However, it also opens the door to new set of security and privacy issues, for example, the private data of users can be accessed by taking control over surveillance devices or activating false fire alarms, etc. These challenges make smart homes feeble to various types of security attacks and people are reluctant to adopt this technology due to the security issues. In this survey paper, we throw light on IoT, how IoT is growing, objects and their specifications, the layered structure of the IoT environment, and various security challenges for each layer that occur in the smart home. This paper not only presents the challenges and issues that emerge in IoT-based smart homes but also presents some solutions that would help to overcome these security challenges. [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]

With the gradual entry into the era of "Internet of Things", large-scale parallel and distributed systems play a crucial role, which motivates us to explore qualitative and quantitative metric to characterize the fault tolerance and reliability of these systems. A more accurate assessment about the fault tolerance and reliability of large-scale parallel and distributed systems can be achieved by h-extra edge-connectivity. It can dynamically measure the size of the separated component under large-scale faulty links. The use of cartesian product operations to assemble large-scale interconnection networks has received extensive attention due to easy operation and scalability. Given a recursively assembled interconnection network G, the distribution of its values of h-extra edge-connectivity is non-uniform. The h-extra edge-connectivity of G, if any, is well-defined for each positive integer 1 ≤ h ≤ ⌊ | V (G) | / 2 ⌋ . This paper focuses on the concentration phenomenon about h-extra edge-connectivity of the n-th cartesian product of interconnection network K 4 (denoted by λ h (K 4 n) ): as n → ∞ , for about 64.5% of positive integers h ≤ 2 × 4 n - 1 , the h-extra edge-connectivities of the n-th cartesian product of interconnection network K 4 are concentrated on 3n numbers. That is, at least [ 3 n g - 3 g t - g (g - 1) ] 4 t links must be deleted to disconnect network K 4 n , provided that the deletion of these links does not isolate any subnetwork with at most h - 1 processors for about 64.5% of positive integers h ≤ 2 × 4 n - 1 , 0 ≤ t ≤ n - 1 and g ∈ { 1 , 2 , 3 } . It is exactly equal to the minimum cardinality of links, such that whose removal will disconnect the network K 4 n with all its resulting components having at least g × 4 t processors. Our results provide a more precise way to evaluate the reliability of interconnection networks. [ABSTRACT FROM AUTHOR]

The management of diabetes involves much compliance, disease awareness, and patient empowerment. Blood glucose levels in people with diabetes are abnormal, resulting in various health problems that affect their kidneys, hearts, and vision. Monitoring the patient's health has the benefit of using the Internet of Things (IoT) and diabetes patient monitoring systems. Because this system uses machine learning to classify data, its value goes beyond patient monitoring. So, this paper proposes a novel deep learning model with an effective feature selection mechanism for diabetes mellitus (DM) prediction in IoT-based healthcare environment. The proposed work starts by collecting the real-time IoT data of the patients from the Pima Indian Diabetes database. The data imbalance problem of the collected dataset is rectified by applying the synthetic minority oversampling technique. Afterward, the balanced dataset is preprocessed by applying missing value imputation and data standardization to improve the classification performance. Then, the hybrid algorithm called k-means clustering-based sailfish optimization is utilized, which performs clustering and optimization to select the essential features from the preprocessed dataset. Finally, the selected features are fed into the kaiming and switan included bidirectional long short-term memory for DM prediction. The proposed system achieves better results than the existing state-of-the-art techniques, which shows its effectiveness in predicting DM in real-time IoT datasets. [ABSTRACT FROM AUTHOR]

The fog-assisted cloud computing gives better quality of service (QoS) to Internet of things (IoT) applications. However, the large quantity of data transmitted by the IoT devices results in the overhead of bandwidth and increased delay. Moreover, large amounts of data transmission generate resource management issues and decrease the system's throughput. This paper proposes the optimized task scheduling and preemption (OSCAR) model to overcome the limitations and improve the QoS. The dataset used for the study is a real-time crowd-based dataset which provides task information. The processes involved in this paper are as follows: (i) Initially, the tasks from the IoT devices are clustered based on the priority and deadline by implementing expectation–maximization (EM) clustering to decrease the computational complexity and bandwidth overhead. (ii) The clustered tasks are then scheduled by implementing a modified heap-based optimizer based on the QoS and service level agreement (SLA) constraints. (iii) Distributed resource management is performed by allocating resources to the tasks based on multiple constraints. The categorical deep Q network is the deep reinforcement learning model is implemented for this purpose. The dynamic nature of tasks from the IoT devices is addressed by performing preemption of tasks using the ranking method, where the tasks with higher priority, with a short deadline replaces less priority task by moving it into the waiting queue. The proposed model is experimented with in the iFogsim simulation tool and evaluated in terms of average response time, loss ratio, resource utilization, average makespan time, queuing waiting time, percentage of tasks satisfying the deadline and throughput. The proposed OSCAR model outperforms the existing model in achieving the QoS and SLA with maximal throughput and reduced response time. [ABSTRACT FROM AUTHOR]

In the development of industrial internet of things (IIoT), the application of mobile edge computing (MEC) has significantly enhanced the efficiency of task computation offloading. However, the risk of data privacy leakage persists during the computation offloading process. Considering the diversity of task data sensitivity and the variability in server security protection capabilities, this paper proposes a method for assessing privacy satisfaction. To ensure offloading efficiency while effectively enhancing privacy security, we have proposed an IIoT cloud-edge-device computation offloading algorithm based on dual-Q network deep reinforcement learning, named D2PCO, to optimize the computation offloading process in IIoT tasks. The incorporation of the dual-Q network notably enhances the algorithm's learning ability and efficiency in dealing with complex decision-making problems. Experimental results show that the proposed D2PCO algorithm significantly improves user privacy satisfaction while ensuring low delay. Compared with MA3MCO, DDPG, shortest distance priority, and random scheduling algorithms, it reduces the average offloading delay by 4.15%, 9.98%, 13.2%, and 26.47% and increases privacy satisfaction by 0.8%, 4.26%, 10.15%, and 30.30%, respectively. [ABSTRACT FROM AUTHOR]

With the popularization of Internet of Things applications, trillions of new devices with different application requirements, such as smart wearables and smart home appliances, will be connected to the network. Hence, it is crucial to ensure the data transmission security of these low-power and multifunctional sensor nodes. In this paper, a novel lightweight block cipher, GFSPX, is proposed for resource-constrained microdevices. The proposed algorithm combines a generalized Feistel structure with the substitution permutation networks structure to design the round function, which effectively addresses the inherent problem of slow diffusion in the traditional Feistel structure. Furthermore, the introduction of Addition or AND, Rotation, XOR operations in the round function to process part of the plaintext reduces the demand for hardware resource of the algorithm. The avalanche test results indicate that the GFSPX algorithm has strong diffusion and can satisfy the avalanche effect in just six rounds. The security analysis results verify the security of the GFSPX algorithm against differential and linear cryptanalysis attacks, algebraic attacks, structural attacks and key scheduling attacks. Finally, the performance analysis results indicate that the hardware implementation cost of GFSPX algorithm is relatively low, requiring only 1715 GE based on 0.13 micron logic process. In addition, the software implementation of this algorithm works well at an encryption rate of 12.31 Mb/s. [ABSTRACT FROM AUTHOR]

Increasing urban traffic and congestion have led to significant issues such as rising air pollution and wasted time, highlighting the need for an intelligent traffic light control (TLC) system to minimize vehicle waiting times. This paper presents a novel TLC system that leverages the Internet of Things (IoT) for data collection and employs the random forest algorithm for preprocessing and feature extraction. A deep belief network predicts future traffic conditions, and a support vector regression network is integrated to enhance prediction accuracy. Additionally, the traffic light control strategy is optimized using reinforcement learning. The proposed method is evaluated through two different scenarios. The first scenario is compared with fixed-time control and the double dueling deep neural network (3DQN) methods. The second scenario compares it with the SVM, KNN, and MAADAC approaches. Simulation results demonstrate that the proposed method significantly outperforms these alternative approaches, showing substantial improvements in average vehicle waiting times by more than 20%, 32%, and 45%, respectively. Using a deep belief network, supplemented by support vector regression, ensures high precision in forecasting traffic patterns. Furthermore, the reinforcement learning-based optimization of the traffic light control strategy effectively adapts to changing traffic conditions, providing superior traffic flow management. The results indicate that the proposed system can substantially reduce traffic congestion and improve urban traffic flow. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) connects objects that sense, communicate, and interact to achieve shared goals, and its integration with manufacturing has led to Industry 4.0. Security is crucial for user acceptance, involving data confidentiality, integrity, authentication, identity management, privacy, and trust among devices. Trust management is vital in IoT and IIoT for reliable services, accurate data collection, device authentication, and secure decision-making. This paper surveys blockchain-based trust mechanisms in IoT and IIoT, categorizing them into four types: basic trust frameworks, enhanced trust interaction, trust data management, and trust-based transaction validation. We analyze these mechanisms to identify their strengths, weaknesses, and current challenges. Blockchain shows promise in improving trust management by decentralizing trust, ensuring transparency, and enhancing security. However, challenges like scalability, energy consumption, and integration with existing IoT infrastructure remain. This survey aims to guide future research by highlighting these issues and proposing potential solutions, contributing to the development of blockchain-based trust management systems in IoT and IIoT. [ABSTRACT FROM AUTHOR]

As a growing up-and-coming branch of the Internet of Things and traditional vehicular ad hoc networks, the Internet of Vehicles (IoV) is intended to perform as a core information carrying and processing platform for Intelligent Transport Systems. However, owing to its dynamic topological structures, large network scale, and mobile limitation, IoV systems still struggle with many unresolved challenges, especially those concerning security and privacy. Therefore, researchers consider security a significant concern due to the variety of vulnerabilities. Recently, existing security researchers have worked extensively to guarantee IoV security. However, multiple challenges in terms of security and privacy are generated by various types of attacks, such as authentication and identification attacks, availability attacks, confidentiality attacks, and data authenticity attacks. In this context, we provide a systematic review of emerging security and privacy vulnerabilities IoV and identify current challenges and the remaining open issues. Furthermore, we will discuss the future of this area. The paper contributes by presenting significant issues and solutions in a clear, smooth, comprehensive, and integrated way. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices generate and transfer a vast volume of data in interaction with various objects. These data rely on cloud computing (CC) or fog computing (FC) for processing and calculations. Unlike CC, FC delivers task processing closer to IoT devices. Consequently, FC leads to reduced latency, increased efficiency, ease of deployment, and flexibility. The primary concern between the service requester and provider when agreeing to task offloading is trust. While several types of research are based on intelligent models for trust management, trust management in FC requires direct and indirect trust computation due to the presence of malicious nodes. To ensure security and maintain the quality of service when interacting with fog servers, trust is essential. In this paper, a new trust model for FC is proposed using a combination of the Genetic Algorithm (GA) and Learning Automaton (LA). The crossover operator in GA by LA is improved. The changes in the search space and finding optimal solutions are dependent on the crossover rate. Thus, determining an optimal value for the crossover operator impacts the problem's solutions. The proposed methods evaluation was done in a MATLAB 2020 simulation environment. The results show that the performance of the proposed method with 200 iterations and 1000 tasks is superior in terms of reliability rate, energy consumption, and delay compared to GA and particle swarm optimization (PSO) algorithm. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and cloud computing are emerging technologies whose use has been proven in various fields of medicine and healthcare. The positive impact of these technologies on improving the performance of medical centers is also evident. Therefore, the purpose of the present study is to investigate the technological requirements of the Cloud Internet of Things (CIoT) in the healthcare industry. This was a quantitative study conducted in two rounds using the Delphi method. A non-random, purposeful, and criterion-based method was used for sampling. A minimum of one paper or research project in the IoTs, cloud computing, or CIoT fields should be completed by participants. In addition to having a master's degree or specialized doctorate and at least 5 years of work experience, they should be faculty members. Fifteen participants in the first round of Delphi and 12 participants in the second round who completed all of the questionnaire's questions made up the research sample. A questionnaire was used to gather information. This questionnaire was designed in four main parts with 14 axes and 48 questions. After collecting the questionnaires in the first round of Delphi, each axis was scored on a five-point Likert scale. Finally, the requirements for the implementation of CIoT in healthcare were determined by the collective agreement of experts. Functional requirements of the network layer and cloud computing, platform layer requirements, and components of quality requirements were extracted. The architectures of the application layer, cloud layer, server layer, network layer, and user layer are explored thoroughly. Furthermore, communication technologies, equipment types, analysis and management, wireless networks and sensors, secure data exchange and management, deployment details, and approaches for security management and control are the main infrastructural keys that are extracted during the Delphi rounds. The findings showed that, with a very high level of importance, the use of wireless local networks was approved more than other network layer components. Compliance with security principles emerged as the most crucial component of the platform layer. We also found the use of hybrid clouds and the necessity of software as a service to be important. All the experts emphasized the need to use application development tools, cloud services, development, and deployment on the platform as a service, data analysis, and application programming interface related to the requirements of the platform layer. Scientifically speaking, they found that the components of secure information exchange, establishing secure protocol communication, key exchange mechanisms, and maintaining information confidentiality are crucial in the field of secure data exchange. Based on the findings, it can be concluded that the implementation of this technology can improve the quality of clinical services and increase the speed of responses and patient satisfaction. It is expected that by identifying the technological requirements of CIoT, a suitable model can be designed for healthcare. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging paradigm that offers remarkable opportunities for data mining and analysis. IoT envisions a world where all smartphones, vehicles, public services facilities, and home appliances that can be connected to the internet act as data sources. Even today, a significant portion of electronic devices, including watches, emergency alarms, parking doors, and many appliances can be linked to IoT systems and remotely controlled. Big data analysis and data mining methods can be utilized to improve the performance of IoT systems and address their challenges in the area of data storage, processing, and analysis. Extensive studies on IoT with big data can make it possible to accumulate tremendous data and transform it into valuable knowledge using data mining techniques. With this background, this paper provides a systematic survey of the literature on the use of big data analytics and data mining methods in IoT. This review aims to identify the lines of research that should receive more attention in future works. To achieve this goal, the articles published between 2010 and 2021 on the subjects of IoT-based big data and IoT-based data mining (60 articles) have been reviewed. These articles fall into four general categories in terms of focus: architecture/platform, framework, applications, and security. The paper provides a summary of the methods used in IoT-based big data analysis and IoT-based data mining in these four categories to highlight the promising avenues of research for future works. [ABSTRACT FROM AUTHOR]

This paper aims to predict the financial time series. Swarm intelligence algorithms usually use metadata to ensure objectivity without the statistical assumptions of the data. This paper proposed a prediction algorithm integrating multiple support vector regression (SVR) models. The algorithm selects different datasets to train these SVR models. This algorithm also adopts reasonable weights to combine the forecasting results of multiple models to reduce the overall prediction error. The weight of each model is dynamically adjusted according to its recent prediction accuracy. Therefore, this algorithm is adaptive and can deal with nonstationary problems. Five international authoritative stock indexes are used to compare the hybrid SVR model with a single SVR model for performance validation from the perspectives of normalized mean squared error, weighted directional symmetry, and root mean squared error. The results demonstrate that the hybrid SVR model has significantly improved the prediction accuracy and generalization ability of the prediction algorithm compared with a single SVR model. It reveals that selecting the appropriate input vector can achieve an excellent prediction effect. [ABSTRACT FROM AUTHOR]

With the fast growth of technologies like cloud computing, big data, the Internet of Things, artificial intelligence, and cyber-physical systems, the demand for data security and privacy in communication networks is growing by the day. Patient and doctor connect securely through the Internet utilizing the Internet of medical devices in cloud-healthcare infrastructure (CHI). In addition, the doctor offers to patients online treatment. Unfortunately, hackers are gaining access to data at an alarming pace. In 2019, 41.4 million times, healthcare systems were compromised by attackers. In this context, we provide a secure and lightweight authentication scheme (RAPCHI) for CHI employing Internet of medical Things (IoMT) during pandemic based on cryptographic primitives. The suggested framework is more secure than existing frameworks and is resistant to a wide range of security threats. The paper also explains the random oracle model (ROM) and uses two alternative approaches to validate the formal security analysis of RAPCHI. Further, the paper shows that RAPCHI is safe against man-in-the-middle and reply attacks using the simulation programme AVISPA. In addition, the paper compares RAPCHI to related frameworks and discovers that it is relatively light in terms of computation and communication. These findings demonstrate that the proposed paradigm is suitable for use in real-world scenarios. [ABSTRACT FROM AUTHOR]

Internet of Things realizes the ubiquitous connection of all things, generating countless time-tagged data called time series. However, real-world time series are often plagued with missing values on account of noise or malfunctioning sensors. Existing methods for modeling such incomplete time series typically involve preprocessing steps, such as deletion or missing data imputation using statistical learning or machine learning methods. Unfortunately, these methods unavoidable destroy time information and bring error accumulation to the subsequent model. To this end, this paper introduces a novel continuous neural network architecture, named Time-aware Neural-Ordinary Differential Equations (TN-ODE), for incomplete time data modeling. The proposed method not only supports imputation missing values at arbitrary time points, but also enables multi-step prediction at desired time points. Specifically, TN-ODE employs a time-aware Long Short-Term Memory as an encoder, which effectively learns the posterior distribution from partial observed data. Additionally, the derivative of latent states is parameterized with a fully connected network, thereby enabling continuous-time latent dynamics generation. The proposed TN-ODE model is evaluated on both real-world and synthetic incomplete time-series datasets by conducting data interpolation and extrapolation tasks as well as classification task. Extensive experiments show the TN-ODE model outperforms baseline methods in terms of Mean Square Error for imputation and prediction tasks, as well as accuracy in downstream classification task. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach that uses a spectral clustering method to cluster patients with e-health IoT devices based on their similarity and distance and connect each cluster to an SDN edge node for efficient caching. The proposed MFO-Edge Caching algorithm is considered for selecting the near-optimal data options for caching based on considered criteria and improving QoS. Experimental results demonstrate that the proposed approach outperforms other methods in terms of performance, achieving decrease in average time between data retrieval delays and the cache hit rate of 76%. Emergency and on-demand requests are prioritized for caching response packets, while periodic requests have a lower cache hit ratio of 35%. The approach shows improvement in performance compared to other methods, highlighting the effectiveness of SDN-Edge caching and clustering for optimizing e-health network resources. [ABSTRACT FROM AUTHOR]

Nowadays, the number of Internet of things (IoT) devices has rapidly increased due to their increasing use in different real-world applications. The sensor devices represent the basic element of the IoT network because they gather data from various environments and situations, while the sink node serves as the network's brain because it processes the data and makes decisions. However, the large amount of data that the sensor devices gather and send to the gateway toward the sink, on the one hand, causes the sensor's limited energy to be depleted and, on the other hand, makes it more difficult to achieve the decisions using these data at the sink. Therefore, before sending data to the gateway, it is important to get rid of any duplicate data while maintaining a high level of data quality. In this paper, a distributed prediction–compression-based mechanism (DiPCoM) for saving power in IoT networks is suggested. DiPCoM makes periodic decisions on sending the data to the gateway. It uses the autoregressive integrated moving average prediction method in each period to predict the next period's data and decide whether the current data should be sent to the gateway. When the decision is made to send the data to the gateway, an effective compression approach is used by DiPCoM to get rid of the duplicate data. It combines different data transmission reduction techniques such as adaptive piecewise constant approximation, differential encoding, symbolic aggregate approximation, and Lempel–Ziv–Welch. Simulation results based on real-world data show that the DiPCoM method is better than other techniques in terms of energy consumption, data reduction ratio, transferred data size, and data accuracy. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is a key concept of the sixth generation (6G) mobile network. In the 6G paradigm, NOMA is a significant energy and spectral efficiency technique, especially in the indoor region, to maintain the quality of service. multiple input multiple outputs (MIMO) base station serves as the main base station due to massive connectivity. In MIMO, spatial modulation (SM) gives a huge data rate and reduces inter-antenna interference. Beamforming permits a proficient intercell interference organization for short-distance data transmission. The prime concept in NOMA is to provide various end users with the same resource block based on a time frame, spreading code, or subcarrier successive interference cancellation. In this article, we propose a power-efficient 6G NOMA-beamforming-based spectrum allocation scheme for the Internet of Things (IoT) devices in the indoor region. MIMO with SM transmits the signal to indoor next-generation node B (gNB). It is a 3GPP-complaint implementation of 5G/beyond 5G base stations. Various IoT devices are connected with those gNBs and share the communication resources. In this paper, we propose an IoT device-interoperability scheme based on the requirements of the IoT devices aiming at improving the network performance. The simulation results show that the proposed model diminishes power consumption and improves signal-to-interferences-plus-noise ratio. [ABSTRACT FROM AUTHOR]

Currently, the Internet of Thing has become an integral part of the world's Internet infrastructure. One of the important matters in this field is the security of the network. Some networks like Software-defined Networking are proposed to improve it. In this paper, cyber-attacks have been simulated and various parameters such as the average rate of receiving traffic, the average response time and network delay have been analyzed. In the following, a new plan for switch buffers and their effect on the state of CPU performance, the controller bandwidth and the amount of packet loss have been discussed. The new buffer structure is proposed using a hash table, which uses a DRAM combination structure. To implement the proposed technique, AS5710-54X-EC switch and floodlight controller were used, and also Eclipse Neon 3.1 was used for writing modules. The results imply that this method had a great impact on detecting attacks in SDN-based IoT frameworks. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) concept is expected to be a crucial component of human life in the near future. In IoT, devices provide functionalities as services. By integrating primitive services of interconnected IoT devices, complex IoT applications are composed. In a large-scale IoT environment, the selection of IoT services is essential. The services must be selected considering Quality-of-Service (QoS), energy consumption, and fairness among IoT services. This paper proposes a multi-objective optimization-based IoT service composition framework for fog-based IoT networks. The proposed solution takes advantage of the NSGA-II (Non-dominated Sorting Genetic Algorithm II). The cloud controller distributes application requests to fog servers as application requests arrive. Fog servers decompose application requests into IoT service requests and then slice IoT service requests into time windows. Each time window is optimized individually using the proposed model considering QoS, energy consumption, and fairness. Experimental evaluation results show that the proposed approach can optimize energy consumption and fairness without causing any QoS degradation. [ABSTRACT FROM AUTHOR]

With the rapid expansion of the Internet of Things and the surge in the volume of data exchanged in it, cloud computing became more significant. To face the challenges of the cloud, the idea of fog computing was formed. The heterogeneity of nodes, distribution, and limitation of their resources in fog computing in turn led to the formation of the service placement problem. In service placement, we are looking for the mapping of the requested services to the available nodes so that a set of Quality-of-Service objectives are satisfied. Since the problem is NP-hard, various methods have been proposed to solve it, each of which has its advantages and shortcomings. In this survey paper, while reviewing the most prominent state-of-the-art service placement methods by presenting a taxonomy based on their optimization strategy, the advantages, disadvantages, and applications of each category of methods are discussed. Consequently, recommendations for future works are presented. [ABSTRACT FROM AUTHOR]

Along with the significant growth of applications and facilities provided by the Internet of Things (IoT) in recent years, security challenges and related issues to privacy become considerable interest of researchers. On the other hand, the de facto IoT routing protocol for low-power and lossy networks called RPL is vulnerable to various types of routing attacks. Many researchers have investigated RPL security solutions focusing on effective detection of prevalent and destructive routing attacks such as blackhole attack, selective forwarding attack, rank attack and so on. Recent studies are proposing trust-based mechanisms with the aim of replacing traditional cryptography-based operations with lightweight security models in order to cover the inherent challenges of IoT devices, including energy and computational limitations. Therefore, in this paper, focusing on the problem of RPL vulnerability against well-known routing attacks, we have proposed a trust-based attack detection model, which investigates traffic behavior in different attack scenarios and detects malicious nodes relying on behavior deviation exactly at the same time as the start of any attack activity. Expected behavior is predicted by our learning model trained from the historical routing behavior pattern, using recurrent neural networks as a powerful deep learning method, which leads to attack detection with high-level accuracy and precision. Both mathematical analysis and simulation results on multiple RPL attack scenarios show clearly that the proposed trust-based defense mechanism is an effective approach capable of timely and precisely detection of routing behavior pattern deviation of malicious nodes exactly at the start time of the attack occurrence, which leads to attack detection and attacker identification based on trust scores extracted from the detected fluctuations between expected and real routing behavior patterns. [ABSTRACT FROM AUTHOR]

The emergence of a new generation of applications such as the Internet of Things, Artificial Intelligence, and 5G has brought fog computing as cloud development toward the edge network. It is done by adding an extra layer of resources to meet low latency, high performance, mobility, and interoperability challenges. The development of fog markets and new beneficial business paradigms such as fog services considerably affect the future of fogonomics (which is defined as the economics of fog computing). Thus, it is a challenging issue to design a market-oriented model to deal with the economics-based resource management in fog computing, which adjusts the resource supply of Fog Resource Providers (FRPs) and the demands of User Services (USs) based on users' requirements while satisfying all parties' utilities. This paper proposes the Fog-Marketing scheme consisting of a Resource Propagation Component (RPC) and a Contract-aware Market Selection Component (CMSC) for resource provisioning in multi-tier fog computing. The RPC is deployed in each fog node to perform hierarchical resource propagation of FRPs to auction-based Fog Decentralized Markets (FDMs) close to USs to increase revenue opportunities, and embedded CMSC in the user agent applies a matching strategy to assign USs and FDMs aims to promote USs' utility. The simulation results under extensive experiments confirmed that the proposed scheme succeeded in reducing the Round-Trip-Time, increasing the revenue of FRPs, and improving the utility of USs compared to existing models. [ABSTRACT FROM AUTHOR]

One of the main sources of information dissemination is social networks. Changes in social and Internet of Things (IoT) relationships can affect the quality of service (QoS) of device-to-device (D2D) communication in fifth-generation (5G) networks. Integration of social networks and IoT networks will allow users to improve their communication abilities with proximity users. The most challenging issues are interference and channel uncertainty due to massive connectivity and random movement of users in an urban dynamic environment. In this paper, D2D-based social Internet of Things (D2D–social IoT) in a 5G network is modeled for urban regions. To address these issues, we formulate a throughput maximization problem under the maximum power and QoS constraints. The problem is divided into multiple subproblems to reduce interference and enhance throughput. First, we leverage the interference channel link for intelligent resource sharing with better QoS by the spatiotemporal method. The second one involves bisection-based power search optimization by utilizing the Mellin transformation method for latency and power minimization. Simulation results demonstrate that the proposed method significantly improves the network performance in terms of throughput, sum rate, and reduction in network latency by up to 19.91%, 26.24%, and 31.57%, respectively. It is expected that an inclusive implementation of the said method can enable resource allocation in 5G-enabled social IoT networks for the development of smart city. [ABSTRACT FROM AUTHOR]

The term "Internet of Things" (IoT) encompasses an entire group of gadgets that are capable of connecting to the Internet in order to gather and share data. The IoT paradigm is being pushed into computer networks by numerous highly advanced intrusions. Cloud computing greatly enhances the success of the IoT by enabling users to perform computing tasks using Internet-based services accessed through connected devices. This seamless integration of cloud technology and the IoT has become a powerful catalyst, revolutionizing the way we operate. The adoption of a distributed architecture, such as cloud computing, exposes the system to potential threats like Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks. To mitigate these risks, the concept of an intrusion detection system (IDS) has been introduced within the cloud environment. Various machine learning (ML) and deep learning (DL) algorithms have been proposed and implemented to effectively detect and respond to such malicious traffic in the cloud system. For dimension reduction during the training process of those algorithms, multiple independent and hybrid techniques have been proposed. This study presents an efficient ML-based real-time IDS framework with proposed hybrid feature selection techniques. Additionally, in this study, a concise comparative analysis has been conducted using five well-known public datasets. The findings presented in this paper reveal that our proposed IDS achieved a maximum accuracy of 99.98% in identifying malicious traffic. [ABSTRACT FROM AUTHOR]

The rapid growth of Internet of Things (IoT) devices has changed human interactions with the environment. IoT networks require specialized defense strategies distinct from traditional corporate contexts. Security measures such as anti-malware software, firewalls, authentication protocols, and encryption techniques are established but face limitations against evolving attack strategies. Therefore, this study proposes an intrusion detection approach for a realistic IoT environment, employing various variants of deep learning models such as deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). The research tested three variants for each model: DNN1, DNN2, DNN3, CNN1, CNN2, CNN3, RNN1, RNN2, RNN3. All these variants are customized and tuned differently to analyze the efficacy of the suggested methodology. Likewise, notable observation highlights the significance of aligning training and validation accuracy curves that indicate controlled overfitting, validating the model's reliability in accurately predicting intrusion and benign network traffic in IoT settings. Results reveal that RNN1 achieves the best results: accuracy of 98.61%, precision of 98.55%, recall of 98.61%, and F1-score of 98.57% compared with other DNN and CNN architectures and benchmark papers. This study advances intrusion detection within IoT networks through a comprehensive evaluation of deep learning models and inspires ongoing research to enhance intrusion detection systems' resilience in dynamic IoT environments. [ABSTRACT FROM AUTHOR]

In IoT applications, prediction models have fundamental challenges such as real-time processing, producing results with considerable/without delay, and taking action against pattern drift. While existing models can excel when data statistics remain relatively stable, real-time systems may encounter difficulties, particularly when confronted with dynamic shifts in data behavior. Analyzing data streams generated by different IoT applications and detecting complex pattern on the fly has become an open area of research. Complex event processing with adaptivity is a must to get desired features in such models. To address this issue, a comprehensive model for prediction has been proposed in this paper. It consists of two phases: (1) the basic model is constructed using historical data, (2) a fast Fourier transform-based adaptive support vector regression (FFT-ASVR) approach is proposed to predict events embedded in IoT data streams. FFT-ASVR predicts abnormal events by experiencing a change in data streams with real-time model updation. The performance of FFT-ASVR with a similar existing method SVM-RBF is presented using real-time traffic data of Madrid city. The proposed approach has significant improvement in terms of mean absolute percentage error (MAPE) for prediction, is adaptive in nature, and is also capable of handling the issue of pattern drift. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a disruptive technology for the future decades. Due to its pervasive growth, it is susceptible to cyber-attacks, and hence the significance of Intrusion Detection Systems (IDSs) for IoT is pertinent. The viability of machine learning has encouraged analysts to apply learning techniques to intelligently discover and recognize cyber attacks and unusual behavior among the IoTs. This paper proposes an enhanced anomaly-based Intrusion Detection Deep learning Multi-class classification model (EIDM) that can classify 15 traffic behaviors including 14 attack types with the accuracy of 95% contained in the CICIDS2017 dataset. Four state-of-the-art deep learning models are also customized to classify six classes of network traffic behavior. An extensive comparative study in terms of classification accuracy and efficiency metrics is conducted between EIDM and several state-of-the-art deep learning-based IDSs showing that EIDM has achieved accurate detection results. [ABSTRACT FROM AUTHOR]

The growth of the Internet of Things (IoT) can lead to improved productivity, scalability, connectivity, and saving time and money. However, the increasing number of IoT-based applications has MAPE the centralized cloud computing paradigm face several challenges such as latency and bandwidth. Nowadays, fog computing with distributed architecture has emerged to support latency-sensitive IoT applications with limited resources. Using the fog instead of the cloud can bring the storage and computing facilities closer to the edge of the network and thus provide better performance for the end user. Meanwhile, IoT applications usually have complex multi-component structures whose efficient placement on fog nodes can overcome the resource restrictions of IoT devices. This problem as the IoT Service Placement Problem (SPP) is NP-hard, where nature-inspired approaches widely provide robust solutions to solve it. In this paper, a meta-heuristic-based evolutionary approach named SPP-ICA is presented to address SPP, which originates from the Imperialist Competitive Algorithm (ICA). SPP-ICA is developed based on MAPE-K autonomous planning model, so that it applies service deployment priority and resource consumption distribution in the placement process. ICA embedded in SPP-ICA leads to more effective placement of IoT services in terms of latency and resource utilization by considering the concepts of elitism and balanced resource consumption. Experimental results show that SPP-ICA performs significantly better than state-of-the-art algorithms with meta-heuristic structure. On average, SPP-ICA deploys more IoT services on fog and reduces service latency by 9%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is becoming an essential aspect of digital healthcare information; the goal is to monitor various health parameters regularly to make healthcare management more efficient and convenient. Gateway devices can broadcast/multicast messages securely to sensors or intended recipients to guarantee the confidentiality of medical readings in the healthcare environment utilizing IoT sensors. In this context, designing and executing a key management framework is critical for the healthcare environment, and working with limited computing and processing capacities is also challenging. The literature on IoT key management is inclined toward centralized solutions, provides solutions with heavy computation and communication costs, and partially addresses resource-constrained devices that guarantee forward and backward secrecy in the healthcare domain. This paper constructs a group key management scheme with node joining and leaving scenarios to provide forward and backward secrecy focusing on lightweight computation. We use the concept of one-way accumulation for secret message exchange by combining elliptic curve cryptography. We designed a scheme that refreshed the established group key as the group's size grows or shrinks, and the approach also applies to classical ciphers conveniently for the number of message exchanges for healthcare nodes. To demonstrate innovation in our method, we mathematically proved the soundness of our session key management scheme for the network model, and simulation findings show that the method is feasible as the processing and communication costs are reduced compared to related schemes. [ABSTRACT FROM AUTHOR]

Crop productivity prediction and recommendation is a significant research area of smart agriculture. This paper proposes an Internet of Things (IoT) framework based on dew computing, edge computing, and federated learning, where soil parameters, environmental parameters, and weather data are analysed to predict the crop productivity of a land, and then recommend suitable crop for the land. The dew layer pre-processes and accumulates the received sensor data, and forwards to the edge server. The edge server analyses the sensor data and the weather data, and then sends the result to the cloud along with the model characteristics and to the mobile device. The proposed framework is simulated in iFogSim. The theoretical analysis shows that the proposed framework has reduced the delay by 60–70% approximately and power consumption by 70–80% approximately than the conventional sensor-cloud framework. We also observe that the proposed framework has reduced the delay by 12–35% approximately and power consumption by 30–50% approximately than the edge-cloud framework. We compare four machine learning algorithms based on their performance in data analysis in terms of precision, recall, accuracy, and F-score. We observe that each classifier obtains more than 95% prediction accuracy. An Android application is also proposed for crop recommendation. [ABSTRACT FROM AUTHOR]

As the 6LoWPAN devices in the Internet of Things (IoT) applications communicate sensitive information over the unattended and insecure channel, it is essential to design suitable authentication methods for such devices. Due to high computation and communication overheads, the traditional certificate-based authentication schemes are predominantly considered impractical for resource-constraint 6LoWPAN devices used in IoT applications. However, a certificate with some lightweight features makes the authentication feasible in those resource-constraint 6LoWPAN devices. This paper proposes a lightweight certificate-based authentication scheme based on cryptographic hash operation and elliptic curve digital signature algorithm. It can mitigate several security threats, including replay, man-in-the-middle, impersonation, malicious device deployment, and spoofing attacks. The automated validation of Internet security protocols and applications (AVISPA) verification tool is used to formally verify the security of the proposed scheme against several known attacks. In addition, the correctness of the authentication scheme is verified using the widely accepted BAN logic. Moreover, a comparative analysis among the proposed and other well-established relevant schemes is carried out in order to figure out the trade-off considering not only the functionality attributes and security but also the involved computation and communication costs. [ABSTRACT FROM AUTHOR]

Both software defined networks and the Internet of Things are new topics that are being heavily employed in the information technology industry and academia. Due to their fame, they have attracted many attacks in the SD-IoT (Software Defined Internet of Things). The attackers' purpose could be to steal or block users' information, as well as to waste network resources on pointless operations, causing the resources to deny legitimate user requests. Distributed denial of service (DDoS) attack is one such attack among those. This paper presents a study on feature extraction for detecting DDoS attack on SD-IoT. Using the Distributed Internet Traffic Generator and hping3 tools, normal and DDoS attack traffic were created, and six features were retrieved from the generated traffic. Some well-known classifiers, such as Random Forest, Light Gradient Boosting Machine (LGBM), Support Vector Machine, and K Nearest Neighbor, are trained and tested with these 6 vector tuples of normal and DDoS attack data to check the efficiency of the extracted features. Since LGBM has been shown to be the most effective classifier across a wide range of metrics, including accuracy, precision, recall, F1, Testing Time, False Alarm Rate, Cohen's Kappa Coefficient, and AUC value, it has been compared with the performance of some other state-of-the-art works. It is found that LGBM with these extracted 6 features is outperforming those works except in one case where the accuracy of proposed work is less than accuracy of that work. So, finally LGBM is deployed in the SDN controller so that it can identify attacks in real-time traffic. [ABSTRACT FROM AUTHOR]

The social internet of things (SIoT) facilitates numerous networking services and novel applications for the IoT, making it more productive and powerful. The state-of-the-art machine learning architectures often face different challenges when processing big data such as increased memory volume, increased training time, and higher computational costs. To tackle these challenges, this paper presents a novel framework for big data classification. Initially, an adaptive filter removes unwanted data and noises. The dimensionality of the filter data is then reduced via the Hadoop map reducer. After that, the battle royale optimization (BRO) algorithm is applied for feature selection. The optimal feature selection process performed by the BRO algorithm improves the accuracy and performance of the proposed fuzzy optimal deep convolutional neural network (FO-DCNN). The FO-DCNN model is formed by integrating the fuzzy-based remora optimization (F-RO) algorithm and deep CNN architecture for robust SIoT data classification. When evaluated using the Twitter, Rotten tomato, skin disease, diabetes, and hepatitis datasets, the proposed model offers improvements in terms of classification accuracy, memory consumption, and computational time. [ABSTRACT FROM AUTHOR]