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Internet of Things (IoT) systems generate a vast volume of data to process, and the volume of data generated by the IoT will continue to expand exponentially in the future. Due to the large-scale nature of such systems and their geographically distributed nature, how to optimally place operators and brokers for data processing across cloud, fog, and edge is the main concern of this paper. Furthermore, in the past, IoT systems relied on cloud-based brokers. If the communication between operators in an IoT application has to be regulated by a broker located in the cloud, the advantages brought by the processing of nodes located at the edge and the fog may be neglected. We consider various factors, such as the impact of different network topologies on operators and brokers, the needs of IoT application users, the performance of sensor layers, and the location characteristics of IoT sensors. In diverse placement strategies, the characteristics of the broker have been given special attention. This paper considers the combined architecture of the operator and broker on the edge, fog, and cloud as an optimization problem of minimizing computational processing and data transmission cost. We propose jointly placing the IoT application's operator and broker on the edge, fog, and cloud nodes. Then we proposed two algorithms: the low-complexity heuristic algorithm named GREEDY and the genetic algorithm called GA. Simulation results show that, in most situations, the GA placement method outperforms others and provides a near-optimal solution. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous derivative of the Internet of Things (IoT) business, the use of various types of information sensing equipment needs to continue to increase. However, power supply conditions are getting tougher. On the other hand, as the function of the devices itself increases, the power consumption also increases, and the lack of energy demand is more obvious. To solve these problems, a dual-frequency RF energy harvesting system is designed in this paper. First of all, in order to expand the effective receiving range of RF energy, this paper designed a 920 MHz hexagonal windmill circular-polarized antenna, and added an energy receiving circuit in the 2.45 GHz band on this basis, which fully alleviated the problem of insufficient energy collection for small devices in the Internet of Things while ensuring a small volume wavelength ratio. In addition, in order to improve the energy conversion efficiency, this paper also designed a dual-frequency rectifier circuit based on conjugate separation matching, using the harmonic signal of 920 MHz signal to broaden the bandwidth of 2.45 GHz. The experimental results show that the peak conversion rates of 920 MHz and 2.45 GHz channels are 72.8%@0dBm and 38.7%@-2dBm, respectively. Overall testing in a Wireless Fidelity (Wi-Fi) room with a commercial Radio Frequency Identification (RFID) reader. The experimental results show that the system can steadily output 3.29 V–3.3 V voltage within 6 m charging distance, which is 20% higher than the output voltage of the single-channel receiving system, and the receiving angle is 36.84% larger. [ABSTRACT FROM AUTHOR]

IoT devices convert billions of objects into data-generating entities, enabling them to report status and interact with their surroundings. This data comes in various formats, like structured, semi-structured, or unstructured. In addition, it can be collected in batches or in real time. The problem now is how to benefit from all of this data gathered by sensing and monitoring changes like temperature, light, and position. In this paper, we propose a predictive analytics framework constructed on top of open-source technologies such as Apache Spark and Kafka. The framework focuses on forecasting temperature time series data using traditional and deep learning predictive analytics methods. The analysis and prediction tasks were performed using Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and a novel hybrid model based on Convolution Neural Network (CNN) and LSTM. The purpose of this paper is to determine whether and how recently developed deep learning-based models outperform traditional algorithms in the prediction of time series data. The empirical studies conducted and reported in this paper demonstrate that deep learning-based models, specifically LSTM and CNN-LSTM, exhibit superior performance compared to traditional-based algorithms, ARIMA and SARIMA. More specifically, the average reduction in error rates obtained by LSTM and CNN-LSTM models were substantial when compared to other models indicating the superiority of deep learning. Moreover, the CNN-LSTM-based deep learning model exhibits a higher degree of closeness to the actual values when compared to the LSTM-based model. [ABSTRACT FROM AUTHOR]

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required. [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]

Nowadays, the Internet of Things (IoT) is used in every domain, especially in the healthcare system it plays a very important role in our life and made our life easier by providing various facilities in every area. On the Internet of Medical Things (IoMT), IoT devices collect real-time data from patients and send it to the cloud through the Internet. These gathered data are extorted to centralized processing and computational power. Centralization can lead to a single point of failure, data modification, snooping, security, and privacy evasion. Blockchain can resolve such issues by providing decentralized computational power and better storage capacity for IoT data. Therefore, integrating IoT and blockchain technologies can be an intelligent way to implement a decentralized IoT-based smart healthcare system. We find that integration of the blockchain and IoT is persuasive and can cause remarkable transformations across the healthcare system. We discussed blockchain and its applicability in the healthcare industry. The paper explores blockchain's dimensions, making the innovative healthcare system more secure and stable. We provide a comparative analysis of well-known recent research on IoT-based smart healthcare system security using blockchain based on different parameters such as architecture, data integrity, medical information sharing, distributed electronic health records, patient encryption key, access control, hardware implementation, etc. The paper focuses on the current security issues in IoT-based smart healthcare systems, architectures, taxonomy, and research gap and their possible solutions we point out various research challenges that should be considered before the deployment of a blockchain network in an IoT setting. This paper will be helpful to new researchers to get an overview of IoT-based healthcare system security. [ABSTRACT FROM AUTHOR]

Existing blockchain approaches exhibit a diverse set of dimensions, and on the other hand, IoT-based health care applications manifest a wide variety of requirements. The state-of-the-art analysis of blockchain concerning existing IoT-based approaches for the healthcare domain has been investigated to a limited extend. The purpose of this survey paper is to analyze current state-of-the-art blockchain work in several IoT disciplines, with a focus on the health sector. This study also attempts to demonstrate the prospective use of blockchain in healthcare, as well as the obstacles and future paths of blockchain development. Furthermore, the fundamentals of blockchain have been thoroughly explained to appeal to a diverse audience. On the contrary, we analyzed state-of-the-art studies from several IoT disciplines for eHealth, and also the study deficit but also the obstacles when considering blockchain to IoT, which are highlighted and explored in the paper with suggested alternatives. [ABSTRACT FROM AUTHOR]

The rapid advancement of IoT technology presents transformative opportunities across various sectors, with education being a prominent beneficiary. Smart classrooms, a product of IoT integration, are being widely adopted to create technology-enhanced, student-centric learning environments that cater to students' information literacy needs, particularly during events like pandemics. This widespread adoption generates substantial amounts of educational data, commonly known as big data, necessitating innovative solutions for analysis and utilization. To solve these challenges, this paper proposes utilizing the Apriori algorithm—a data mining technique renowned for uncovering valuable patterns and associations within extensive datasets. This paper evaluates the impact of various information resources with differing quality, considering individuals' information literacy skills. Utilizing data mining techniques, it delves into university students' information literacy data, integrating it with the university library resources to establish a data-driven information literacy education model. It then focuses on criteria, components, and effective methods for instructing college students in information literacy. Finally, a diverse group of students, from first-year undergraduates to doctoral candidates at a specific university, is studied for their engagement in information literacy instruction. Based on the experimental findings, sophomore students exhibited the highest level of participation at 75.9% accuracy, while postgraduate students received more information literacy training than undergraduates and Ph.D. students. When comparing this method to others, such as SVM, KNN, LR, RF, and DT, it achieved superior performance. Additionally, the quality of information literacy training in university libraries was assessed through three dimensions: student learning, behavior, and achievements. Only junior, senior, and first-year graduate students scored above 4, with scores of 4.18, 4.15, and 4.26, respectively. [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]

Internet of things establishes communication among heterogeneous devices. IoT network is low power and lossy network known as LLN. The components in LLN use low power for its operations. The Internet Engineering Task Force (IETF) has defined routing protocol for standardized LLN, i.e., routing protocol for low-power and lossy networks (RPL). One of the major challenges in RPL is efficient conservation of node energy to improve the life of the LLN network. In the RPL network, most of the energy is consumed while regulating and controlling the packets rather than transmission. The algorithm used for regulating and controlling packet in RPL is called trickle timer algorithm. Hence to improve the lifetime of network it is essential to modify the existing trickle timer algorithm. In this paper, we have proposed a new algorithm called EE-trickle. The performance of EE-trickle is compared with existing trickle using the simulator Cooja and using open test bed of future Internet of things lab. From the experiments, it is identified that EE-trickle provides better PDR along with less energy consumption than the existing trickle. Hence, the paper helps the future researchers who work on energy consumption in RPL to make use of EE-trickle in their experiment rather than existing trickle. [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 important part of mechanical equipment is rotating machinery, used mostly in industrial machinery. Rolling element bearings are the utmost dominant part in rotating machinery, so even small defects in these components could result in catastrophic system failure and enormous financial losses. Hence, it is crucial to create consistent and affordable condition monitoring and fault diagnosis systems that estimate severity level and failure modes and to create an appropriate maintenance strategy. The studies reveal that the fault diagnostic system focuses on single fault diagnosis of the shaft-bearing system. However, in real scenarios, the occurrence of a single fault is very unlikely. Thus, multifault diagnosis of the shaft-bearing system is of greater significance. This paper aims at steadily and broadly summarizing the development of the intelligent multifault diagnostic and condition monitoring systems. In addition, there is a rapid development of application of Internet of things, cloud computing and artificial intelligence techniques for fault diagnosis. In this paper, we summarize the study of various fault diagnostic system built on the architecture and application of these cutting-edge technologies for predictive maintenance of mechanical equipment. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet of Things (IoT) networks provide benefits to humans in numerous domains by empowering the projects of smart cities, healthcare, industrial enhancement and so forth. The IoT networks include nodes, which deliver the data to the destination. However, the network nodes' connectivity is affected by the nodes' removal caused due to the malicious attacks. The ideal plan is to construct a topology that maintains nodes' connectivity after the attacks and subsequently increases the network robustness. Therefore, for constructing a robust scale-free network, two different mechanisms are adopted in this paper. First, a Multi-Population Genetic Algorithm (MPGA) is used to deal with premature convergence in GA. Then, an entropy based mechanism is used, which replaces the worst solution of high entropy population with the best solution of low entropy population to improve the network robustness. Second, two types of Edge Swap Mechanisms (ESMs) are proposed. The Efficiency based Edge Swap Mechanism (EESM) selects the pair of edges with high efficiency. While the second ESM named as EESM-Assortativity, transforms the network topology into an onion-like structure to achieve maximum connectivity between similar degree network nodes. Further, Hill Climbing (HC) and Simulated Annealing (SA) methods are used for optimizing the network robustness. The simulation results show that the proposed MPGA Entropy has 9% better network robustness as compared to MPGA. Moreover, both the proposed ESMs effectively increase the network robustness with an average of 15% better robustness as compared to HC and SA. Furthermore, they increase the graph density as well as network's connectivity. [ABSTRACT FROM AUTHOR]

Sensor clouds are formed by IP-enabled wireless sensors and Internet of Things devices that are used for sensing and actuation in commercial and industrial applications. Data collected by the sensors are consolidated by distributed cloud data consolidation (DCS) servers to be utilized as raw sensory information by applications running data analytics and actuation functions. Alternatively, DC servers may feed sensor data to the cloud-hosted Big Data Analytics (BDS) servers. Sensor clouds and their respective DCS servers, as well as BDS servers, may form different security realms. These security realms' ownership structures are complicated and differ from standard database servers, necessitating a dependable authentication technique to provide trusted access to DC and BDS servers. This paper proposes a new multiparty authentication framework to authenticate applications requesting access to the DCS and BDS servers without direct human or application access to the sensors and actuators. Only DC servers are permitted to communicate with sensors/actuators, and only applications certified by a Session Authority Cloud are granted access to DCS/BDS servers via an authentication protocol that includes many information and key exchanges. This solution may assure the reliable deployment of sensor clouds in different critical application domains (i.e., industry, commercial, national security, and defense, etc.) while reducing the potential of direct espionage of sensed/actuated systems. Linear Temporal Logic is used to explicitly analyze and establish the correctness of the presented framework. OPNET modeling and simulations are used to illustrate the protocol's design and operations. The results demonstrate that multiparty authentication is conceivable for Sensor cloud computing systems. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) as an emerging technology is based on the idea that smart things can connect to the Internet and exchange the collected data in a peer-to-peer paradigm. Due to its inherent features, IoT can be utilized in real-world scenarios and its expansion can improve human well-being. Internet of things is applied quite closely to humans and transmits serious information such as healthcare information, financial data, and private information through an insecure communication platform. Since almost all tasks are performed with minimal human intervention, and adversary may deploy its nodes among other legitimate elements of IoT, providing an effective mutual authentication is vital. In this Systematic Literature Review, authentication of IoT and its literature are reviewed systematically. In particular, it has endeavored that the collected literature covers the papers conducted from 2018 to 2022. Moreover, this study seeks to provide a comprehensive answer to six important Research Questions in the context of authentication of IoT that often engage the minds of scholars. It is hoped that this survey will be an effective guide for future research by addressing the relevant challenges, analyzing open issues, and providing future research directions. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has garnered considerable attention from academic and industrial circles as a pivotal technology in recent years. The escalation of security risks is observed to be associated with the growing interest in IoT applications. Intrusion detection systems (IDS) have been devised as viable instruments for identifying and averting malicious actions in this context. Several techniques described in academic papers are thought to be very accurate, but they cannot be used in the real world because the datasets used to build and test the models do not accurately reflect and simulate the IoT network. Existing methods, on the other hand, deal with these issues, but they are not good enough for commercial use because of their lack of precision, low detection rate, receiver operating characteristic (ROC), and false acceptance rate (FAR). The effectiveness of these solutions is predominantly dependent on individual learners and is consequently influenced by the inherent limitations of each learning algorithm. This study introduces a new approach for detecting intrusion attacks in an IoT network, which involves the use of an ensemble learning technique based on gray wolf optimizer (GWO). The novelty of this study lies in the proposed voting gray wolf optimizer (GWO) ensemble model, which incorporates two crucial components: a traffic analyzer and a classification phase engine. The model employs a voting technique to combine the probability averages of the base learners. Secondly, the combination of feature selection and feature extraction techniques is to reduce dimensionality. Thirdly, the utilization of GWO is employed to optimize the parameters of ensemble models. Similarly, the approach employs the most authentic intrusion detection datasets that are accessible and amalgamates multiple learners to generate ensemble learners. The hybridization of information gain (IG) and principal component analysis (PCA) was employed to reduce dimensionality. The study utilized a novel GWO ensemble learning approach that incorporated a decision tree, random forest, K-nearest neighbor, and multilayer perceptron for classification. To evaluate the efficacy of the proposed model, two authentic datasets, namely, BoT-IoT and UNSW-NB15, were scrutinized. The GWO-optimized ensemble model demonstrates superior accuracy when compared to other machine learning-based and deep learning models. Specifically, the model achieves an accuracy rate of 99.98%, a DR of 99.97%, a precision rate of 99.94%, an ROC rate of 99.99%, and an FAR rate of 1.30 on the BoT-IoT dataset. According to the experimental results, the proposed ensemble model optimized by GWO achieved an accuracy of 100%, a DR of 99.9%, a precision of 99.59%, an ROC of 99.40%, and an FAR of 1.5 when tested on the UNSW-NB15 dataset. [ABSTRACT FROM AUTHOR]

The widespread growth of the Internet of Things (IoT) in various application domains makes device security and data privacy a critical concern. Indeed, the continuous discovery and exchange of resources between IoT devices through central Resource Directories (RD), running on standardized application-layer protocols, such as Constrained Application Protocol (CoAP) causes trust and reliability issues. Blockchain (BC) offers a promising reliable and non-repudiable solution through consensus and decentralization. Nevertheless, its applicability in IoT environments is still challenging. This paper proposes DRDChain: a lightweight permissioned blockchain framework for discovering and consuming IoT resources. DRDChain deploys Distributed RDs (DRDs) as validating peers and constrained IoT devices as BC clients. Resource registration and discovery operations in DRDChain are modeled as smart contracts and support multiple data storage approaches. A prototype of DRDChain is implemented using Hyperledger Iroha and CoAPthon. Extensive evaluations in a realistic IoT environment have demonstrated the effectiveness and efficiency of DRDChain in terms of reliability, time, throughput, and storage size. [ABSTRACT FROM AUTHOR]

Fog computing provides a suitable development for real-time processing in the Internet of Things (IoT). Attribute-based encryption (ABE) is the main method to control data access in fog computing. A residue number system (RNS) can speed up multiplication and exponential operations by converting very large integers to small integers. This paper proposes a fine-grained lightweight access control scheme using ABE modified with RNS properties (RNS-ABE) with fog computing. Decryption is implemented with the Chinese remainder theorem (CRT), and a new access structure based on the CRT secret sharing scheme is also introduced. Security of the proposed scheme proved based on RNS properties and the complicated problem of factoring a very large integer into its large prime factors, like RSA encryption. The time cost comparison shows that the total encryption and decryption time of our scheme is more efficient than the lightweight schemes with the underlying operation of bilinear pairing. [ABSTRACT FROM AUTHOR]

With rapid development and deployment of artificial intelligence methods, the Industrial Internet of Things (IIoT) has highly developed to fast tracing industrial communications and optimizing manufacturing processes. In Industry 5.0, hyper-automation process as one of technological trends navigates industrial entities to intelligent devices of the IIoT, cloud computing, smart robotics, smart agile software and embedded components by high complexity and reliability. By increasing data communication in the IIoT environments and cloud computing, the security and safety of hyper-automation process is also increasingly unstable and challengeable with respect to cyber-attacks, unstructured malwares and abnormal activities. With the diversification and unexpected behaviors of cyber-security threats, traditional cyber-attack detection systems have some critical problems with increasing massive data including unappropriated feature selection and extraction, high computation time in prediction and inaccurate classification models. Due to the above-mentioned challenges, this paper presents a new cloud-based cyber-attack detection architecture based on Ensemble Bagged Trees Detection (EBTD) algorithm for predicting malicious behaviors and types of cyber-attacks for hyper-automation process in the IIoT. The proposed architecture uses Analysis of Variance (ANOVA) and a priority-based feature selection and extraction model to find the optimal features with highly dependent on the network traffic, computation time, malicious behaviors, and types of attacks. Then, experimental results are conducted using the technical data sets UNSW-NB15 and NSL-KDD. The simulation results show that the proposed architecture performance performs better than other case studies and prediction models and effective on optimization of large-scale cyber-attack detection systems for critical hyper-automation process in the IIoT environment. [ABSTRACT FROM AUTHOR]

Healthcare (HC) is among the most promising sectors for assessing Internet of things (IoT) based technological advances, as patients can use wearable or injected medical sensors to monitor medical parameters any place and at any time. The data gathered by IoT devices can be sent to medical professionals, and doctors have real-time access to their patient's data. Nevertheless, the IoT devices within the network have limited resources and minimal computing capability, causing energy conservation issues. Even though clustering saves energy in network nodes, existing clustering methods could be more effective because of the higher Energy Consumption (EC), poorly balanced network load, and enhanced end-to-end delays. Furthermore, the integrity and security of medical information have become significant issues for HC applications. This paper proposes a secure and energy-efficient data transmission (DT) framework for IoT-based HC (IoT-HC) systems using enhanced mayfly clustering-based Q learner routing (EMCQLR) and exponential key-based elliptical curve cryptography (EKECC) techniques. The proposed work consists of the following steps. Double hash biometric-based authentication (DHABA) is initially used for IoT user authorization that prevents the HC network from unauthorized data access. The cluster head (CH) is then selected using the enhanced mayfly optimization algorithm (EMOA) to build clusters of IoT medical sensors and collect data from the nodes. Data is routed to the sink node by checking for data duplication. The path-weighted Q reinforcement learning (PWQRL) model can perform data routing. Finally, the EKECC algorithm encrypts the data packets received from the body sensors, providing security to the patient's data when DT is performed over an unsecured wireless channel. The performance of the suggested system is evaluated using the energy, loss ratio, throughput, success rate, and delay. The simulation outcomes show that the proposed method outperforms existing methods. Furthermore, false data elimination check and reliability metrics show that our proposed scheme ensures information privacy, message authenticity, and integrity while requiring minimal communication overhead and computation. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) offers a vast infrastructure where numerous devices interact to collect data or perform simple processing activities. These devices are, usually, equipped with sensors, software and storage capabilities being able to process the collected data and exchange knowledge with their peers. Data and knowledge may be transferred, in an upwards mode, to the edge infrastructure and Cloud to be the subject of more complex processing. Advanced processing may be also realized upon clusters of IoT devices or edge nodes, i.e., a set of interconnected devices/nodes.Such group of nodes may work to achieve a common goal, thus, we may desire to rely on their collected data to support activities like federated learning.A training process, when necessary, may be delivered across the cluster of nodes to conclude the view of the group. In this paper, we focus on the development of a monitoring process upon the collected data and a grouping method upon multiple datasets formulated by IoT devices (hosted by edge nodes) in order to deliver the most similar ones. Our vision is to continuously identify similar datasets to support knowledge extraction through the adoption of techniques like the aforementioned federated learning. We utilize a correlation detection method combined with a probabilistic model to conclude the most similar datasets. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) technology has gained a reputation in recent years due to its ease of use and adaptability. Due to the amount of sensitive and significant data exchanged over the global Internet, intrusion detection is a challenging task in the vast IoT network. A variety of hostile behaviors and attacks are now detected by intrusion detection systems (IDSs), which are difficult or impossible for a single method to identify. An Improved Binary Golden Jackal Optimization (IBGJO) algorithm and Long Short-Term Memory (LSTM) network are used in this paper to develop a new IDS model for IoT networks. Firstly, the GJO is improved by opposition-based learning (OBL). A binary mode of the improved GJO algorithm is used to select features from IDS data in order to determine the best subset selection. IBGJO uses OBL strategy to improve the performance of the GJO and prevents the algorithm from getting trap in local optima by controlling initial population. LSTM is used in the IBGJO-LSTM model to classify samples. Although high detection rates are achieved by machine learning techniques, the efficiency of these methods decreases with the increase in the size of the dataset. To overcome these problems, deep learning methods are more suitable for distinguishing samples from huge amount of data. The proposed model was assessed using the NSL-KDD and CICIDS2017 datasets. For CICIDS2017 and NSL-KDD, the proposed model was 98.21% accurate. The results show that the recognition accuracy of the proposed model is higher than the models BGJO-LSTM, Binary Whale Optimization Algorithm-LSTM (BWOA-LSTM) and Binary Sine Cosine Algorithm-LSTM (BSCA-LSTM). This is likely because the binary mode of the improved GJO algorithm is able to more effectively select the most relevant features from the IDS data and the LSTM is able to more accurately classify the samples. Also, the proposed model has a significantly higher percentage accuracy than Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB). [ABSTRACT FROM AUTHOR]

Internet of Things (IoTs) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, IoT devices are highly vulnerable to cyber-attacks, which may result in security breaches and data leakages. To effectively prevent these attacks, a variety of machine learning-based network intrusion detection methods for IoT networks have been developed, which often rely on either feature extraction or feature selection techniques for reducing the dimension of input data before being fed into machine learning models. This aims to make the detection complexity low enough for real-time operations, which is particularly vital in any intrusion detection systems. This paper provides a comprehensive comparison between these two feature reduction methods of intrusion detection in terms of various performance metrics, namely, precision rate, recall rate, detection accuracy, as well as runtime complexity, in the presence of the modern UNSW-NB15 dataset as well as both binary and multiclass classification. For example, in general, the feature selection method not only provides better detection performance but also lower training and inference time compared to its feature extraction counterpart, especially when the number of reduced features K increases. However, the feature extraction method is much more reliable than its selection counterpart, particularly when K is very small, such as K = 4 . Additionally, feature extraction is less sensitive to changing the number of reduced features K than feature selection, and this holds true for both binary and multiclass classifications. Based on this comparison, we provide a useful guideline for selecting a suitable intrusion detection type for each specific scenario, as detailed in Table 14 at the end of Sect. 4. Note that such the comparison between feature selection and feature extraction over UNSW-NB15 as well as theoretical guideline have been overlooked in the literature. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoTs) is a complex and large network of all kinds of equipment and things that covers various areas such as military networks, industrial networks, smart urban infrastructures, education, and many other areas, high volume of traffic, and the growing number of connected equipment. to these networks has made intrusion detection in these networks a challenging issue; Therefore, protecting these networks from various attacks is of particular importance and can be established through planning and establishing effective security controls. One of these important and effective methods of security control is the use of an intrusion detection system (IDS). Due to the ability of machine learning and deep learning methods to learn from existing real examples, they are used to prevent, predict, and intelligently and quickly track complex and unknown future examples. The most important ability and feature of these methods are to detect and predict new attacks, which in many cases are mutated or inspired by previous attacks. Therefore, IoTs control and security systems use and develop new methods in creating safe and intelligent communication between their devices and equipment, which are activated using advanced deep learning and machine methods. Also, the success and advancements of deep learning methods in various fields and fields with extensive data have made activists in the field of the internet interested in these methods. In this paper, to solve the mentioned problems and challenges, we also present a new and effective method of detection based on long short-term memory (LSTM)–recurrent neural network (RNN) and kernel principal component analysis (PCA). To achieve data preprocessing, high accuracy detection rate, feature extraction, and attack detection is embedded in an end-to-end or global detection method. We have used the NSL-KDD dataset to evaluate the results and check the presented idea. The results of our experiments and simulations show that the proposed IDS-LSTM attack detection strategy and idea has better performance than several detection and prediction strategies that use neural networks, support vector machines (SVM). [ABSTRACT FROM AUTHOR]

With the rapid convergence of the Internet of Things (IoT) and command and control communication networks, there is a swift surge in computation-intensive applications and heterogenous devices. Building an edge computing network near the protocol gateway not only facilitates network protocol conversion, but also alleviates computational load on the gateway. Nevertheless, traditional protocol gateways necessitate the inclusion of conversion methods for all communication protocols in the network for data interoperability with other devices. Consequently, constructing low-complexity protocol conversion programs, and the more efficient and equitable allocation of edge server resources, are critical challenges that need to be addressed. This paper investigates the joint problem of task offloading and heterogeneous protocol device access in command and control communication networks, deriving the benefit parameters of task offloading. We propose a real-time data unified access platform, which, by distributing configuration files, enables the IoT gateway to generate protocol conversion programs, thereby transforming all device protocols into a unified common protocol. Furthermore, to enhance the efficiency of heterogeneous device access to the network, we design a data parsing/encapsulation algorithm based on the real-time data unified access platform, supporting bidirectional mapping of two types of data. Experimental validation confirms the reasonability and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Location privacy (LP) plays an important role in location-based services (LBS) offered by different service providers. Many applications request users to enable the location services in their smart devices and access geodata from their smartphones which becomes a security threat. To enhance the individual's privacy while accessing LBS systems, this paper proposed a novel Hilbert curve (HC) based algorithm for generating k-dummy anonymous locations. These anonymous location helps to generate anonymous trajectories when a user requests a point of interest (POI) from the LBS in a vehicular cloud network (VCN). The proposed technique can generate a dummy block to fill it with the k-dummy location of each request from the user. The proposed approach consists of the following four stages. The first one starts with registering the different smart internet of things devices (IoT) in the VCN. The second one includes processing the devices' trajectories into VCN. The third one generates the k-dummy location of trajectories in LP and applies the HC to fill the square spaces. The Fourth stage consists in requesting anonymous POI from the LBS. The results show the proposed approach's effectiveness on parameters like flexibility and reliability of filling the square space in different iterations. [ABSTRACT FROM AUTHOR]

The term “Internet of Things” (IoT) refers to a system of networked computing devices that may work and communicate with one another without direct human intervention. It is one of the most exciting areas of computing nowadays, with its applications in multiple sectors like cities, homes, wearable equipment, critical infrastructure, hospitals, and transportation. The security issues surrounding IoT devices increase as they expand. To address these issues, this study presents a novel model for enhancing the security of IoT systems using machine learning (ML) classifiers. The proposed approach analyzes recent technologies, security, intelligent solutions, and vulnerabilities in ML IoT-based intelligent systems as an essential technology to improve IoT security. The study illustrates the benefits and limitations of applying ML in an IoT environment and provides a security model based on ML that manages autonomously the rising number of security issues related to the IoT domain. The paper proposes an ML-based security model that autonomously handles the growing number of security issues associated with the IoT domain. This research made a significant contribution by developing a cyberattack detection solution for IoT devices using ML. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. Compared to previous research, the proposed approach achieved a 99.9% accuracy, a 99.8% detection average, a 99.9 F1 score, and a perfect AUC score of 1. The study highlights that the proposed approach outperforms earlier machine learning-based models in terms of both execution speed and accuracy. The study illustrates that the suggested approach outperforms previous machine learning-based models in both execution time and accuracy. [ABSTRACT FROM AUTHOR]

Facing the exponential demand for massive connectivity and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets. Particularly, the operators have to cope with the continuous prosperity of the Internet of things (IoT) along with the ever-increasing deployment of machine-type devices (MTDs). In this regard, due to its compelling benefits, non-orthogonal multiple access (NOMA) has sparked a significant interest as a sophisticated technology to address the above-mentioned challenges. In this paper, we consider a hybrid NOMA scenario, wherein the MTDs are divided into different groups, each of which is allocated an orthogonal resource block (RB) so that the members of each group share a given RB to simultaneously transmit their signals. Firstly, we model the densely deployed network using a mean field game (MFG) framework while taking into consideration the effect of the collective behavior of devices. Then, in order to reduce the complexity of the proposed technique, we apply the multi-armed bandit (MAB) framework to jointly address the resource allocation and the power control problem. Thereafter, we derive two distributed decision-making algorithms that enable the users to autonomously regulate their transmit power levels and self-organize into coalitions based on brief feedback received from the base station (BS). Simulation results are given to underline the equilibrium properties of the proposed resource allocation algorithms and to reveal the robustness of the proposed learning process. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoT) devices has given rise to applications that demand real-time responses and minimal delay. Fog computing has emerged as a suitable platform for processing IoT applications, extending cloud computing services to the edge of the network. This enables more cost-effective and time-efficient processing at the network's edge. However, determining how to allocate tasks to fog nodes presents a fundamental challenge, involving factors like energy consumption and limited fog server capacity, impacting quality of service parameters such as delay. This paper introduces a mathematical formula for resource allocation to minimize delay and energy consumption while considering quality of service criteria. The subsequent step involves presenting a hybrid genetic algorithm (GA) and the gray wolf optimization (GWO), constituting an improved hybrid approach where the GA exhaustively explores the solution space to reduce the risk of converging to a locally optimal point. The combination of these algorithms produces multiple solutions. Despite incurring processing costs and computation delays, the implementation of these algorithms is crucial for enhancing the Quality of Service (QoS). In conclusion, the results indicate that the simultaneous use of positive aspects from both algorithms significantly improves execution time, final task completion time compared to the other methods. [ABSTRACT FROM AUTHOR]

Function-as-a-service has reduced the user burden by allowing cloud service providers to overtake operational activities such as resource allocation, service deployment, auto-scaling, and load-balancing, to name a few. The users are only responsible for developing the business logic through event-triggered functions catering to an application. Although FaaS brings about multiple user benefits, a typical challenge in this context is the time incurred in the environmental setup of the containers on which the functions execute, often referred to as the cold-start time leading to delayed execution and quality-of-service violations. This paper presents an efficient scheduling strategy FASE that uses a finite-sized warm pool to facilitate the instantaneous execution of functions on pre-warmed containers. Test-bed evaluations over AWS Lambda confirm that FASE achieves a 40% reduction in the average cold-start time and 1.29 × speedup compared to the baselines. [ABSTRACT FROM AUTHOR]

With the proliferation of the Internet of Things and smart devices, there exists an urge to address the critical computation demands of end users for several real-time applications. Fog computing (FC) targets to address the computation requirements nearer to the end user in almost real-time. Mainly, the fog environments (FE) exhibit characteristics of resource constraint and wide heterogeneity. In addition, the complex fog environment enforces the fog resources (FR) deployment either within fixed or distributed over random geographical locations. In the literature, there exist several solutions for the optimization of FR allocation. However, the location-dependent FR allocation has not been addressed thoroughly. Hence, this paper proposes a novel hybrid clustering-based deep Q-network for location-based optimal resource allocation (HCDQN-ORA) model. Here, the first objective is to minimize the search operation for identifying suitable FR and to meet the quality of service (QoS) demand using an enhanced fog resource clustering (E-FRC) algorithm. The second objective aims to achieve optimal FR allocation among the set of location-dependent FRs using reinforcement learning techniques. Problem formulation has been modelled using Markov decision process. A deep Q-network algorithm with two variants, namely enhanced optimal resource allocation using deep Q-network (EORA-DQN) and enhanced optimal resource allocation using deep Q-network with experience replay (EORA-DQN-ER), has been incorporated. To analyse the statically located and randomly distributed FR environment, the experimental results exhibit that, in the case of clustering of FRs, E-FRC is more efficient than conventional clustering algorithms. Next, the location-dependent optimal FR allocation problem incorporates the performance analysis based on the convergence of loss function and the parametric analysis based on batch size, respectively. Results exhibit that EORA-DQN performs better only for statically allotted resources. However, the proposed EORA-DQN-ER outperforms both types of FR with a success rate of 89.6% of optimal allocation with the batch size of 200. [ABSTRACT FROM AUTHOR]

Securing confidential data against unauthorized users leads to access control policies with the rapid progression of Internet of Things (IoT) devices. Because of high mobility subscribers, the dynamic IoT environment is subjected to high signaling overhead which remains a challenging issue to guarantee data dissemination to legitimate users. The group's key management schemes are the central mechanism to deal with dynamic environments. But they are centralized concepts that cause scalability issues and suffer in handling large numbers of subscribers. Therefore, this paper proposes a modified deep reinforcement oppositional wolf learning-based group key management (MDROWL-GKM) system to monitor the data obtained in IoT properly. It does not maximize the network traffic as well as computational overhead when a group member leaves or joins. With the inclusion of an opposition-based learning gray wolf optimization algorithm, the overload issue of the modified deep reinforcement method is eliminated and the performance is enhanced. The efficacy of the proposed MDROWL-GKM system is investigated using different measures namely storage overhead, computation overhead, access response time, space complexity, re-evaluation time, policy adjustment accuracy, and communication overhead. The experimental analysis proves that the proposed MDROWL-GKM system is superior to other state-of-the-art techniques, particularly with high policy adjustment accuracy (96%), less communication overhead (8 μs) and area under curve (AUC) rate (0.982). [ABSTRACT FROM AUTHOR]

The advent of Internet of Things (IoT) in agriculture has revolutionized the way farmers monitor and manage their crops. IoT-enabled sensors can provide real-time data on various environmental parameters such as temperature, humidity, soil moisture, and crop growth, which can be used to make informed decisions and optimize crop yield. However, the vast amount of data generated by these sensors poses a significant challenge in terms of data processing and communication. To address this challenge, clustering is often used to group the sensors into clusters and elect a cluster head (CH) to communicate with the gateway node. The selection of an appropriate CH and the optimal path for data transmission are critical factors that affect the performance of the IoT system. In this paper, we propose a novel approach to optimize the CH selection and path selection using modified fuzzy logic, Whale optimization algorithm (WOA) and enhanced crow swarm optimization (ECSO). Fuzzy logic is used to evaluate the relevant parameters such as energy, distance, overhead, trust, and node degree to select the most suitable CH. ECSO is then employed to find the optimal path for data transmission based on the selected CH. We evaluate the proposed approach using simulation experiments in a smart agriculture scenario. The results show that the proposed approach outperforms existing approaches in terms of throughput, packet delivery ratio, delay, and energy efficiency. The proposed system demonstrates substantial advantages in terms of throughput, with a remarkable throughput value of 68 Mbps, outperforming the other methods, including EQSR (12.5 Mbps), E2S-DRL (60 Mbps), and OSEAP. The packet delivery ratio (PDR) is also significantly higher for the proposed system, reaching 90.9%. The proposed approach can significantly improve the performance of IoT-enabled smart agriculture systems, leading to better crop yield and higher profitability for farmers. The results of the simulation experiments demonstrate the superiority of the proposed approach over the existing one's throughput, packet delivery ratio (PDR), delay, and energy consumption efficiency is found in the result section. [ABSTRACT FROM AUTHOR]

This paper aims to propose a prediction method based on Deep Learning (DL) and Internet of Things (IoT) technology, focusing on the ecological security and tourist satisfaction of Ice-and-Snow Tourism (IST) to solve practical problems in this field. Accurate predictions of ecological security and tourist satisfaction in IST have been achieved by collecting and analyzing environment and tourist behavior data and combining with DL models, such as convolutional and recurrent neural networks. The experimental results show that the proposed method has significant advantages in performance indicators, such as accuracy, F1 score, Mean Squared Error (MSE), and correlation coefficient. Compared to other similar methods, the method proposed improves accuracy by 3.2%, F1 score by 0.03, MSE by 0.006, and correlation coefficient by 0.06. These results emphasize the important role of combining DL with IoT technology in predicting ecological security and tourist satisfaction in IST. [ABSTRACT FROM AUTHOR]

An industry-wide paradigm change has been sparked by the growth of Internet of Things (IoT)-based Wireless Sensor Networks (WSNs), which has made reliable and effective routing methods necessary. This thorough analysis looks at how Machine Learning (ML) techniques may be used to solve the problems that come with WSN routing. A summary of standard routing algorithms and an examination of their shortcomings comprise the first portion of the paper. The integration of ML approaches, such as reinforcement learning and supervised and unsupervised learning, is then explored in order to improve WSN routing efficiency. The article examines the difficulties and factors related to ML-based routing, including data quality, energy efficiency, scalability, and security. Applications and case studies show how ML is really used in WSN routing, offering insights into effective tactics and lessons discovered. Evaluation metrics and performance assessments are included in a separate section that uses simulation and experimental data to compare ML-based and conventional techniques. Looking forward, the study describes new breakthroughs in ML for WSNs and points out unresolved issues, providing a guide for future research paths. The important results and their consequences are outlined in the conclusion, which also highlights how ML has the potential to revolutionize WSN routing in the future. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) as an emerging technology is widely used in various applications such as remote healthcare, smart environment, and intelligent transportation systems. It is necessary to address users' concerns about cost, ease of use, privacy, and comprehensive security to grow the popularity of this technology. Intrusion Detection System (IDS) plays an indispensable role in security and preventing unauthorized users to access authorized network resources through analyzing network patterns. Several techniques such as metaheuristic algorithms, machine learning, fuzzy logic, and artificial intelligence algorithms can be applied to increase the accuracy of IDS, feature selection, and network patterns classification. In this paper, a hybrid intrusion detection system based on Support Vector Machine (SVM) and Grey Wolf Optimization (GWO) is presented that utilizes the advantages of these algorithms. In the proposed approach, the support vector machine has been used to train and differentiate anomaly records from normal records and grey wolf optimization has been used to find the kernel function, feature selection, and adjust optimal parameters for the SVM in order to improve the classification. The conducted simulations prove that the proposed approach outperforms in terms of detection accuracy, precision, recall, and F-score on both NSL-KDD and TON_IoT datasets. [ABSTRACT FROM AUTHOR]

The technological breakthroughs of Internet of thing (IoT) applications in various sectors are nowadays contributing to the development of smart mobile IoT (SM-IoT). Lack of authentication and encryption schemes make SM-IoT more susceptible to major security and privacy issues. Signcryption has evolved as a new paradigm in recent years that provides better authenticity and security to the messages and minimizes both computation cost and communication costs simultaneously. To secure the communicated message on the sender and receiver sides, this paper proposes an efficient and secure decentralized communication system to offer personalized service for the users. The proposed network framework comprises four key entities namely trusted service providers, gateways, resource senders, and receivers. The master private keys are generated at the initial setup phase. Then, the optimal partial private keys are extracted and broadcasted through the channel by the key extraction process utilizing the fuzzy whale sine cosine levy algorithm. The ciphertext is generated for the sender on signcryption using a modified Identity-based Cryptography approach. The signcryption ciphertext generated is broadcasted via gateways to the receiver by measuring verification parameters. Finally, the designcryption process is performed using the certificateless hybrid signcryption scheme to retrieve the plaintext message. The proposed secure communication network is evaluated using performance metrics such as bilinear pairing time, modular experimentation time, pairing-based experimentation time, elliptic curve (EC) scalar point multiplication time, map-to-point hash function time, and EC point addition time. The experimental results revealed the effectiveness of the proposed secure communication framework on both the sender and receiver sides. [ABSTRACT FROM AUTHOR]

This paper proposes a numerically and experimentally validated printed wideband antenna with a planar geometry for Internet of Things (IoT) applications. This design tackles the challenges associated with deploying IoT sensors in remote areas or across extensive geographical regions. The proposed design exploits a coplanar-waveguide-fed modified microstrip line monopole for excitation of circularly polarized waves radiating in the broadside direction. The primary design is based on perturbations of the microstrip line protracted from a grounded coplanar waveguide. The capacitively coupled short rectangular stubs are periodically inserted alternately and excited asymmetrically on each side of the microstrip line parallel to the direction of the electric field vector. The sequential phase excitation of the periodic stubs generates a rectangular-cascaded electric field, which suppresses the stop band at the open end. As a result, the antenna radiates in the broadside direction. The impedance bandwidth of the antenna exceeds 8 GHz in the 28 GHz mm-wave band, i.e., it ranged from 25 to 33.5 GHz. Additionally, an axial ratio below 3 dB is achieved within the operating band from 26 to 33.5 GHz with the alterations of the surface current using straightforward topological adjustments of the physical parameters. The average in-band realized gain of the antenna is 10 dBic when measured in the broadside direction. These results indicate that the proposed design has the potential to improve the connectivity between IoT devices and the constantly varying orientation of satellites by mitigating the polarization mismatch. [ABSTRACT FROM AUTHOR]

DDoS attacks launched by IoT botnets can be classified into low-rate and high-rate DDoS attacks, which depict two distinct scenarios of data imbalance, namely, the minimal and maximal proportion of attack traffic. Developing a detection model that can effectively address two data imbalance scenarios concurrently is crucial in safeguarding computers against DDoS attacks. This necessitates the development of a model with enhanced generalizability. However, in the examination of cross-evaluation across datasets which is closely associated with the generalizability of models, there is a limited focus on addressing the issue of data imbalance. This oversight has led to a significant decrease in model performance when compared to training and testing on a single dataset. To identify guiding principles within this context, the literature suggests employing an undersampling technique on benign instances during the preprocessing phase to eliminate redundant data. Additionally, the literature conducts simulations of various cross-evaluation scenarios and evaluates the performance of classifiers. This evaluation is done after applying representative oversamplers, undersamplers, and the method proposed in this paper. After analyzing the experiment results, it is advisable to employ oversamplers for low-rate DDoS datasets and undersamplers for high-rate DDoS datasets. Moreover, optimizing SMOTE-based algorithms for specific models will yield optimal performance. [ABSTRACT FROM AUTHOR]

Over the past few decades, the harmful effects of industrial activity and consumer society have resulted in global warming. Governments and international organizations are looking for ways to monitor activities of individuals and companies to assess their ecological impact. Unfortunately, such an approach would easily be seen as a mass surveillance tool. This is why we are proposing in this paper a novel privacy-aware global infrastructure for ecological footprint calculator based on the Internet of things and blockchain. Indeed, we take advantage of the data collection capacity of the Internet of things, the anonymization provided by public key identification and encryption, and the immutability of blockchain to implement this global system. A three-stage approach was used to validate our architecture: modeling in Petri nets to verify that the infrastructure fulfills all the required missions, implementing the three central authorities with python to record parameters such as durations, and finally modeling in queuing networks to demonstrate stability. The blockchain and Internet of things parts are used in a purely abstract manner relying on standard concepts; therefore, we have not implemented them. The experiments have produced very promising results. We have shown that for the simplest form of queue modeling, the involved servers have a utilization rate that is close to 50 % and that the overall waiting time remains below one minute. [ABSTRACT FROM AUTHOR]

Advancements in computing and communication technology have increased the number of devices connected to the Internet, leading to the emergence of the Internet of Things (IoT) technology. However, currently available network security measures are mainly designed for devices with higher computing power and cannot be directly applied to IoT devices. Client authentication is a fundamental aspect of security in IoT systems and is often achieved using digital signatures. Current IoT client authentication methods, such as RSA and ECDSA, are resource-intensive and vulnerable to attacks. Non-interactive zero-knowledge proof approaches are more suitable for IoT networks. MQTT is a widely used communication protocol in the IoT environment, but the client authentication feature of the standard MQTT protocol is poor. This paper proposes improving the elliptic curve cryptography-based non-interactive zero-knowledge proof authentication algorithm ECDSA. The proposed algorithm, T-ECDSA, uses a timestamp-based key to encrypt/decrypt the signature, valid only for a short period, making signature reuse invalid. The T-ECDSA is incorporated into the MQTT broker to avoid the requirement of a different dedicated server for authentication purpose. The experimental results demonstrate better security and improved time and execution complexity for the T-ECDSA compared to the standard ECDSA. [ABSTRACT FROM AUTHOR]

The increasing number of network attacks has led to the development of intrusion detection systems. However, these methods often face limitations such as high traffic flow data dimensions, which can reduce attack detection rates and noise sensitivity, affecting anomaly detection performance. This paper introduces a new model based on recurrent deep learning and instance-level horizontal reduction to detect anomalies and network attacks. The model uses nested sliding windows, which move with a specific step in the data and generate a different number of histogram outputs based on the type of anomaly in the data. Evaluation results on five databases show that the proposed model achieves a high accuracy of 99% in detecting different attacks, demonstrating the success of this new approach combined with deep recurrent neural networks in detecting anomalies. [ABSTRACT FROM AUTHOR]

Manufacturers have traditionally relied on regular maintenance and inspections to ensure the proper functioning of all technical devices, machines, and components. In the era of Industry 4.0, these precautionary measures are supplemented and extended considerably by applying predictive maintenance. This form of maintenance is almost symbolic of Industry 4.0 and promises tremendous success. However, reliable data collection, integrating Industry 4.0 into the pre-existing manufacturing infrastructure, and analysing production machine status and production-related data present real challenges. A solution presented in this paper addressing these issues is an FPGA-based edge device development for data analysis and fault detection. In the proposed system, the MFCC method is implemented on a Pynq-Z2 board as pre-processing IP cores used to convert the vibration data collected to detect and diagnose machinery-bearing faults into 2D feature representations, which are then utilised for fault prediction based on optimised CNN network architecture. The Pynq-Z2 platform can be connected to the internet network directly through the Ethernet port and remotely controlled, making it perfect for IoT applications. We opted for the NCS2 accelerator from Intel to implement the proposed CNN network that will be connected to the Pynq-Z2 platform through the USB port. An experimental test through an IIoT application was carried out to validate the proposed system. The results showed that the system could perform pre-processing and prediction tasks in less than 40 ms. [ABSTRACT FROM AUTHOR]

This paper presents a review of voltammetric and related methods implemented with low-cost, mainly lab-made instrumentation. In recent years, there has been a growing interest in developing low-cost electrochemical instrumentation. Advances in electronics and software have made it easier to access high-performance components for the construction of potentiostats. Currently, lab-made instrumentation for electrochemical research can be very simple, with minimal components to perturb the system and record the response signal. It can also be very advanced, using high-performance electronic components and software development. Several authors have presented the feasibility of designing and manufacturing electrochemical instruments for voltammetry, chronoamperometry, chronopotentiometry, and electrodeposition. This review presents some available options, ranging from basic to advanced. The aim is to provide readers with the full range of possibilities for using their own instruments, especially in cases where access to commercial apparatus is difficult or where specific applications are needed, for example in the Internet of Things or artificial intelligence. [ABSTRACT FROM AUTHOR]

The core premise of cancelable biometrics lies in the creation of a distinct biometric template for every individual, which can be either canceled or regenerated as needed. This process requires the use of a uniquely-defined key during the generation of such template. The generated templates are tailored to be key-specific. This ensures that each distinct key will generate a unique template, while preserving the integrity and security of the original biometric data, ensuring that it remains uncompromised. In this paper, a cancelable biometric system based on electrocardiography (ECG) and photoplethysmography (PPG) signals is introduced. A signal fusion process is implemented for the two traits to generate a single template per user. In order to enhance the security of generated templates, a well-designed permutation stage is implemented according to a user-specific key. The permutation key is obtained through a well-designed look-up table created by the authors. The user verification is conducted on the cancelable template, without the need for any inversion processes. The user verification scheme depends on a two-pronged approach: robust feature extraction followed by the application of a machine learning (ML) classifier. The mel-frequency cepstral coefficients (MFCCs) extraction algorithm is employed for feature extraction due to the low frequency range of the adopted biometric signals and the nonlinearity of the filter bank used for MFCC extraction. Several ML classifiers are adopted to validate the system with cancelable templates without any inversion process. Simulation results with multilayer perceptron (MLP) and logistic regression (LR) classifiers demonstrated superior effectiveness of the proposed authentication framework, with accuracy rates up to 100% and 99.7% on the pulse transit time PPG and BIDMC datasets, respectively. Hence, the proposed system proves effective access control and user verification in the Internet-of-Things (IoT) applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to model and monitor the energy dynamics of smart devices within the context of the Internet of Things (IoT). The proposed approach employs eXtended Hybrid Petri nets (xHPN) to emulate the behavior of interconnected smart devices forming a wireless network. The novelty of this study lies in the utilization of a fluidic representation to model the battery behavior of smart devices, allowing for the simulation of continuous energy consumption and replenishment via renewable energy harvesting to reflect real-world scenarios. Furthermore, in order to conserve energy, we introduce a new sleeping mechanism named the Triple Sleeping Strategy (TSS). By considering the mean battery charge and the mean sleeping percentage as evaluation metrics, the experimental study showcases the predictive capabilities of the developed model in simulating the performance of IoT networks prior to their actual deployment. Comparative analysis against recent works that use simple and double sleeping strategies, demonstrates the benefits of our approach, in terms of energy efficiency and device lifespan. For instance, when the device is configured with a 90 % sleeping percentage, TSS maintains a decent mean battery level for ten days, almost 8% higher than the double sleeping strategy. Furthermore, the presented case study demonstrates the ability of the proposed model to select appropriate parameters and configurations such as solar panel area and position, battery capacity, packet length, and deployment zone, to cope with the desired performance criteria. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has been emerging as an essential aspect of smart applications that are presently there in wireless communication. Gateways play an important role in IoT as they are capable of filtering the data that is being communicated to the cloud platforms. The data is sent via intermediary gateways utilizing a variety of wireless technologies to application servers located in the IoT cloud. Using standard routing strategies to link millions of IoT devices to the internet results in gateway congestion and overload resulting in decrease in IoT network throughput. But for cost-effectiveness, optimal gateway placement is required and this can be possible by integrating Solution Specific Gateways (SSGs) thus it creates a multi-technology gateways management scenario. In this paper, an Enhanced Multi-technology Gateway Management (EMTGM) scheme has been proposed based on the efficient selection of SSGs along with context aware handover decision making based on multiple objectives. The proposed scheme is further strengthened as both AHP and TOPSIS concepts are integrated for effective decision making which makes it a more efficient one. The proposed framework is simulated on Mobisim and its effectiveness has been analyzed with respect to existing approaches under the network parameters like End to end Delay, Packet Delivery Ratio, Residual Energy, Energy Consumption and Throughput by varying the arrival tasks, node count and simulation time. From the results, it is inferred that the proposed scheme outperforms existing schemes and thus is a quite effective one. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things, the real-time monitoring of residents' sports health service system has become a key demand. As a common optical sensing technology, light sensor has the advantages of high sensitivity and real-time performance. Therefore, this paper discusses the application potential of real-time monitoring of Internet of Things based on light sensor in residents' physical health service system, and evaluates its effectiveness and feasibility in monitoring users' movement, physical signs and environmental information. A real-time monitoring system of Internet of Things based on light sensor is designed and integrated into the residents' sports health service system. Multiple optical sensor modules are used to collect real-time user movement, physical signs and environmental information. By analyzing and processing sensor data, the performance of the system in real-time monitoring of user movement, physical signs and environmental information was evaluated. Experiments show that the real-time monitoring system based on light sensor can effectively monitor the user's movement, physical signs and environmental information in real time. The system has a high degree of sensitivity and accuracy, and can accurately identify the user's type of exercise, heart rate changes, and environmental parameters. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is crucial for deploying a novel Artificial Intelligence (AI) model for both network and application management. However, using classical centralized learning algorithms in the IoT environment is challenging, given massively distributed private datasets. Advancements in AI have helped us solve various use cases, but it operates under two significant challenges. Firstly, the data exists in separate clusters, and secondly, the current AI has limited data privacy and security. Federated learning (FL) aims to preserve data privacy through distributed learning methods that keep the data in storage silos. Likewise, differential privacy improves data privacy by measuring the privacy loss in communication among the elements of FL. The paper proposes two adaptive approaches for making model training differentially private in a vertical federated environment. The first one uses random feature selection to train different machine learning models, and performance improvement is also proposed. The second approach uses a tree structure, i.e., Classification and Regression Trees, using some defined constraints. Further, we created a scheme to help identify malicious users/devices in a federated network cluster using parity checks for every FL iteration. [ABSTRACT FROM AUTHOR]

Federated learning is a distributed machine learning approach that enables a large number of edge/end devices to perform on-device training for a single machine learning model, without having to share their own raw data. We consider in this paper a federated learning scenario wherein the local training is carried out on IoT devices and the global aggregation is done at the level of an edge server. One essential challenge in this emerging approach is IoT devices selection (also called scheduling), i.e., how to select the IoT devices to participate in the distributed training process. The existing approaches suggest to base the scheduling decision on the resource characteristics of the devices to guarantee that the selected devices would have enough resources to carry out the training. In this work, we argue that trust should be an integral part of the decision-making process and therefore design a trust establishment mechanism between the edge server and IoT devices. The trust mechanism aims to detect those IoT devices that over-utilize or under-utilize their resources during the local training. Thereafter, we introduce DDQN-Trust, a double deep Q learning-based selection algorithm that takes into account the trust scores and energy levels of the IoT devices to make appropriate scheduling decisions. Finally, we integrate our solution into four federated learning aggregation approaches, namely, FedAvg, FedProx, FedShare and FedSGD. Experiments conducted using a real-world dataset show that our DDQN-Trust solution always achieves better performance compared to two main benchmarks: the DQN and random scheduling algorithms. The results also reveal that FedProx outperforms the competitor aggregation models in terms of accuracy when integrated into our DDQN-Trust solution. [ABSTRACT FROM AUTHOR]