Search

In recent days, a wide range of Internet of Things (IOT) related applications are employed for automated services. Various issues such as security, reliability and fault tolerance has restricted the use of IoT services in real time environments. Also, the transmitted data are prone to various types of attacks. Therefore, there is a need for secure data transmission and different types of attack has to be classified. Thus, a novel encryption technique is proposed in this study for affording data security in IoT system and a novel deep learning technique is proposed for the effective categorization of attacks in IoT data. Initially, the input data is hashed using Amended Merkle Tree approach (AMerT). Then, the data are encrypted using Secret Elliptic curve cryptography (SEllC). Next, the encrypted data are assembled in blockchain framework which is an Interplanetary File System (IPFS). Finally, decryption is carried out to access the given input data and the attacks are classified by proposing a novel deep learning technique called Attention Bidirectional Gated unit assisted Residual network (Att-BGR). Here, in order to improve the classification accuracy attention is used along with Bidirectional Gated unit assisted Residual network. Here, the Experimentation is carried out via Python and the efficiency of proposed work is determined by evaluating varied metrics. The comparison results exhibits that the proposed technique obtains better classification accuracy having an accuracy of 98.38%. Also, the encryption approach utilized has a minimum encryption time of 114s which is lower than the existing techniques.

The Internet of Things (IoT) has been applied in many areas for some time, ranging from a small dimmer gadget to lighting of a large building, from homogeneous component switching circuits to heterogeneous network connections. It is a many-combine-in-one system that operates by ad hoc connections. The digital twin (DT) represents the components and bi-directional data exchange between physical and virtual representation within the designated environment. This study looks into the power consumption of small scale IoT systems by visualizing at its design stage using DT technique. Their connection has brought about unforeseen power to multidisciplinary research and development of cyber-physical systems. The resulting combination offers a new realm of visualization process support to conceptual design that is once just an imaginative idea without seeing the real thing. Future visualization research will enhance the connected systems to yield a betterment of real life IoT applications.

Plant pest and disease management, especially in the early stages of infestation, is a critical challenge that poses significant threats and has potential to devastate agricultural crops, causing total yield loss and food insecurity. Traditional inspection methods are time-consuming and prone to errors due to limited labor expertise. Therefore, to tackle these challenges, harnessing advanced technologies such as artificial intelligence (AI), Machine Learning/Deep Learning (ML/DL), and Internet of Things (IoT) is essential for managing and mitigating agriculture hazards. This research presents a comprehensive review of the state-of-the-art DL architectures integrated with IoT-based systems applied to plant pest and disease detection (PPDD) by investigating different potential approaches that have been employed using DL and IoT up to the year 2024 to address challenges in agriculture. Convolutional Neural Network (CNN) architectures for image recognition, object detection, and their integration with IoT, embedded into mobile devices and unmanned aerial vehicles (UAV) are explored. Moreover, the research discusses the advantages and limitations of these techniques, emphasizing their architecture design, efficiency and accuracy. The findings demonstrate that there is a tradeoff between robustness and complexity among existing techniques, and authors recommend future trends aimed at creating robust models with fewer parameters that are more accurate and easily implementable on small IoT-based and portable devices suitable for in-field and real-time applications. Furthermore, while existing review papers discuss either DL or IoT separately, this research paper uniquely focuses on their combined models, providing a comprehensive overview of the synergistic potential of leveraging IoT-driven technologies alongside advanced DL algorithms to ease the task of researchers in the field of precision agriculture particularly in PPDD.

The Internet of Things transmits data collected from remote sensors and shares public and private data through the Internet or other communication networks. The IoT healthcare data uses decentralized cloud security for data transmission and storage of healthcare data. Privacy and security are critical components for managing private data and personalization. Due to the Big Data environment in healthcare data processing the sensitive data collection from Electronic Personal Healthcare Records poses challenges and difficulties. The unauthorized policies allow adversaries to obtain data and keys for sharing healthcare records. So Sensitive information in personal healthcare records needs to be highly protected in the medical field. The excessive insensitivity of the current privacy regulations causes delays in complexity and poor performance. Exposure-risk response interactions are necessary to calculate the burden of environmental ailments. To tackle this issue, this paper introduces Blockchain-based Decentralized Secure Sharing using the Shuffled Random Starvation Link Encryption algorithm to protect the sensitive PHR records in the smart IoT. Initially, the proposed quasi-sensitive attribute Identification method is used to analyze and categorize the sensitive cases of PHR. Then encrypt the sensitive attributes using the novel Shuffled Random Starvation Link Encryption algorithm to protect the user’s private data. The Key Authentication Policy is used to verify the key node aggregation and peer-end verification to share information between users. As a result, the proposed system outperforms previous approaches in reliability and security.

LoRaWAN is a widely adopted protocol for Internet of Things applications with energy constraints and large coverage area demands. The protocol standardization defines a star-of-stars topology, where end-devices send their packets directly to a gateway that forwards the messages to the network server, where they are handled depending on the application. LoRaWAN is at the top of the Long Range (LoRa) physical layer based on the Chirp Spread Spectrum (CSS) modulation, where signals are spread along the frequency by the Spreading Factor (SF). The modulation technique gives the LoRaWAN long range coverage characteristics, that could reach 45 kilometers in theory. Furthermore, the protocol adopts the Industrial, Medical, and Scientific (ISM) frequency band plans, which impose spectrum occupation limitations, and the constraints are defined by a device Duty Cycle (DC). The DC imposes a restriction of 0.1% up to 10% in transmission time, limiting the total amount of messages an end-deceive could send. In order to increase even more the coverage area, many works proposed the adoption of multihop techniques in LoRaWAN networks. Proposed solutions are based on routing and relay approaches, both with benefits and limitations. However, the literature does not explore the DC constraints on the multihop LoRaWAN solutions since packets might be enqueued. The packet delay increases due to the time spent in the buffer, and the delivery ratio decreases because packets are dropped when the buffer is full. To understand such impact, we provide a model to generalize the DC behavior on multihop LoRaWAN networks, focused on the packet delivery ratio and delay metrics. We evaluate our model analytically and experimentally. First, we conducted a numerical comparison between the standard network with the multihop Long Range Wide Area Network (LoRaWAN) over duty cycle constraints. The comparison considers a rural area and the coverage area limited by the 802.11 ah propagation model. The results elucidated the tradeoff between packet size, number of packets, and delay. The two-hop solution could decrease the delay compared with direct transmission using high spreading factors. However, in dense networks, the intermediate device buffer increases the packet delivery delay. It could reduce the packet delivery ratio because the device must respect the duty cycle to forward packets. On the other hand, higher spreading factors decrease the payload size and need more packets to transmit the same message. Then, we compared analytical and experimental results in four relayed scenarios with different numbers of devices and different intermediate device buffer sizes. The results show the DC significantly increases the network delay and packet losses. The intermediate device also causes packet prioritization, making the network unfair. The multihop strategies are relevant for many applications, including coverage area extension, but the solutions must consider the DC constraints as a limitation to their adoption.

Deep Edge Intelligence targets the deployment of deep learning algorithms at the edge devices to make them contribute to the overall intelligence in the Internet of Things. While training deep networks requires computational resources, edge devices usually lack high computational power. Decentralized online learning methods provide a solution for gathering limited information from edge devices to collectively improve prediction performance. However, methods that require multiple rounds of data transfer between edge components for an acceptable accuracy increase not only the training time but also the risk of communication errors due to connectivity failures that result in packet delays and losses. Besides, every device at the edge may not experience the same training data due to limitations set by physical constraints such as the location of devices and environmental factors. To overcome these bottlenecks, this study proposes a convolutional ensemble learning approach, coined EdgeConvEns, that facilitates training heterogeneous weak models on edge and learning to ensemble them by fusing the limited knowledge obtained from edge where data on edge are heterogeneously distributed. In this setup, it is possible for an edge device to barely train a shallow network that fits its resources. Losses that occur while transferring their feature representations to the edge server are handled by variational autoencoders, which learn to fill in missing representations. The last stage is the proposed convolutional ensemble model at the edge server which uses all representations during training and inference to boost the prediction performance. The overall proposed approach can also be employed in setups where edge devices can finely tune pre-trained inferences. To accelerate the training on edge devices, a flexible chained-tiling method is also proposed and demonstrated on field programmable gate array (FPGA) devices with various computational capacities. Extensive experiments demonstrate that EdgeConvEns can outperform the state-of-the-art performance with fewer communications and less data in training scenarios selected from the literature.

While Industry 4.0 improves human productivity, it also raises sustainability and social challenges. Industry 5.0, as a supplement and logical continuation of the Industry 4.0 paradigm, focuses on the development of a human-centric, sustainable, and resilient manufacturing system. This paper reviews the existing literature. First, it discusses the definition and implementation framework for Industry 5.0. Then, it expounds the application status of Industry 5.0 in the field of intelligent manufacturing from four perspectives: digital manufacturing and intelligence, human-centric intelligent manufacturing and production process management, decentralized and resilient production, and sustainable production. It summarizes the role of Industry 5.0 technology in various intelligent manufacturing scenarios, as well as the challenges it faces, and concludes by analyzing Industry 5.0’s potential development direction and future technologies. This paper believes that the application of Industry 5.0 technology will effectively improve the production capacity of intelligent manufacturing systems and promote the development of intelligent manufacturing systems in a safe, efficient, sustainable and resilient direction; the development of Industry 5.0 will focus on giving full play to human creativity, avoiding repetitive labor through human-robot collaboration, and thereby realizing human value.

As the growth of IoT networks increases exponentially, the number of cyber attacks is also increasing on IoT networks day-by-day. This results in the vital requirement of cyber security mechanisms to secure IoT networks from cyberattacks. To build such a security mechanism, researchers and cybersecurity practitioners need relevant IoT datasets. However, until now, only a few publicly available IoT network intrusion datasets exist in the literature. Moreover, these datasets lack coverage of IoT network traffic containing IoT application layer protocols like AMQP, XMPP, STOMP, etc. and various IoT-based attacks. So, in this work, we generate a new realistic and comprehensive IoT network intrusion dataset called MU-IoT for IoT cybersecurity. The network traffic contained in the MU-IoT dataset is collected from the Manipur University (MU) IoT Smart Lab, which is our own IoT network testbed. The testbed includes more than 30 devices, which include both physical and emulated IoT devices, and general-purpose devices such as laptops, desktops, servers, etc., that are usually present in a Smart Lab. The dataset contains normal network traffic generated from various applications and attack network traffic comprised of 16 attack types, including IoT-based attacks, which are categorized into six categories. From the testbed, we collected 22.8 GB of raw network data and extracted 15.8 GB of preprocessed network data by using our own feature extraction approach. The preprocessed network data contains 121 flow-based features and 3 class labels of more than 34.8 million records. Additionally, this dataset covers extensive IoT-specific application protocols and a variety of normal network behaviours, which is a notable advantage over existing datasets. The MU-IoT dataset can be utilized to develop and validate machine learning-based Intrusion Detection and Mitigation Systems (IDMS). Moreover, the MU-IoT dataset helps to validate the centralized and federated learning-based IDMS. In addition, we also include the prior exploratory data analysis with the ranking of the features given by various feature selection algorithms as well as from our own feature ranking approach and the performance given by some common machine learning algorithms, as a future reference to the research community. The MU-IoT dataset is publicly available in the link https://manipuruniv.ac.in/CSDmuIOTdataset/.

This paper presents a comprehensive examination of smart farming solutions through a systematic review of literature available in various digital repositories. Methodologically, we categorize the devices and technologies utilized in these solutions into sensors, actuators, gateways, power supplies, networking, data storage, data processing, and information delivery. Through this analysis, we identify the most commonly employed devices and technologies in smart farming solutions and discuss their utilization within the proposed categories. By synthesizing the gathered information, we offer insights into the current landscape of smart farming, accompanied by recommendations for the selection of devices and technologies tailored to each category. This research contributes to the understanding of smart farming technology and aids stakeholders in making informed decisions regarding the implementation of such solutions.

The integration of IoT with fuzzy logic provides innovative solutions for various medical applications. IoT enables seamless connectivity between medical devices, real-time monitoring, and data collection. Fuzzy logic has the ability to handle uncertainty and imprecision, thereby enhancing decision-making, medical diagnosis, and treatment. By combining IoT with fuzzy logic, medical providers can develop an intelligent system that improves the quality of healthcare systems. Moreover, the integration of IoT with fuzzy logic enables healthcare providers with powerful predictive analytics capabilities, enabling early detection of diseases and proactive interventions. Several research papers in the literature have investigated the use of IoT with fuzzy logic in medical applications. This review focuses on the use of IoT with fuzzy logic in medical applications. The contributions of this paper are to review the current state of the literature and the potential direction of the medical application of fuzzy system with IoT, and propose a novel scheme that combines Generative Artificial Intelligence (GAI) such as ChatGPT with a fuzzy-based IoT framework. The benefits, challenges, power management, and privacy are also discussed in this paper. The publications are classified according to the type of medical applications. In addition, they are classified according to the type of fuzzy logic used into four categories. We present this review to show the current status in this field. We used Scopus and Google Scholar databases as the basis for presenting this review. The number of publications, number of citations, and other information are discussed to show the achievements in this field. The related research is examined, compared, and classified, focusing on the advantages, challenges, and future research areas.

The Internet of Things (IoT) has significantly improved location-based services in smart cities, such as automated public transportation and traffic management. Estimating the location of connected devices is a critical problem. Low Power Wide Area Network (LPWAN) technologies are used for localization due to their low power consumption and long communication range. Recent advances in Machine Learning have made Received Signal Strength Indicator (RSSI) fingerprinting with LPWAN technologies effective. However, this requires a connection between devices and gateways or base stations, which can increase network deployment, maintenance, and installation costs. This study proposes a cost-effective RSSI fingerprinting solution using IQRF technology for IoT device localization. The region of interest is divided into grids to provide training locations, and measurements are conducted to create a training dataset containing RSSI fingerprints. Pattern matching is performed to localize the device by comparing the fingerprint of the end device with the fingerprints in the created database. To evaluate the efficiency of the proposed solution, measurements were conducted in a short-range local area ( $80\times 30$ m) at 868 MHz. In the measurements, four IQRF nodes were utilized to receive the RSSIs from a transmitting IQRF node. The performances of well-known ML classifiers on the created dataset are then comparatively assessed in terms of test accuracy, prediction speed, and training time. According to the results, the Bagged Trees classifier demonstrated the highest accuracy with 96.87%. However, with an accuracy of 95.69%, the Weighted k-NN could also be a reasonable option for real-world implementations due to its faster prediction speed (37615 obs/s) and lower training time (28.1 s). To the best of the authors’ knowledge, this is the first attempt to explore the feasibility of the IQRF networks to develop a RSSI fingerprinting-based IoT device localization in the literature. The promising results suggest that the proposed method could be used as a low-cost alternative for IoT device localization in short-range location-based smart city applications.

In hospitals and pharmacies, the demand for efficient medication management (e.g., item storage and access) is rapidly increasing, and the scale of medication management systems is gradually expanding. Intelligent medication management systems based on Internet of Things (IoT) can enhance the system efficiency and largely save labor costs through contactless medication dispensing and robotic item access. A typical medication management system that can store medicines usually includes several cabinets, each cabinet contains several drawers, and each drawer contains medicine boxes. In this paper, we propose, Flexit, a framework of flexible location encoding schemes that can encode the locations of cabinets, drawers, and boxes for efficient item access in an intelligent medication management system. Flexit includes a human/user interface, a central control unit, and a medication storage system, built upon medicine cabinets. Given storage space constraints, we propose a hybrid data compression method that combines trees and bitmaps, which is further optimized according to Huffman coding principles. Additionally, given a location code, the user/robot is capable to decode a number of locations, and Flexit employs a location-aware path planning algorithm to help access the stored items at these decoded locations at the minimal time-distance combined cost. We evaluate the performance of the data compression and path-planning algorithms through experiments over datasets, which show superior performance compared to existing methods, i.e., the compression algorithm can save 5-10 times the storage space on average, and the path-planning algorithm can reduce the system’s runtime by 20-50%.

This study proposes a two-stage strategy to ensure the successful execution of critical tasks. In the first stage, an Enhanced Task Offloading (ETO) algorithm is introduced to determine the appropriate computing layer—edge, fog, or cloud—for offloading incomplete tasks. The algorithm makes this decision by assessing the availability of idle computing resources relative to the task’s computational requirements. Additionally, it verifies the status of the server (on/off) before offloading; if the server is unavailable, the algorithm proceeds to check the next layer. In the second stage, the strategy employs a Multi-objective Firefly (MFA) algorithm to assign the optimal computational device within the selected layer. Experimental simulations compare the proposed strategy with a benchmark task offloading algorithm. The results demonstrate the superiority of the proposed strategy, achieving reductions in energy consumption and delay and maximizing resource utilization compared to the baseline algorithms.

The increasing use of Internet of Things (IoT) devices in critical sectors has increased exposure to security threats, making protecting these systems a priority challenge. Based on static configurations, traditional security approaches have proven ineffective in the face of the dynamic nature of emerging threats, as they cannot adapt in real time to changes in the environment or new attack vectors. This work proposes an adaptive security framework for Internet of Things (IoT) systems capable of autonomously detecting, mitigating, and adapting to various threats, improving precision and response times, and optimizing energy consumption. The framework was implemented in a distributed Internet of Things environment, using adaptive architectures based on the Robot Operating System (ROS) and microservices orchestration with Kubernetes. The results showed a significant improvement in response time, with a reduction of 44%, reaching an average of 250 milliseconds, compared to 450 milliseconds for static approaches. Furthermore, a 92% precision in threat detection was achieved, reducing false positives to 4% and false negatives to 6%. Power consumption was controlled, reaching a maximum of 160 milliamp-hours after facing multiple threats, confirming the system’s efficiency in resource-limited environments. These results demonstrate that the proposed adaptive framework is a robust and efficient solution for security in Internet of Things environments, overcoming the limitations of traditional approaches and ensuring adequate protection without compromising energy efficiency.

This paper proposes an Enhanced Edge-Linked Caching (EELC) scheme for Internet of Things (IoT) environments under Information-Centric Networking (ICN), employing an advanced use of Proximal Policy Optimization (PPO), a form of deep reinforcement learning, to inform the caching decisions of edge nodes. The rapid proliferation of IoT devices has led to significant challenges in managing content efficiently within networks, particularly in terms of scalability, latency, and energy consumption. Traditional IP-based architectures are inadequate in addressing these challenges, necessitating a shift towards a content-centric approach provided by ICN. By leveraging PPO, EELC dynamically adapts to changing IoT network conditions, optimizing caching strategies to enhance energy efficiency and improve network responsiveness. In our simulation, we verify the performance of EELC in comparison to the Edge-Linked Caching (ELC) and Leave Copy Everywhere (LCE) approaches under different cache sizes and Zipf-distributed content requests. EELC performs far better than ELC under energy efficiency, cache hit ratios, and server hit reduction in all tested scenarios. This indicates that EELC could be a potential approach for significantly improving network efficiency and responsiveness in IoT networks.

The Industry 5.0 revolution has launched a new age of intelligent manufacturing equipment, which is crucial to our society and economy. Industry 5.0 aims to enhance human potential by integrating cutting-edge IT technology, Artificial Intelligence (AI), the Internet of Things (IoT), robots, and augmented reality into everyday living, especially in smart industrial settings, to maximize human potential. Smart Production Processes (SP2), accessible standards, and resource sharing on the web have increased network vulnerabilities as enterprises adopt them. These vulnerabilities primarily target industry IoT systems, threatening private data. Existing system issues include network vulnerabilities, production capability, and data management. Traditional applications struggle with industrial data’s bulk and complexity, causing data analysis, security, and privacy challenges. Modern industrial systems use AI and ML for big data analysis and data processing to overcome these issues. Optimizing human-machine synergy minimizes costs and equipment maintenance and boosts efficiency. An ML-assisted Smart Production Process (ML-SP2) and Intrusion Detection System (ML-IDS) have been proposed to assess and address Industrial 5.0 concerns. Data capture, predictive maintenance and optimization, transparent decision-making, and proactive maintenance are integrated into the manufacturing process with the ML-SP2. The ML-IDS detects network vulnerabilities using ensemble methods and manages distribution efficiently. The ML-IDS uses the variety of ML techniques such as random forest, decision tree and support vector machines that identifies the intruder with maximum prediction accuracy. In addition, the intruder activities are observed with the help of the convolution networks that improve the overall intruder activities recognition. The smart industrial production phase’s effectiveness

Digital Earth (DE), a technology offering real-time visualisation of Earth’s processes, has shown promising results in aiding decision-making for a sustainable world, raising awareness about individual impacts on our planet, and supporting the United Nations Sustainable Development Goals (UN SDGs) agenda. However, both DE and SDGs face a common obstacle: Data Quality (DQ). This review investigates the challenge of DQ in the context of DE for SDGs and explores how IoT can address this challenge and extend the reach of DE to support SDGs. Furthermore, the study discusses three core aspects; first, the potential of IoT as a data source that supplements satellite data for DE for SDGs, second, the DQ challenge that is specific to an IoT-enabled DE for SDGs is illustrated through scenarios identified from the literature, and third, solutions and perspectives that address the DQ challenge. This study underscores the necessity of addressing the DQ challenge and discusses some potential solutions to foster effective interdisciplinary collaboration, knowledge sharing, and data reusability. The study provides a viewpoint for understanding and addressing the DQ challenge for an IoT-enabled DE for SDGs to support the UN SDGs agenda for a sustainable world by 2030.

While the Guarantee Ratio (GR) is critically important in delay-sensitive fog applications, the existing deadline-aware task assignment strategies prioritize the balance of utilization over this criterion. Therefore, this paper introduces FUSION: a fuzzy-based task management policy, which provides a high GR with the least possible makespan. FUSION considers the effect of propagation, uplink/downlink delays, and also the bandwidth between the layers on the tasks’ completion time during offloading. It benefits from a fuzzy offloader, along with a VM-ranking strategy based on a fuzzy quantified proposition. Hence, it uses two simple and efficient fuzzy ranking approaches, i.e., Decomposition and OWA. By employing fuzzy-based models, FUSION can handle uncertainty in rapidly changing fog environments with time-varying task sets with minimal computation complexity against existing meta-heuristic algorithms. FUSION considers tasks’ size with respect to VM’s processing capacity (MIPS), arrival rate, length, deadline, processing time, and execution time. In addition to VMs’ load, and busy time, FUSION considers laxity as one of its VM-ranking objectives. FUSION also conducts load-balancing, but only when it can improve the rankings to not affect the GR. Based on the iFogSim simulations, FUSION provides a higher GR in 63% of the scenarios compared to state-of-the-art. Furthermore, evaluations of the offloader algorithm indicate that FUSION provides higher GR in more than 55% of the scenarios.

Wheat serves as a crucial agricultural commodity and a primary dietary staple for numerous global populations. However, it faces persistent threats from various diseases targeting wheat leaves, ultimately impacting its production. Accurate and prompt automated disease diagnosis through advanced computer vision is crucial for safeguarding wheat quality. However, the literature relied on inadequate feature selection acquired from computationally expensive backbones followed by shallow layered networks. It is resultantly limiting their capacity to recognize and prioritize diseased areas effectively. Therefore, this paper introduces an optimal features-assisted lightweight framework that integrates EfficientNet-B3 with a spatial attention (SA) mechanism to capture healthy and unhealthy patterns effectively. The proposed framework harnesses this capability to address the vital regions affected by wheat diseases via an optimized, lightweight, and attentive network. Subsequently, we thoroughly analyzed several backbone features to identify robust hyperparameters conducive to achieving our lightweight objective. Furthermore, we employed SA blocks to fortify the network, directing attention efficiently towards diseased regions. The efficacy of the proposed network is validated through comprehensive evaluations conducted on both our proposed and LWDCD2020 benchmarks. Comparative analyses with existing methods consistently showcase superiority, firmly establishing our proposed approach as a viable network for wheat disease recognition. additionally, we also collected our own dataset, namely the Wheat Disease Five Classes Classification Dataset (WD5CC), and included diverse images.

In this paper, we propose a modular security approach using a positioning security engine featuring Global Positioning System (GPS) location features that can uniquely identify the Internet of Things (IoT) user device. Our approach aims to reinforce the security and viability of IoT-centric solutions for various innovative applications, including IoT Mobile payment, Smart city heterogeneous networks, communication services, safety, and location-based services integration. To achieve our goal of securitization and viability, we target consumer IoT devices equipped with built-in location-based GPS chips, which are vulnerable to hackers where the existing cryptographic authentication-based protocols demand power and computation resources required for authentication protocols is not sufficient to carry end to end secure transaction in an IoT environment. Therefore, to compensate this lack of environment capability to carry the end-to-end secure transaction on IoT devices when emitting various radio signals, we implement a modular security approach to compensate the lack of capabilities. This leads to an optimal security facilitated by Simple Public Key Infrastructure following the Pretty Good Privacy Web of Trust approach. Moreover, our implementation on the development board Arduino succeeded in providing and extended secure capable environment for carrying secure transactions. The results show a communication success rate of 70, 80 and 90 percent between Security Engine component called modules, with 70 percent of successful Secure Sockets Layer (SSL) key exchange by every identified user in average 15 seconds simulation running time for every two by third round of simulation.

In recent years, the rapid growth of Internet of Things (IoT) devices has introduced significant security risks, emphasizing the need for effective IoT device management. Traditional device identification methods, such as traffic analysis and machine learning, which rely heavily on static features and prior knowledge, often struggle to maintain accuracy in dynamic IoT environments characterized by frequent changes and large-scale deployments. To address this challenge, we propose the Feature Extraction-Deep IoT (FE-DIoT) method to improve the accuracy and reliability of IoT device identification in large-scale networks by integrating a feature selection algorithm with a classification model. Our approach leverages the Dynamic Weight Adjustment-Based Recursive Feature Elimination (DWA-RFE) algorithm to effectively minimize redundancy, thus enhancing the robustness of the model. Additionally, we implement a Deep Cross Network with Feature Extraction (DCN-FE) module to improve device classification precision by extracting the most significant features from network traffic data. The experimental results in our dataset and other public datasets demonstrate that FE-DIoT significantly outperforms existing methods in terms of robustness and classification accuracy. These findings indicate that FE-DIoT provides a practical and effective solution to improve IoT device management and security in complex large-scale networks.

The Internet of Things (IoT) has expanded rapidly, generating vast amounts of data that require effective protection. Traditionally, the attribute-based searchable encryption (ABSE) as been used within IoT to maintain data privacy and integrity. However, existing ABSE approaches often rely on centralized systems that are susceptible to single-point failures and are inefficient in terms of decryption and data retrieval. Therefore, we propose a novel blockchain-assisted multi-keyword searchable encryption scheme (BAMKSE) that utilizes the decentralized nature of blockchain to overcome these limitations. This scheme integrates ciphertext policy attribute-based encryption (CP-ABE) with key policy attribute-based encryption (KP-ABE) to provide advanced access control and mutual authentication, while offloading the computational burden of decryption to cloud-based pre-decryption services. Security analyses demonstrate the resilience of BAMKSE against chosen keywords attacks (CKAs) and chosen-plaintext attacks (CPAs), offering an effective solution for secure and efficient IoT data handling.

Accurate plant identification is critical for applications such as automated agriculture and plant monitoring systems. However, traditional classification methods often face challenges in balancing accuracy and computational efficiency, particularly when handling large datasets or real-time processing. This research aims to develop a classification scheme that efficiently identifies plant types based on color and shape attributes, achieving high accuracy with minimal computational complexity. To address this, we propose a two-level classification approach using a Naive Bayes classifier in a hierarchical structure. The first stage utilizes simple color features to categorize the majority of images with high accuracy and low computational overhead. In cases where classification remains uncertain, the second stage extracts additional color and shape attributes, offering a more refined analysis of complex samples. The scheme is implemented within an Internet of Things (IoT)-enabled data acquisition framework, enabling real-time image data collection. The system was evaluated using four types of artificial plants placed in a growth chamber equipped with image sensors and LED lighting, with data processed through a cloud service. The results demonstrate that the two-level classifier outperforms single-level approaches, maintaining high accuracy by deferring more complex samples to the second stage without significantly increasing computational costs. This hierarchical classification scheme successfully balances efficiency and accuracy, making it well-suited for large-scale applications such as smart greenhouses, where reliable and rapid plant classification is essential.

The tremendous progress of 5G technology has transformed the landscape of the Internet of Things (IoT), allowing for fast data speeds, low delay, and widespread connection that is crucial for a variety of applications, including smart cities and industrial automation. In the context of 5G enabled IoT networks, colored noise introduces varying levels of interference across different frequency bands, which can significantly degrade the performance of 5G LDPC decoding. This paper presents a novel Deep learning approach for 5G channel LDPC code decoding tailored for next-generation IoT applications. The proposed method integrates an Iterative Normalized Min-Sum (NMS) algorithm with a Convolutional Neural Network (CNN) to enhance the performance of LDPC decoding in the presence of colored noise, a common interference in real-world communication channels. Through extensive simulations and analysis, our approach demonstrates a significant performance improvement, achieving a 3.8 dB enhancement at a Bit error rate of $10^{-6}$ . This is achieved by accurately estimating and mitigating channel noise, thereby ensuring reliable data transmission for critical IoT applications. The findings indicate that our approach to decoding technique not only enhances error correction capabilities but also adapts to varying channel conditions, optimizing IoT network performance and efficiency. This research contributes a robust solution to the challenges posed by colored noise in 5G-enabled IoT networks, promoting the deployment of more reliable and efficient IoT systems.

The escalating number of Internet of Things devices driven by the surging popularity of smart devices has led to the accumulation of large quantities of personal data. While this data can be used to create more intelligent and efficient devices, it also raises concerns about data protection and privacy. Though there are a variety of security threats like man-in-the-middle, replay, remote hijacking, password guessing, Denial of Service, and malware attacks, Distributed DoS attacks play a vital role in the IoT environment. The reason is that DDoS attacks can be initialized by any intruder at a cheaper cost. In some parts of the world, some platforms support hackers unknowingly. DDoS attacks can affect the functionality of a smart solution and sometimes it can lead to life threats in the healthcare sector when an IoT wearable doesn’t function at the expected time frame. The urge of the hour is to develop methodologies to adapt to a real-time environment and which react immediately when there is an intrusion. This paper focuses on developing a hybrid framework based on Ensemble Learning for detecting the DDoS attack types in a very short time frame and alerting the users. The model addresses key challenges identified in existing approaches, including model size, deployment issues, and detection speed. The proposed MRMR-PCA-based Bagged Decision Tree Ensemble Model was tested on the CICIoT2023 dataset, which contains twelve different types of DDoS attacks. The experimental results prove that this model has a boost in accuracy by 2.5%, a reduction in training time by 86%, and a spike in prediction speed by 48.28%. This enhances the security of sensitive data in cloud storage as well as during communication offering a powerful tool for rapid intrusion detection.

Chronic diseases, such as diabetes, cause serious challenges worldwide due to their long-lasting effect on health and quality of life. Diabetes, considered a high glucose level, requires continuous management to avoid complications like kidney failure, low vision, and heart disease. Leveraging state-of-the-art technology, especially IoT devices, shows great potential for optimizing the recognition and management of diabetes. These devices, including smart glucose monitors and wearable sensors, provide real-time data on crucial health metrics, enabling early intervention and proactive monitoring. Furthermore, incorporating deep learning (DL) techniques improves data analysis and recognizes risk factors and subtle patterns related to diabetes. By integrating IoT technology with DL techniques, the healthcare system can empower patients with tools for self-management and develop more conventional approaches for earlier diagnosis and personalized treatment plans, ultimately improving long-term health outcomes and reducing the burden of diabetes. This study develops a new IoT-enabled Driven Early Detection of Chronic Disease using the DL and Dimensionality Reduction (EDCD-DLDR) approach. The EDCD-DLDR method aims to enable IoT devices to collect patient medical data and employ the DL model for earlier diagnosis of diabetes. In the EDCD-DLDR technique, the IoT-based data acquisition process is initially involved, and the collected data gets normalized using Z-score normalization. The EDCD-DLDR technique uses an artificial rabbit optimizer-based feature selection (ARO-FS) approach for dimensionality reduction. In addition, the detection of diabetes is achieved by the attention bidirectional gated recurrent unit (ABiGRU) model. The pelican optimization algorithm (POA) based hyperparameter selection model is included to improve the performance of the ABiGRU network. A comprehensive set of simulations is made to highlight the performance of the EDCD-DLDR method on the Kaggle dataset. The experimental validation of the EDCD-DLDR method underscored a superior accuracy value of 97.14% over existing techniques.

5G wireless network technology is widely used, and data protection is becoming more important as data transmitted and received over the network increases. As the number of Internet of Things devices rapidly increases, data leakage attacks targeting lightweight devices are increasing. Machine learning models have limitations in being applied to lightweight devices because of their large computational complexity and latency in the learning process. Improving utility while protecting data using existing data privacy protection techniques is difficult. Guaranteeing privacy and data utility is difficult because processing large amounts of data with low-capacity memory is complicated. Therefore, a memory-efficient mechanism is needed while ensuring privacy and data utility in data transmission conditions. We propose an optimal feature selection mechanism that maximizes privacy and utility by optimizing the privacy-utility trade-off for resource-constrained lightweight device environments. The proposed mechanism extracts privacy-sensitive features by selecting features requiring privacy protection, removing privacy-sensitive features, making it difficult for attackers to identify data even if they intercept them during data transmission. It demonstrated improved accuracy and memory usage compared to conventional models and improved the accuracy of legitimate nodes by 17.6% compared to models with differential privacy, reduced the accuracy of attackers by 15.45% compared to models without privacy protection techniques, and memory usage by 18.26% under 100% data sampling ratio conditions. Thus, it has been proven to be a secure and efficient model for data transmission environments by effectively improving data utility, privacy, and memory usage.

The Internet of Things (IoT) has revolutionized numerous sectors, with healthcare being a prominent beneficiary. One key area of advancement is Ambient Assisted Living (AAL), which leverages Information and Communication Technologies (ICT) like IoT to improve the quality of life for vulnerable populations, particularly seniors. However, despite efforts to tailor solutions to the needs of these environments, the prevailing approach often becomes overly focused on intricate implementation details rather than the core characteristics of the problem domain. This narrow focus inhibits generalization and leads to unsustainable solutions due to these environments’ diverse, dynamic, and scalable nature. In response, emerging technology paradigms, such as Fog Computing (FC), have emerged to enhance performance and other quality aspects. FC enables the deployment of distributed, latency-aware applications and services. Therefore, this paper introduces FOGAAL, a Domain-Specific Language (DSL) tool crafted to design AAL architectures, including the FC advantages. To demonstrate FOGAAL’s practical applicability, this contribution uses FOGAAL to build an AAL architecture for a real scenario; in addition, it has an empirical evaluation employing the Technology Acceptance Model (TAM) to gauge user perceptions while utilizing the tool for modeling AAL Architectures. The evaluation, conducted as a quasi-experiment, underscore the tool’s alignment with requirements and its potential for adoption, as evidenced by software engineers who have expressed their intention to use FOGAAL when creating architecture for AAL environments.

The Internet of Things and motion recognition-based healthcare rehabilitation system (IMRHRS) is an emerging innovation with the potential to enhance physicians’ rehabilitation services. This study investigated physicians’ switching behavior from conventional rehabilitation systems to IMRHRS. We developed a research model for empirical examination employing the stimulus-organism-response paradigm and integrated the constructs of Information System Success and Technology Acceptance Models for a clearer picture. We conducted this study in two tertiary hospitals in Hefei, China, collected data from 158 physicians, and examined it using Smart-PLS. We proposed quality attributes (information, service, and system quality) as external stimuli, performance attributes (perceived ease of use and usefulness) as organisms, and switching behavior as physicians’ responses. This study revealed that information, service, and system qualities significantly impacted perceived ease of use. In contrast, information and system quality have no effect on perceived usefulness. Additionally, perceived ease of use and usefulness significantly affected physicians’ switching behavior. The results of this study apply to several groups, such as researchers, government healthcare authorities, policymakers, and medical informatics firms.

This paper proposes an Internet of Things (IoT) system to effectively monitor and analyze ecological environments in real time. Firstly, we present the overall architecture of the proposed IoT system including multiple layers. Then, focusing on several important modules within the cloud layer, this paper elaborates on the corresponding design concepts and implementation methods. Moreover, leveraging Geographic Information System technology, spatial data visualization is designed and implemented at the application layer to provide users with intuitive perceptions of ecological environment changes. Finally, this proposed IoT system is deployed and operated in a suburban environment while extensive experiments are conducted. The experimental results indicate that the system works well in terms of graphical user interface performance, network utilization efficiency, sensory data collection, demonstrating the feasibility and effectiveness of the system design.

A large number of Internet of Things (IoT) devices have been deployed in numerous applications (e.g., smart homes, healthcare, smart grids, manufacturing processes, and product supply chains). However, IoT networks’ wide range and heterogeneity make them prone to cyberattacks. Most IoT devices have limited resource capabilities (e.g., memory capacity, processing power, and energy consumption) to function as conventional intrusion detection systems (IDSs). Many research approaches to lightweight IDSs have been taken, namely energy-based IDSs, machine learning/deep learning (ML/DL)–based IDSs, and federated learning (FL)–based IDSs. FL has become a promising solution for IDSs in IoT networks because it reduces overhead in the learning process by engaging IoT devices during the training process. In this paper, we present a comprehensive survey on FL for IDSs in an IoT environment with resource-constrained devices. We investigate the existing studies of FL in different architectures, namely centralized (client-server), decentralized (device-to-device), and semi-decentralized. The study’s findings highlight the necessity for enhancing the FL framework to better suit IoT networks. This enhancement is crucial, particularly in addressing two key challenges: the need to lightweight FL client’s models to accommodate the resource constraints of IoT devices and having a design aggregation algorithm capable of effectively handling the heterogeneity and limited resources inherent in IoT devices. Finally, we discuss the open challenges and future directions for scientists and researchers interested in FL-based IDS for IoT environments.

The Internet of Things (IoT) plays a crucial role in our daily lives. Many objects are being equipped with electronic components in order to facilitate their interconnection and integration with the Internet. Due to the growing usage of IoT devices and generated data, to ensure the reliability of the network, it is necessary to utilize scalable and effective trust management systems. This study introduces the SCoTMan model, which integrates social interactions with the capability of smart contracts for effective trust management in Social IoT (SIoT). Blockchain can enhance trust management protocols by providing a global view of data and improving the propagation of trust values. Implementing trust management algorithms in traditional decentralized architectures can be challenging, but using smart contracts can provide an efficient solution. We implemented the model on the Hyperledger Fabric platform and performed a comprehensive assessment. The model efficiently performs indirect trust computations by choosing optimized counselors and minimizing storage requirements. Multiple experiments have been carried out to assess and validate the efficiency of the model in functionality metrics such as trust convergence and success rate, as well as non-functional metrics like transaction delay, computation and communication overhead, and memory usage. We also conducted a comparative analysis to emphasize the method’s advantages over existing approaches in the literature. We introduced the Total Storage Cost metric for better comparison. The results show substantial improvements in trust convergence under limited Total Storage Cost. The proposed method demonstrates that it is possible to establish scalable trust management in the SIoT by utilizing smart contracts and social metrics.

The fundamental objective of the Internet of Things (IoT) and Named Data Networking (NDN) architectures is to facilitate the provision of communication services. The existing Internet infrastructure presents various challenges associated with its location-based architecture, including those related to latency, bandwidth, and power consumption. This paper explains the NDN-based IoT caching architecture and discusses the caching module, focusing on the selection of an optimal caching approach to address the issues above. This study selected two distinct caching categories, namely centrality-based and probability-based approaches. The selection of these caching strategies was based on their prominence within the research community. In order to determine the most optimal caching strategies, the Icarus network simulator is employed to conduct a comprehensive evaluation of the selected strategies. The evaluation of the performance is conducted based on several key metrics, including the hit ratio, content retrieval latency, and average hop count. The Popularity-Aware Closeness Centrality strategy and the Efficient Popularity-Aware Probabilistic Caching strategy demonstrated superior performance when employing centrality-based caching and probabilistic-aware caching categories, respectively, in order to enhance network performance.

The privacy protection of the transmitted data is considered a vital issue encountered by most of the Internet of Things (IoT) platforms. In recent years, the privacy of users has been protected by employing many location privacy-protection algorithms. These methods often use static phantom node selection that lacks adaptability. This paper proposed an innovative Fractional Flamingo Archery Optimization (FFAO) to select the optimal phantom node and identify a suitable routing path for source location privacy in an IoT-enabled blockchain healthcare network. The hybrid approach is based on multi-objective optimization parameters, such as distance, energy, neighbor list, trust, and heterogeneity, to enhance adaptability and security. In addition, blockchain is integrated with FFAO to add an extra layer of protection. The performance of FFAO during optimal routing path selection is validated through simulation in MATLAB environment, and it attained a maximum safety period of 563501.7m, network lifetime of 119.679s, and energy of 0.125J. These findings highlight the effectiveness, scalability, and improved location privacy compared to existing techniques for real-world IoT healthcare applications.

Internet of Things (IoT) has more security issues due to the data being shared in an open platform. Integrating blockchain into IoT for security is a new development in computational communication systems. However, attackers are adapting their methods and creating new vulnerabilities in blockchain-based IoT platforms. Furthermore, when the blockchain is integrated with IoT networks, vulnerabilities, privacy issues, and security threats are amplified due to malicious transactions and active attacks. This paper proposes BlockDLO, an approach to IoT security that combines blockchain technology with deep learning. A five-phase architecture is proposed for the edge computing blockchain. In its first phase, network localization is resolved with chaotic map-based identification and authentication. The second phase proposes page rank-based clustering for edge computing. Then, BlockDLO combines the shared-chain technique with a deep distributed file system to address issues with block creation and ledger distribution, and an ethereum smart contract to address data security concerns. The communication route optimization is done with page rank centrality search optimization in its next phase. Finally, the integration of deep learning model to detect malicious data in the IoT network is done using the authenticated received data. BlockDLO creates an efficient intrusion detection system by combining a deep convolution neural network with blockchain. The proposed system is trained using public data sources and tested using an in-house network testbed. The results demonstrate that the proposed system outperforms existing work in terms of energy usage, packet loss rate, end-to-end delay, routing overhead, network lifetime, accuracy, and security strength.

In the context of the rapid development of the digital economy, digital homes have emerged as a crucial component, significantly contributing to the output of the digital economy. This study employs CiteSpace software to conduct a quantitative analysis of journal articles containing the keywords “digital home” and “digital family” from 2005 to 2023. The study utilized CiteSpace VI software to perform keyword co-occurrence, clustering analysis, trend mapping, and emergent word analysis on data sourced from the Web of Science (WoS) and China National Knowledge Infrastructure (CNKI) databases. This approach quantitatively analyzed and compared the research hotspots and developmental trends in digital homes, both domestically and in foreign countries. The analysis reveals distinct focal points in the research topics covered in Chinese and foreign literature. Foreign publications predominantly concentrate on network construction and user experience. In contrast, Chinese works are more focused on the application of technology and network security. Moreover, Chinese studies are inclined towards exploring segmented technology application scenarios, such as the integration of smart home and Internet of Things (IoT) technologies, whereas foreign studies often discuss enhancing network efficiency and user interface convenience. The findings suggest that future developments in digital homes should increasingly consider cross-cultural user experiences and data security to adapt to global trends. Through systematic research on digital homes, this paper provides theoretical and practical guidance for planning and constructing digital homes and offers new perspectives and impetus for the development of the digital economy. Future studies could explore the application differences of digital homes across various cultural backgrounds and how to enhance the digital living experience of users while ensuring data security.

Cybersecurity continues to be a significant problem for all industries involved in digital activities, and it is specified as the cyclical surge in security occurrences. Considering more Internet of Things (IoT) devices are being employed in the medical field, homes, transportation, offices, and other locations, malicious attacks are arising more regularly. While IoT provides numerous advantages to users or service providers, security and privacy remain a significant problem. An Intrusion Detection System (IDS) can be employed to mitigate cyber threats in such an interconnected network. However, several existing performances for IDS in IoT need more extensiveness of the categories of attack the network was showing, higher-level feature dimensional, systems built on out-of-date databases, and a shortage of consideration of the imbalanced databases. Therefore, this study presents a Distributed Multiclass Cyberattack Detection using Golden Jackal Optimization with Deep Learning (DMCD-GJODL) technique for IoT networks. The main aim of the DMCD-GJODL method is to ensure security in the IoT environment by detecting cyberattacks using the DL model. In the DMCD-GJODL method, the min-max scalar is primarily used to scale the input data. The DMCD-GJODL method applies a Chaotic Crow Search Optimization Algorithm (CSSOA) based feature selection approach to select features. Moreover, the Bi-Directional Gated Recurrent Unit (BiGRU) approach can be exploited to detect and classify cyberattacks. Eventually, the GJO methodology can boost the BiGRU approach’s hyperparameter choice, enhancing the overall classification process. The performance evaluation of the DMCD-GJODL approach takes place on the BoT-IoT dataset. Extensive comparative results stated the betterment of the DMCD-GJODL approach in detecting cyberattacks with a maximum accuracy of 98.70%, precision of 98.92%, recall of 97.62%, and F-score of 98.25%.

With the Internet of Things (IoT) rapidly increasing in popularity, it is necessary to progress Beyond fifth-generation (B5G) applications to support access to a large number of devices under the strict Ultra-Reliability and Low-Latency Communication (URLLC) requirements, which are intimately tied to strict delay requirements. This article focuses on minimizing co-channel interference, implementing efficient transmitter power, satisfying the Quality-of-Service (QoS), and assigning the energy harvesting requirement of radio frequencies using a Power Splitting Ratio (PSR) to maximize energy efficiency. This article proposes an adaptive approach algorithm for power allocation and PSR to reduce energy consumption caused by a nonconvex optimization problem, to achieve a high strict level of URLLC, QoS aware immediate reward, and huge connection. In addition, the proposed Deep Reinforcement Learning-Based Reduction Action Space (DRL-RAS) allows achieving a high degree of massive access to a huge number of devices by tackling the rate requirement of IoT devices with high intelligence to increase learning efficiency by maximizing the immediate reward function and ensuring strict URLLC. The simulation’s results demonstrate how DRL-RAS decreases co-channel interference based on decision selection to provide strict URLLC. The performance of energy efficiency and QoS are based on achieving a highly strict level of URLLC. The suggested DRL-RAS training strategy performs better, gaining QoS satisfaction levels of 17.21% and 8.67% when the needed QoS rises from 0.88 bits/sec/Hz to 1 bit/sec/Hz. Moreover, the proposed DRL-RAS eliminates error prediction during the training process by updating the weight of DRL-RAS to minimize the loss function and maximize energy efficiency.

Predictive maintenance (PdM) has become a critical strategy for improving the efficiency and reliability of industrial machinery. Integrating machine learning methods into a PdM system provides a promising solution for optimizing maintenance strategies, preventing equipment failures on the production line, and reducing downtime. This research presents a data-driven approach for detecting faults in industrial machines using sensor data. The method aims to optimize system performance, resulting in economic savings including energy consumption, and maintenance costs. The approach outlined in this research includes the establishment of a PdM system designed for yarn production machines empowered by machine learning methods. The effectiveness of PdM applications depends on careful selection of machine learning methods. This study examines four machine learning algorithms and a deep learning algorithm for predictive modeling. The algorithms were trained on historical data to identify underlying patterns and correlations between operational parameters and failure events. The trained models were deployed in the PdM system to continuously monitor the health condition of industrial machines on ThingSpeakTM IoT interface platform in real-time. This research also presents a systematic process for developing a predictive maintenance framework. The process includes data acquisition from industrial machinery, preprocessing, feature selection, model training, and deployment. The effectiveness of the proposed system is validated through extensive experimentation and case studies conducted in an industrial setting. Evaluation metrics revealed that the deep learning algorithm outperformed the other approaches, achieving an accuracy score of 0.96 and a prediction validation score of 0.86. The results show significant improvements in predictive accuracy and enhanced operational efficiency compared to reactive maintenance approaches.

As the Metaverse promises to revolutionize human interaction and digital life, the role of edge computing emerges as a critical enabler. This paper presents a comprehensive survey of edge-enabled Metaverse applications, technological innovations, challenges, solutions, and future trajectories within the industry. We dissect the landscape of Metaverse applications across various sectors, analyzing how edge computing empowers real-time, immersive experiences. We delve into the cutting-edge advancements in decentralized computing infrastructure, edge networking, and artificial intelligence shaping the Metaverse, highlighting their potential to overcome latency, bandwidth, and privacy challenges. Additionally, we explore enabling technologies such as 5G and IoT, which facilitate seamless connectivity and data processing. We also address significant challenges, including the need for scalable and resilient infrastructure, data security concerns, and the integration of diverse technologies, proposing viable solutions like enhanced edge AI algorithms and robust cybersecurity frameworks. Finally, we chart prospective trajectories for edge-enabled Metaverse development, identifying key trends and potential disruptive forces that will shape the industry’s future. Our survey aims to serve as a definitive resource for researchers, developers, and industry leaders by providing a holistic understanding of edge computing’s pivotal role in realizing the boundless potential of the Metaverse.

The water crisis, global warming and climate changes have become recently prominent world issues. Saving and conserving water have become, therefore, an imperative for water resources’ sustainability. In this context, dramatic innovations have come to light, e.g., Precision Agriculture. Numerous research projects on this subject have been conducted, as illustrated not only by the huge number of research works in this topic, but also by the dozens of surveys dedicated to. Surveys regarding smart agriculture are of two categories, surveys on smart agriculture in general in which smart irrigation is a subtopic among others, and surveys dedicated to smart irrigation. Existing surveys, however, suffer from limitations including low paper numbers, linear description without structural classification, and missing key information. To fill this gap, we propose, in this paper, a survey on SI that classifies the large body of literature into categories in a structured way. To this end, we have employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to determine the inclusion or exclusion of articles. We have reviewed a total of 610 publications out of which 227 met the inclusion criteria. We have categorized the selected references into three conceptual clusters corresponding to: (1) 41.8% focusing on field measurements, based on IoT sensors, (2) 37% involving Remote Sensing (RS) and (3) 21.1% Artificial Intelligence (AI) methods. Each category is thoroughly described, including the inputs and outputs, along with a description of the various tasks employed in irrigation management. This systematic re-view is expected to serve as a useful reference for research on smart irrigation management methods.

Ensuring availability is a critical requirement for the Internet of Things (IoT). CIDER, a recovery architecture, and its follow-up scheme, Lazarus, are solutions to address this issue. CIDER introduced a new hardware module, the Authenticated Watchdog Timer (AWDT), to keep IoT devices running in normal mode as long as trusted authenticated tickets are received from a hub. If valid tickets are not received, the AWDT resets the device, and a recovery procedure takes over. Lazarus, a more advanced solution, implemented the AWDT in ARM TrustZone (TZ). CIDER and Lazarus promised to unconditionally recover control in case of compromised firmware. In this work, we analyze both and demonstrate that the schemes do not give such unconditional recovery. In particular, we identify two major problems. Attackers can easily make devices unavailable by manipulating the tickets. Even more severe is the fact that a runtime attack, utilizing a potential weakness in the trusted firmware, can also make a device unavailable and prevent the recovery mechanism. We notice that when the AWDT is implemented in TZ, a richer device security state information can be tracked securely, allowing the device to handle attacks in a much more robust way with a better availability guarantee. We design and implement a new solution using the same hardware as Lazarus. Our design introduces two new boot modes called LZ_VERIFY and MIN_APP. The former verifies whether the disruption is caused by a network issue or a runtime attack, enhancing the device’s decision-making for subsequent actions. The latter is when the device expends minimal computational effort to maintain usability while the attack is ongoing. Assuming the time spent in MIN_APP as uptime, our evaluation shows that under network attack conditions, the new design improves device availability from 1.66% to 99.95% compared to Lazarus, a significant improvement. A similar design is also possible using a hardware AWDT, as used by CIDER.

With the rapid development of artificial intelligence and a new generation of network technologies, the Internet of Things (IoT) is expanding worldwide. Malicious agents consistently exploit new technical vulnerabilities to access the various IoT systems used in critical industries, medical diagnosis, military, and defense systems. To mitigate these threats, IoT networks should be equipped with intrusion detection systems capable of detecting threat vectors in an attempt to compromise the systems. Moreover, many researchers have integrated privacy-preserving technologies such as homomorphic encryption, differential privacy, and secure multiparty computation with machine learning algorithms. Furthermore, federated learning, which shares only model parameters rather than data, provides distributed privacy-preserving learning; therefore, federated learning is secure and reliable for the implementation of intrusion detection systems in IoT environments. This survey examined the utilization and applications of privacy-preserving mechanisms, explicitly focusing on privacy-preserving federated learning for intrusion detection systems in IoT environments. This survey also highlights future research directions and open research questions. Privacy-preserving federated learning can significantly contribute to the rapid and efficient detection and prevention of various threat vectors that target IoT ecosystems.

Precision agriculture is a crucial field that addresses food security for the growing population. This article presents an innovative approach merging deep learning with a 5G Private Mobile Network (PMN) to construct a smart agriculture system. We simulated a 5G PMN infrastructure using UERANSIM and Free5GC with the Quantum Key Distribution Function (QKDF) integration. The 5G connectivity enhances agricultural practices through real-time crop healthcare monitoring and reliable transmission of irrigation control commands. In addition to using 5G, our proposed system integrates a lightweight model and three YOLOv8 models for AI-powered irrigation and crop health monitoring. While the lightweight model automatically generates irrigation schedules based on current weather conditions and soil moisture levels, optimizing crop water needs, the three YOLOv8 models are trained to predict growth stage, health status, and nutritional status (nitrogen, phosphorus, and potassium). All deep learning models are optimized for lettuce and feature lightweight architecture for easy deployment on low-power devices. Evaluation on test datasets reveals promising accuracy scores: the irrigation schedule generation model achieves 73%, the growth stage model attains 93.8%, the health status model reaches 87%, and the nutritional status model obtains 83%. This research contributes to improving precision agriculture while providing practical solutions to enhance irrigation management and crop health monitoring. We have also developed a QKDF that is integrated with the 5G core network to improve the security of the proposed system by establishing secure session keys with a length of 256 bits.

Wireless sensor networks have become pervasive in various applications, including environmental monitoring, smart cities, precision agriculture, and healthcare. In particular, linear wireless sensor networks that span considerable distances are increasingly deployed for applications such as pipeline monitoring and transportation systems. However, ensuring the reliability of long wireless sensor networks poses significant challenges due to the unique characteristics of the network topology and the constraints imposed by the resource-constrained sensor nodes. There is a notable lack of methods for the analysis and optimization of the reliability of large-scale sensor networks, and in this paper, we partially fill this gap. We propose a method for assessing the tradeoff between redundancy of forward error correction and reliable packet delivery. We also consider approaches to improve the reliability of linear sensor networks based on increasing topology connectivity and provide a corresponding method for the exact calculation of the all-terminal reliability. Numerical experiments demonstrate the relevance and effectiveness of our results. This research contributes to a deeper understanding of the underlying principles governing the reliability of long wireless sensor networks and provides valuable insights for the design, optimization, and management of such networks in real-world applications.

Traditional networks become more difficult and complex due to their location-based communication policies. Researchers have proposed a new way of communication to overcome issues of traditional networks named Named Data Networking (NDN). In NDN, packets carry the name of the data instead of the source or destination IP addresses. Similarly, the Internet of Vehicles (IoVs) is a developing application of the Internet of Things (IoT) that has the potential to significantly increase network communication. To communicate helpful information with a particular group of vehicles, Named Internet of Vehicles (NIoVs) uses a broadcast pattern. However, there are several issues with security and packet duplication in NIoVs. In this paper, a solution related to the routing issue named broadcast storm within NIoV architecture is proposed. Denial of Service (DoS) attacks are also discussed in the context of NIoVs. A novel method called “Interest Flooding Attack Countermeasure and Controlled Packet Propagation (IFAC-CPP)” is proposed to counter both broadcast storms and DoS attacks. The first part of the proposed scheme deals with the problem of flooding attacks, while the second part deals with the issue of broadcast storms. This scheme is implemented in the communication environment of NIoVs in a highway scenario, with a focus on safety applications. Simulation results are compared with PP-FIRM and PP-FIRM with an attacker, demonstrating that the proposed scheme in this paper achieves better results in terms of network performance and efficiency.

This paper proposes MicroSync, a high-accuracy and low-power synchronization method based on two ideas: a hybrid timer and communication scheduling. The hybrid timer is implemented by combining two types of timers: a real-time clock and high-frequency clock timers. While high-frequency clock timers offer high resolution at the cost of increased power consumption, real-time clocks operate at lower frequencies, providing power efficiency but lower time resolution. The combination of these two timers results in a synchronized timer with low power consumption and high accuracy. Furthermore, we proposed two types of communication scheduling using this hybrid timer: high-accuracy and low-power scheduling. High-accuracy scheduling leverages Slave Latency, a feature of Bluetooth Low Energy (BLE), to minimize communication frequency, reduce the time gap between communication and synchronization, and prevent error accumulation caused by frequency drift. Both synchronization schemes were implemented on a commercially available Bluetooth transceiver IC. The evaluation results show that the high-accuracy scheduling achieves 400-ns synchronization accuracy with less than $300~\mu $ W power consumption. The low-power scheduling also achieves 1- $\mu $ s synchronization accuracy with less than $150~\mu $ W power consumption under same conditions.

Preventive maintenance of heating, ventilation, and air conditioning (HVAC) systems aims to monitor and detect early-stage failures, thereby enhancing thermal comfort satisfaction and reducing energy consumption. However, it is a complex and uncertain factor, and technicians must understand the root cause of the problem to interpret prefailure events accurately. This study addresses this concern using a causal machine learning model, which encodes HVAC system behavior, indoor ambiance, and the outdoor environment based on random variables, with a particular focus on air conditioning units. It models their causal representation concerning prefailures using the structural causal model. It employs d-separation and d-connection to justify cause-and-effect relationships with the model and applies the expectation maximization algorithm to fit model parameters given observational data. The causal model is verified using a do-operator to rationalize the sound explanations (d-separated and d-connected) and employing odds ratio and confidential interval to prove their statistical strength. The model achieves high causal significance, with odds ratios of the submodel lying between 0.3-0.9 and 1.4-6.0 (exclude 1.0). The results indicate that the model and its explanations can encode human-like interpretation and achieve high causal significance aligned with real-world prefailure events. This refers to a reasoning process that is similar to that of humans, where information is included or excluded based on explanations of cause and effect. This confirms that the contribution of this study is suitable for causal-based decision-making systems for HVAC preventive maintenance.

Ubiquitous computing is a key component of future wireless communications and Internet of Things (IoT) applications. By using distributed fog computing, data obtained from sensors can be timely processed and different tasks can be computed efficiently. As a result, IoT applications can make timely decisions and improve application reliability. In this paper, we consider a Partial Offloading to Multiple Helper (POMH) scenario where IoT device divide their tasks into subtasks that can be computed in a parallel manner. We propose a modified many-to-many matching-based task offloading algorithm to reduce the task latency in a POMH scenario. To overcome the externalities due to the dynamic preference profile of fog nodes, an intelligent proposal-based scheme is also introduced. The performance analysis of the proposed techniques shows that they provide quicker task computation as compared to different techniques available in the literature.

Protection against unwanted intrusions is crucial for preserving the integrity and security of connected devices in the context of Internet of Things (IoT) networks. The growing number of IoT devices has made several industries more vulnerable to cyberattacks and security breaches, including smart homes, industrial automation, and healthcare. In response to this pressing dilemma, the goal of this project is to create a novel method for intrusion detection in Internet of Things systems utilizing Denoising Autoencoder (DAE) models. Traditional intrusion detection methods often prove inadequate in Internet of Things scenarios due to resource restrictions, dynamic network topologies, and a diversity of communication protocols. By utilizing DAEs’ unsupervised learning and feature extraction skills, our suggested approach creates a system that can identify and stop intrusion attempts in real-time. The evaluation of the study additionally makes use of the NSL-KDD and CICIDS 2017 datasets. DAE integration yields an unequaled accuracy of 99.991% when the CICIDS 2017 dataset is used, and an accuracy of 99.4% when the NSL-KDD dataset is used. The CICIDS 2017 dataset analysis reveals several notable performance measures, including an accuracy of 1.0, a precision of 0.995, and an F1-score of 0.998. Analyses of the NSL-KDD dataset also produce outstanding results, with an F1-score of 0.989, recall of 0.991, accuracy of 0.994, and precision of 0.984. The results also show how well the suggested DAE-based intrusion detection method works to stop unauthorized users from accessing IoT devices, which lowers the risk of issues with system integrity, privacy, and security. By strengthening resilience against evolving cyber threats in the networked Internet of Things landscape, this research enhances cybersecurity strategies tailored to address the unique challenges encountered by IoT ecosystems.