Intelligent Internet Of Things Research Articles

Smart Water Quality Monitoring with IoT Wireless Sensor Networks.

Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies. The more recent wireless sensor network (WSN)-based techniques are evolving to alleviate the problems of monitoring, coverage, and energy management, among others. The inclusion of the Internet of Things (IoT) in WSN techniques can further lead to their improvement in delivering, in real time, effective and efficient water-monitoring systems, reaping from the benefits of IoT wireless systems. However, they still suffer from the inability to deliver accurate real-time data, a lack of reconfigurability, the need to be deployed in ad hoc harsh environments, and their limited acceptability within industry. Electronic sensors are required for them to be effectively incorporated into the IoT WSN water-quality-monitoring system. Very few electronic sensors exist for parameter measurement. This necessitates the incorporation of artificial intelligence (AI) sensory techniques for smart water-quality-monitoring systems for indicators without actual electronic sensors by relating with available sensor data. This approach is in its infancy and is still not yet accepted nor standardized by the industry. This work presents a smart water-quality-monitoring framework featuring an intelligent IoT WSN monitoring system. The system uses AI sensors for indicators without electronic sensors, as the design of electronic sensors is lagging behind monitoring systems. In particular, machine learning algorithms are used to predict E. coli concentrations in water. Six different machine learning models (ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor) are used on a sourced dataset. From the results, the best-performing model on average during testing was the AdaBoost regressor (a MAE¯ of 14.37 counts/100 mL), and the worst-performing model was stochastic gradient boosting (a MAE¯ of 42.27 counts/100 mL). The development and application of such a system is not trivial. The best-performing water parameter set (Set A) contained pH, conductivity, chloride, turbidity, nitrates, and chlorophyll.

Efficient Connectivity in Smart Homes: Enhancing Living Comfort through IoT Infrastructure.

Modern homes are experiencing unprecedented levels of convenience because of the proliferation of smart devices. In order to improve communication between smart home devices, this paper presents a novel approach that particularly addresses interference caused by different transmission systems. The core of the suggested framework is an intelligent Internet of Things (IoT) system designed to reduce interference. By using adaptive communication protocols and sophisticated interference management algorithms, the framework minimizes interference caused by overlapping transmissions and guarantees effective data sharing. This can be accomplished by creating an optimization model that takes into account the dynamic nature of the smart home environment and intelligently allocates resources. By maximizing the signal quality at the destination and optimizing the distribution of frequency channels and transmission power levels, the model seeks to minimize interference. A deep learning technique is used to augment the optimization model by adaptively learning and predicting interference patterns from real-time observations and historical data. The experimental results show how effective the suggested hybrid strategy is. While the deep learning model adjusts to shifting interference dynamics, the optimization model efficiently controls resource allocation, leading to better data reception performance at the destination. The system's robustness is assessed in various kinds of situations to demonstrate its flexibility in responding to changing smart home settings. This work not only offers a thorough framework for interference reduction but also clarifies how deep learning and mathematical optimization can work together to improve the dependability of data reception in smart homes.

Blockchain-Enabled Intelligent IoT Protocol for High-Performance and Secured Big Financial Data Transaction

In the recent era, the communication network with the support of many wireless technologies is giving benefits for remote access. Such a communication model increases the flexibility for data storage with the management of network resources efficiently with the integration of the Internet of Things (IoT). Although, the industrial Internet of Things (IIoT) has enabled the development of numerous machine learning-based solutions to provide real-time applications. However, most of the solutions are not prepared to cope with heterogeneous services and the huge amount of device data in the digital world. Furthermore, conducting financial transactions over the Internet raises several security issues. Such restrictions compromise the sensitive data of financial institutions and also degrade the trust of network users in the system. Thus, this article presents a secured blockchain model for high-performance computing in a big data environment, which aims to protect the business activities for financial interaction with intelligent services of software-defined network (SDN) architecture. First, to keep the security credentials, the SDN controller creates an association between the IoT devices and maintains local and global records. Second, the machine learning approach is explored using reliable and fault-tolerant methods to support the network scalability and extract the updated routing information for transmitting financial data. The proposed protocol also provides data integrity with a high level of network availability and copes with the financial security of big data by investigating cryptographic approaches. Our proposed protocol is tested using simulation, and various experiments are performed to show its efficacy in terms of network throughput, computing overhead, data delay, response time, and dropped packets as compared to tunicate swarm algorithm-based optimized routing mechanism (TORM) and RouteChain.

IoT Attack Detection using Machine Learning and Deep Learning in Smart Home

The Internet of Things (IoT) has revolutionized the traditional Internet, pushing past its former boundaries by implementing smart linked gadgets. The IoT is steadily becoming a staple of everyday life, having been implemented into various diverse applications, such as cities, smart homes, and transportation.  However, despite the technological advancements that the IoT brings, various new security risks have also been introduced due to the development of new types of attacks. This prevents current intelligent IoT applications from adaptively learning from other intelligent IoT applications, which leaves them in a volatile state. In this paper, we conducted a structured literature review (SLR) on Smart Home's IoT attack detection using machine learning and deep learning. Four journal databases were used to perform this review: IEEE, Science Direct, Association for Computing Machinery (ACM), and SpringerLink. Sixty articles were selected and studied, where we noted the various patterns and techniques present in the framework of the selected research. We also took note of the different machine learning and deep learning methods, the types of attacks, and the Network layers present in Smart Home. By conducting an SLR, we analyzed the numerous techniques of IoT attack detection for smart homes proposed by various theoretical studies. We enhanced the studied literature by proposing a new solution for better IoT attack detection in smart homes.

Progressive Adoption of RINA in IoT Networks: Enhancing Scalability and Network Management via SDN Integration

Thousands of devices are connected to the Internet as part of the Internet of Things (IoT) ecosystems. The next generation of IoT networks is expected to support this growing number of Intelligent IoT devices and tactile Internet solutions to provide real-time applications. In view of this, IoT networks require innovative network architectures that offer scalability, security, and adaptability. The Recursive InterNetwork Architecture (RINA) is a clean slate network architecture that provides a scalable, secure, and flexible framework for interconnecting computers. SDN technology is becoming a de facto solution to overcome network requirements, making RINA adoption difficult. This paper presents an architecture for integrating RINA with SDN technologies to lower the barriers of adopting RINA in IoT environments. The architecture relies on a RINA-based distributed application facility (DAF), a RINA southbound driver (SBI), and the RINA L2VPN. The RINA-based DAF manages RINA nodes along the edge–fog–cloud continuum. The SBI driver SDN enables the hybrid centralized management of SDN switches and RINA nodes. Meanwhile, the RINA L2VPN allows seamless communication between edge nodes and the cloud to facilitate the data exchange between network functions (NFs). Such integration has enabled a progressive deployment of RINA in current IoT networks without affecting their operations and performance.

End-to-end security enabled intelligent remote IoT monitoring system

Introduction: Internet of things (IoT) compose of million of devices connected together over the internet. IoT plays a vital role now a days and especially in future, the most of the monitoring and data collection. The data should be secure while collection and as well in the process of transferring till the destination whether Service Organization Control (SOC) or to cloud for storage. In this paper, a secure IoT based intelligent monitoring system is proposed.Methods: An intelligent IoT station that interacts via cellular connection to relay data to the cloud is constructed using the Waspmote platform. The algorithm is injected to automatically filter and only keep the new data for transfer to avoid redundancy. The advanced encryption standard (AES) 256-bit method is enabled for onboard data encryption and then the generated cipher text is transmitted. The encrypted data is then stored over the cloud to ensure privacy. Moreover, the mobile application (mApp) is developed to be installed on handheld devices for calling the secure data from the cloud, decrypting it, and displaying it as per user input, whether real-time or historical.Results and Discussion: The encryption algortihm helps in securing the proposed monitoring system from brute force, man in the middle, phishing, spoofing, and denial of service (DoS) attacks. The results of the real testbed experimentation demonstrate the complexity evaluation and reliability of IoT monitoring systems with end-to-end data security in terms of encryption algorithm delay and data rate, respectively.

Integration of data science with the intelligent IoT (IIoT): current challenges and future perspectives

The Intelligent Internet of Things (IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approaches, such as Artificial Intelligence (AI) and machine learning, to make accurate decisions. Data science is the science of dealing with data and its relationships through intelligent approaches. Most of the state-of-the-art focus is on the topic independently, either on data science or IIoT. Therefore, to address the gap, this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT (IIoT) system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics. The paper analyzes the data science or big data security and privacy features, including network architecture, data protection, and continuous monitoring of data, which face challenges in various IoT-based systems. Extensive insights into IoT data security, privacy, and challenges are visualized in the context of data science for IoT. In addition, this study reveals the current opportunities to enhance data science and IoT market development. The current gap and challenges faced in the integration of data science and IoT are comprehensively presented, followed by the future outlook and possible solutions to the existing challenges.

Reliable data transmission for a VANET-IoIT architecture: A DNN approach

The challenges and resilience of vehicular ad hoc network (VANET) and deep neural network (DNN) hybrid architectures in terms of reliability in smart cities have attracted much global interest stemming from the rollout of the next generation of intelligent networks. In this paper, we propose a novel distributed DNN (D-DNN) scheme with blockchain to support the Internet of Intelligent Things (IoIT) infrastructure in the VANET environment of the future. In particular, because the communication links between edge nodes are very unstable in VANETs, a new neuro-fuzzy server that serves the dual roles of finding reliable links between edge nodes and performing optimal routing path selection is proposed. Next, a blockchain layer is employed at the edge nodes, which are initially scrutinized before establishing communication links to ensure reliability during data transfer. Then, the proposed D-DNN (PD-DNN) scheme is applied to enhance the performance of the VANET-IoIT architecture by improving the data flow and convergence rate and mitigating erratic variations in output. To address reliability concerns, the coverage probability (CP) metric is investigated as a measure of network connectivity. Furthermore, we present an analysis of the PD-DNN scheme in comparison with the traditional DNN (T-DNN) scheme. Finally, simulation results for VANET-IoIT scenarios show that, subject to data protection and privacy constraints, the CP values corresponding to different communication links are improved to a greater extent under our scheme than under the traditional scheme, demonstrating the feasibility of the proposed scheme.

The Role of Artificial Intelligence of Things in Achieving Sustainable Development Goals: State of the Art.

With the environmental and societal changes, the achievement of sustainable development goals (SDGs) and the realization of sustainability in general is now more important than ever. Through a bibliometric analysis and scientific mapping analysis, this study aims to explore and provide a review regarding the role of artificial intelligence (AI), the Internet of Things (IoT), and artificial intelligence of things (AIoT) in realizing sustainable development and achieving SDGs. AIoT can be defined as the combination of AI with IoT to create more efficient and data-driven interconnected, intelligent, and autonomous IoT systems and infrastructure that use AI methods and algorithms. The analysis involved 9182 documents from Scopus and Web of Science (WoS) from 1989 to 2022. Descriptive statistics of the related documents and the annual scientific production were explored. The most relevant and impactful authors, articles, outlets, affiliations, countries, and keywords were identified. The most popular topics and research directions throughout the years and the advancement of the field and the research focus were also examined. The study examines the results, discusses the main findings, presents open issues, and suggests new research directions. Based on the results of this study, AIoT emerged as an important contributor in ensuring sustainability and in achieving SDGs.

Guest editorial: Deep learning techniques in intelligent internet of things and 5G communication networks

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Game-Theoretic Optimization Of Intelligent Iot Networks For Enhanced Resource Management In Precision Agriculture

The burgeoning application of Internet of Things (IoT) technologies in agriculture has revolutionized precision farming practices. Intelligent IoT networks equipped with sensors, actuators, and edge computing capabilities offer real-time monitoring and intelligent control over crucial agricultural parameters. However, optimizing resource allocation and network performance in these dynamic environments remains a complex challenge due to competing interests among network devices and potential interference between neighboring farms. This paper proposes a novel approach for optimizing intelligent IoT networks in precision agriculture using game theory. We first model the network as a non-cooperative game where individual devices act as rational players aiming to maximize their own utilities, represented by factors like data transmission success, energy efficiency, and resource utilization. We then employ Nash equilibrium and its refinements to determine stable and efficient network configurations. To address the potential for strategic manipulation and ensure collective benefit, we further introduce cooperative game mechanisms, such as coalition formation and resource sharing protocols, to incentivize collaborative behavior among devices. The efficacy of the proposed approach is evaluated through extensive simulations with realistic agricultural scenarios. Results demonstrate significant improvements in network performance metrics, including higher data throughput, reduced energy consumption, and improved resource utilization compared to traditional non-game-theoretic approaches. We conclude by discussing the real-world implementation challenges and future research directions in game-theoretic optimization of intelligent IoT networks for sustainable and efficient precision agriculture.

Intelligent IoT framework with GAN‐synthesized images for enhanced defect detection in manufacturing

AbstractThe manufacturing industry is always exploring techniques to optimize processes, increase product quality, and more accurately identify defects. The technique of deep learning is the strategy that will be used to handle the issues presented. However, the challenge of using AI in this domain is the small and imbalanced dataset for training affected by the severe shortage of defective data. Moreover, data acquisition requires significant labor, time, and resources. In response to these demands, this research presents an intelligent internet of things (IoT) framework enriched by generative adversarial network (GAN). This framework was developed in response to the needs outlined above. The framework applies the IoT for real‐time data collection and communication, while GAN are utilized to synthesize high‐fidelity images of manufacturing defects. The quality of the GAN‐synthesized image is quantified by the average FID score of 8.312 for non‐defective images and 7.459 for defective images. As evidenced by the similarity between the distributions of synthetic and real images, the proposed generative model can generate visually authentic and high‐fidelity images. As demonstrated by the results of the defect detection experiments, the accuracy can be enhanced to the maximum of 96.5% by integrating the GAN‐synthesized images with the real images. Concurrently, this integration reduces the occurrence of false alarms.

Artificial Intelligence for Internet of Things as a Service: Small or Big Data, Private or Public Model, Centralized or Federated Learning?

Artificial Intelligence for Internet of Things as a Service: Small or Big Data, Private or Public Model, Centralized or Federated Learning?

Retracted: Analysis of Perioperative Nursing Intervention Effect of Cerebrovascular Intervention Patients Based on Intelligent Internet of Things

Retracted: Analysis of Perioperative Nursing Intervention Effect of Cerebrovascular Intervention Patients Based on Intelligent Internet of Things

Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold

With the development of intelligent IoT applications, vast amounts of data are generated by various volume sensors. These sensor data need to be reduced at the sensor and then reconstructed later to save bandwidth and energy. As the reduced data increase, the reconstructed data become less accurate. Usually, the trade-off between reduction rate and reconstruction accuracy is controlled by the reduction threshold, which is calculated by experiments based on historical data. Considering the dynamic nature of IoT, a fixed threshold cannot balance the reduction rate with the reconstruction accuracy adaptively. Aiming to dynamically balance the reduction rate with the reconstruction accuracy, an autonomous IoT data reduction method based on an adaptive threshold is proposed. During data reduction, concept drift detection is performed to capture IoT dynamic changes and trigger threshold adjustment. During data reconstruction, a data trend is added to improve reconstruction accuracy. The effectiveness of the proposed method is demonstrated by comparing the proposed method with the basic Kalman filtering algorithm, LMS algorithm, and PIP algorithm on stationary and nonstationary datasets. Compared with not applying the adaptive threshold, on average, there is an 11.7% improvement in accuracy for the same reduction rate or a 17.3% improvement in reduction rate for the same accuracy.

High Performance Rotating Triboelectric Nanogenerator with Coaxial Rolling Charge Pump Strategy.

With the development of society and the advancement of technology, the emergence of the Internet of Things (IoT) has changed people's lifestyles and raised the demand for energy to a new level. However, there are some drawbacks in terms of energy supply for IoT sensors, such as limited battery capacity and limitations in replacement and maintenance. Therefore, it has become urgent to develop a sustainable green energy source (wind energy) using the surrounding environment. Meanwhile, triboelectric nanogenerators (TENGs) with advantages such as flexible structure, low manufacturing cost, and environmental friendliness provide enormous potential for constructing self-powered sensing systems. In this work, we present a novel coaxial rolling charge pump TENG (CR-TENG) based on wind energy to enhance the output performance and durability. The rolling friction charge pump TENG directly injects positive and negative charges into the main TENG, which is more wear-resistant compared to sliding friction, and greatly increases the charge density and output power. In addition, the charge pumping part and the main TENG adopt the coaxial design, reducing the complexity of the structural design. On comparing the output performance of the CR-TENG under the initial state, rectifier bridge supplemental charge strategy, and charge pump supplemental charge strategy, results shown that the output voltage performance of the CR-TENG can be improved by 5800% under the charge pump supplemental charge strategy. Moreover, the output performance of the CR-TENG remains stable after 72,000 cycles. The output power of the CR-TENG can reach 1.21 mW with a load resistance of 3 × 107 Ω. And the CR-TENG can charge a 0.1 μF capacitor to 5 V in just 1.6 s. This work provides new insights for the rotary durable high output charge pump compensating a triboelectric nanogenerator and demonstrates the important potential of harvesting environmental energy to supply intelligent IoT nodes.

CSformer: Enhancing deep learning efficiency for intelligent IoT

The rapid development of deep learning technology has led to increasing demand for more intelligent, automated, and humanized Internet of Things (IoT) devices. Deep learning models, while endowing IoT devices with the capability to learn higher-level features, concurrently impose more demanding computational and storage prerequisites on the hardware. To tackle the challenge and enable the practical application of deep learning models in IoT devices, we propose a novel efficient Transformer called CSformer, which incorporates intra-layer cluster and inter-layer selection. Intra-layer cluster is performed using a k-means++ based generation algorithm to improve cluster accuracy. To address the issues of information loss caused by clustering, we propose cluster center information enhancement and clustering loss calculation modules. The inter-layer selection strategy selects tokens according to their contribution, consistently diminishes redundancy, and prioritizes the retention of crucial information. By consistently reducing the sequence length, the inter-layer selection significantly improves training speed and reduces the memory occupation of the model. The experimental results indicate that in two common scenarios for intelligent IoT, namely text classification and sequence labeling, CSformer significantly outperforms the baseline models. Specifically, in the text classification task, our model achieves an average 22.66% reduction in memory consumption, a 37.98% decrease in time consumption, and a superior 9.58% performance improvement compared to baseline models across six datasets. Additional experiments substantiate the efficacy of the intra-layer cluster and inter-layer selection modules, as demonstrated through ablation experiments, overall performance, and visualization. The intra-layer cluster module enhances the performance of existing models by achieving more precise clustering and mitigating information loss, leading to significant performance improvements. The inter-layer selection module enhances the efficiency of existing studies by reducing model memory consumption and improving computational efficiency through the selective retention of essential tokens. This can effectively facilitate future research in applying advanced deep learning models to intelligent IoT, expanding the range of application scenarios and tasks within the field of intelligent IoT.

An Overview of the Implications of Artificial Intelligence (AI) in Sixth Generation (6G) Communication Network

The 6G communication network is anticipated to be a cutting-edge next-generation communication network that will enhance the value of the intelligent Internet of Things (IoT). The emergence of many domains within artificial intelligence (AI) has paved the way for significant opportunities in the development of 6G technology. These opportunities include the enhancement of human intelligence, the integration of various devices and systems through the Internet of Everything (IoE), improvements in the quality of experiences, and enhancements in the overall quality of life. The integration of AI and 6G networking technology is anticipated to bring about a transformative shift, transitioning from a focus on interconnected devices to a paradigm centered around interconnected intelligent systems. This article provides an overview of the extent to which AI is poised to revolutionize 6G networking technologies. This study is primarily concerned with the implementation of appropriate applications that address human needs and challenges. Furthermore, this research highlights the significance of technology that has the potential to provide value for emerging technologies.

Enhancing intelligent IoT services development by integrated multi-token code completion

The Internet of Things (IoT) is a revolutionary network of interconnected devices embedded with sensors and software that enables seamless communication, data sharing, and intelligent decision-making in the form of IoT services. To facilitate the efficient development of IoT services, code completion technique provides a promising solution by providing suggestions for missing code snippets. The development trend of IoT services is to support more mobile device terminals. Mobile devices are portable and easy to use, allowing IoT device operation and management anytime and anywhere. However, the current multi-token completion methods struggle to guarantee code generation quality under the constraints of low resources and low latency, making it difficult to fully support IoT service development. We propose a multi-token code completion framework, S2RCC, which completes code from skeleton to refinement with dual encoder and dual decoder. The framework consists of two phases: first, the code skeleton, which is the simplification of code containing structure-sensitive tokens, is predicted based on the semantics of the code context; second, the broken context is repaired with the predicted skeleton, and then parsed into the code structure so that the specific tokens can be generated combining the semantics and structure of context. Furthermore, we then provide an implementation of the framework, representing the repaired code as an improved Heterogeneous code graph and fusing the semantics and structure of code context by the three-layer stacked attention. We conducted experiments on multi-token completion datasets, showing that our model has achieved the state-of-the-art with the smallest possible scale and the fastest generation speed.

Generative Adversarial Networks (GANs) for Audio-Visual Speech Recognition in Artificial Intelligence IoT

This paper proposes a novel multimodal generative adversarial network AVSR (multimodal AVSR GAN) architecture, to improve both the energy efficiency and the AVSR classification accuracy of artificial intelligence Internet of things (IoT) applications. The audio-visual speech recognition (AVSR) modality is a classical multimodal modality, which is commonly used in IoT and embedded systems. Examples of suitable IoT applications include in-cabin speech recognition systems for driving systems, AVSR in augmented reality environments, and interactive applications such as virtual aquariums. The application of multimodal sensor data for IoT applications requires efficient information processing, to meet the hardware constraints of IoT devices. The proposed multimodal AVSR GAN architecture is composed of a discriminator and a generator, each of which is a two-stream network, corresponding to the audio stream information and the visual stream information, respectively. To validate this approach, we used augmented data from well-known datasets (LRS2-Lip Reading Sentences 2 and LRS3) in the training process, and testing was performed using the original data. The research and experimental results showed that the proposed multimodal AVSR GAN architecture improved the AVSR classification accuracy. Furthermore, in this study, we discuss the domain of GANs and provide a concise summary of the proposed GANs.