Internet Of Things Techniques Research Articles

Investigation on Wireless Energy Harvesting Techniques for Internet of Things

The Internet of Things (IoT), by collecting, processing, and uploading information from its surroundings, makes the preliminary base for the construction of a smart city. To utilize IoTs fully, establishing wireless sensor networks (WSNs) as a subset technology of the system is pivotal. Among all energy sources, the radio frequency energy, with its extensive coverage, easy accessibility, and characteristic of being free of spatial limitations, is the optimal choice for energy harvesting [1]. This project aims to conduct a thorough investigation on a wideband wireless energy harvester with high efficiency and verify its application prospect in portable power sources. I have comprehensively studied and modeled the components of a wireless energy harvesting system. I further fabricated the wireless energy harvester based on the optimized circuit diagram. It successfully featured a high sensitivity of 2701 V/W at 1.05 GHz or a wide bandwidth ranging from 0.5 GHz to 2.5 GHz. Finally, I used the devised sensor in real-life scenarios and successfully powered a thermos hygrometer and a LED. I built a network of multiple energy harvesters with an in-situ monitoring system, analogous to the IoT. In this work, I have proposed a methodology to fabricate wireless energy harvesters with high sensitivity and wide bandwidth. This research will bridge the gap between the scientific exploration for novel devices and their application in real-life scenarios.

Development of Internet of Things and Artificial Intelligence for intelligent sanitation systems: A literature review

Adequate sanitation is crucial for human health and well-being, yet billions worldwide lack access to basic facilities. This comprehensive review examines the emerging field of intelligent sanitation systems, which leverage Internet of Things (IoT) and advanced Artificial Intelligence (AI) technologies to address global sanitation challenges. The existing intelligent sanitation systems and applications is still in their early stages, marked by inconsistencies and gaps. The paper consolidates fragmented research from both academic and industrial perspectives based on PRISMA protocol, exploring the historical development, current state, and future potential of intelligent sanitation solutions. The assessment of existing intelligent sanitation systems focuses on system detection, health monitoring, and AI enhancement. The paper examines how IoT-enabled data collection and AI-driven analytics can optimize sanitation facility performance, predict system failures, detect health risks, and inform decision-making for sanitation improvements. By synthesizing existing research, identifying knowledge gaps, and discussing opportunities and challenges, this review provides valuable insights for practitioners, academics, engineers, policymakers, and other stakeholders. It offers a foundation for understanding how advanced IoT and AI techniques can enhance the efficiency, sustainability, and safety of the sanitation industry.

DiSA-CF: A distance-integrated self-attention model for collaborative filtering in web service recommendation

The ubiquity of the Internet of Things (IoT) across diverse applications underscore its pivotal role in seamlessly integrating physical devices to facilitate efficient data collection, analysis, and automation. Consequently, ensuring the Quality of Service (QoS) emerges as a critical imperative. While numerous studies have proposed methodologies focusing on resource optimization, network management, and data processing techniques, several previous approaches to QoS prediction may face constraints in the Internet of Things (IoT) environment, where the dynamic nature of IoT environments demands predictive models capable of adapting to varying conditions. Integrating user and service location data into QoS prediction models is paramount, as it enables personalized service delivery tailored to the user’s specific context, thus enhancing the user experience and overall system performance. This paper presents a novel approach to QoS prediction in IoT, harnessing self-attention and collaborative filtering (CF) techniques while incorporating location-based features such as distance from the user to the service. Experimental evaluations on benchmark datasets reveal that our proposed model improves prediction accuracy by up to 7 % compared to existing methods, underscoring its efficacy in enhancing QoS provisioning in IoT environments.

Development of an On-Shaft Vibration Sensing Module for Machine Wearable Rotor Imbalance Monitoring

Rotor imbalance is considered to be one of the main mechanical faults of rotating machinery; it may result in bearing damage and even catastrophic system failure. Recent progress in the Internet of Things (IoT) has promoted the application of novel sensing and computing techniques in the industry, and it is promising to employ novel IoT techniques for imbalance detection to avoid potential failures. Existing sensing techniques suffer from the impact of bearing structure dynamics, loss of accuracy during their lifetime, and security risks introduced by the sensor cabling and supports, which may, in turn, interfere with the machine operations due to inappropriate design and installation. This investigation provides an on-shaft machine wearable vibration sensing technique for effectively monitoring the running state of rotors while minimizing the interference with their operations. In this work, key investigations include the following: (1) theoretical modeling and an analysis of rotor imbalance, and its measurement with an on-shaft micro-electromechanical system (MEMS) accelerometer; (2) the development of a wirelessly powered, cordless on-shaft vibration measurement (OSVM) sensor for unobtrusive sensing of the vibration of rotating shafts; (3) the in-sensor computing design for optimizing the distribution of computing resources and decreasing data transmission. The tests and evaluation of the proposed techniques were conducted with a rotor test rig to demonstrate their feasibility. The presented investigation is a typical example of applying new sensing and computing paradigms to improve the flexibility and convenience of applications, which is a good reference for related investigations and practices.

Advancements in Telehealth: Enhancing Breast Cancer Detection and Health Automation through Smart Integration of IoT and CNN Deep Learning in Residential and Healthcare Settings

The rapid evolution of telehealth, or telemedicine, has spurred crucial technological advancements aimed at addressing the early stages of complex cancer conditions, where conventional diagnostic methods face challenges. This research introduces a cancer detection system that utilizes Internet of Things (IoT)-based patient records and machine learning. The primary objective is to automate real-time breast cancer monitoring and detection in residential institutions and smart hospitals, thus enhancing the delivery of quality cancer healthcare. Background: Traditional diagnostic methods, particularly physical inspection, exhibit inherent limitations in identifying breast cancer at early stages. This research responds to this challenge by leveraging innovative technologies, such as IoT and deep learning-based techniques, to overcome the constraints of conventional approaches. Objective: The primary goal of this study is to develop and implement a cancer detection system that integrates IoT-based patient records and machine learning for real-time breast cancer monitoring in residential and healthcare settings. Method: The research employs a synergistic combination of IoT technology for collecting images of residential users and Convolutional Neural Network (CNN), a deep learning technique, for early cancer prediction. The focus lies on contributing to the overall well-being of individuals who may unknowingly be living with cancer. Result: Simulated outcomes after 25 epochs are presented, emphasizing the training accuracy of the model and its validation accuracy using the proposed VGG16 classifier. Graphical representations of the results indicate consistent performance metrics, with both validation and training accuracy exceeding 99%. Specifically, the training accuracy measures at an impressive 99.64%, while the validation accuracy stands at 99.12%. Main Findings: The study demonstrates the effectiveness of the integrated IoT and deep learning techniques in achieving high accuracy rates for early breast cancer prediction. The findings affirm the potential of this approach to assist dermatologists in identifying breast malignancies at treatable stages. Conclusion: This research establishes a foundational framework for the integration of IoT and deep learning techniques, presenting a promising avenue for advancing early cancer detection in smart healthcare systems. The proposed cancer detection system holds significant potential for improving healthcare outcomes and contributing to the overall well-being of individuals at risk of breast cancer.

Exploring security threats and solutions Techniques for Internet of Things (IoT): from vulnerabilities to vigilance

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

Deep Learning Algorithms for IoT Based Crop Yield Optimization

Precision agriculture, with its objectives of optimizing crop yields, decreasing resource waste, and enhancing overall farm management, has emerged as a revolutionary technology in modern agricultural practices. The advent of deep learning techniques and the Internet of Things (IoT) has brought about a paradigm shift in monitoring, decision-making, and predictive analysis within the agriculture industry. This review paper investigates the relationship between deep learning, the (IoT), and agriculture, with an emphasis on how these three domains might work together to optimize crop yields through intelligent decision-making. The integration of deep learning techniques with (IoT) technology for precision agriculture is thoroughly analyzed in this study, covering recent developments, obstacles, and possible solutions. The paper investigates the role of deep learning algorithms in analyzing the vast amounts of data generated by IoT devices in agriculture. It scrutinizes various deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants applied for crop disease detection, yield prediction, weed identification, and other crucial tasks. Furthermore, this review critically examines the integration of IoT-generated data with deep learning models, highlighting the synergistic benefits in enhancing agricultural decision-making, resource allocation, and predictive analytics. This review underscores the pivotal role of IoT and deep learning techniques in revolutionizing precision agriculture. It emphasizes the need for interdisciplinary collaboration among agronomists, data scientists, and engineers to harness the full potential of these technologies for sustainable and efficient farming practices.

Trustworthy Localization in IoT Networks: A Survey of Localization Techniques, Threats, and Mitigation.

The Internet of Things (IoT) has revolutionized the world, connecting billions of devices that offer assistance in various aspects of users' daily lives. Context-aware IoT applications exploit real-time environmental, user-specific, or situational data to dynamically adapt to users' needs, offering tailored experiences. In particular, Location-Based Services (LBS) exploit geographical information to adapt to environmental settings or provide recommendations based on users' and nodes' positions, thus delivering efficient and personalized services. To this end, there is growing interest in developing IoT localization systems within the scientific community. In addition, due to the sensitivity and privacy inherent to precise location information, LBS introduce new security challenges. To ensure a more secure and trustworthy system, researchers are studying how to prevent vulnerabilities and mitigate risks from the early design stages of LBS-empowered IoT applications. The goal of this study is to carry out an in-depth examination of localization techniques for IoT, with an emphasis on both the signal-processing design and security aspects. The investigation focuses primarily on active radio localization techniques, classifying them into range-based and range-free algorithms, while also exploring hybrid approaches. Next, security considerations are explored in depth, examining the main attacks for each localization technique and linking them to the most interesting solutions proposed in the literature. By highlighting advances, analyzing challenges, and providing solutions, the survey aims to guide researchers in navigating the complex IoT localization landscape.

Harnessing IoT and machine learning for sustainable agriculture: Predictive crop yield modeling in smart farming

<p>The integration of Internet of Things (IoT) technology in agriculture has transformed traditional farming methods, enabling the emergence of smart agriculture systems. This research paper focuses on utilizing IoT and machine learning techniques to forecast crop yields based on climatic and soil conditions. The study utilizes a dataset sourced from Kaggle, which includes information on 22 distinct crops, such as Maize, Wheat, Mango, Watermelon, and others. The dataset encompasses crucial climatic factors like temperature, humidity, and rainfall, as well as essential soil conditions necessary for optimal crop growth. By employing advanced machine learning algorithms on this dataset, the objective is to develop accurate models capable of predicting crop yields. This, in turn, assists farmers in making well-informed decisions regarding crop management and optimizing agricultural productivity. The outcomes of this research have significant implications for the agricultural industry, providing valuable insights into crop yield estimation and supporting the implementation of sustainable farming practices.</p>

Low-Power Design Techniques for Internet of Things (IoT) Devices: Current Trends and Future Directions

Low-Power Design Techniques for Internet of Things (IoT) Devices: Current Trends and Future Directions

Enhancing Agricultural Productivity through Smart Farming with IoT

Technology has greatly advanced the agriculture industry, making it more data- driven and intelligent. The Internet of Things has grown at an exponential rate, causing significant changes in many industries, radical change creates opportunities as well as challenges because it destroys current farming methods. The Internet of Things (IoT) and wireless sensors' possible uses in agriculture are covered in this article. It also discusses some of the challenges that could occur from integrating this technology with traditional farming methods. The IoT tools and techniques used in agricultural applications are explained in detail. Based on this article, we can determine current IoT trends in agriculture.

Protecting the Internet of Things (IOT) with Machine Learning and Deep Learning Techniques

Abstract- Deep learning (DL) and Machine learning (ML) as an IoT paradigm have improved problem-solving, and as a result, their application has expanded to many different fields. This has led to the idea that there are two powerful ways to use data—deep learning (DL) and machine learning (ML)—to solve specific problems. Thus, this article's objective is to provide a thorough analysis of "Scanning Machines and Deep Learning Techniques for Internet of Things (IOT) Security and Privacy," which addresses the current state of IoT research as well as its joint endeavor with DL. This technique stops the adversary from discovering the training data for the target model by utilizing differential privacy. The research's authors concluded that machine learning and deep learning algorithms were developed relatively recently and were never intended for use in cryptography applications. However, researchers with the necessary skills can use deep learning and machine learning to develop cryptography. Index Terms- Internet of Things, Deep Learning, Machine Learning, Security

IoT Techniques: Exploring Edge Computing Challenges and Ethical Implications in Interconnected Device Systems

The Internet of Things (IoT) denotes a system where various devices and items are interconnected, gathering and sharing data through built-in sensors and communication systems. Edge Computing: The emergence of edge computing within IoT calls for immediate data processing at its origin, introducing complexities in effective distributed computing and consistent data management. Ethical Concerns: With the expanding use of IoT across various industries, navigating ethical issues related to data privacy, user consent, and appropriate utilization becomes more intricate and essential. The proposed system integrates advanced IoT techniques with optimized edge computing strategies to address immediate data processing challenges and implement robust ethical safeguards, ensuring efficient distributed computing and responsible data management across interconnected devices. Two Advantages of purposed system are IoT Benefits: The Internet of Things (IoT) fosters smooth connections between devices, boosting efficiency in operations and facilitating instant monitoring and decision-making. Utilizing IoT functionalities allows enterprises to extract valuable insights from data analytics, resulting in enhanced product innovation and enriched customer interactions. Edge Computing Benefits: Edge computing diminishes delays by handling data near its origin, improving response speeds and enabling swift decisions without over-reliance on centralized infrastructures. Integrating edge computing into IoT setups reduces data traffic, resulting in financial savings and streamlined network performance. Advantages of Ethical Considerations: Tackling ethical issues within IoT builds user confidence, encouraging wider acceptance of interconnected systems and emphasizing conscientious data handling. Giving precedence to ethical aspects can pave the way for consistent regulations and standards, establishing a basis for trustworthy and clear IoT implementations. Benefits of the Proposed System: Merging sophisticated IoT methodologies with edge computing approaches bolsters system dependability, adaptability, and efficiency, catering to dynamic data handling needs adeptly. By embedding strong ethical measures, the envisioned system reassures stakeholders, upholding adherence to standards while championing ethical data practices. The suggested system embodies a holistic strategy that combines cutting-edge IoT and edge computing methods, emphasizing ethical aspects, to tackle pivotal issues related to data handling, network optimization, and ethical data stewardship in interconnected device environments.

Review on Lightweight Cryptography Techniques and Steganography Techniques for IOT Environment

In the modern world, technology has connected to our day-to-day life in different forms. The Internet of Things (IoT) has become an innovative criterion for mass implementations and a part of daily life. However, this rapid growth leads the huge traffic and security problems. There are several challenges arise while deploying IoT. The most common challenges are privacy and security during data transmission. To address these issues, various lightweight cryptography and steganography techniques were introduced. These techniques are helpful in securing the data over the IoT. The hybrid of cryptography and steganography mechanisms provides enhanced security to confidential messages. Any messages can be secured by cryptography or by embedding the messages into any media files, including text, audio, image, and video, using steganography. Hence, this article has provided a detailed review of efficient, lightweight security solutions based on cryptography and steganography and their function over IoT applications. The objective of the paper is to study and analyze various Light weight cryptography techniques and Steganography techniques for IoT. A few works of literature were reviewed in addition to their merits and limitations. Furthermore, the common problems in the reviewed techniques are explained in the discussion section with their parametric comparison. Finally, the future scope to improve IoT security solutions based on lightweight cryptography and steganography is mentioned in the conclusion part.

BATTERY MONITORING SYSTEM FOR ELECTRIC VEHICLE USING IOT

This paper describes the utility of internet-of-things (IoT) in monitoring the overall performance of electric car battery. It is clean that an electric vehicle definitely relies upon at the source of energy from a battery. But, the amount of power supplied to the vehicle is reducing regularly that ends in the performance degradation. That is a chief issue for battery manufacture. On this paintings, the concept of monitoring the overall performance of the car the use of IoT techniques is proposed, in order that the monitoring may be completed at once. The proposed IoT-primarily based battery monitoring device is includes two most important elements i) monitoring device and ii) consumer interface. Primarily based on experimental effects, the gadget is successful to locate degraded battery overall performance and sends notification messages to the person for similarly motion.

A Review on Machine Learning-based Malware Detection Techniques for Internet of Things (IoT) Environments

A Review on Machine Learning-based Malware Detection Techniques for Internet of Things (IoT) Environments

Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System

With the emergence of the 5G network, the count of analysis papers associated with the 6G Internet of Things (IoT) has rapidly increased due to the rising attention of researchers in next-generation technology, 6G networks and IoT techniques. Owing to this, grasping the overall research topics and directions is a complex task. To mutually address the significant issues of 6G cellular IoT, i.e., information transmission, data aggregation and power supply, we proposed a variance-based integrating model for the 6G-IoT approach that considers energy, communication and computation (ECC). Initially, the base station (BS) charges huge IoT devices concurrently utilizing WPT in the downlink. After that, IoT devices gather the energy to perform the communication task and the computation task in the uplink in a similar spectrum. Also, the model integrates the optimization of transmit beams via the Improved Ant Colony Optimization (IACO) model to balance the system performance, power consumption and computational complexity. Further, this study exploited activated Remote Radio Units (RRUs) to improve the network performance and energy efficiency in the downlink model. The simulation outcomes evaluate the performance of the proposed work over the conventional models concerning error analysis. From the results, the MSE value in the IACO work is much lower, around 0.011, while the compared schemes achieved comparatively higher MSE values.

Biomimetic Spun Silk Ionotronic Fibers for Intelligent Discrimination of Motions and Tactile Stimuli.

Innovation in the ionotronics field has significantly accelerated the development of ultraflexible devices and machines. However, it is still challenging to develop efficient ionotronic-based fibers with necessary stretchability, resilience, and conductivity due to inherent conflict in producing spinning dopes with both high polymer and ion concentrations and low viscosities. Inspired by the liquid crystalline spinning of animal silk, this study circumvents the inherent tradeoff in other spinning methods by dry spinning a nematic silk microfibril dope solution. The liquid crystalline texture allows the spinning dope to flow through the spinneret and form free-standing fibers under minimal external forces. The resultant silk-sourced ionotronic fibers (SSIFs) are highly stretchable, tough, resilient, and fatigue-resistant. These mechanical advantages ensure a rapid and recoverable electromechanical response of SSIFs to kinematic deformations. Further, the incorporation of SSIFs into core-shell triboelectric nanogenerator fibers provides outstanding stable and sensitive triboelectric response to precisely and sensitively perceive small pressures. Moreover, by implementing a combination of machine learning and Internet of Things techniques, the SSIFs can sort objects made of different materials. With these structural, processing, performance, and functional merits, the SSIFs prepared herein are expected to be applied in human-machine interfaces.

A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

The Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the backbones of Industry 4.0, where data quality is crucial for decision support. Data quality in these systems can deteriorate due to sensor failures or uncertain operating environments. Our objective is to summarize and assess the research efforts that address data quality in data-centric CPS/IoT industrial applications. We systematically review the state-of-the-art data quality techniques for CPS and IoT in Industry 4.0 through a systematic literature review (SLR) study. We pose three research questions, define selection and exclusion criteria for primary studies, and extract and synthesize data from these studies to answer our research questions. Our most significant results are (i) the list of data quality issues, their sources, and application domains, (ii) the best practices and metrics for managing data quality, (iii) the software engineering solutions employed to manage data quality, and (iv) the state of the data quality techniques (data repair, cleaning, and monitoring) in the application domains. The results of our SLR can help researchers obtain an overview of existing data quality issues, techniques, metrics, and best practices. We suggest research directions that require attention from the research community for follow-up work.

A Survey on Formal Verification and Validation Techniques for Internet of Things

The Internet of Things (IoT) has brought about a new era of connected devices and systems, with applications ranging from healthcare to transportation. However, the reliability and security of these systems are critical concerns that must be addressed to ensure their safe and effective operation. This paper presents a survey of formal verification and validation (FV&V) techniques for IoT systems, with a focus on the challenges and open issues in this field. We provide an overview of formal methods and testing techniques for the IoT and discuss the state explosion problem and techniques to address it. We also examined the use of AI in software testing and describe examples of tools that use AI in this context. Finally, we discuss the challenges and open issues in FV&V for the IoT and present possible future directions for research. This survey paper aimed to provide a comprehensive understanding of the current state of FV&V techniques for IoT systems and to highlight areas for further research and development.