Internet Of Things Applications Research Articles

IoHT Based Tele-Healthcare Support System for Feasibility and Perfor-mance Analysis

With the rapid development in Internet of Things (IoT) it come up with the subbranch Internet of Healthcare Things or the Internet of Medical Things. With the help of portable healthcare sensors and the wearable devices now, ambulance can be converted into critical diagnostics unit on the wheels. Monitoring the home care patient is not the just part of IoT application where doctor can remotely monitor the patient situation by the means of sensors. It requires a complete system may called as telemedicine where multiple components will play similar priority role to monitor the patient status using sensors, let the doctor connect the patient, data analytics calculate the progression, AI engine will detect the emergency and act upon. The proposed system is to design and developing centralized tele-healthcare support system and systematic analytics sharing with multiple level of user with privacy protection. This will allow different authorities to take advantages from patient generated records.

Enhancing Security and Energy Efficiency in Wireless Sensor Networks for IoT Applications

Wireless Sensor Networks (WSNs) are fundamental components within the Internet of Things (IoT) scene, empowering information collection and transmission in different applications. This research papers on upgrading the security and vitality effectiveness of WSNs for ideal IoT usefulness. Novel strategies were created and assessed to address these vital perspectives. Security improvements included the usage of lightweight cryptographic calculations, intrusion detection systems (IDS), and secure directing conventions. These measures essentially reinforced organized strength against cyber dangers, with interruption discovery rates surpassing 95% and secure steering conventions accomplishing a strength score of over 97%. Vitality productivity optimizations enveloped communication convention improvements, information accumulation strategies, and energetic control administration methodologies. These procedures come about in outstanding decreases in vitality utilization, with optimized conventions diminishing vitality utilization by up to 30% and information conglomeration techniques yielding vitality reserve funds of up to 50%. Test assessments were conducted utilizing both simulation-based approaches and real-world organizations. Recreation comes about, obtained utilizing NS-3, showcasing the viability of the proposed strategies over different network topologies, activity designs, and attack scenarios. Real-world experimentation employing a WSN testbed approved the viable achievability and viability of the techniques. Comparative investigations with existing writing highlighted the oddity and importance of the commitments, checking considerable progressions in WSN security and vitality proficiency.

Smart Class System Prototyping through Sustainable Infrastructure Based on Mobile Application SPACE (Smart Program - Class Architecture) Integrated Smart Building Management System

Optimizing innovation in the use of health technology in infrastructure will directly impact the conditions of social interaction in the community, including within the scope of the school area. The Smart Class concept collaborates with learning carried out in a hybrid way. However, it is not yet fully optimized by digitizing the existing infrastructure with the concept of managing school facilities and infrastructure. The implementation of the Smart Class uses the intelligent building management system (IBMS), where this system integrates all parts of the building with the Internet of Things (IoT), facilities, and infrastructure and can minimize pandemic transmission in the academic sphere by accessing via mobile app. This research aims to analyze the design of the smart program class architecture integrated monitoring system to implement IoT-integrated health protocols, especially in the middle school scope. This research uses qualitative and quantitative methods with descriptive analysis through the development of user research at the system design stage. The output of this innovation is how schools implement digital technology comprehensively, starting from the application of IoT, sensors, and databases in a real-time mobile app environment. There are three main things: the data input, data processing, and output value. The intervention of school needs can be expanded with the integration of the learning management system in the form of integrated learning management the learning process, which is in line with monitoring the use of infrastructure in schools. Through this Smart Class concept, schools can still operate optimally without any applicable health policies and can provide excellent academic services.
 Keywords: smart class system prototyping, sustainable infrastructure mobile application, SPACE smart building management system

Cloud storage tier optimization through storage object classification

AbstractCloud storage adoption has increased over the years given the high demand for fast processing, low access latency, and ever-increasing amount of data being generated by, e.g., Internet of Things applications. In order to meet the users’ demands and provide a cost-effective solution, cloud service providers offer tiered storage; however, keeping the data in one tier is not cost-effective. In this respect, cloud storage tier optimization involves aligning data storage needs with the most suitable and cost-effective storage tier, thus reducing costs while ensuring data availability and meeting performance requirements. Ideally, this process considers the trade-off between performance and cost, as different storage tiers offer different levels of performance and durability. It also encompasses data lifecycle management, where data is automatically moved between tiers based on access patterns, which in turn impacts the storage cost. In this respect, this article explores two novel classification approaches, rule-based and game theory-based, to optimize cloud storage cost by reassigning data between different storage tiers. Four distinct storage tiers are considered: premium, hot, cold, and archive. The viability and potential of the proposed approaches are demonstrated by comparing cost savings and analyzing the computational cost using both fully-synthetic and semi-synthetic datasets with static and dynamic access patterns. The results indicate that the proposed approaches have the potential to significantly reduce cloud storage cost, while being computationally feasible for practical applications. Both approaches are lightweight and industry- and platform-independent.

Hybrid CNN Approach for Unknown Attack Detection in Edge-Based IoT Networks

INTRODUCTION: In the constantly growing Internet of Things (IoT), device security is crucial. As IoT gadgets pervade our lives, detecting unforeseen assaults is crucial to protecting them. Behavioral analysis, machine learning, and collaborative intelligence may be needed to protect against new dangers. This short discusses the need of detecting unexpected IoT attacks and essential security strategies for these interconnected environments.OBJECTIVES: This research uses the BoT-IoT dataset to create an enhanced IoT intrusion detection system. The goals are to optimize a CNN architecture for effective pattern recognition, address imbalanced data, and evaluate model performance using precision, recall, F1-score, and AUC-ROC measures. Improving IoT ecosystem reliability and security against unknown assaults is the ultimate goal.METHODS: The proposed methods use the BoT-IoT dataset to create a comprehensive IoT intrusion detection system. This involves tuning a Convolutional Neural Network (CNN) architecture to improve pattern recognition. Oversampling and class weighting address imbalanced data issues. RESULTS: The comprehensive evaluation of our innovative unknown attack detection method shows promise, suggesting it may be better than existing methods. A high accuracy, precision, recall, and f-measure of 98.23% were attained using an advanced model and feature selection methods. This achievement was achieved by using features designed to identify unknown attacks in the dataset, proving the proposed methodology works.CONCLUSION: This research presents an improved IoT Intrusion Detection System using the BoT-IoT dataset. The optimised Convolutional Neural Network architecture and imbalanced data handling approaches achieved 98.23% accuracy.

Agricultural transformation: A story about the Internet of Things

Precision agriculture, a sustainable solution for enhancing crop productivity, utilizes enabling technologies, primarily centred around the Internet of Things (IoT), to optimize irrigation and increase crop yields by adapting farming techniques to specific conditions. This study presents the adoption of a low-cost and eco-friendly IoT initiative in rural farms in Vietnam, which is an unexpected location. It marks the country’s first IoT start-up for agriculture during the 4.0 Industrial Revolution. To answer three research questions, we conducted a contemporary case study mixed with a couple of field experiments in a real-life context to learn how an IoT application was blended with local culture to transform traditional agriculture to a modern and digitalized industry. The pilot implementation yielded promising results, with positive feedback from farmers. Following necessary modifications, the system has been widely adopted across provinces in Vietnam. The study identified three key factors contributing to the start-up’s success: ‘Thiên thời, Địa lợi, Nhân hòa’. We believe that the start-up can assist millions of local farmers to get on the 4.0 train and that adopting IoT to initiate smart agriculture in Vietnam has sent a strong message ‘To be successful, global technology has to integrate into local culture’.

Dual-band GPS/LoRa antenna for internet of thing applications

This paper presents the design and characterization of a compact dual-band microstrip antenna for GPS and long range (LoRa) internet of thing (IoT) applications. The antenna operates at 868 MHz and 1.57 GHz and has a gain of 3.35 dBi and 5.08 dBi, respectively. The antenna design is optimized using CST microwave studio software (MWS®), and both simulation and measurement results are in close agreement. The antenna features a directional E-plane and omnidirectional H-plane radiation pattern in each band of operation. The proposed antenna’s compact size and dual-band capability make it suitable for IoT applications that require GPS and LoRa communication in a small form factor. The results presented in this paper demonstrate the feasibility and effectiveness of the proposed antenna design.

Dynamic Data Advertising and Packet Loss Analysis Using BLE Legacy Advertising

Bluetooth Low energy (BLE) advertisements have great potential in Internet of Things applications like monitoring systems, which involve the real-time transfer of data collected from sensors interfaced to multiple power-constrained devices. Since sensor data conveys information about the present state of the system, packet loss is vital in such applications. The paper proposes a customized BLE legacy advertising packet structure to advertise dynamic sensor data, and proposes two techniques: One-Set-Multiple-Events (OSME) and One-Set-X-Events (OSXE), to advertise the dynamic sensor data using the non-connectable and non-scannable undirected advertising mode of BLE. Using appropriate advertising parameters with OSXE, each advertising packet gets successfully advertised <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$X$</tex-math></inline-formula> times, whereas OSME neither guarantees successful advertisement of all the packets, nor does it allow control over the number of transmitted copies of each packet. Using OSXE, experimental analysis is carried out to study packet loss when single BLE node transmits alone, and when 25 nodes transmit simultaneously. Mathematical analysis has been carried out for packet collision for multiple nodes. Experimental results show that packet loss is also impacted by factors like advertising set duration, scan window, scanner's link layer activities like advertising report generation and channel switching. Using OSXE, packet loss close to 0% is obtained for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$X=3$</tex-math></inline-formula> when 25 nodes advertise simultaneously.

OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems

Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process. In order to address these issues, an effective meta heuristic based feature selection and deep learning techniques are developed for enhancing the IDS. The Osprey Optimization Algorithm (OOA) based feature selection is proposed for selecting the highly informative features from the input which leads to an effective differentiation among the normal and attack traffic of network. Moreover, the traditional sigmoid and tangent activation functions are replaced with the Exponential Linear Unit (ELU) activation function to propose the modified Bi-directional Long Short Term Memory (Bi-LSTM). The modified Bi-LSTM is used for classifying the types of intrusion attacks. The ELU activation function makes gradients extremely large during back-propagation and leads to faster learning. This research is analysed in three different datasets such as N-BaIoT, Canadian Institute for Cybersecurity Intrusion Detection Dataset 2017 (CICIDS-2017), and ToN-IoT datasets. The empirical investigation states that the proposed framework obtains impressive detection accuracy of 99.98 %, 99.97 % and 99.88 % on the N-BaIoT, CICIDS-2017, and ToN-IoT datasets, respectively. Compared to peer frameworks, this framework obtains high detection accuracy with better interpretability and reduced processing time.

A bridged‐switch energy‐efficient switching scheme for SAR ADCs with 2× input range

AbstractIn recent years, successive approximation register (SAR) analogue‐to‐digital converters (ADCs) have been widely applied in Internet of Things (IoT) sensor devices. To reduce the power consumption of ADCs, a bridged‐switch energy‐efficient switching scheme for SAR ADCs with a 2× input range is proposed. Due to the use of top‐plate sampling, overall shift and bridged switch, the energy consumption of the first three comparisons and reset is zero, meanwhile the energy consumption of the fifth comparison is greatly reduced. The proposed switching scheme has an average energy consumption of 13.084CVref2, which reduces the switching energy by 99.04% compared to the conventional switching scheme. The proposed switching scheme can achieve a 2× input range with the same supply voltage, effectively reducing ADC power consumption and making it more suitable for IoT applications.

Transformative Applications of IoT in Diverse Industries: A Mini Review

By integrating physical objects and facilitating data-driven decision-making, the Internet of Things (IoT) is transforming several sectors. Through the provision of individualised treatment plans, real-time health data analysis, and remote patient monitoring, it is vital to the modernization of healthcare systems. IoT technologies are essential to the development of smart cities, resource allocation optimisation, public safety improvement, and traffic congestion reduction. IoT-driven smart farming automates machinery, optimises irrigation, and monitors crop conditions. As IoT makes it possible to create smart grids, save energy waste, and increase grid dependability, the energy landscape is changing. IoT makes it easier to apply Industry 4.0 ideas in the manufacturing sector, converting conventional factories into networked, intelligent systems. Reducing operating costs and increasing productivity are the outcomes of implementing IoT-enabled sensors, robots, and data analytics to improve supply chain management, predictive maintenance, and production efficiency. Innovation, sustainability, and efficiency are becoming more and more possible as a result of the Internet of Things' integration across many industries. This review also showcases the relevant prospects of IoT applications in the fields mentioned.

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.

Incorporating the Internet of Things (IoT) Learning Module into the Smart Building Course

The utilization of Internet of Things (IoT) devices is increasing very rapidly, creating a demand for skilled professionals who can navigate the complexities of this technology. It is a crucial educational challenge to prepare undergraduates to become contributors in this emerging field. The curriculum needs to be updated to offer students both theoretical learning and practical application so they can benefit from it. This paper introduces a comprehensive IoT learning module to the final-year students of civil engineering in an elective course, Smart Building. The proposed learning module includes an introduction to IoT concepts and applications, IoT devices, communication protocols, cloud platforms, user interfaces, and IoT risk management and security, all of which can be easily integrated into existing courses. Results from the module are presented using direct and indirect assessments, including assignments, hands-on practical, examinations, and surveys. These assessments are designed to evaluate the students’ understanding and the challenges they encountered in their IoT learning process. The findings indicate that a significant majority, surpassing 90% of the students, have demonstrated understanding of a minimum of three essential components of the fundamental IoT architecture, despite their limited background knowledge at the beginning of the course. Additionally, this learning module can serve as a valuable resource for other educators who intend to deliver IoT-related courses.

Architectural Framework and Feasibility of Internet of Things-Driven Mars Exploration via Satellite Constellations

This study outlines a technical framework for Internet of Things (IoT) communications on Mars, leveraging Long Range (LoRa) technology to connect Martian surface sensors and orbiting satellites. The designed architecture adapts terrestrial satellite constellation models to Martian environments and the specific needs of interplanetary communication with Earth. It incorporates multiple layers, including Martian IoT nodes, satellite linkage, constellation configuration, and Earth communication, emphasizing potential Martian IoT applications. The analysis covers four critical feasibility aspects: the maximum communication range between surface IoT nodes and orbiting satellites, the satellite constellation’s message processing capacity to determine IoT node volume support, the communication frequency and visibility of IoT nodes based on the satellite constellation arrangement, and the interplanetary data transmission capabilities of LoRa-based IoT devices. The findings affirm LoRa’s suitability for Martian IoT communication, demonstrating extensive coverage, sufficient satellite processing capacity for anticipated IoT node volumes, and effective data transmission in challenging interplanetary conditions. This establishes the framework’s viability for advancing Mars exploration and IoT in space exploration contexts.

A Topical Review on Enabling Technologies for the Internet of Medical Things: Sensors, Devices, Platforms, and Applications.

The Internet of Things (IoT) is still a relatively new field of research, and its potential to be used in the healthcare and medical sectors is enormous. In the last five years, IoT has been a go-to option for various applications such as using sensors for different features, machine-to-machine communication, etc., but precisely in the medical sector, it is still lagging far behind compared to other sectors. Hence, this study emphasises IoT applications in medical fields, Medical IoT sensors and devices, IoT platforms for data visualisation, and artificial intelligence in medical applications. A systematic review considering PRISMA guidelines on research articles as well as the websites on IoMT sensors and devices has been carried out. After the year 2001, an integrated outcome of 986 articles was initially selected, and by applying the inclusion-exclusion criterion, a total of 597 articles were identified. 23 new studies have been finally found, including records from websites and citations. This review then analyses different sensor monitoring circuits in detail, considering an Intensive Care Unit (ICU) scenario, device applications, and the data management system, including IoT platforms for the patients. Lastly, detailed discussion and challenges have been outlined, and possible prospects have been presented.

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.

Potential applications of internet of things: A comprehensive analysis

Internet of Things (IoT) is the amalgamation of hardware, like sensors and trackers, which monitor several parameters of the environment or physical objects, and software that processes all the data gathered by hardware. Globally, the IoT market is anticipated to reach 53.8 billion USD by 2025. This enhancing demand is due to its innate ability to automate, which drives several industries to adopt IoT. In addition, minimum memory cost, processing, and storage with an increase in Big Data (BD), cloud, and conjunction of industrial networks and the internet are the added factors for the increase in IoT development. Due to this significance, IoT has applications in numerous areas like medical management, farming, wearable technology, smart energy meters, smart cities, etc. The applications are not limited to the examples mentioned above. Considering this, existing studies have considered different applications and attempted to execute them. As different applications have been focused on by these studies, the present review intends to provide a compilation of potential applications of IoT as considered by conventional research between 2018 and 2022. The study also intends to explore the advantages and disadvantages of different IoT applications (deliberated by conventional studies) through tabular analysis. Further, this review emphasizes IoT’s major key challenges and countermeasures to resolve its security issues. Finally, the study affords recommendations that will assist all IoT experts in bringing IoT products with enhanced security into the market.

Real-World Implementation and Performance Analysis of Distributed Learning Frameworks for 6G IoT Applications

This paper explores the practical implementation and performance analysis of distributed learning (DL) frameworks on various client platforms, responding to the dynamic landscape of 6G technology and the pressing need for a fully connected distributed intelligence network for Internet of Things (IoT) devices. The heterogeneous nature of clients and data presents challenges for effective federated learning (FL) techniques, prompting our exploration of federated transfer learning (FTL) on Raspberry Pi, Odroid, and virtual machine platforms. Our study provides a detailed examination of the design, implementation, and evaluation of the FTL framework, specifically adapted to the unique constraints of various IoT platforms. By measuring the accuracy of FTL across diverse clients, we reveal its superior performance over traditional FL, particularly in terms of faster training and higher accuracy, due to the use of transfer learning (TL). Real-world measurements further demonstrate improved resource efficiency with lower average load, memory usage, temperature, power, and energy consumption when FTL is implemented compared to FL. Our experiments also showcase FTL’s robustness in scenarios where users leave the server’s communication coverage, resulting in fewer clients and less data for training. This adaptability underscores the effectiveness of FTL in environments with limited data, clients, and resources, contributing valuable information to the intersection of edge computing and DL for the 6G IoT.

Enabling The E-NOSE: Evaluating Network Technologies for Optimized Odor Data Transmission in IoT and Edge Computing Applications

Electronic Noses (e-noses) are advanced sensors that are being increasingly used in the Internet of Things (IoT) field. They have various applications in environmental monitoring and medical diagnostics. Optimal transmission of odor-related data is essential for fully harnessing the capabilities of these electronic noses in various network environments. This research paper examines the issue of transmitting odor data in Internet of Things (IoT) applications by assessing recent progress in communication and network technologies. A thorough analysis of various network alternatives, such as Ethernet, cellular (4G/5G), Wi-Fi, Bluetooth, Zigbee, and LoRa, is provided, accompanied by an elaborate table displaying precise technical specifications. The analysis of LoRa, a Low-Power Wide-Area Network (LPWAN) technology, encompasses an investigation into its architecture, modulation method, device classifications, and protocols such as LoRaWAN. The analysis of Edge Computing and cloud-based approaches for managing odor data in IoT culminates in a tabular presentation that succinctly outlines their advantages and disadvantages. This study provides valuable information to enhance the efficiency of transmitting odor data and effectively utilize electronic nose technology in different emerging applications in the Internet of Things (IoT) field.

SECURING PUBLIC HEALTH: IOT AND BIG DATA IN FOOD SAFETY TRACEABILITY

This research paper investigates the transformative impact of Internet of Things (IoT) technologies on enhancing food safety and quality measurements for public health in the foodindustry. As global demands for diverse and safe food products rise, the integration of innovative technologies becomes crucial. Developing a sensible and trustworthy food safety traceability system and effectively executing traceability are the cornerstones of any solution to these issues. Today, traceability solutions play a critical role in restoring consumer and market trust while guaranteeing food safety and quality. The study explores real-world applications of IoT, focusing on its role in monitoring and managing the entire food production cycle. By interconnecting devices and sensors, IoT emerges as a multifaceted solution that revolutionizes traditional methodologies, promising toelevate safety and quality standards throughout the food supply chain. In the dynamic realm of food production, IoT proves to be a transformative force, providing real-time insights and reshaping how we ensure safety and maintain the quality of the food we consume with food safety traceability.