Internet Of Things Applications Research Articles

Intelligent System for Secure Tamper Protocol Model with IoT BlockchainArchitecture for Data Mitigation

In an era characterized by the proliferation of Internet of Things (IoT) devices and the critical importance of data integrity, the need for robust security mechanisms has never been greater. This paper introduces a novel "Secure Tamper Protocol Model" (STPM) integrated with an IoT-based blockchain architecture, designed to address the growing challenges of data mitigation in IoT ecosystems. This research explores the application of Symmetric Homomorphic Hidden Markov Models (SHHMMs) in the context of anomaly detection, with a focus on %G - IoT environments. SHHMMs have shown remarkable promise in accurately identifying anomalies within diverse datasets. The study presents numerical findings indicating the model's high accuracy, precision, recall, and F1-Score, with an initial accuracy of 98.2% reaching 99.0% at Epoch 100. Comparative analysis against traditional methods like Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) models consistently highlights SHHMM's superior performance, demonstrated through Packet Delivery Ratio (PDR), Packet Loss, End-to-End Delay, and Overhead metrics. The integration of blockchain technology further enhances the practicality of SHHMM in ensuring data integrity and security. This research contributes to the advancement of anomaly detection techniques in 5G- IoT applications, offering a blend of precision and robustness.

Analyzing the State-of-the-Art in Descriptive Statistics, Storytelling, and Performance Parameter Confirmation for IoT Applications

This paper gives thorough look at the most up-to-date methods in describing statistics, telling stories, and confirming performance parameters for Internet of Things (IoT) apps. It is very important to use descriptive statistics to summarize and explain the features of IoT data. We talk about different statistical measures, like mean, median, mode, variance, and standard deviation, and stress how important they are for understanding how IoT data is distributed. In the Internet of Things (IoT), storytelling means using stories to share lessons gained from analyzing data. We talk about how important storytelling methods are for helping people understand and use complex IoT data. We also look at different ways to tell stories, such as data visualization, story frameworks, and ways to get people involved. Confirming performance parameters is important for making sure that IoT applications are reliable and accurate. We look at different ways to check performance factors, like computer modeling, hypothesis testing, and confidence ranges. We also talk about the problems that can come up and the best ways to handle them when confirming performance parameters in IoT settings. We find important trends and problems in summary statistics, telling stories, and confirming performance parameters for IoT applications through this research. We stress the need for advanced analysis methods to handle the huge amount, speed, and range of IoT data that is being collected. We also stress how important it is to have good communication strategies when turning data ideas into choices that IoT users can act on. This paper gives researchers, practitioners, and decision-makers who work with IoT systems useful information by giving a full picture of the most recent progress in descriptive statistics, stories, and confirming performance parameters for IoT.

Bibliometric Analysis on the Application of IoT in Smart Manufacturing System in Industry 4.0

The rapid integration of Internet of Things (IoT) technologies in smart manufacturing systems has been a pivotal development within the framework of Industry 4.0. This study conducts a comprehensive bibliometric analysis to examine the evolution, current trends, and emerging research areas related to IoT applications in smart manufacturing. By analyzing data from a decade of scholarly publications, this research identifies key themes, influential authors, and collaborative networks that have shaped the field. The findings reveal that foundational technologies such as the internet, cloud computing, and big data are central to the development of smart manufacturing, while newer innovations like digital twins and blockchain are gaining prominence. The study also emphasizes the importance of strategic collaboration among researchers and industry professionals in advancing IoT adoption. The practical implications suggest that manufacturers should prioritize both foundational digital infrastructure and emerging IoT applications to enhance efficiency, security, and sustainability. This analysis provides a roadmap for future research and practical implementation, highlighting the critical areas where IoT can further revolutionize smart manufacturing systems.

Pelatihan Internet of Things (IoT) di SMA Negeri 1 Donorojo dalam Rangka Program Kerja KKN UNISNU Jepara

In the Industrial Revolution 4.0 era began to enter the virtual realm through connectivity between humans, machines, and data spread in various places and known as the Internet of Things (IoT) where the technology is needed in the current era. Internet of Things (IoT) is a technology that connects physical devices, vehicles, household appliances, and various other items. The Community Service Method used is offline training. The results of this Community Service are 30 participants who participated in the training with initially there were only 4 participants who understood the concept and application of IoT to 29 participants with a percentage of 86.2%, followed by participants' interest in the IoT field from 3 participants to 28 participants with a percentage of 89.2%, and activeness in question and answer sessions and discussions from 5 participants to 30 participants with a percentage of 83.3%.

Reviewing the impact of artificial intelligence and machine learning in enhancing IoT applications performance

The convergence of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) has revolutionized various industries by enhancing IoT applications' efficiency and performance. This paper presents a comprehensive review of the impact of AI and ML in optimizing IoT systems. This study investigates how AI/ML algorithms contribute to real-time decision-making, energy efficiency, data management, and security in IoT networks through a qualitative research methodology. Additionally, it identifies the limitations of current research and provides directions for future work.

A novel lightweight multi-factor authentication scheme for MQTT-based IoT applications

The present authentication solutions employed in the Internet of Things (IoT) are either inadequate or computationally intensive, given the resource-constrained nature of IoT devices. This challenges the researchers to devise efficient solutions to embed an important security tenet like authentication. In IoT, the most popular machine-to-machine communication protocol used at the application layer is Message Queuing Telemetry Transport (MQTT). However, the MQTT protocol inherently lacks security-related functions, like authentication, authorization, confidentiality, access control, and data integrity, which is unacceptable for IoT-driven mission-critical applications when connected over public networks. In such a situation, the security is hardened by employing a transport layer security protocol like TLS, which entails significant computational overheads. This paper presents a novel scheme to enhance MQTT security by providing a lightweight multi-factor authentication scheme based on Elliptical curve cryptography. The proposed scheme uses a low-cost signature and a fuzzy extractor to correct errors in imprinted biometrics in noisy environments. This scheme attains mutual authentication, generates a securely agreed-upon session key for secret communication, and guarantees perfect forward secrecy. Furthermore, the rigorous informal security analysis shows the proposed scheme resists cryptographic attacks, including known session critical attacks. Furthermore, an empirical study has been carried out to assess the effectiveness of the proposed scheme in the Cooja simulated environment.

Training IoT Development for Enhancing Search and Rescue Tracking and Educational Tools

The Internet of Things (IoT) Development Training at Universidade Oriental Timor Lorosa’e aims to address challenges in tracking search and rescue teams and enhance educational tools. This training is designed to provide an in-depth understanding of IoT technology and its applications in operational and educational contexts. The methods used include theoretical sessions, hands-on practice, and project evaluation. The evaluation results show a significant increase in participants' knowledge levels: understanding of IoT concepts increased from 40% to 85%, knowledge of IoT applications rose from 35% to 80%, the ability to use IoT devices improved from 30% to 75%, and skills in developing IoT systems advanced from 25% to 70%. The success of this training reflects the positive impact of IoT technology in enhancing participants' skills, potentially bringing long-term benefits to both fields discussed at Universidade Oriental Timor Lorosa’e.

Evaluating risk of IoT adoption in the food supply chain using an integrated interval-valued spherical fuzzy generalised TODIM method

The existing literature has shown that the Internet of Things (IoT) has proven to be beneficial for managing operations in the food supply chain. However, the potential failures resulting from these new technologies have not been thoroughly examined. Therefore, the aim of this study is to create a new framework for analyzing the risks associated with implementing IoT in the food supply chain. To achieve this, we introduce an extended failure mode and effect analysis framework using an integrated generalised Toma de Decisão Iterativa Multicritério (TODIM) method within the interval-valued spherical fuzzy setting. Initially, we developed a risk structure consisting of four dimensions and sixteen sub-factors to facilitate the implementation of IoT technology in the food supply chain. We then introduced an interval-valued spherical fuzzy-weighted Bonferroni mean operator to gather expert opinions and create a group risk matrix considering the interaction between the input risk data. Subsequently, we proposed a newly developed interval-valued spherical fuzzy TODIM method based on the Logarithmic percentage change-driven objective weighting (LOPCOW) method to evaluate and rank the risks associated with IoT application in the food supply chain. This method can capture the bounded rational decision behavior of experts and the uncertain preference of risk parameters. Based on the identified risk factors, we implemented a proposed risk analysis framework in line with the preferences of the experts. The results indicate that "human resource risk" and "uncertainty in technology adoption" are the most and least significant risks, respectively. We also conducted sensitivity and comparison studies to test the reliability and rationality of the proposed framework. The main findings can assist policymakers and managers in mitigating and minimizing the risks connected with IoT applications in the food supply chain. Additionally, these outcomes can aid stakeholders in making well-informed decisions about resource allocation for risk management.

The impacts of emotional intelligence and IOT on operational efficiency in manufacturing: A cross-cultural analysis of Nigeria and the US

This review paper explores the dual impacts of Emotional Intelligence (EI) and the Internet of Things (IoT) on Operational Efficiency (OE) in the manufacturing sectors of Nigeria and the United States, providing a comprehensive cross-cultural analysis. In an era where manufacturing industries globally are undergoing rapid technological advancements and organizational transformations, understanding the synergistic role of human and technological factors is crucial. EI, which encompasses the ability to recognize, understand, and manage emotions, significantly influences leadership effectiveness, team dynamics, and overall workplace harmony. Concurrently, the integration of IoT technologies facilitates real-time data monitoring, predictive maintenance, and enhanced automation, leading to substantial improvements in OE. The paper first delineates the conceptual frameworks of EI and IoT, highlighting their individual contributions to manufacturing efficiency. Subsequently, it discusses the comparative analysis of the manufacturing environments in Nigeria and the US, examining how cultural contexts shape the application and impact of EI and IoT. In the United States, advanced technological infrastructure and a culture of innovation foster the widespread adoption of IoT and the integration of EI in organizational practices. Conversely, Nigeria's manufacturing sector, while facing challenges such as infrastructural deficits and skill gaps, shows potential for growth through targeted investments in technology and human capital development. Through this cross-cultural lens, the paper identifies key differences in the perceptions and applications of EI, the extent of IoT integration, and the resulting OEs. The findings suggest that while the US benefits from a mature ecosystem for both EI and IoT, Nigeria's unique socio-economic landscape necessitates tailored strategies to harness these tools effectively. The analysis concludes with policy and practical implications, offering insights for enhancing manufacturing efficiency through balanced and culturally sensitive approaches to technological and EI advancements. This comprehensive review underscores the importance of synergizing human and technological capabilities to drive sustainable growth and competitiveness in the global manufacturing sector. Keywords: Emotional Intelligence, Internet of Things, Operational Efficiency, Manufacturing, Cross-Cultural Analysis, Nigeria and United States.

A ranking framework for the selection of IoT cloud platforms using hybrid multi-attribute decision-making method

PurposeThe Internet of Things (IoT) cloud platforms provide end-to-end solutions that integrate various capabilities such as application development, device and connectivity management, data storage, data analysis and data visualization. The high use of these platforms results in their huge availability provided by different capabilities. Therefore, choosing the optimal IoT cloud platform to develop IoT applications successfully has become crucial. The key purpose of the present study is to implement a hybrid multi-attribute decision-making approach (MADM) to evaluate and select IoT cloud platforms.Design/methodology/approachThe optimal selection of the IoT cloud platforms seems to be dependent on multiple attributes. Hence, the optimal selection of IoT cloud platforms problem is modeled as a MADM problem, and a hybrid approach named neutrosophic fuzzy set-Euclidean taxicab distance-based approach (NFS-ETDBA) is implemented to solve the same. NFS-ETDBA works on the calculation of assessment score for each alternative, i.e. IoT cloud platforms, by combining two different measures: Euclidean and taxicab distance.FindingsA case study to illustrate the working of the proposed NFS-ETDBA for optimal selection of IoT cloud platforms is given. The results obtained on the basis of calculated assessment scores depict that “Azure IoT suite” is the most preferable IoT cloud platform, whereas “Salesman IoT cloud” is the least preferable.Originality/valueThe proposed NFS-ETDBA methodology for the IoT cloud platform selection is implemented for the first time in this field. ETDBA is highly capable of handling the large number of alternatives and the selection attributes involved in any decision-making process. Further, the use of fuzzy set theory (FST) makes it very easy to handle the impreciseness that may occur during the data collection through a questionnaire from a group of experts.

Low power techniques for internet of things implementation: A review

The motive of designing a low-power system is to have a long battery life. One can carefully apply low-power design techniques to achieve low power consumption with proper system functioning to increase battery life. One such system where many devices are generally battery-operated and connected to the internet is the Internet of Things (IoT). IoT devices are used widely to gather data to propel actions and transmit the data for data analysis, connectivity, and automation. So much research is focused on IoT communication and computation in IoT devices, which increases power consumption. This survey explores low-power design strategies used at the architectural level. Additionally, it highlights the IoT features that give opportunities to apply low-power design methodologies to achieve less power consumption in IoT. Low-power design techniques are critical for the efficient functioning of Internet of Things (IoT) applications, where devices often operate in resource-constrained environments with limited power sources like batteries. Many applications of IoT are noticed including the domains such as industry, environment, and society, which are highly power-consuming because of the features such as computation, communication, security, and fault tolerance. Researchers have already reported the usefulness of many such techniques including architecture-level low-power design techniques. Still, it is observed that the work carried out is inadequate regarding power consumption, and communication bandwidth bottlenecks. In the present work, a rigorous literature survey is carried out based on low-power design techniques to support sustainable growth of IoT applications across various domains and power optimization.

Unveiling the Power of the Internet of Things: Exploring Services, Applications, and Overcoming Challenges

The Internet of Things (IoT) has transcended its futuristic perception and become an omnipresent reality. Its pervasive nature encompasses devices, sensors, clouds, big data, and business interactions. This revolutionary concept amalgamates traditional embedded systems with wireless microsensors, automation-driven control systems, and other elements to establish a vast infrastructure. The integration of wireless communication, micro electro mechanical devices, and the Internet has given rise to novel IoT applications. The IoT is essentially a network of interconnected objects accessible through the Internet, each object uniquely identifiable. The advent of IPv6, superseding IPv4, plays a pivotal role in expanding the address space for IoT development. The primary objective of IoT applications is to imbue objects with intelligence, eliminating the need for human intervention. However, the proliferation of smart nodes and the exponential data generated by each node present new challenges pertaining to data privacy, scalability, security, manageability, and other critical issues, which we delve into in this comprehensive exploration

A True Random Number Generator Design Based on the Triboelectric Nanogenerator with Multiple Entropy Sources.

The triboelectric nanogenerator (TENG) has the potential to serve as a high-entropy energy harvester, enabling the self-powered operation of Internet of Things (IoT) devices. True random number generator (TRNG) is a common feature of encryption used in IoT data communication, ensuring the security of transmitted information. The benefits of multiplexing TENG and TRNG in resource-constrained IoT devices are substantial. However, current designs are limited by the usage scenarios and throughput of the TRNG. Specifically, we propose a structurally and environmentally friendly design based on the contact-separation structure, integrating heat fluctuation and charge decay as entropy sources. Furthermore, filtering and differential algorithms are recommended for data processing based on TENG characteristics to enhance randomness. Finally, a TENG-based TRNG is fabricated, and its performance is verified. Test results demonstrate a random number throughput of 25 Mbps with a randomness test pass rate approaching 99%, demonstrating suitability for resource-constrained IoT applications.

Memory-Efficient Trust Management for IoT Nodes Enabling Secure Communication in Trusted Environments

The Internet of Things (IoT) is changing quickly, so making sure that gadgets that are linked can talk to each other safely is very important. This paper describes a new way to handle trust that is specifically made for IoT nodes that work in safe settings. Conventional models frequently have a parcel of additional memory needs and are difficult to compute, which is made more regrettable by IoT gadgets that do not have a part of assets. Our proposed approach bargains with these issues by employing a belief administration framework that doesn't utilize a parcel of memory and finds an adjustment between security needs and the restricted assets that IoT hubs have. The foremost inventive thing around our strategy is that it can alter belief levels on the fly based on modern data and past experiences. Our framework takes up as small memory as conceivable whereas still having solid security measures since it employs lightweight cryptography conventions and quick information structures. This lets Internet of Things (IoT) gadgets utilize secure communication strategies without abating them down or making them less dependable. Context-aware belief assessment could be a key portion of our framework. This implies that belief levels are continually changed based on real-time encompassing variables and the way hubs are connected with each other. This adaptable alter makes beyond any doubt that belief choices remain in line with changing working conditions and threat scenarios. We moreover made beyond any doubt that our strategy works well with current IoT plans, permitting distinctive sorts of systems to work together and develop. We demonstrate that our system works in a wide range of IoT application cases by running a part of models and real-world tests on it. This study shows an easy-to-use and expandable way to improve trust management in IoT settings, with a focus on memory efficiency without losing safety. By using our approach, IoT stakeholders can reduce the risks that come with old trust models. This will make IoT environments more reliable and adaptable in the future.

Empowering IoT Developers with Privacy-Preserving End-User Development Tools

Internet of Things applications (IoT) have the potential to derive sensitive user data, necessitating adherence to privacy and data protection laws. However, developers often struggle with privacy issues, resulting in personal data misuse. Despite the proposed Privacy by Design (PbD) approach, criticism arises due to its ambiguity and lack of practical tools for educating software engineers. We introduce Canella, an integrated IoT development ecosystem with privacy-preserving components leveraging End-User Development (EUD) tools Blockly@rduino and Node-RED, to help developers build end-to-end IoT applications that prioritize privacy and comply with regulations. It helps developers integrate privacy during the development process and rapid prototyping phases, offering real-time feedback on privacy concerns. We start by conducting a focus group study to explore the applicability of designing and implementing PbD schemes within different development environments. Based on this, we implemented a proof-of-concept prototype of Canella and evaluated it in controlled lab studies with 18 software developers. The findings reveal that developers using Canella created more privacy-preserving applications, gained a deeper understanding of personal data management, and achieved better privacy compliance. Our results also highlight Canella's role in educating and promoting privacy awareness, enhancing productivity, streamlining privacy implementation, and significantly reducing cognitive load. Overall, developers found Canella and its privacy-preserving components useful, easy to use, and easy to learn, which could potentially improve IoT application privacy. Watch the demo video.

Impact of Internet of Things (IoT) applications on HR analytics and sustainable business practices in smart city

AimsThe research discovers how IoT contributes to workspace optimization, utilizing occupancy sensors to streamline office layouts, improve energy efficiency, and enhance the overall work environment in smart cities in India. Subject and methodsIn the present research study, both descriptive and exploratory research design were implemented and respondents include the Experts, HR Analysts and Regular Employees of Services organizations. The independent and dependent variables were identified and multiple regression analysis was executed for data analysis using SPSS software. ResultsThe results or outcomes of the research summarizes the positive response of technological upgradation in HR practices in modern organizations. HR Analytics interconnect with applications of IoT that facilitates for better resource utilization and monitoring system. ConclusionThe study concludes by presenting a comprehensive framework for HR professionals to effectively integrate IoT into their analytics practices, emphasizing the need for collaboration, communication, and the establishment of clear privacy policies.

PKM Kelompok Remaja dalam Menerapkan Aplikasi Berbasisis IoT (Internet of Things) untuk Wirausaha Mandiri Berbasis Start-Up

Vocational education plays an important role in bridging the gap between educational outcomes and labor market needs, especially in the Industry 4.0 era. With the rapid development of technology, especially artificial intelligence and automation, vocational education must adapt its curriculum to prepare graduates with relevant skills. The Internet of Things (IoT) is one of the key technologies that increases efficiency and opens up new opportunities, especially for the younger generation in entrepreneurship. The Community Service Program (PKM) uses an educational-persuasive approach to increase youth's knowledge and skills in utilizing IoT for start-up-based independent entrepreneurship. The training held, including IoT application simulations, showed an increase in participants' knowledge and abilities. Evaluation results based on questionnaires show that the majority of participants felt a significant increase in their understanding of IoT material and applications. In conclusion, this program succeeded in developing technology-based entrepreneurial competencies for teenagers, while providing practical insight into the application of IoT in business.

Empowering IoT connectivity: unveiling the potential of IoT.Js for enhanced interconnectivity

ABSTRACT The concept of a global Internet of Things (IoT), as proposed by Bart, revolutionizes connectivity by assigning unique digital identities to everyday objects, thereby enabling the development of smarter devices. However, the advancement and expansion of the IoT face significant obstacles and constraints, including energy efficiency and memory usage, which require further investigation. To address these challenges, this study presents a novel framework named IoT.js. This framework aims to facilitate the interconnection of lightweight devices within the web-based IoT ecosystem. These devices, ranging from microcontrollers to those with limited memory capacities, stand to benefit from IoT.js’s utilization of JavaScript for IoT application development. By promoting collaboration among lightweight devices with small memory sizes, IoT.js offers a promising solution to the resource constraints encountered in IoT deployments. This study contributes to the advancement of the IoT landscape by introducing a new approach that not only enables the efficient use of limited resources but also facilitates the seamless interconnection of lightweight devices within the IoT network. Additionally, the study conducts performance analysis to measure the efficacy of the proposed framework. Through rigorous evaluation of key metrics, such as energy efficiency and memory usage, the research findings validate the effectiveness of IoT.js in addressing the challenges associated with IoT deployment. Overall, the proposed IoT.js based approach represents a significant step forward in enhancing interconnectivity within the IoT ecosystem, thereby unlocking new possibilities for the development of innovative IoT applications and services.

Machine learning‐driven implementation of workflow optimization in cloud computing for IoT applications

AbstractThe optimization of workflow scheduling in Internet of Things (IoT) environments presents significant challenges due to the dynamic and heterogeneous nature of these systems. Traditional techniques must often adapt to fluctuating network conditions and varying data loads. To address these limitations, we propose a novel approach that leverages Automated Machine Learning (AutoML) integrated with cloud computing to optimize workflow scheduling for IoT applications. Our solution automates machine learning model selection, training, and tuning, significantly enhancing computational efficiency and adaptability. Through extensive experimentation, we demonstrate that our AutoML‐driven approach surpasses conventional algorithms across several key metrics, including accuracy, computational efficiency, adaptability to dynamic environments, and communication efficiency. Specifically, our method achieves a scheduling accuracy improvement of up to 25%, a reduced computational overhead by 30%, and a 40% enhancement in adaptability under dynamic conditions. Furthermore, the scalability of our solution is critical in cloud computing contexts, enabling efficient handling of large‐scale IoT deployments by leveraging cloud resources for distributed processing. This scalability ensures that our approach can effectively manage increasing data volumes and device heterogeneity inherent in modern IoT systems.

IoT-driven optimization of a NxN enhanced pipeline multiplier

As the Internet of Things (IoT) continues to evolve, the demand for efficient hardware solutions is crucial. The paper explores the significance of hardware multiplication in IoT devices and discusses the challenges and opportunities in implementing dedicated hardware accelerators for multiplication operations. It specifically focuses on a highly efficient NxN-bit folded pipeline multiplier designed using advanced 22 nm CMOS technology and a modified hierarchical design approach. This multiplier achieves remarkable performance enhancements by reducing critical path delay through pipelining, resulting in a three-fold increase in operational speed compared to conventional multipliers. Additionally, it consumes only 0.2 mW of power and reduces chip area consumption by over 28 %. The paper emphasizes the importance of minimizing computational latency and power consumption to enable IoT devices to execute complex algorithms with improved speed and resource utilization. It also addresses trade-offs, such as area overhead and design flexibility, associated with hardware multiplication. The insights provided contribute to ongoing efforts to optimize hardware components for IoT applications, fostering the development of more capable and responsive IoT ecosystems. The study presents a rapid and energy-efficient alternative for multiplication in digital hardware, addressing the need for high-performance and energy-efficient devices in the context of the IoT.