Internet Of Things Applications Research Articles

Developing Wireless Communication Technique for Usage in IoT Applications

The Internet of Things (IoT) is another registering worldview that imagines standard ordinary articles being changed into significant items. In view of its benefits over wired advancements, like simpler arrangement, less expensive structures, compact help, flexibility, and straightforwardness of association, remote innovations will be the best option for interfacing IoT gadgets. There are assortments of remote advances that can be utilized for IoT; these innovations cover a wide scope of distances, from a couple of centimeters to numerous kilometers. The Internet Engineering Task Force (IETF) introduced the 6LowPAN convention in this article, and the ZigBee association set up the ZigBee convention over the low-power IEEE802.15.4 standard. Sensors are gathered in remote sensor networks applications to illuminate sensor centers. Commonly, battery power is utilized to drive these center points. In IoT applications, these centers should keep going for quite a long time without waiting be re-energized. IoT helps with settling on choices in view of genuine information gathered from a colossal number of ordinary gadgets that have been improved with information through the expansion of detecting, handling, and correspondence capacities. Remote correspondence is for the most part utilized by IoT gadgets to speak with different gadgets.

Nepali Virtual AI Assistant

With the development of Artificial Intelligence (AI), much of our regular redundant work that does not require critical thinking is replaced by machines. The scope of the Internet of Things (IoT) is also increasing due to AI. IoT acts as a suitable platform for the application of AI in real life. In this paper, we present a Virtual AI Assistant that combines AI with IoT. Our system uses an ESP8266 (Node MCU) chip as the core microcontroller for the home automation application. It is an IoT-compatible microcontroller with a cloud connectivity feature that is accomplished with the help of WiFi (Wireless fidelity). This allows home appliances such as lights, fans, rice cookers, etc. to be controlled by desktop or mobile applications from any part of the world. Our system uses speech recognition as its primary source for controlling the application. The speech recognition feature can detect and distinguish people’s voices which is a handy feature in terms of security and privacy. Other than IoT, software features such as note-making, application initiators, web navigation, and automatic typing through speech recognition are also implemented in this system. Our work also makes use of the Nepali language for IoT applications for switching home appliances on and off. The AI model was trained from scratch manually for the Nepali language as proper audio datasets for the Nepali language are not available. Our work in the development of Nepali Virtual AI Assistant is to integrate hardware and software with AI to create user-friendly automation and ease in people’s daily lives.

IoT - Toxic Gas Detection System in Sewage

The application of IoT and machine learning in gas detection systems for sewage offers an innovative approach to real-time gas detection and enhances environmental safety in industrial and urban environments. This system uses advanced technologies such as the Internet of Things (IoT), machine learning algorithms, and high-performance sensors like the MQ135 and the DHT11 to achieve the best results in the gas concentration measurement and collection. It identifies abnormalities and determines which emission control measures are most effective for specific release points and similar situations. The design of the device includes sensor nodes that are primarily responsible for data collection and a central microcontroller (MCU) that operates a machine learning algorithm for efficient anomaly detection and predictive maintenance. The system uses the IoT connection to regularly send data to cloud platform (Blynk), enabling real time monitoring of gas levels and environmental conditions. The system generates a visual image of the captured data that can be accessed online. Distinctive attributes like accurate gas detection, continuous monitoring, predictive maintenance, remote assessment, and comprehensive data visualization, all contribute to smart decision-making for environmental safety.

An empirical investigation into the enhancement of decision-making capabilities in corporate sustainability leadership through Internet of Things (IoT) integration

This research investigates the impact of Internet of Things (IoT) applications on decision-making capacity (DMC) development in Corporate Sustainable Leadership (CSL). The research focuses on how IoT enhances Environmental, Social, and Governance (ESG) practices within organizations, addressing key sustainability challenges and supporting Sustainable Development Goals (SDGs). Through quantitative and qualitative analyses, the study identifies three primary findings: (1) IoT applications directly enhance power source management in the environmental domain, improving Business Strategy and Long-term Growth (BSLG) and Measurement and Reporting (M&R) capabilities. This is achieved through real-time monitoring, smart building management, and predictive maintenance. In the social sphere, IoT strengthens supply chain transparency, bolstering Stakeholder Management (SM) through real-time tracking and blockchain-integrated systems for product authenticity verification. IoT comprehensively empowers performing risk management in the governance realm, improving Organizational Culture (OC) through early warning systems and predictive analytics for risk mitigation. (2) IoT integration facilitates Data-Driven Decision Making (DDDM), Real-Time Responsiveness (RTR), and Business Risk Management (BRM), key components of IoT-DMC. Furthermore, it supports the development of Effective Stakeholder Management (ESM), Resource Efficiency (RE), and Sustainable Competitive Outcomes (SCO) within IoT-CSL. (3) While highlighting the potential of IoT in advancing CSL, the research also acknowledges challenges such as initial investment costs, data privacy concerns, technological complexity, energy consumption of IoT devices, and electronic waste management. The study concludes that successful IoT implementation in CSL requires careful planning, robust data management, and a holistic approach considering both benefits and potential drawbacks.

Internet of Things, critical infrastructures, and the governance of cybersecurity in 5G network slicing

Over the last decade, cybersecurity has become a disruptive challenge driven by the shift to 5G networks. 5G is an application-agnostic general-purpose technology (GPT) characterized by its major role as multidimensional upstream technology for a large and open set of downstream Internet of Things (IoT) applications and use cases in various network industries and, more generally, within the economy as a whole. The focus of this paper is on the network economics of cybersecurity governance, considering the interplay between the recent EU cybersecurity regulatory framework, the extensive standardization efforts of international standardization organizations, and the economic incentives to tackle 5G network performance and security challenges. Based on the economic concept of 5G-based network slicing, the entrepreneurial challenges driven by the Internet of Things (IoT) are analyzed with an eye towards the complementarity between the physical side and virtual side of IoT applications. Network slicing gains particular relevance in the context of different types of logically separated, QoS-differentiated 5G bandwidth capacities, which are combined with other virtual resources such as sensor networks, data storage and processing, together with end-to-end slice security. It is shown in this paper that economic incentives for the implementation of 5G cybersecurity cannot be considered in isolation but must be viewed in combination with other performance requirements of 5G network slices. This is the driver for implementing network slicing architectures within 5G networks that feature a multitude of heterogeneous network slices with different QoS differentiation and various cybersecurity requirements.

Study to integrate Delay-Tolerant Network protocols in IoT LEO constellations for flood prevention

In the era of the Internet of Things (IoT) the development of Direct-to-Satellite IoT (DtS-IoT) applications are becoming increasingly relevant. These applications are based on enabling IoT devices to communicate directly with satellites. In this scenario, LEO satellites can provide global IoT service coverage, becoming essential to connect devices in remote areas. As an example, the deployment of NarrowBand-IoT (NB-IoT) service is being actively investigated with the apparition of Non-Terrestrial Network (NTN) and DtS-IoT concepts. Although some initiatives propose seamless and ubiquitous service, these features may not be required for IoT applications. This relaxes the requirements in the satellite constellation architecture, enabling it to be more sparse. The connection between sensors, satellites and ground segment becomes intermittent and discontinuous in these non-dense architectures. Delay/Disruptive-tolerant protocols are essential to coexist with this network characteristics. Unfortunately, traditional IoT protocols have not been designed with this delay-tolerant feature. This work tackles this challenge by integrating Delay-Tolerant Network protocols with NB-IoT architecture. Specifically, the integration is based on interconnecting the Bundle Protocol and IP traffic through a novel interface. This development has been divided in three phases: (1) The definition of an architecture and protocol stack to tackle NTN IoT scenarios with discontinuities, (2) the analysis from simulated data of the resulting protocol stack in a realistic scenario based on flooding prevention, and (3) the implementation and validation of it in a laboratory testbed. The proposed case study uses a Direct-to-Satellite IoT architecture to create an early warning flooding detection system. The simulation results provide insights of the achieved performance with these architectures when servicing hundreds of sensing nodes.

Enhancing internet of things security and efficiency through advanced elliptic curve cryptography-based strategies in fog computing

<p>Fog computing (FC) has evolved as a significant paradigm within the internet of things (IoT) ecosystem, serving as a crucial link between edge devices and centralised cloud computing resources. This research paper investigates advanced methodologies for improving the security and efficiency of FC in the IoT domain. The primary emphasis is placed on the utilisation of elliptic curve cryptography (ECC) to accomplish these goals. This study examines the difficulties encountered in ensuring the security of IoT deployments based on FC. It also presents novel solutions based on ECC to mitigate these obstacles. Moreover, this study investigates techniques for enhancing the efficiency and allocation of resources in IoT applications within a FC environment. This study seeks to offer significant insights into the application of ECC-based techniques for enhancing the security and efficiency of FC in the context of the IoTs. These insights are derived through a combination of theoretical analysis and practical implementations. To evaluate the effectiveness of the proposed system, an analysis is conducted to examine the encryption time, decryption time, and correlation coefficients. These metrics are then compared to those of existing state-of-the-art approaches.</p>

Enhancing 5G energy efficiency through digital twin networks: A comprehensive review

AbstractIn the era of 5G, seamless mobility handovers are critical in densely populated regions like Malaysia to mitigate disruptions and inefficiencies. 5G networks offer unprecedented data speeds and reliability, essential for advancing mobile communication and Internet of Things applications. However, ensuring continuous connectivity and service during mobility remains challenging, especially in urban settings. Digital twin technology presents a promising solution to enhance 5G handover mechanisms. A digital twin network (DTN) mirroring Malaysia's 5G infrastructure is proposed, utilising real‐time data and user behaviour insights to optimise energy consumption during handovers. The focus is on energy‐efficient protocols and algorithms, reviewed through a systematic literature review. The DTN aims to enhance mobility handover efficiency through predictive handovers and adaptive resource allocation, bolstered by sustainable practices such as edge computing. The potential of DTNs to optimise 5G handover processes is explored, starting with the foundational concepts of 5G mobility and digital twins, highlighting the need for improved strategies in high‐mobility scenarios. Methodologies leveraging digital twins to predict network conditions, simulate handover scenarios, and proactively manage decisions are examined, reducing latency and packet loss. Case studies demonstrate how digital twins adapt dynamically to network changes and user mobility, thereby improving quality of service and user experience. Malaysia's specific 5G mobility challenges are addressed with a tailored DTN emphasising energy efficiency, evaluated through practical applications. Evaluation criteria assess effectiveness with in‐depth analysis of methods, performance metrics, limitations, and recommendations for future research. Challenges and future directions including scalability, security, and real‐time data processing, are discussed, aiming to integrate digital twin technology with 5G networks for enhanced connectivity. This abstract provides a roadmap for leveraging digital twins to optimise 5G network performance sustainably, guiding future research and implementation strategies.

Key technologies and applications of collaboration between digital power grid and Internet of Things

AbstractThe integration of electricity technology and information technology, such as the Internet of Things (IoT), enables the construction of new power systems, along with the innovation of application scenarios and business scope. The key technologies of power IoT and the data flow process are summarised first. The IoT technology and application scenario requirements of power generation, transmission, loading, and storage of new power systems are studied. Thus, the nature of the collaborative development of the digital power grid and the IoT is demonstrated from the perspective of data processing in power IoT and application requirements in power systems. The key problems and solutions faced by the power IoT under the digital transformation are described, and the cross‐integration of key technologies and promotion of application scenario innovation are prospected. Finally, the key issues of future technological development were discussed, providing reference ideas for fully leveraging the value of energy and electricity data production factors and promoting the construction of a digital electricity ecosystem.

Reinforcement learning-enabled swarm intelligence method for computation task offloading in Internet-of-Things blockchain

Blockchain technology, based on decentralized data storage and distributed consensus design, has became a promising solution to address data security risks and provide privacy protection in the Internet-of-Things (IoT) due to its tamper-proof and non-repudiation features. Although blockchain typically does not require the endorsement of third-party trust organizations, it mostly needs to perform necessary mathematical calculations to prevent malicious attacks, which results in stricter requirements for computation resources on the participating devices. By offloading the computation tasks required to support blockchain consensus to edge service nodes or the cloud, while providing data privacy protection for IoT applications, it can effectively address the limitations of computation and energy resources in IoT devices. However, how to make reasonable offloading decisions for IoT devices remains an open issue. Due to the excellent self-learning ability of Reinforcement Learning (RL), this paper proposes a RL enabled Swarm Intelligence Optimization Algorithm (RLSIOA) that aims to improve the quality of initial solutions and achieve efficient optimization of computation task offloading decisions. The algorithm considers various factors that may affect the revenue obtained by IoT devices executing consensus algorithms (e.g., Proof-of-Work), It optimizes the proportion of sub-tasks to be offloaded and the scale of computing resources to be rented from the edge and cloud to maximize the revenue of devices. Experimental results show that RLSIOA can obtain higher-quality offloading decision-making schemes at lower latency costs compared to representative benchmark algorithms.

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.

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.