Potential Of Internet Of Things Research Articles

IoT applications in asset management: A review of accounting and tracking techniques

In the digital age, the confluence of Internet of Things (IoT) technologies with asset management practices heralds a transformative era, promising unprecedented efficiency and strategic advantage. This paper delves into the intricacies of this integration, exploring the evolution, challenges, and potential of IoT in reshaping asset management paradigms. With a keen aim to elucidate the transformative impact of IoT and navigate the complexities of its integration into asset management systems, the study adopts a qualitative research methodology, underpinned by a systematic literature review. This approach enables a comprehensive exploration of the subject matter, ensuring a nuanced understanding of the IoT's potential and pitfalls within the asset management domain. The findings illuminate a landscape where IoT applications significantly enhance asset utilization rates, underscored by empirical success stories and quantifiable improvements in operational efficiency. However, the journey reveals barriers—technical, organizational, and cultural—that impede seamless IoT adoption. Addressing these challenges, the paper offers strategic recommendations, emphasizing the need for strategic alignment, inter-organisational collaboration, and robust data management strategies to overcome integration hurdles. Conclusively, the study not only achieves its aim of highlighting the potential of IoT in asset management but also charts a course for future endeavors in this field. It posits that the strategic integration of IoT technologies stands as a cornerstone for the next generation of asset management practices, promising a future marked by enhanced efficiency, economic viability, and strategic agility. The recommendations provided serve as a compass for organizations embarking on the IoT integration journey, guiding them towards a horizon of transformative potential.

DEVELOPMENT OF DAG BLOCKCHAIN MODEL

In this study the authors present an innovative approach to resolving scalability and efficiency challenges in blockchain technology through the integration of Directed Acyclic Graphs (DAGs). This approach helps to overcome the limitations of traditional blockchain systems, particularly in transaction processing. The classic blockchain has some problems as slow transaction processing and poor scalability. The authors offer Directed Acyclic Graph (DAG) as a scalable and energy-efficient alternative. The paper outlines the development of a DAG-based blockchain model, utilizing Python and Flask alongside the Ed25519 cryptographic curve. It conducts a comparative analysis of DAG with traditional consensus mechanisms like Proof of Work and Proof of Stake, underscoring the efficiency and security benefits of employment of DAG. The research methodology includes an extensive literature review and the construction of a practical model to demonstrate DAG's applicability in blockchain networks. Particularly notable is the exploration of DAG's potential in Internet of Things (IoT) ecosystems, addressing critical issues such as energy inefficiency and network communication challenges in existing consensus algorithms. The authors calculated the performance of the model and compared it with similar models on several evaluation criteria. The simulation results of our proposed model show an improvement in performance and security by minimizing end-to-end delay, time cost, energy consumption, and throughput. The model eliminates the limitations of classic blockchain systems, such as high latency and low scalability. It structures transactions and blocks as a DAG, which provides fast validation and high scalability without compromising security. The research demonstrates the transformative implications of DAG for advancing blockchain technology.

Impact of Internet of Things (IoT) on Home Automation and Energy Management

The integration of Internet of Things (IoT) technologies into home automation and energy management systems has sparked a transformative wave, reshaping the way energy is consumed and managed within households. Employing a mixed-methods research approach, this study investigated the multifaceted impact of IoT-driven solutions. Through structured surveys and qualitative interviews, the study revealed a 20% reduction in energy consumption after IoT implementation, highlighting the potential for sustainable energy practices. Participants reported heightened satisfaction with the convenience of remote control and observed significant cost savings in energy bills. However, data privacy and security concerns emerged as challenges, emphasizing the need for broad safeguards. The study contributes to the evolving discourse on IoT's potential to revolutionize domestic energy consumption and automation, emphasizing the synthesis of efficiency and user comfort in smart homes

Stamp-Like Energy Harvester and Programmable Information Encrypted Display Based on Fully Printable Thermoelectric Devices.

Thermoelectric (TE) devices exhibit considerable application potential in Internet of Things and personal health monitoring systems. However, TE self-powered devices are expensive and their fabrication process is complex. Therefore, large-scale preparation of the TE devices remains challenging. In this work, simple screen-printing technology is used to fabricate a user-friendly and high-performance paper-based TE device, which can be used in both stamp-like paper-based TE generators and infrared displays. When used as a paper-based TE generator, an output power of 940.8 µW is achieved with a temperature difference of 40 K. The programmable infrared pattern based on the TE array display could be used to realize encryption and anti-counterfeiting properties. Moreover, a visual extraction algorithm is used to develop a mobile application for processing and decoding the infrared quick response code information. These findings offer an exciting approach to using paper-based TEGs in applications such as energy harvesting devices, optical encryption, anti-counterfeiting, and dynamic infrared display.

Transceiving Signals by Mechanical Resonance: A Miniaturized Standalone Low Frequency (LF) Magnetoelectric Mechanical Antenna Pair With Integrated DC Magnetic Bias

Low frequency(LF) communication systems offer significant potential in internet of things(IoT) applications due to their low propagation loss and long transmission range. However, traditional LF electrical antennas are too bulky for IoT. Due to their small dimensions, magnetoelectric mechanical antennas are promising, however, the state of art magnetoelectric antennas either have bulky external magnetic bias or have limited operation range. In this paper, we present a miniaturized standalone LF magnetoelectric transmitting/receiving antenna pair with integrated magnetic bias to significantly improve the operation range by 360%. As the operation principle of the antenna is based on mechanical resonance, its dimension is reduced by four orders of magnitude compared with an electrical antenna counterpart. The transmitting and receiving antennas are the same in structure and dimensions: they are both composed of magnetostrictive Terfenol-D and piezoelectric PZT laminate with dimensions of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$38\times 12\times19.4$ </tex-math></inline-formula> mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The integrated magnetic bias significantly reduces the dimension compared with magnetoelectric antennas with external bias. Performance measurement demonstrates that a maximum operation distance of 4 m is achieved without DC magnetic bias, while maximum operation distance with DC magnetic bias is 9 m, which is significantly improved by 225%. Compared with the spiral LF antenna presented in prior study, the radiation efficiency of the magnetoelectric antenna is enhanced by 17.96 times, resulting in 360% operation distance improvement from 2.5m at an input power of 4.5W to 9m at an input power of 2.03W. The LF magnetoelectric mechanical antenna pair may be a promising candidate for IoT applications.

Trajectory Prediction and Channel Monitoring Aided Fast Beam Tracking Scheme at Unlicensed mmWave Bands

Unlicensed 60 GHz millimeter band has a great potential in industrial Internet of things (IIoT) owing to its continuous large bandwidth. However, the signal transmission in this band suffers from high propagation loss, thus beamforming is adopted to provide directional gain. With the increasing number of beams, beam alignment and tracking in mobility scenario may incur unacceptable latency and overhead, and the existing beam management mechanism is no longer suitable. To reduce the latency and signaling overhead during beam tracking, we propose a fast beam tracking scheme with the help of trajectory prediction and channel monitoring. More specifically, we firstly quantify the beam coherent time to reduce the frequency of beam searching. Then, a two-stage heuristic trajectory prediction and channel monitoring aided fast beam tracking scheme is proposed to obtain the optimal beam pairs in the process of terminal movement and make sure that the interference on the directional beam is under the limit. Simulation results verify the effectiveness of the beam coherent time and the advantages of the proposed scheme in terms of complexity, outage probability, and effective spectrum efficiency.

Considering Risk in Infrastructure Internet of Things: Observations and Recommendations

This article explores the impact of the Internet of Things (IoT)-the emerging set of embedded sensors, actuators, controllers, and communications devices-on future utilities and infrastructures from the perspective of risk, reliability, and resilience. Given the need to safeguard public infrastructure, utility managers are typically well versed in managing risk; however, the IoT presents a new and fundamentally different component with a highly uncertain risk profile. These IoT systems will be densely deployed, highly integral, and likely autonomous in operation, meaning that they could directly affect the operational reliability of a utility. While much has been written about the IoT's potential to offer benefits and possibly harm future infrastructure, this article looks at the topic from the perspective of the utilities? risk and presents a set of observations and recommendations concerning utility deployment of the IoT. I highlight why utility and infrastructure operators should carefully consider risk as they incorporate this embedded, often autonomous networked technology into their systems.

Blockchain-Enabled Wireless Internet of Things: Performance Analysis and Optimal Communication Node Deployment

Blockchain has shown a great potential in Internet of Things (IoT) ecosystems for establishing trust and consensus mechanisms without involvement of any third party. Understanding the relationship between communication and blockchain as well as the performance constraints posing on the counterparts can facilitate designing a dedicated blockchain-enabled IoT systems. In this paper, we establish an analytical model for the blockchain-enabled wireless IoT system. By considering spatio-temporal domain Poisson distribution, i.e., node geographical distribution in spatial domain and transaction arrival rate in time domain are both modeled as Poisson point process (PPP), we first derive the distribution of signal-to-interference-plus-noise ratio (SINR), blockchain transaction successful rate as well as overall throughput. Based on the system model and performance analysis, we design an algorithm to determine the optimal full function node deployment for blockchain system under the criterion of maximizing transaction throughput. Finally, the security performance is analyzed in the proposed networks with three typical attacks. Solutions such as physical layer security are presented and discussed to keep the system secure under these attacks. Numerical results validate the accuracy of our theoretical analysis and optimal node deployment algorithm.