Internet Of Things Devices Research Articles

Improving the performance of IoT devices that use Wi-Fi

<p>Providing quality service to users of the internet of things (IoT) entails addressing two crucial aspects: one related to security and the other concerning the limited resources of IoT devices. We will face a challenge while using timesensitive applications within a network that utilizes a high-performance Wi-Fi technology with exceeding energy consumption. Due to this research challenge, we propose a new algorithm, IoT-quality of service (QoS), designed to achieve a true balance between enhancing the security aspects of IoT devices and improving network-hardware performance. Thus, the algorithm efficiently manages the limited energy resources by monitoring energy levels, communication quality, and queuing delay at access points. This is accomplished by utilizing a streamlined identity management system capable of achieving authentication and access authorization with reduced loading for IoT devices. The research hypothesis underwent validation through a comparative analysis of its performance against the conventional model of a Wi-Fi-based IoT device. This evaluation was conducted utilizing the NS3 simulator and was based on a predetermined set of parameters influencing the examined performance metrics, including power consumption, throughput, delay, and response time. The findings exposed the superiority of the proposed algorithm.</p>

Air quality monitoring system based on low power wide area network technology at public transport stops

<span>Mass migration from rural areas to urban areas has caused problems of traffic congestion, high industrial concentration and inequity in the distribution of housing in the world's capitals, generating a significant threat to sustainable development and public health due to air pollution air. In the Peruvian context, the importance of real-time monitoring of air quality is highlighted according to the standards established by the government. Several studies propose real-time environmental monitoring systems using internet of thing (IoT) technologies, electrochemical and optical sensors to measure pollutants, highlighting the need for data analysis. The objective of the paper is to show the implementation of IoT devices called sensor nodes, with long range wide area network (LoRaWAN) transmission technology for continuous monitoring of polluting gas concentrations. In addition, they are integrated into a central node called gateway to perform real-time monitoring through a web application. As an initial result, IoT devices demonstrated their effectiveness for real-time monitoring. Despite being a prototype-level result, the next stage involves its deployment at public transport stops in Lima. Overcoming the limitations of the solution, this paper establishes the foundation for future research on pollution and public health.</span>

Timing issues on power side-channel leakage of advanced encryption standard circuits designed by high-level synthesis

In recent years, field programmable gate array (FPGA) have been used in many internet of things (IoT) devices and are equipped with cryptographic circuits to ensure security. However, they are exposed to the risk of cryptographic keys being stolen by side-channel attacks. Countermeasures against side-channel attacks have been developed, but they are becoming more of a threat to IoT devices due to the diversity of attacks. Therefore, it is necessary to understand the basic characteristics of side-channel attacks. Therefore, this study clarifies the relationship between two timing issues, the clock period of the circuit and the power sampling interval, and the amount of side-channel leakage. We design seven advanced encryption standard (AES) circuits with different clock periods and conduct empirical experiments using logic simulations to clarify the correlation between the two timings and the amount of side-channel leakage. T-test is used to evaluate the leakage amount, which is evaluated based on four metrics. From the results, we argue that the clock period and sampling interval do not interfere with each other in the side-channel leakage amount.

TaPaFuzz: Hardware-accelerated RISC-V bare-metal firmware fuzzing using rapid job launches

Fuzz testing serves as a key technique in software security aimed at identifying unexpected program behaviors by repeatedly executing the target program with auto-generated random inputs. Testing is integral to IoT device security but is hampered by the minimal observability features of typical in-market IoT devices. Moreover, the slow nature of a RISC-V software emulation on x86 host CPUs and the inaccuracies introduced by compiling IoT applications to a different ISA for execution on host systems pose significant challenges. Our software-hardware co-design surmounts these hurdles. Fuzzing jobs are prepared and evaluated on a host computer, while the actual execution with high-throughput tracing is performed on an FPGA. Advances in the host-to-FPGA interface together with an accelerated reset procedure between Fuzzer jobs effectively hide the costly host-FPGA communication, increasing the single-thread fuzzing performance by up to factor 11.7x that of the leading QEMU-based fuzzer AFL++ running on a very fast x86 CPU. We demonstrate practical usability by evaluating our framework on a collection of bare-metal applications.

Practical and Secure Password Authentication and Key-Agreement-Scheme-Based Dual Server for IoT Devices in 5G Network

Practical and Secure Password Authentication and Key-Agreement-Scheme-Based Dual Server for IoT Devices in 5G Network

Enhancing stress detection in wearable IoT devices using federated learning and LSTM based hybrid model

Enhancing stress detection in wearable IoT devices using federated learning and LSTM based hybrid model

Enhancing IOT Security: Investigating Lightweight Encryption Algorithms for Resource-Constrained Devices

Abstract: In this work, we examine three LWCs: namely AES-128, SPECK, and ASCON, considering their presentation on resource-constrained IoT devices. With the large-scale deployment of IoT in all areas of industry, ensuring the security and efficiency of cryptographic implementations has become highly critical because typical IoT devices are usually characterized by limited memory, processing power, and energy resources. The study ascertains the memory usage, throughput, energy efficiency, and strength of security in the chosen algorithms by highly popular resource-constrained boards such as Arduino Nano and Arduino Micro. The benchmarks have shown that SPECK has the highest throughput, which is much efficient for constrained environments. One of the significant features of ASCON is that it has minimized memory usage and high energy efficiency. Therefore, it suits for long-term usage in IoT. AES-128 is secure but resource-intensive, so its usage may be irrelevant in very constrained environments. The results are of great value for IoT developers as a contribution to the selection of cryptographic algorithms based on the conditions and resource restrictions of the IoT devices involved so that optimized security solutions are achieved.

Optimizing IOT Network Performance: A Study on Low-Power Wide-Area Network (LPWAN) Technologies for Smart City Applications

Abstract: This research explores with a specific focus on smart city applications, how to optimise the execution of Web of Things (IoT) networks by utilising breakthroughs in Low-Powered Wide Area Networks (LPWAN). As IoT devices proliferate and generate vast amounts of data, having a competent network becomes essential, especially when dealing with low device thickness and limited power availability. This analysis provides a detailed energy consumption model tailored to LoRaWAN, based on a deeper understanding of its functional components in Internet of Things scenarios. The review uses MATLAB for replication and looks at two clear scenarios: the standard allocation of spreading factors without optimisation and an improved method that uses particle swarm optimisation (PSO) for dynamic spreading factor work. The results show that the network energy consumption dropped significantly, from 6.3997×10^ (- 17) Joules to 2.5230×10^(- 17) Joules, along with increased throughput, decreased parcel misfortune, and decreased idleness. Through a detailed analysis of several parameters, such as network density and data transfer efficiency, this study demonstrates the feasibility of the suggested optimisation model in terms of improving battery efficiency and overall network performance. Finally, the findings demonstrate how efficient LPWAN systems may provide robust and useful IoT environments, which are essential for the successful implementation of smart city initiatives.

Application of Internet of Things (IoT) in Energy Infrastructure: Lessons for the Future of Operations and Maintenance

The application of the Internet of Things (IoT) in energy infrastructure is revolutionizing operations and maintenance practices, driving efficiencies, and enhancing sustainability across the sector. This paper explores the transformative impact of IoT technologies on energy infrastructure, focusing on how they facilitate real-time monitoring, predictive maintenance, and data-driven decision-making. By leveraging IoT devices and systems, energy operators can optimize performance, reduce operational costs, and minimize downtime, ultimately contributing to more resilient and sustainable energy systems. The discussion highlights key IoT applications within energy infrastructure, including smart grids, renewable energy systems, and conventional power plants. Smart sensors and devices enable continuous data collection, providing insights into equipment performance, energy consumption, and environmental conditions. Predictive analytics, powered by IoT data, allows operators to anticipate equipment failures and conduct maintenance proactively, thereby extending asset life and improving reliability. Additionally, the abstract addresses the challenges associated with IoT integration in energy infrastructure, such as cybersecurity risks, data management issues, and the need for interoperability among diverse systems. It emphasizes the importance of establishing robust security protocols to protect sensitive data and ensure the integrity of IoT networks. Furthermore, the role of regulatory frameworks and industry standards in promoting the safe and effective deployment of IoT technologies is discussed. The paper also examines case studies of successful IoT implementations in energy infrastructure, showcasing the lessons learned from these initiatives. These examples illustrate the potential for IoT to enhance operational efficiency, reduce greenhouse gas emissions, and support the transition to cleaner energy sources. In conclusion, the abstract underscores that the adoption of IoT technologies in energy infrastructure is not just a trend but a necessity for the future of operations and maintenance. As the energy sector continues to evolve, embracing IoT will be crucial for achieving sustainability goals and meeting the growing demands for reliable energy supply.

Digital tools and AI: Using technology to monitor carbon emissions and waste at each stage of the supply chain, enabling real-time adjustments for sustainability improvements

The increasing urgency of addressing climate change has necessitated the adoption of innovative technologies to monitor and mitigate carbon emissions and waste throughout the supply chain. This paper explores the role of digital tools and artificial intelligence (AI) in enabling real-time tracking and analysis of environmental impact at each stage of the supply chain. By integrating IoT devices, big data analytics, and machine learning algorithms, organizations can gain comprehensive insights into their operations, identifying key areas where emissions and waste can be reduced. The use of AI-powered predictive analytics allows companies to model various scenarios, optimizing resource allocation and operational efficiency while minimizing environmental footprints. This research also highlights successful case studies where companies have implemented these technologies, resulting in significant sustainability improvements and cost savings. Furthermore, the paper discusses the challenges associated with data integration, system interoperability, and the need for industry-wide standards to ensure effective monitoring and reporting. The importance of stakeholder collaboration is emphasized, as engaging suppliers, customers, and regulatory bodies is essential for achieving comprehensive sustainability goals. Ultimately, this paper advocates for the strategic implementation of digital tools and AI as pivotal enablers of sustainable supply chain management, providing organizations with the capability to adapt to changing environmental regulations and consumer expectations. By leveraging technology to monitor and reduce carbon emissions and waste, businesses can enhance their competitiveness while contributing to a more sustainable future.

Comprehensive framework for data fusion in distributed ledger technologies to enhance supply chain sustainability

This review proposes a comprehensive framework that integrates data fusion with Distributed Ledger Technologies (DLT) to enhance sustainability in supply chain management. In today’s global supply chains, ensuring transparency, efficiency, and environmental responsibility is critical, yet the lack of real-time visibility and data fragmentation presents significant challenges. The framework addresses these issues by merging data from multiple sources, including IoT devices, operational databases, and external environmental factors, using advanced data fusion algorithms. DLT, with its decentralized, immutable, and transparent nature, ensures the integrity and security of the data, allowing all stakeholders to access accurate and tamper-proof information. The fusion of data within a DLT infrastructure not only improves traceability and accountability but also enables the automation of sustainability checks via smart contracts. These contracts can trigger actions based on predefined sustainability metrics such as carbon emissions, energy consumption, and resource efficiency. Furthermore, predictive analytics and machine learning algorithms integrated into the system provide real-time monitoring and optimization of sustainability performance throughout the supply chain. The proposed review offers numerous benefits, including enhanced transparency, reduced operational costs, improved sustainability outcomes, and risk mitigation. It also addresses challenges such as scalability, data privacy, and regulatory compliance, offering solutions to overcome these hurdles. By exploring case studies of successful implementations, this review demonstrates the practical applications and future potential of combining DLT and data fusion for sustainable supply chain management, positioning it as a critical tool for organizations aiming to meet environmental and regulatory demands in an increasingly digital and eco-conscious world.

Enhancing IoT Security Using GA-HDLAD: A Hybrid Deep Learning Approach for Anomaly Detection

The adoption and use of the Internet of Things (IoT) have increased rapidly over recent years, and cyber threats in IoT devices have also become more common. Thus, the development of a system that can effectively identify malicious attacks and reduce security threats in IoT devices has become a topic of great importance. One of the most serious threats comes from botnets, which commonly attack IoT devices by interrupting the networks required for the devices to run. There are a number of methods that can be used to improve security by identifying unknown patterns in IoT networks, including deep learning and machine learning approaches. In this study, an algorithm named the genetic algorithm with hybrid deep learning-based anomaly detection (GA-HDLAD) is developed, with the aim of improving security by identifying botnets within the IoT environment. The GA-HDLAD technique addresses the problem of high dimensionality by using a genetic algorithm during feature selection. Hybrid deep learning is used to detect botnets; the approach is a combination of recurrent neural networks (RNNs), feature extraction techniques (FETs), and attention concepts. Botnet attacks commonly involve complex patterns that the hybrid deep learning (HDL) method can detect. Moreover, the use of FETs in the model ensures that features can be effectively extracted from spatial data, while temporal dependencies are captured by RNNs. Simulated annealing (SA) is utilized to select the hyperparameters necessary for the HDL approach. In this study, the GA-HDLAD system is experimentally assessed using a benchmark botnet dataset, and the findings reveal that the system provides superior results in comparison to existing detection methods.

Linguistic Secret Sharing via Ambiguous Token Selection for IoT Security

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, including weak authentication, insufficient data protection, and firmware vulnerabilities. To address these issues, we propose a linguistic secret sharing scheme tailored for IoT applications. This scheme leverages neural networks to embed private data within texts transmitted by IoT devices, using an ambiguous token selection algorithm that maintains the textual integrity of the cover messages. Our approach eliminates the need to share additional information for accurate data extraction while also enhancing security through a secret sharing mechanism. Experimental results demonstrate that the proposed scheme achieves approximately 50% accuracy in detecting steganographic text across two steganalysis networks. Additionally, the generated steganographic text preserves the semantic information of the cover text, evidenced by a BERT score of 0.948. This indicates that the proposed scheme performs well in terms of security.

Xiaomi: An Analysis of Market Strategy under the High Cost-Performance Ratio

Abstract: Xiaomi Corporation, which was established in 2010 in Beijing, is well-known for its smartphones along with other smart hardware as well as IoT devices. It is the second-largest manufacturer of smartphones in the world, behind Samsung, with a market share of about 12%, most of which run on the MIUI (now HyperOS) operating system. It mainly focuses on providing products that are of excellent quality and performance but still maintain affordability which attracts customers from around the world. This research delves into Xiaomi's adaptive marketing tactics as a reaction to evolving market dynamics and changing consumer demands, and examines Xiaomis market plan by looking at three clear instances. The first is how Xiaomi keeps its strong position in the smartphone market by bringing out improved models every year with a steady price tag. This helps to maintain their image of being very good value for money. Second, the choice of the company to put $300,000 for three years into remaking its logo with Japanese designer Kenya Hara. This highlights how redesigning has a strategic effect on brand image and worldwide identification. Thirdly, this study talks about Xiaomis start in the car industry when it introduced the first car model called Xiaomi SU7 in month April. It shows a major shift from making only smartphones to also producing electric cars.

A Review of Literature to Derive Best Practices for Big-Data Audits in Saudi Context

In 2014, leading scholars in the field of big data auditing outlined a research agenda to explore these impacts, setting the foundation for further investigation. This paper explores the impact of big data on audit evidence, particularly in the context of Saudi Arabia’s transition to a more diversified economy under Vision 2030. This paper also seeks to synthesize current research and establish best practices for auditors in Saudi Arabia, emphasizing the importance of adapting audit processes to a growing economy that increasingly generates vast amounts of data. In recent decades, auditing practices have undergone substantial transformation by broadening the scope of audit goals and incorporating diverse, non-traditional sources of evidence, such as unstructured data from social media or IoT devices. These new data sources, while increasing audit accuracy and depth, pose challenges regarding data reliability and integrity. Over the past decade, many of these research questions have been addressed, particularly in relation to how big data enhances auditors' abilities to detect fraud, improve audit quality, and provide more accurate risk assessments. By implementing AI algorithms and advanced analytics, auditors can gain deeper insights while also addressing key concerns such as data veracity, compliance with International Financial Reporting Standards (IFRS), and the guidelines set by the Saudi Organization for Certified Public Accountants (SOCPA). As Saudi Arabia's economy diversifies and becomes more data-driven, understanding and integrating big data into audit practices will be essential for maintaining high audit standards and supporting economic growth.

Implementation of Chaotic Neural Key Generation Algorithm For IoT Devices

This paper presents a new method for generating encryption keys for Internet of Things (IoT) devices. This method combines chaos theory with neural networks to create secure and efficient keys. We use the Lorenz chaotic system to generate complex patterns and a Convolutional Neural Network (CNN) to learn and predict these patterns. This approach is designed to address the unique security challenges of IoT devices, which often have limited computing power. We tested our method with different key sizes and evaluated its performance using accuracy, loss, entropy, and correlation metrics. The results show that key sizes between 256 and 512 bits offer the best balance between model performance and security for IoT devices. We also conducted Diehard statistical tests, which our key generation method passed successfully, demonstrating its ability to produce high-quality random keys.

Energy-efficient trajectory optimization algorithm based on K-medoids clustering and gradient-based optimizer for multi-UAV-assisted mobile edge computing systems

The mobile edge computing system supported by multiple unmanned aerial vehicles (UAVs) has gained significant interest over the last few decades due to its flexibility and ability to enhance communication coverage. In this system, the UAVs function as edge servers to offer computing services to Internet of Things devices (IoTDs), and if they are located distant from those devices, a significant amount of energy is consumed while data is transmitted. Therefore, optimizing UAVs’ trajectories is an indispensable process to minimize overall energy consumption in this system. This problem is difficult to solve because it requires multiple considerations, including the number and placement of stop points (SPs), their order, and the association between SPs and UAVs. A few studies in the literature have been presented to address all of these aspects; nevertheless, the majority of them suffer from slow convergence speed, stagnation in local optima, and expensive computational costs. Therefore, this study presents a new trajectory optimization algorithm, namely ITPA-GBOKM, based on a newly proposed transfer-based encoding mechanism, gradient-based optimizer, and K-Medoids Clustering algorithm to tackle this problem more accurately. The K-medoid clustering algorithm is used to achieve better association between UAVs and SPs since it is less sensitive to outliers than the K-means clustering algorithm; the transfer function-based encoding mechanism is used to efficiently define this problem’s solutions and manage the number of SPs; and GBO is utilized to search for the best SPs that could minimize overall energy consumption, including that consumed by UAVs and IoTDs. The proposed ITPA-GBOKM is evaluated using 13 instances with several IoTDs ranging from 60 to 700 to show its effectiveness in dealing with the trajectory optimization problem at small, medium, and large scales. Furthermore, it is compared to several rival optimizers using a variety of performance metrics, including average fitness, multiple comparison test, Wilcoxon rank sum test, standard deviation, Friedman mean rank, and convergence speed, to show its superiority. The experimental results indicates that this algorithm is capable of producing significantly different and superior results compared to the rival algorithms.

An Efficient Pairing-Free Ciphertext-Policy Attribute-Based Encryption Scheme for Internet of Things

The Internet of Things (IoT) is a heterogeneous network composed of numerous dynamically connected devices. While it brings convenience, the IoT also faces serious challenges in data security. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptography method that supports fine-grained access control, offering a solution to the IoT’s security issues. However, existing CP-ABE schemes are inefficient and unsuitable for IoT devices with limited computing resources. To address this problem, this paper proposes an efficient pairing-free CP-ABE scheme for the IoT. The scheme is based on lightweight elliptic curve scalar multiplication and supports multi-authority and verifiable outsourced decryption. The proposed scheme satisfies indistinguishability against chosen-plaintext attacks (CPA) under the elliptic curve decisional Diffie–Hellman (ECDDH) problem. Performance analysis shows that our proposed scheme is more efficient and better suited to the IoT environment compared to existing schemes.

Super Low Profile 5G mmWave Highly Isolated MIMO Antenna with 360∘ Pattern Diversity for Smart City IoT and Vehicular Communication

This study introduces a planar folded super low profile 5G mmWave monopole antenna for small IoT devices with superior efficiency and gain. Low-loss Rogers RT-5880 substrate material has been utilized to achieve higher efficiency, gain, and diversity performance in a relatively compact size. The unit element planar monopole antenna has been designed within 6.15×5.13×0.8mm3 with a partial ground. Four unit element antenna was orthogonally arranged on alternating sides of the substrate plane with narrow inter-component spacing in the multiple input multiple output (MIMO) system. The integration of the double negative (DNG) metamaterial array on the antenna led to a cumulative improvement in the overall performance of the antenna, which includes gain, efficiency, and isolation. The minimum isolation of the antenna increased by 11 dB with substrate-loaded metamaterial. The proposed MIMO antenna model with substrate-loaded metamaterial demonstrates 8 GHz instantaneous bandwidth (33.1 GHz - 41.1 GHz), 0.001 envelope correlation coefficient (ECC), diversity gain (DG) greater than 9.99 dB, channel capacity loss (CCL) less than 0.2bits/s/Hz, and a total active reflection coefficient (TARC) of 25 dB, which suggests that the diversity characteristics of the MIMO antenna system is impressive. Moreover, a densely packed 12-element antenna model has been introduced in this study with 360∘ pattern diversity, capable of receiving signals from any direction. Three 4-element MIMO systems have been arranged in an architecture to develop the 12-element MIMO system, and the size of the 12-element MIMO architecture is 15×15×15mm3. Despite having this compact size and dense packing, the 12-element MIMO antenna model shows similar diversity performance as the 4-element antenna model. Analog equivalent circuit models or resistor-inductor-capacitor (RLC) equivalent systems for the antenna models were created. A concept of a 128-element massive MIMO antenna has been presented to support the end devices, creating an application scenario of an IoT-interconnected smart city.

Health IoT Threats: Survey of Risks and Vulnerabilities

The secure and efficient collection of patients’ vital information is a challenge faced by the healthcare industry. Through the adoption and application of Internet of Things (IoT), the healthcare industry has seen an improvement in the quality of delivered services and patient safety. However, IoT utilization in healthcare is challenging due to the sensitive nature of patients’ clinical information and communicating this across heterogeneous networks and among IoT devices. We conducted a semi-systematic literature review to provide an overview of IoT security and privacy challenges in the healthcare sector over time. We collected 279 studies from 5 scientific databases, of which 69 articles met the requirements for inclusion. We performed thematic and qualitative content analysis to extract trends and information. According to our analysis, the vulnerabilities in IoT in healthcare are classified into three main layers: perception, network, and application. We comprehensively reviewed IoT privacy and security threats on each layer. Different technological advancements were suggested to address the identified vulnerabilities in healthcare. This review has practical implications, emphasizing that healthcare organizations, software developers, and device manufacturers must prioritize healthcare IoT security and privacy. A comprehensive, multilayered security approach, security-by-design principles, and training for staff and end-users must be adopted. Regulators and policy makers must also establish and enforce standards and regulations that promote the security and privacy of healthcare IoT. Overall, this study underscores the importance of ensuring the security and privacy of healthcare IoT, with stakeholders’ coordinated efforts to address the complex and evolving security and privacy threats in this field. This can enhance healthcare IoT trust and reliability, reduce the risks of security and privacy issues and attacks, and ultimately improve healthcare delivery quality and safety.