Internet Of Things Applications Research Articles

IoT security with blockchain: A review

The relationship between the Internet of Things (IoT) and Blockchain has emerged to address the challenges of security, data integrity and transparency of IoT devices. Blockchain, with its distributed and decentralized nature, ensures reliable data transmission between IoT devices and security by preventing data manipulation. It also enhances the security and transparency of IoT applications by supporting automated and trusted transactions through smart contracts. In this context, Blockchain plays a critical role in making the IoT ecosystem more reliable and effective. In this study, we review studies that emphasize the importance of Blockchain technology for IoT security and propose various solutions. The proposed solutions include scalable frameworks for secure transactions in dynamic applications, Hyperledger Sawtooth-based frameworks that support secure logging of industrial activities, specialized access control mechanisms such as Trust-Based Access Control Mechanism (TABI), and IoT Cop monitoring framework that offers advanced security features. In addition, blockchain-based access control protocols, authentication systems and security models are aimed at enhancing IoT security. While most of the studies in the literature focus on the current challenges of IoT and Blockchain integration, they also reveal future research opportunities. These studies are briefly mentioned and the necessary ones are expressed in a table. In this way, it is aimed to create the necessary infrastructure for those who will work in this field.

Intrusion Detection System Fog Security Model for the Smart Cities and Urban Sensing

In smart cities, Intrusion Detection Systems (IDS) play a critical role in enhancing cybersecurity and safeguarding the interconnected network of devices and systems. Smart cities leverage various technologies, such as the Internet of Things (IoT), sensors, and communication networks, to optimize urban services and improve overall efficiency. However, this increased connectivity also introduces vulnerabilities that could be exploited by malicious actors. This paper presents a thorough exploration of contemporary developments in the domains of security and optimization within the context of smart cities. The rapid integration of Internet of Things (IoT) devices and technologies in urban environments necessitates robust security frameworks and efficient resource management strategies. The paper begins by scrutinizing Intrusion Detection Systems (IDS) tailored for smart city networks, evaluating their efficacy in mitigating diverse cyber threats. Novel approaches, such as Anomaly Detection and Firewall systems, are analyzed alongside traditional IDS, providing a comprehensive overview of the security landscape. In parallel, the paper introduces innovative clustering techniques, with a focus on the proposed Flora Optimization Weighted Clustering (FOWC) model. Inspired by biological growth mechanisms, FOWC presents a unique paradigm for optimizing sensor placements in smart cities. A comparative analysis with existing clustering algorithms like LEACH and Genetic Algorithm underscores FOWC’s superior performance in terms of Detection Rate, Precision, Recall, and F1 Score, positioning it as a promising solution for urban sensor network optimization. Beyond security and optimization, the paper addresses broader challenges in the realm of smart cities, including secure IoT applications, intrusion detection for IoT-enabled environments, and the integration of artificial intelligence for enhanced cyber defense. The research studies presented collectively contribute to a comprehensive understanding of the evolving landscape of smart cities, offering insights with practical implications for policymakers, urban planners, and technologists. Keywords: Smart cities; internet of things (iot); intrusion detection system (ids); fog computing; security

Improving the IT Competence of Vocational High School Teachers with Internet of Things Training Based on ESP 8266

Abstract. This training aims to improve participants' understanding and skills of SMK Muhammadiyah 3 Yogyakarta Teachers in developing IoT applications using the ESP8266 module. The employed training techniques encompass both theoretical and practical modules, as well as interactive group discussions. Trainees are interested in technology and want to expand their knowledge of IoT. During the training, participants were given an understanding of the basic concepts of IoT, an introduction to the ESP8266 module, and how to use the Arduino IDE for microcontroller programming. The results of this dedication show a significant increase in participants' understanding and skills after attending the training. Participants can understand the basic principles of wireless communication, install, and configure the ESP8266 module, and develop simple IoT applications. Evaluation of the participants showed high satisfaction with this training. The training was deemed highly beneficial and applicable to the participants' everyday routines. Participants also feel more confident in facing challenges in developing IoT applications. The training program is expected to yield enduring advantages, foster indigenous ingenuity, and enhance community engagement with the Internet of Things (IoT) technology.

Job-Deadline-Guarantee-Based Joint Flow Scheduling and Routing Scheme in Data Center Networks.

Many emerging Internet of Things (IoT) applications deployed on cloud platforms have strict latency requirements or deadline constraints, and thus meeting the deadlines is crucial to ensure the quality of service for users and the revenue for service providers in these delay-stringent IoT applications. Efficient flow scheduling in data center networks (DCNs) plays a major role in reducing the execution time of jobs and has garnered significant attention in recent years. However, only few studies have attempted to combine job-level flow scheduling and routing to guarantee meeting the deadlines of multi-stage jobs. In this paper, an efficient heuristic joint flow scheduling and routing (JFSR) scheme is proposed. First, targeting maximizing the number of jobs for which the deadlines have been met, we formulate the joint flow scheduling and routing optimization problem for multiple multi-stage jobs. Second, due to its mathematical intractability, this problem is decomposed into two sub-problems: inter-coflow scheduling and intra-coflow scheduling. In the first sub-problem, coflows from different jobs are scheduled according to their relative remaining times; in the second sub-problem, an iterative coflow scheduling and routing (ICSR) algorithm is designed to alternately optimize the routing path and bandwidth allocation for each scheduled coflow. Finally, simulation results demonstrate that the proposed JFSR scheme can significantly increase the number of jobs for which the deadlines have been met in DCNs.

An Analysis of Methods and Metrics for Task Scheduling in Fog Computing

The Internet of Things (IoT) uptake brought a paradigm shift in application deployment. Indeed, IoT applications are not centralized in cloud data centers, but the computation and storage are moved close to the consumers, creating a computing continuum between the edge of the network and the cloud. This paradigm shift is called fog computing, a concept introduced by Cisco in 2012. Scheduling applications in this decentralized, heterogeneous, and resource-constrained environment is challenging. The task scheduling problem in fog computing has been widely explored and addressed using many approaches, from traditional operational research to heuristics and machine learning. This paper aims to analyze the literature on task scheduling in fog computing published in the last five years to classify the criteria used for decision-making and the technique used to solve the task scheduling problem. We propose a taxonomy of task scheduling algorithms, and we identify the research gaps and challenges.

Research on Detection System for Machinery Parts Based on ZigBee Technology

ZigBee technology is a low-power, low-cost, short-range wireless communication technology for various IoT (Internet of Things) applications. Machinery parts detection system monitors the quality, size, shape, and other parameters of machinery parts in real time to ensure the accuracy and quality of the production process. In this article, we propose a machinery parts detection scheme based on ZigBee technology. By deploying wireless sensor networks, the detection data concerning machinery parts could be transmitted in real time, thus reducing the complexity of wiring and improving detection efficiency. ZigBee based machinery parts detection system would provide an efficient and convenient means of detection for the manufacturing industry, thus enjoying a wide range of application prospects.

AN ULTRA-WIDEBAND PATCH ANTENNA FOR FUTURE INTERNET OF THINGS APPLICATIONS

The Internet of Things (IoT) era is driving up demand for wireless connectivity, changing the tech sector. The creation of a compact, inexpensive, and effective antenna is crucial for supporting IoT devices. In this study, a hybrid strategy (HS)-based dense rectangular patch antenna for future Internet of Things applications (IoT) is demonstrated. A slotted patch and a partial ground with defected ground structure (DGS) are combined to create HS, leading to in a small antenna with a large impedance bandwidth, good impedance matching, increased radiation efficiency, and high gain. The antenna is designed on an FR4_epoxy substrate with a compact size of 30 × 20 × 0.8 mm3, a dielectric constant of 4.4, a thickness of 0.8 mm, a loss tangent of 0.02 and fed by a 50 Ω microstrip feed line. It is analyzed and simulated at the operating frequency of 5.2 GHz for ultra-wideband (UWB) (3.1–10.6 GHz) applications using high-frequency structure simulator (HFSS) software. The simulation results reveal that the antenna offers a broad bandwidth of 19.9 GHz (3.10 GHz to 23 GHz), a gain of 7.97 dB with an efficiency of 0.9651 (96.51%). The radiation pattern is bidirectional in both the E-plane and H-plane. Hence, the proposed antenna is very promising for future IoT applications due to its compact size and wide bandwidth characteristics. It also enhances connectivity and performance, lowers interference, opens up new applications, and encourages the widespread use of IoT technology, ultimately leading to the development of a more sophisticated and connected society.

Multi-objective cost-aware bag-of-tasks scheduling optimization model for IoT applications running on heterogeneous fog environment

Recently introduced by CISCO, the fog computing paradigm extends cloud-based computing services near the Internet of Things (IoT) devices to serve their applications, aiming to increase performance. This new computing paradigm tries to meet the requirements of emerging IoT applications, which mostly are geographically distributed and location-aware, low-cost, and delay-sensitive. One of the frequently applied applications is a bag-of-tasks (BoT) of IoT applications, each of which includes several independent tasks. However, inefficient task scheduling approaches for parallel executions may degrade the delivered quality of service (QoS) to users and also increase the providers’ total cost of ownership (TCO). The reliable execution commensurate with users’ service level agreement (SLA) and affordable services tailored to their budget at the same time is favorable for both users and fog service providers because the highly reliable execution is a reputational metric that increases the users’ trust and adherence. To this end, this paper formulates the task scheduling for the execution of BoT in IoT applications to a multi-objective optimization problem with execution time, costs and reliability perspectives. To measure total costs, both the execution cost and monetary cost models are presented. To measure the resource reliability, the new scheduling failure factor (SFF) metric is also presented, which is a kind of cost parameter. To solve this combinatorial NP-Hard problem, a multi-objective cost-aware discrete grey wolf optimization-based algorithm (MoDGWA) is proposed. To verify the performance of MoDGWA, diverse scenarios were conducted and the performance of the proposed algorithm was compared against other state-of-the-arts. The extensive simulation results prove the average dominance of MoDGWA, and it has 0.55 %, 7.28 %, 10.20 %, and 45.83 % reduction against other state-of-the-art in terms of makespan, ToC, TSFF, and the cost score respectively. Note that, the implementation of the MoDGWA is available on https://github.com/mirsaeid123/Mirsaeid.

Lightweight image super-resolution for IoT devices using deep residual feature distillation network

The 5th industrial revolution is characterized by an extensive interconnection of embedded devices, which offer a range of services, including the monitoring of their environments. Images captured from remote cameras require enhancements for effective analysis. Despite recent progress in single-image super-resolution techniques by yielding impressive results through deep convolutional neural networks, the complexity of these advanced models renders them impractical for use on miniaturized Internet of Things (IoT) devices, primarily due to their limited computational capabilities and memory constraints. Furthermore, the rapid evolution of IoT devices necessitates efficient image super-resolution techniques, while existing advanced methods, based on deep convolutional neural networks, are too resource-intensive for these devices, and this gap highlights the need for a more suitable solution. In this study, we introduce a lightweight, efficient super-resolution model specifically designed for IoT devices. This model incorporates a novel deep residual feature distillation block (DRFDB), which leverages a depthwise-separable convolution block (DCB) for effective feature extraction. The focus is on reducing computational and memory demands without compromising on image quality. The proposed DCB extracts coarse features from given input features as calculation units, using two operations, depthwise and pointwise convolutions. These two operations are able to significantly reduce the number of parameters and floating-point operations while maintaining a PSNR value higher than the 90% threshold. We modify the proposed DCB and introduce a multi-kernel depthwise-separable convolution block (MKDCB) to fine-tune the model. The experiments, conduct on various standard datasets such as DIV2K, Set5, Set14, Urban100, and Manga109 by demonstrating that our model significantly outperforms existing methods in terms of both image quality and computational efficiency. The model shows improved performance metrics like PSNR, while requiring fewer parameters and less memory usage, making it highly suitable for IoT applications. This study presents a breakthrough in super-resolution for IoT devices, balancing high-quality image reconstruction with the limited resources of these devices.

Detecting denial of sleep attacks by analysis of wireless sensor networks and the Internet of Things

The Internet of Things (IoT) amalgamates a large number of physical objects that are distinctively identified, ubiquitously interconnected and accessible through the Internet. IoT endeavors to renovate any object in the real world into a computing device that has sensing, communicating, computing and control capabilities. There are a budding number of IoT devices and applications and this escort to an increase in the number and complexity of malicious attacks. It is important to defend IoT systems against malicious attacks, especially to prevent attackers from acquiring control over the devices. Energy utilization is significant for battery-enabled devices in the IoT and wireless sensor networks which are operated long time period. The Denial-of-Sleep attack is an explicit type of denial-of-service attack that beleaguered a battery-powered device’s power supply that results in the exhaustion of this critical resource. This paper focuses on the survey on Denial of sleep attacks in Wireless Sensor networks and the IoT.

Pelatihan Dasar IoT Menggunakan Tinkercad Bagi Siswa SMK Kristen Immanuel Pontianak

Internet of Things (IoT) technology as one of the technologies in the Industrial Revolution 4.0 era allows communication between electronic devices and sensors via the internet. This is very helpful for human life. Various applications of IoT have expanded in the fields of medicine, agriculture, logistics, energy, and many more. With the rapid growth of IoT even in the industrial world, professionals who have capabilities in this field are needed. Responding to the need for manpower in the Industrial Revolution 4.0 era, this community service activity was carried out to equip students at Vocational High Schools (SMK). The implementation of this activity succeeded in providing understanding and knowledge to the students of Christian Vocational High School Immanuel, Pontianak. This is indicated by 63% of the student participants experiencing an increase in their pre-test and post-test scores.

A network communication speedup model using multiple fungible and non-fungible paths

The advent of industry 5.0 has seen technologies such as cyber-physical systems, sensors, mobile devices and a host of others, connected to the Internet of Things (IoT). The sheer growth of the IoT and the data transmitted across it calls for faster speeds (speedup), which in turn are facilitated by more bandwidth and better use of existing bandwidth. Speedup itself is not new and has been extensively studied in the literature in high performance computing and related fields. In this paper, some methods to achieve theoretical speedup in network communications are explored and tested using simulations and extrapolations. We also explored the theoretical limits of speedup. Some theoretical limits of speedup are also proposed, along with a model to estimate the speedup that can be achieved using larger container sizes over the existing bandwidth. We have also proposed a way to estimate the speedup that can be achieved when more bandwidth is provided using multiple fungible and non-fungible paths. It is anticipated that our proposed speedup model will assist the manufacturers of IoT sensors and smart devices, as well as network administrators and software developers in determining what theoretical speedup can be achieved with a certain network configuration, and in building devices and software capable to achieve practical speedups as close as possible to the predicted theoretical speedups. This in turn will lead to faster response and data transmission times in IoT and other network applications that leverage the speedup achieved.

Cryptanalysis and Improvement of Several Identity-Based Authenticated and Pairing-Free Key Agreement Protocols for IoT Applications.

Internet of Things (IoT) applications have been increasingly developed. Authenticated key agreement (AKA) plays an essential role in secure communication in IoT applications. Without the PKI certificate and high time-complexity bilinear pairing operations, identity-based AKA (ID-AKA) protocols without pairings are more suitable for protecting the keys in IoT applications. In recent years, many pairing-free ID-AKA protocols have been proposed. Moreover, these protocols have some security flaws or relatively extensive computation and communication efficiency. Focusing on these problems, the security analyses of some recently proposed protocols have been provided first. We then proposed a family of eCK secure ID-AKA protocols without pairings to solve these security problems, which can be applied in IoT applications to guarantee communication security. Meanwhile, the security proofs of these proposed ID-AKA protocols are provided, which show they can hold provable eCK security. Some more efficient instantiations have been provided, which show the efficient performance of these proposed ID-AKA protocols. Moreover, comparisons with similar schemes have shown that these protocols have the least computation and communication efficiency at the same time.

Smart Agriculture: Technologies, Practices, and Future Directions

The Internet of Things (IoT) has ushered in a new era of innovative agriculture research. Because IoT is still in its early stages, it must be widely tested before it can be widely deployed in many agricultural applications. In this paper, I look at different prospective IoT applications, as well as the unique concerns and constraints connected with IoT deployment for better farming. The gadgets and wireless communication technologies linked with IoT in agricultural and farming applications are thoroughly researched to focus on the unique requirements. Sensor-enabled IoT systems that deliver intelligent and smart services for smart agriculture are being investigated.
 Several case studies are offered to investigate the existing IoT-based solutions implemented by various organizations, individuals, and groups depending on their deployment criteria. The difficulties in these solutions are addressed, as are the factors for improvement and the future roadmap of work with IoT. Smart farming is a trend that emphasizes the use of information and communications technology (ICT) in machinery, equipment, and sensors in network-based hi-tech farm supervision cycles. Innovative technologies such as the Internet of Things (IoT) and cloud computing are expected to spur growth and kickstart the usage of robotics and artificial intelligence in agriculture. Such radical departures are upsetting established agricultural practices while also posing several obstacles. This study explores the techniques and equipment utilized in wireless sensor applications in IoT agriculture, as well as the predicted problems encountered when integrating technology with traditional farming practices. Furthermore, this technical knowledge is useful to growers during crop times ranging from planting to harvest, and applications in packing and transportation are being researched.

An Intelligent Adaptive Neuro-Fuzzy for Solving the Multipath Congestion in Internet of Things

The Internet of Things (IoT) has recently become a significant focus in research circles. IoT facilitates the integration of numerous physical entities with the Internet. Adhering to a standardized structure is imperative to manage the vast amount of information effectively. Although many researchers in the field of IoT have proposed various layered architectural designs, none have yet fulfilled all the requisite architectural criteria. Network congestion occurs when the volume of data packet traffic surpasses the network's handling capacity. Apart from addressing congestion issues, it is crucial to harmonize network resources like energy, bandwidth, and latency. The Quality of Service (QoS) in IoT applications chiefly depends on proficient congestion management, which is the central subject of this research. The research employs the Adaptive Neuro-Fuzzy Inference System (ANFIS) to regulate congestion, while the Membership Function (MF) undergoes adjustments through the application of the Modified Squirrel Search Algorithm (MSSA). This ANFIS amalgamates the advantages of Fuzzy Logic (FL) and Artificial Neural Networks (ANN) to form a unique framework. Utilizing ANFIS, adaptive analysis services are available to interpret complex patterns and nonlinear interactions, featuring quick learning capabilities. The MSSA aids in tweaking the Membership Function within the ANFIS model, achieving a successful global convergence rate. An adaptive method considering predator presence probability is employed to harmonize the algorithm's exploration and exploitation functionalities, further bolstered by a dimensional search approach. The simulation results demonstrate that the proposed Swarm Intelligence Adaptive Neuro-Fuzzy Inference System (SI-ANFIS) method significantly reduced traffic overhead and attained an impressive accuracy rate of 93.58%.

DCSP: A delay and cost-aware service placement and load distribution algorithm for IoT-based fog networks

The service placement problem in a fog-cloud pertains to the intricate task of ascertaining the optimal placement locations for diverse service replicas that constitute an Internet of Things (IoT) application within a distributed computing architecture. Nonetheless, due to the heterogeneous nature of fog nodes and cloud data centers, the constantly evolving nature of IoT applications, the pressing demand for real-time data processing with minimal latency, and the imperative of cost optimization, service placement remains an arduous and intricate challenge. Heuristic algorithms are widely employed to derive near-optimal solutions. The present study introduces a novel heuristic approach to service placement and load distribution in fog-cloud environments called Delay and Cost-aware Service Placement (DCSP). This approach takes into account both delay and cost and thereby enables the optimal utilization of resources, delay, and cost for delay-sensitive services. DCSP features continual adjustment of the number of service replicas based on workload and resource availability, ensuring efficient usage of fog and cloud computing resources and optimal delay for each request. The results of simulation experiments that compare various algorithms demonstrate the superiority of DCSP, which outperforms other methods in cost, average delay, and resource utilization. The experimental results not only illustrate the exceptional superiority of our method over MOHGA, MVC, and the Local method in terms of delay and utilization but also demonstrate our efforts to maintain cost at a comparable level. Our method surpasses all of them in utilization, achieving remarkable improvements ranging from 7.4 % to 49.7 %. Furthermore, it outperforms them in terms of delay, with significant enhancements ranging from 2.1 % to 19.3 %.

Dynamic coefficient symmetric polynomial-based secure key management scheme for Internet of Things (IoT) networks.

With the extensive application and continuous expansion of the Internet of Things (IoT), the access of a large number of resource-limited nodes makes the IoT application face a variety of security vulnerabilities and efficiency limitations, and the operating efficiency and security of IoT are greatly challenged. Key management is the core element of network security and one of the most challenging security problems faced by wireless sensor networks. A suitable key management scheme can effectively defend against network security threats. However, among the key management schemes that have been proposed so far, most of them do not take into account the efficiency in terms of connectivity rate and resource overhead, and some of them even have security risks. In this article, based on the symmetric polynomial algorithm, a dynamic coefficient symmetric polynomial key management scheme is proposed to better solve the IoT security problem. In this scheme, the nodes' IDs are mapped into the elements of the shared matrix M by the identity mapping algorithm, and these elements are used to construct polynomials P(x,y) to generate pairwise keys. The communicating nodes have their own coefficients of P(x,y) and thus have higher connectivity. The overall performance evaluation shows that the scheme significantly improves the resilience against node capture and effectively reduces the communication and storage overheads compared to the previous schemes. Moreover, the scheme overcomes the λ-security of symmetric polynomial key management scheme, and is able to provide a large pool of polynomials for wireless sensor networks, facilitating large-scale application of nodes.

Flexible Offloading and Task Scheduling for IoT Applications in Dynamic Multi-Access Edge Computing Environments

Nowadays, multi-access edge computing (MEC) has been widely recognized as a promising technology that can support a wide range of new applications for the Internet of Things (IoT). In dynamic MEC networks, the heterogeneous computation capacities of the edge servers and the diversified requirements of the IoT applications are both asymmetric, where and when to offload and schedule the time-dependent tasks of IoT applications remains a challenge. In this paper, we propose a flexible offloading and task scheduling scheme (FLOATS) to adaptively optimize the computation of offloading decisions and scheduling priority sequences for time-dependent tasks in dynamic networks. We model the dynamic optimization problem as a multi-objective combinatorial optimization problem in an infinite time horizon, which is intractable to solve. To address this, a rolling-horizon-based optimization mechanism is designed to decompose the dynamic optimization problem into a series of static sub-problems. A genetic algorithm (GA)-based computation offloading and task scheduling algorithm is proposed for each static sub-problem. This algorithm encodes feasible solutions into two-layer chromosomes, and the optimal solution can be obtained through chromosome selection, crossover and mutation operations. The simulation results demonstrate that the proposed scheme can effectively reduce network costs in comparison to other reference schemes.

A decision-making mechanism for task offloading using learning automata and deep learning in mobile edge networks

The development of mobile networks has led to the emergence of challenges such as high delays in storage, computing and traffic management. To deal with these challenges, fifth-generation networks emphasize the use of technologies such as mobile cloud computing and mobile edge computing. Mobile Edge Cloud Computing (MECC) is an emerging distributed computing model that provides access to cloud computing services at the edge of the network and near mobile users. With offloading tasks at the edge of the network instead of transferring them to a remote cloud, MECC can realize flexibility and real-time processing. During computation offloading, the requirements of Internet of Things (IoT) applications may change at different stages, which is ignored in existing works. With this motivation, we propose a task offloading method under dynamic resource requirements during the use of IoT applications, which focuses on the problem of workload fluctuations. The proposed method uses a learning automata-based offload decision-maker to offload requests to the edge layer. An auto-scaling strategy is then developed using a long short-term memory network which can estimate the expected number of future requests. Finally, an Asynchronous Advantage Actor-Critic algorithm as a deep reinforcement learning-based approach decides to scale down or scale up. The effectiveness of the proposed method has been confirmed through extensive experiments using the iFogSim simulator. The numerical results show that the proposed method has better scalability and performance in terms of delay and energy consumption than the existing state-of-the-art methods.

Recent Trends of Integration of Blockchain Technology With the IoT by Analysing the Networking Systems: Future Research Prospects

In recent times, attention has surged towards entities with the potential to revolutionize various sectors. The integration of Internet of Things (IoT) and blockchain technologies, known as IoT-blockchain, offers numerous advantages, including heightened security, privacy, traceability, transparency, and reduced costs. This abstract delves into the taxonomy and prominent platforms of blockchain applications for IoT in networking systems, exploring recent advancements, obstacles, and future research avenues. IoT blockchain's crucial aspect lies in establishing decentralized networks, enabling secure collaboration and data interchange among diverse devices without a central governing entity. Platforms like Ethereum, Hyperledger, and IOTA facilitate the creation and management of these networks. Recent developments focus on enhancing security, scalability, and efficiency through novel consensus mechanisms and cryptographic techniques. Challenges persist, including the need for improved interoperability, integration with existing systems, efficient governance, regulatory structures, and the identification of use cases and business models for widespread adoption. The examination of successful governance, regulatory frameworks, and potential adoption catalysts completes the discourse on IoT blockchain technology.