Internet of Things (IoT)

In today's world, the Internet of Things (IoT), which is a fairly new technology, has become a popular topic for discussion. Meanwhile, the increasing demand for personalized healthcare with the assistance of new technologies has created new applications called e-health IoT applications; however, researchers are still attempting to find its applications, therefore they have not been able to focus on comparing these applications. We have aimed at understanding the benefits of e-health IoT applications in comparison to one another. Therefore, this study is an attempt to provide a list of e-health IoT applications for individuals and to prioritize them. The Fuzzy Analytical Hierarchy Process (FAHP) method has been used, which is a method for Multi-Criteria Decision Making (MCDM) and a useful tool for prioritizing multiple alternatives based on criteria. Eight important criteria, based on a comprehensive literature review and experts’ opinions, were determined. Then, by using the FAHP method, the weight of each criterion was calculated. As a result, seven applications identified for individuals were prioritized based on the weight of each criterion and the score of each application in each criterion. Health Effectiveness, Empowerment, Safety, Privacy, and Peace of Mind are the most important criteria in e-health IoT applications for individuals; Cost Saving, round-the-clock Access, and Time-Saving are in the next levels of importance. The results also show that Chronic disease management, Medication reminders, Health monitoring, Air quality, Fall detection, Sleep control and Fitness were respectively ranked as first, second, third, fourth, fifth, sixth and seventh among the IoT applications.

Digital twin-enabled neural networks will develop innovative processes in feature selection and simulation. In addition, this methodology will have development in autonomous driving, natural language processing, healthcare, and many other fields. Recently sensors have been widely used for environment monitoring, and massive data has to be processed efficiently and effectively, which requires managed neural architectures for sustainable computing. The sustainable digital twin-empowered architectures create new biological evolution simulation algorithms and intelligent system architectures for supervised and unsupervised learning. Some of today's fundamental artificial intelligence issues, including adaptive machine learning and neuromorphic cognitive models, can be overcome by this methodology. The goals of this special issue on digital twin-enabled neural network architecture management for sustainable computing aim to pay attention to the researchers and industries towards recent advances in decision-making algorithms, neural network models and architectures for faster processing.

The global demand for food can be eliminated by precision farming. This research work proposes a low-cost IoT-enabled handy device to measure soil water content. Three different sensor probes are designed in COMSOL Multiphysics 5.4 and fabricated using PCB Technology. The designed sensor probes are calibrated to effectively measure moisture content for three different soil types (silt/sandy/clay). An electronic system has been programmed according to Optimized-Moisture-Value (OMV) algorithm to read and collect the soil moisture information. Three sensor probes, capacitance, and voltage responses are analyzed using linear fitting. It has been observed from the response data that model B's performance is better than the other two presented models in terms of soil moisture. The obtained goodness of fitness value for model B is around 0.999 for all the categories of soils. The electronic system is built around W78E054D and ESP8266 controllers. The W78E054D controller is used to excite the sensor probe with a signal having a frequency of 500 kHz. The IoT-enabled controller ESP8266 reads and collects the soil moisture data according to the OMV algorithm.

In the Industry 4.0 era, many pioneering industries are leveraging emerging technologies such as the Internet of Things (IoT) as solutions in the digital age. One of the largest and most active industries in Iran is the food industry, which stands to benefit significantly from these advancements. Achieving a sustainable competitive advantage is often possible at the level of the supply chain, where companies use information and communication technologies, such as IoT, to coordinate information, finances, and materials among supply chain actors. This research aimed to identify the key success factors (KSFs) for implementing IoT in the food supply chain. Firstly, through a systematic literature review, the KSFs for IoT implementation in the food supply chain were identified. To develop a measurement model, confirmatory factor analysis using structural equation modeling was employed, making the research applied-descriptive. A questionnaire was designed and completed by 142 members of the "Amadeh Laziz" supply chain (a case study), who were selected using a stratified random sampling method. Confirmatory factor analysis and LISREL 8.83 were then used to validate the proposed model. Finally, the cause-and-effect relationship between KSFs in IoT implementation in the food supply chain was analyzed using Grey DEMATEL. Based on the confirmatory factor analysis findings, the KSFs in implementing IoT in the food supply chain were identified as technical, economic, legal, cultural and social, security, applicability of IoT throughout the supply chain, and implementation of IoT applications. Thus, the measurement model included eight factors and 27 measures. According to the cause-and-effect relationship findings, "Implementation of IoT applications" and "Economic" factors were found to be mostly influenced, while "Applicability of IoT throughout the supply chain" and "Technical" factors were recognized as the most influential. The results of this research can guide food producers and technology policymakers in their supply chains and help avoid trial and error in IoT implementation by leveraging global and national experiences.

Background: IoT Smart networks are the latest creation of smart technology where Internet of Things, Artificial Intelligence, and Blockchain technologies have merged. Such technologies have the possibility of increasing performance, security and the degree of expansion in different fields like smart city, health and manufacturing. As it is, there are several issues that organisations continued to encounter when implementing both these systems in order to address diversified network requirements. Objective: The study aims to define how IoT, AI, and Blockchain technologies can be integrated to develop smart networks and how their integration will address the issues of performance, data integrity, and resource utilization in smart networks. Methods: The solution consisted of three components: IoT for instant data gathering, AI for modeling and efficient traffic control, Blockchain for secure data storage. Analyses of various objectives such as data throughput, latency, energy consumption, and security were conducted for smart city applications through simulations. Results: The linked matrix obtained a 45% increase in data transfer rate, a 40% cut in response time and a 50% enhancement of power utilization compared to other systems. Purchases made using blockchain were correct to the last digit, achieved with a success rate of 99.9%, and there were no cases of hacking. AI algorithms minimized congestion levels of the network by 55%, and IoT devices remained available 98% of the time. Conclusion: The incorporation of the IoT, AI and Blockchain enhances the effectiveness and assures the stability of smart networks greatly. From these findings, there is a significant potential for broad utility thus the need for research on the scale, integration, and testing of these in practice.

Background: The growth of the number of connected devices and the extent of Internet of Things (IoT) integration has led to new and emerging needs such as the management of big data, real-time reaction, efficient bandwidth utilization, and security considerations. Due to the intrinsic latency, network load and argue of scalability, standard cloud computing models do not suffice these requirements. In response to this, edge computing the function of analyzing data closer to its source hence leading to performance gains. Objective: This article explores the impact of incorporating edge computing in the optimization of IoT systems specifically in aspects like latency minimization, bandwidth utilization, security, processing capability, flexibility in expansion, and data reliability. Methods: A combined computational model was used to mimic edge and cloud platforms. Performance metrics were evaluated under three primary IoT scenarios: traffic management of smart cities, industrial applications, and health care management applications. Regression models and confidence intervals also provided general support to the findings. Results: The findings showed edge computing to be a more effective substitute for cloud-based systems; proving that latency can be reduced by 82%, and data bandwidth by 65-68%. Perennial threats including interception of data were cut by 50-66% while processing was done at 73% higher efficiency. Other criteria such as scalability and data consistency also pointed out the application of edge computing for resilience in more extensive IoT environment. Conclusion: Essentially, edge computing helps overcome limitations of cloud-based IoT systems, and is therefore imperative to real-time, secure, and scalable IoT. Future work should consider the integration of hybrid edge-cloud models, self-healing schemes, and more robust rigorous security solutions in order to fine-tune its applicability.

Background: Smart city technology solutions have recently ramped up the utilization of drones with Internet of Things (IoT) technologies for improving smart city systems. IoT sensors combined with real-time communication ad hoc network drones are also another area with great potential including traffic monitoring, environment management, disaster management, etc. Nevertheless, issues regarding energy consumption and density, the number of nodes that can be incorporated into the network, as well as the issue of avoiding collisions between the signal sent by one node with the signals that may be transmitted by other nodes are still observed as essential impediments to the wide application of WSNs. Objective: The article seeks to propose and assess algorithms for operating drone-IoT systems whilst dealing with issues like energy efficiency, real-time data communication, avoiding mid-air collisions, and dealing with the increasing number of systems in crowded urban areas. Methods: This study utilizes a two-time algorithm technique that was adopted from the prior study. The first algorithm provides a method for speed and position control of drones, ensuring that the distance between the drones is sufficient and not violable. The second algorithm is centered on energy reduction, which selects the precise energy usage by employing path planning in real time. The effectiveness of these algorithms was determined using simulation models with respect to metrics including latency, energy consumption, and scalability. Results: The proposed system revealed the systems’ improvements in energy efficiency, fewer collisions, and strong scalability of drone management. Main conclusions possible to conclude during the experiment reveal the system’s generic aptitude to the different urban situations and its stability in changing traffic conditions. Conclusion: The article presents a scalable and efficient solution for extending drone applications to smart cities using IoT platforms. In this way, the results can serve as the further theoretical and experimental base for investigating the trends of management and the infrastructure of cities.

Background: Smart contract is defined as a self-executing contract that runs on the distributed ledger technology, called block chain and has attracted much attention as a promising application for improving efficiency, accountability and reliability in telecommunications and related sectors. But problems like scalability issues, recurrent resource inefficiencies, and threats posed by new quantum computing technologies hinder their broad usage and effectiveness. Solving these problems is crucially important to further development of blockchain systems and to provide for them ongoing stability in complex contexts. Objective: Towards this goal, the current study proposes a comprehensive blockchain framework that incorporates these computational intelligence techniques and quantum-safe cryptography in an effort to address scalability, security, and efficiency issues. This research aims at solving practical problems and identifying the potential applications for blockchain in telecommunication and other fields. Methods: An evidence-based approach including detailed literature reviews, qualitative expert interviews, and simulation studies was adopted. Experimental conditions involved latency, throughput, energy, and scalability factors in order to assess single-photon detection. Telecommunications providers engaged in pilot tests to determine the practical usability of the system. Results: The improvement in the aspects of the system that was proposed were high improvements that were achieved as follows: 75% improvement in scalability, 25% improvement in latency, and the preferred quantum-resistant cryptography. Substantial gain in energy efficiency was estimated to be 40%, while field implementations ensured versatility of the system in the areas that differ from a city or even desert. Conclusion: These findings provide support to the proposition that blockchain systems hold the key to revolutionizing telecommunications. With that, the solution of the critical limitations of this research makes it the basis for further development to maintain blockchain technology secure, scalable, and sustainable in the quantum period.

This study introduces a federated learning-based architecture designed to support highly scalable and decentralized anomaly detection in IoT-integrated aquaponics systems. Emphasizing rigorous data privacy, the framework employs PrefixSpan for sequential pattern mining to extract significant temporal behaviors from heterogeneous distributed datasets. IoT sensors deployed across 11 aquaponic ponds collected extensive datasets, each exceeding 170,000 entries, capturing vital indicators such as temperature, pH, turbidity, and fish growth metrics. The proposed FL model demonstrated strong correlations—exceeding 0.9—between water quality conditions and fish development, validating the system’s predictive robustness. Notably, Pond 6 and Pond 10 yielded 1269 and 1339 sequential patterns respectively, confirming the exceptional scalability of the model. The architecture also achieved a 35% reduction in communication latency compared to conventional centralized systems, enabling responsive and efficient anomaly detection in real time. In parallel, a Top-k mining approach was employed to benchmark pattern interpretability as well as computational efficiency because it revealed trade-offs in sensitivity versus frequency-based simplification. Recent studies that focus upon aquaponics have also validated the operational superiority of the system in anomaly detection that is privacy-aware via comparison across models. The comparison highlighted its alignment to sustainable smart farming objectives. By addressing the limitations of centralized data handling, this framework offers a resilient, scalable, and privacy-aware approach to intelligent aquaponics management.