Things Technology Research Articles

Integration of Foundation Models and Federated Learning in AIoT-Based Aircraft Health Monitoring Systems

The study presents a comprehensive framework for integrating foundation models (FMs), federated learning (FL), and Artificial Intelligence of Things (AIoT) technologies to enhance aircraft health monitoring systems (AHMSs). The proposed architecture uses the strengths of both centralized and decentralized learning approaches, combining the broad knowledge capture of foundation models with the privacy-preserving and adaptive nature of federated learning. Through extensive simulations on a representative aircraft fleet, the integrated FM + FL approach demonstrated consistently superior performance compared to standalone implementations across multiple key metrics, including prediction accuracy, model size efficiency, and convergence speed. The framework establishes a robust digital twin ecosystem for real-time monitoring, predictive maintenance, and fleet-wide optimization. Comparative analysis reveals significant improvements in anomaly detection capabilities and reduced false alarm rates compared to traditional methods. The study conducts a systematic evaluation of the benefits and limitations of FM, FL, and integrated approaches in AHMS, examining their implications for system robustness, scalability, and security. Statistical analysis confirms that the integrated approach substantially enhances precision and recall in identifying potential failures while optimizing computational resources and training time. This paper outlines a detailed aviation ecosystem architecture integrating these advanced AI technologies across centralized processing, client, and communication domains. Future research directions are identified, focusing on improving model efficiency, ensuring generalization across diverse operational conditions, and addressing regulatory and ethical considerations.

Artificial Intelligence and the Internet of Things in Recreation: A Systematic Literature Review

This study aimed to examine the literature on the use of artificial intelligence and the Internet of Things in the field of recreation and leisure and present the results within themes identified inductively from the data. We employed a systematic review methodology, consisting of determining appropriate selection criteria, choosing data sources, extracting data, categorizing the results, and reporting. Using the Web of Science database, we identified a total of 69 articles published between 2017 and 2024. After filtering and screening for keywords, 23 full-text articles related to artificial intelligence and the Internet of Things in the field of recreation and leisure were included in the analysis. Relevant studies were evaluated according to year, journal, focus, country, type of technology, recreation area, and results obtained. Findings from the reviewed articles are discussed under six themes: safety, ecosystem, personalized recreation experience, wearable technology, health, and potential recreation and leisure areas. We observed that the most frequently investigated topic in the studies was recreational tourism, with a general focus on outdoor recreation. The studies often referred to nature conservation and planned and safe personal leisure time. In conclusion, we determined that artificial intelligence and Internet of Things technologies have various applications in the field of recreation, but relevant studies are limited.

Telemedicine: Bridging the Gap in Providing Primary Care to Rural Area Patients Across India

Telemedicine has emerged as a transformative solution to address the significant healthcare disparities faced by rural India, where over 70% of the population resides. In this article, the evolution of telemedicine in India, its current landscape, and its potential to revolutionize primary healthcare delivery in rural areas are reviewed. Despite progress in establishing Primary Health Centers, rural healthcare infrastructure remains underutilized due to staffing shortages and other challenges. Telemedicine offers a lifeline by bridging critical gaps in primary care, with government initiatives such as E-Sanjeevni and private entities such as M-Swasth and Apollo TeleHealth expanding access to healthcare services. The COVID-19 pandemic accelerated the adoption of more advanced, patient-centric telemedicine models incorporating artificial intelligence, machine learning, and Internet of Things technologies. These innovations aim to provide rural communities with healthcare standards comparable to urban areas. However, challenges persist, including technological barriers, digital literacy gaps, and concerns about data security and privacy. To fully realize telemedicine’s potential, a multifaceted approach is necessary. This includes culturally adapting platforms for rural populations, strengthening data protection legislation, enhancing digital infrastructure, and increasing digital literacy. The collaborative efforts of government bodies, private enterprises, healthcare professionals, and non-profit private organizations are crucial in overcoming these challenges. By embracing telemedicine and addressing its implementation barriers, India can bridge the healthcare gap, improve the quality of life for millions of rural residents, and potentially catalyze economic development in rural areas.

A Block-chain Based Mechanism for Securely Storing Data on Cloud and IOT

The burgeoning paradigm of cloud computing has made a number of tools available to researchers. It is a cutting-edge technology that keeps revolutionizing many sectors in every imaginable method. It is a promising technology that gives users and researchers new perspectives, development, and many amazing factors. The integration of cloud and block-chain technology and internet of things (IOT). The benefits both service providers and customers by enabling the use of resources that can be authenticated while protecting anonymity. This joining method is distinct and environmentally. This cooperative method sheds light on the different paths or facets of computers that can solve issues other than security-related ones. The primary problem at the moment is cloud security. For service providers, it's critical to protect client privacy and avoid data loss. Using client data privacy solutions, the current review study focused on block-chain with combination of IOT and data security. In order to strengthen security and expand the power or utility of cloud systems, a comprehensive examination is also carried out. Block-chain technology has the ability to increase speed and anonymity while providing numerous advantages to cloud-based and IOT based applications.

ADVANCES IN THE DESIGN AND OPTIMIZATION OF SMART IRRIGATION SYSTEMS FOR SUSTAINABLE URBAN VERTICAL FARMING

Urban vertical farming has emerged as a sustainable and innovative approach to addressing the increasing global demand for food in rapidly growing and densely populated cities, where traditional agriculture faces significant challenges due to space and resource constraints. A primary issue in these systems is the efficient management of critical resources, particularly water and energy, which are essential for maintaining high crop productivity and environmental sustainability. This study introduces, develops, and evaluates a mathematical model that integrates Internet of Things (IoT) technology to optimize water and energy usage in a hydroponic vertical farming setup. The model utilizes real-time environmental data collected from IoT sensors to dynamically adjust irrigation and energy consumption, ensuring minimal waste while sustaining optimal conditions for plant growth. Extensive simulations conducted using Python demonstrate substantial improvements in Water Use Efficiency (WUE) and significant energy savings, validating the model’s effectiveness. The study also presents practical case studies from regions like Singapore, Qatar, and Malaysia, showcasing how the integration of renewable energy sources, such as solar photovoltaic panels, with advanced smart irrigation technologies can lead to up to 50% growth rate improvements. Despite existing challenges, such as high initial capital investments, technical complexities, and the need for continuous maintenance, the findings indicate that modular and scalable system designs offer a promising path forward. Future research should aim to reduce overall costs and enhance system adaptability for various urban environments. Ultimately, this research provides a scalable and efficient framework for advancing urban agriculture, with the potential to contribute significantly to global food security and promote the sustainability of urban ecosystems.

AIoT-Based Visual Anomaly Detection in Photovoltaic Sequence Data via Sequence Learning

Anomaly detection is a common analytical task aimed at identifying rare cases that differ from the majority of typical cases in a dataset. In the management of photovoltaic (PV) power generation systems, it is essential for electric power companies to effectively detect anomalies in PV sequence data, as this helps operators and experts understand and interpret anomalies within PV arrays when making response decisions. However, traditional methods that rely on manual labor and regular data collection are difficult to monitor in real time, resulting in delays in fault detection and localization. Traditional machine learning algorithms are slow and cumbersome in processing data, which affects the operational safety of PV plants. In this paper, we propose a visual analytic approach for detecting and exploring anomalous sequences in a PV sequence dataset via sequence learning. We first compare the sequences with their reconstructions through an unsupervised anomaly detection algorithm (Long Short-Term Memory) based on AutoEncoders to identify anomalies. To further enhance the accuracy of anomaly detection, we integrate the artificial intelligence of things (AIoT) technology with a strict time synchronization data collection and real-time processing algorithm. This integration ensures that data from multiple sensors are synchronized and processed in real time. Then, we analyze the characteristics of the anomalies based on the visual comparison of different PV sequences and explore the potential correlation factors to analyze the possible causes of the anomalies. Case studies based on authentic enterprise datasets demonstrate the effectiveness of our method in the anomaly detection and exploration of PV sequence data.

IoT—A Promising Solution to Energy Management in Smart Buildings: A Systematic Review, Applications, Barriers, and Future Scope

The use of Internet of Things (IoT) technology is crucial for improving energy efficiency in smart buildings, which could minimize global energy consumption and greenhouse gas emissions. IoT applications use numerous sensors to integrate diverse building systems, facilitating intelligent operations, real-time monitoring, and data-informed decision-making. This critical analysis of the features and adoption frameworks of IoT in smart buildings carefully investigates various applications that enhance energy management, operational efficiency, and occupant comfort. Research indicates that IoT technology may decrease energy consumption by as much as 30% and operating expenses by 20%. This paper provides a comprehensive review of significant obstacles to the use of IoT in smart buildings, including substantial initial expenditures (averaging 15% of project budgets), data security issues, and the complexity of system integration. Recommendations are offered to tackle these difficulties, emphasizing the need for established processes and improved coordination across stakeholders. The insights provided seek to influence future research initiatives and direct the academic community in construction engineering and management about the appropriate use of IoT technology in smart buildings. This study is a significant resource for academics and practitioners aiming to enhance the development and implementation of IoT solutions in the construction sector.

Optimize Stormwater Management for Green Roofs Based on IoT Technology and Sensor Networks

The intensification of global climate change and the significant increase of extreme rainfall events have made traditional urban drainage systems face great adjustments. As a key component of green infrastructure to avoid urban flood risk, green roofs are receiving increasing attention due to their unique capabilities in stormwater management. This paper explores the potential of Internet of Things (IoT) technology and sensor networks to enhance green roof stormwater management. Integrated real-time monitoring of key environmental parameters, including soil moisture, temperature and rainfall, enables the system to dynamically adjust roof drainage and irrigation strategies. This greatly improves the rainwater retention capacity and reduces the pressure on the municipal sewage system. The results show that the combination of IoT technology and sensor networks not only improves the efficiency of green roof management in combating climate change, but also promotes the efficient use of water resources and enhances the adaptability and sustainability of the system. This research provides the necessary technical support for the wider adoption of green roofs in urban environments and offers viable solutions to future challenges posed by climate change.

Design of a hazard prediction system with intelligent multimodal fusion based on artificial intelligence & internet of things technology: taking a crib as an example.

How to design an intelligent multimodal fusion hazard prediction system using AIoT (Artificial Intelligence & Internet of Things) technology to predict some potential dangers. This article will provide some ideas and methods. This article designs an intelligent multimodal fusion hazard prediction system based on AIoT technology. The system mainly consists of an IoT hardware device and an AI multimodal and multi-dimensional Hazard Prediction Algorithm. This article will take baby cribs as an example, using this system to empower traditional baby cribs and transform them into intelligent baby cribs. In this example, the system can detect the real-time status of the baby and predict upcoming dangers, including kicking the quilt, wetting the bed, fever, crying, climbing over the crib, and turning over. Assuming that intelligent systems can detect the status of infants in real-time and make predictions before potential dangers occur, to promptly alert parents. Perhaps it can prevent some irreversible dangers from occurring. This system solves the pain point problem of parents having to take care of their babies after working hard. In this scenario, the system provides a design scheme for an intelligent multimodal fusion hazard prediction system with a temporary care function. The application of this system to baby cribs not only reduces the burden on parents but also ensures the safety and comfort of the baby. At the same time, the system has transferability, and its design concept can be transferred to other application scenarios, such as hospital care for infants, young children, or elderly patients, as well as childcare stations for abandoned children in society. This innovative system design scheme has a positive significance for family harmony and social development.

A novel spatial pyramid-enhanced indoor visual positioning method

As a key application of Internet of Things (IoT) technology, visual localization plays an important role in everyday life. However, pedestrians in images can obstruct environmental features, negatively impacting the performance of visual localization systems. To address this issue, we propose a Spatial Pyramid-Enhanced MixVPR visual localization method (SPE-VL) that aims to enhance image feature descriptions through multi-scale spatial information, thereby mitigating the effects of pedestrian occlusion on localization accuracy. The SPE-VL method is divided into two main phases: sensor-based matching range constraint and image feature extraction and matching. In the matching range constraint phase, we propose a direction decision method based on a machine learning classifier that utilizes smartphone sensor data to restrict the direction of image matching, reducing the likelihood of mismatches. In the feature extraction and matching phase, we propose a Transformer-based feature cross-enhancement method that leverages local features and spatial contextual information to enhance features, improving both image retrieval accuracy and localization precision. Experimental results indicate that the SPE-VL method demonstrates higher localization accuracy and robustness compared to existing state-of-the-art methods, providing new insights and solutions for the application of visual localization in complex environments.

Penerapan Internet of Things Pada Kandang Jangkrik Berbasis Arduino Uno R3

The development and advancement of technology today are becoming increasingly complex,with one of the significant advancements being the Internet of Things (IoT) technology. IoT hasbeen implemented in various sectors, particularly in the livestock sector. However, there are stillissues where cricket farmers continue to use conventional methods in the breeding process,resulting in suboptimal processes and leading to the death of their livestock. The aim of thisresearch is to develop a system that applies IoT technology to assist cricket farmers in managingthe breeding process within cricket enclosures. These farmers face challenges in feeding theircrickets and controlling the temperature and humidity within the enclosures.The research methodis carried out by collecting needs, designing a system in the form of a system design, andimplementing the system. The system uses Arduino uno R3 as a control center, RTC DS3231 as atimer for feed using a servo motor, DHT11 to detect temperature and humidity in the cage whichwill activate the fan and heating lamp as a solution. The system uses the NodeMCU ESP8266 wifimodule which is connected to the Blynk application to monitor temperature and humidity in thecage. With this system, it helps make it easier for cricket farmers in the cultivation process.

Secured energy data transaction for prosumers under diverse cyberattack scenarios

Due to the increasing use of renewable energy sources and the advancement of smart grid technology, bilateral energy transactions between prosumers have attracted significant interest as a potential solution for efficient and decentralized energy distribution. Prosumers can establish direct energy exchanges by utilizing internet of things (IoT) technologies and arrangements with smart metering capabilities, eliminating the need for middlemen and allowing for more effective use of renewable energy sources. However, these direct energy exchanges between prosumers can be susceptible to cyber-threats, which hinder secure and effective energy transactions while protecting privacy. To enable safe and seamless energy transactions among prosumers and the grid, the cyber-security of IoT devices should be of paramount significance as a possible solution. Therefore, this paper focuses on securing the energy transactions among prosumers facilitated by smart meters. It aims to address potential threats against data integrity, confidentiality, and availability from the prosumers’ point of view and develop a comprehensive framework for securing energy transactions based on artificial intelligence (AI). The proposed structured roadmap not only identifies compromised trading data but also prevents prosumers from reacting to it by replacing the contaminated as well as missing trading data. A comparative analysis on AI-based algorithms indicates that decision tree (DT) outperforms support vector machine (SVM) and multi-layer perceptron (MLP) for the proposed framework to profile the corrupted trading data identification and categorization in order to provide effective outcomes. Additionally, the proposed framework adopts a deep learning (DL)-based model for the replacement of compromised trading data. All the numerical analyses, along with extensive simulation results, justify, the efficacy of the proposed framework.

Rancang Bangun Sistem Monitoring Kebocoran ydrofluorocarbon (HFC) pada Cold Storage System Berbasis IoT di Kapal AHTS

This research aims to design and developing a hydrofluorocarbon (HFC) leak monitoring system in the cold storage cooling system on the Anchor Handling Tug Supply (AHTS) ship by utilizing Internet of Things (IoT) technology. HFC leaks in cooling systems can cause environmental hazards and operational inefficiencies. The proposed system integrates sensors and IoT devices to continuously monitor HFC levels, detect leaks in real-time, and provide instant alerts to crew and management. The system architecture includes sensors for HFC detection, a microcontroller for data processing, and a communication module for data transmission to a central monitoring platform. Real-time data can be accessed through a user-friendly interface, enabling timely intervention and maintenance. Initial testing shows that the system is effective in identifying and reporting HFC leaks, potentially reducing environmental impacts and improving the safety and efficiency of cold storage operations on AHTS vessels. Further development and extensive field testing are planned to refine the system and validate its performance across a wide range of operational conditions.

Worker Presence Monitoring in Complex Workplaces Using BLE Beacon-Assisted Multi-Hop IoT Networks Powered by ESP-NOW

The increasing adoption of Internet of Things (IoT) technologies has facilitated the creation of advanced applications in various industries, notably in complex workplaces where safety and efficiency are paramount. This paper addresses the challenge of monitoring worker presence in vast workplaces such as shipyards, large factories, warehouses, and other construction sites due to a lack of traditional network infrastructure. In this context, we developed a novel system integrating Bluetooth Low Energy (BLE) beacons with multi-hop IoT networks by using the ESP-NOW communications protocol, first introduced by Espressif Systems in 2017 as part of its ESP8266 and ESP32 platforms. ESP-NOW is designed for peer-to-peer communication between devices without the need for a WiFi router, making it ideal for environments where traditional network infrastructure is limited or nonexistent. By leveraging the BLE beacons, the system provides real-time presence data of workers to enhance safety protocols. ESP-NOW, a low-power communications protocol, enables efficient, low-latency communication across extended ranges, making it suitable for complex environments. Utilizing ESP-NOW, the multi-hop IoT network architecture ensures extensive coverage by deploying multiple relay nodes to transmit data across large areas without Internet connectivity, effectively overcoming the spatial challenges of complex workplaces. In addition, the Message Queuing Telemetry Transport (MQTT) protocol is used for robust and efficient data transmission, connecting edge devices to a central Node-RED server for real-time remote monitoring. Moreover, experimental results demonstrate the system’s ability to maintain robust communication with minimal latency and zero packet loss, enhancing worker safety and operational efficiency in large, complex environments. Furthermore, the developed system enhances worker safety by enabling immediate identification during emergencies and by proactively identifying hazardous situations to prevent accidents.

Metode Pengumpulan Data Debit Air Pada Saluran Distribusi Air PDAM Makassar Berbasis IoT

Water is an important resource for human survival as well as various economic sectors. PDAM is responsible for providing water supply to the community. One of the major challenges faced in managing water supply is the problem of water loss or leakage. This research aims to overcome this problem by designing an innovative method to collect water discharge data on the distribution channel of PDAM Makassar using Internet of Things (IoT) technology. This method uses the ESP32-CAM to take images of the water meter on the main pipe and save it to a database. Optical Character Recognition (OCR) technology is used to convert the image into text that can be processed. The test results show that the average error in meter readings is 0.215% with an average accuracy rate of 98%. Overall the system works well in reading digital meters and is reliable in monitoring water consumption visually and in real-time. Thus, this method can be an effective solution in detecting water leaks and improving the efficiency of water supply management by PDAMs, as well as saving resources and operational costs.

Security analysis and provision of authentication protocol, based on peer-to-peer structure in IOT platform

The Internet of Things technology allows you to transfer data to any asset through communication networks such as the Internet or intranet. One of the most important problems is security, which includes confidentiality, authenticity validation, and completeness. Among security problems, the most important ones are ensuring authenticity and protecting privacy. The elliptic curve encryption algorithm is famous protocols for maintaining privacy and verifying authenticity. Despite the capabilities they have and provide good security against network attacks, they face challenges such as response time, delay in the authentication and authentication process, as well as the amount of memory consumed. Two-factor authentication (2FA) is a security process where users provide two different authentication factors to authenticate themselves. It also provides a higher level of security than authentication methods that rely on single-factor authentication (SFA). This article presents two novel methods for 2FA that use encryption techniques, scramble architecture, and chord architecture. These methods allow for the authentication process to be initiated by both parties involved in the communication. Three scenarios and three metrics of reaction time, memory usage, and connection latency were used to assess the efficacy of the suggested approach. The average reaction time is improved and is 0.012 s when the lengths of the finger table in the chord structure are increased to 10, 15, and 20. The findings show that the suggested approach reduces computing complexity, reaction time, and communication latency. It has an average reaction time of 0.016 s, an average memory use of 30,360 kb, and an average connection latency of 0.042 s.

AI-Assisted Automated Identification of Human Activities Using Ficus religiosa Leaf-Based Triboelectric Nanogenerator.

Movement monitoring and effective identification of different actions are the keys that help in fitness services, health status, clinical studies, etc. In this technological era, Internet of Things (IoT) technologies, including smart wireless devices and sensors, are very effectively used for monitoring human activities, but the demand for sustainable and green power sources is a crucial issue with these devices. Triboelectric nanogenerators (TENG) are proven to be promising applications in these devices because they harvest energy from the surrounding environment and eliminate the use of batteries as power sources. As a green energy source, this study emphasizes the fabrication of biodegradable materials-based TENGs, which are eco-friendly and are related to clean and green energy as per the UN's sustainable development goals SDG 7 (affordable and clean energy). In the present work, a natural Ficus religiosa leaf (FRL) of the F. religiosa tree is used in designing and fabricating a TENG (FRL-TENG). Also, an approach is discussed to compare the performance of FRL-TENG with TENGs fabricated from other waste biodegradable materials such as garlic tunic, onion tunic, and eggshell membrane (ESM). During the experimental study, it is observed that the FRL-based TENG produced maximum voltage in comparison to other material combinations selected in this study. The generated electric output from these TENG combinations is also used to power an array of tens of green-light-emitting diodes (LEDs). Furthermore, this paper also proposes the use of FRL-TENG as a wearable sensor to collect information and monitor the physical activities of the user, viz., walk, jump, and run. To recognize the movement status, the FRL-TENG sensor is integrated with an extra randomized tree-based machine learning model for accurately distinguishing the user's three activities with an accuracy of 96%. The work showcases an innovative approach to encourage customized uses of TENG sensors in human motion monitoring and permits the development of intelligent, self-powered systems for new applications.

RNEP: Random Node Entropy Pairing for Efficient Decentralized Training with Non-IID Local Data

The proliferation of edge devices and advancements in Internet of Things technology have created a vast array of distributed data sources, necessitating machine learning models that can operate closer to the point of data generation. Traditional centralized machine learning approaches often struggle with real-time big data applications, such as climate prediction and traffic simulation, owing to high communication costs. In this study, we introduce random node entropy pairing, a novel distributed learning method for artificial neural networks tailored to distributed computing environments. This method reduces communication overhead and addresses data imbalance by enabling nodes to exchange only the weights of their local models with randomly selected peers during each communication round, rather than sharing the entire dataset. Our findings indicate that this approach significantly reduces communication costs while maintaining accuracy, even when learning from non-IID local data. Furthermore, we explored additional learning strategies that enhance system accuracy by leveraging the characteristics of this method. The results demonstrate that random node entropy pairing is an effective and efficient solution for distributed learning in environments where communication costs and data distribution present significant challenges.

Development and Modeling of the Internet of Things Network for Patients IT Diagnostics

The work of a network that implements algorithms for IT diagnostics of neurological diseases based on the Internet of Things technology has been developed and modeled. The network includes a smartphone, a platform, a neural network, and applications. First, the voices of sick patients are entered from the smartphone to train the neural network, and then the examined patients for IT diagnostics. Data is transferred between the smartphone and the platform (ThingSpeak) via the MQTT protocol. The smartphone’s mobile application extracts the voice functions of the examined patients and records them on the Internet of Things network platform. Recognition is performed using the trained neural network. The structure and algorithm of the ThingSpeak platform are presented. IT diagnostics data are displayed in the application on the smartphone. The patient data used in the study are taken from the ADReSS 2020 Challenge program, which contains speech data of patients with Alzheimer’s disease and healthy people.

Latest advancements and prospects in the next-generation of Internet of Things technologies.

The Internet of Things (IoT) is a sophisticated network of objects embedded with electronic systems that enable devices to collect and exchange data. IoT is a recent trending leading technology and changing the way we live. However, it has several challenges especially efficiency, architecture, complexity, and network topology. The traditional technologies are not enough to provide support. It is evident from the literature that complex networks are used to study the topology and the structure of a network and are applied to modern technologies. Thus, the capability of powerful computational tools and the existence of theoretical frameworks enable complex networks to derive new approaches in analyzing IoT-based technologies in terms of improving efficiency, architecture, complexity, and topology. In this direction, limited research has been carried out. The integration aspect remains a key challenge. Therefore, in order to fill this gap. Herein, we design a comprehensive literature review. In this research effort, we explore a newly leading emerging technology named the Social Internet of Things (SIoT). It is developed to overcome the challenges in IoT. We discuss the importance and the key applications of SIoT. We first presented a conceptual view along with a recent technological roadmap. The big data play an important role in the modern world. We discuss big data and the 5 Vs along with suitable applications and examples. Then, we highlighted the key concepts in complex networks, scale-free, random networks, and small-world networks. We explored and presented various graph models and metrics aligned with social networks and the most recent trends. The novelty of this research is to propose a synergy of complex networks to the IoT, SIoT, and big data together. We discuss the advantages of integration in detail. We present a detailed discussion on complex networks emerging technologies and cyber-physical systems (CPS). Briefly, our literature review covers the most recent advancements and developments in 10 years. In addition, our critical analysis is based on up-to-date surveys and case studies. Finally, we outline the impact of recent emerging technologies on challenges applications, and solutions for the future. This paper provides a good reference for researchers and readers in the IoT domain.