IoT Applications Research Articles

Improving edge AI for industrial IoT applications with distributed learning using consensus

Improving edge AI for industrial IoT applications with distributed learning using consensus

Automatic Modulation Classification for low-power IoT applications

Automatic Modulation Classification for low-power IoT applications

Flexible and Energy-Efficient Crypto-Processor for Arbitrary Input Length Processing in Blockchain-Based IoT Applications

Flexible and Energy-Efficient Crypto-Processor for Arbitrary Input Length Processing in Blockchain-Based IoT Applications

A Low-Profile Circularly Polarized Millimeter-Wave Broadband Antenna Analyzed with a Link Budget for IoT Applications in an Indoor Scenario.

Broadband antennas with a low-profile generating circular polarization are always in demand for handheld/ portable devices as CP antennas counter multipath and misalignment issues. Therefore, a compact millimeter-wave antenna is proposed in this article. The proposed antenna structure comprises two circular rings and a circular patch at the center. This structure is further embedded with four equilateral triangles at a 90° orientation. The current entering the radiator is divided into left and right circular directions. The equilateral triangles provide the return path for current at the differential phase of ±90°, generating circular polarization. Structural development and analysis were initially performed through the characteristic mode theory. It showed that Modes 1 to 4 generated good impedance matching from 20 to 30 GHz and Modes 1 to 5, from 30 to 40 GHz. It also demonstrated the summation of orthogonal modes leading to circular polarization. The antenna-measured reflection coefficient |S11| > 10 dB was 19 GHz (23-42 GHz), and the axial ratio at -3 dB was 4.2 GHz (36-40.2 GHz). The antenna gain ranged from 4 to 6.2 dBi. The proposed antenna was tested for link margin estimation for IoT indoor conditions with line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The communication reliability with co- and cross-polarization was also studied under these conditions, and the results proved to be satisfactory.

Design of the Wearable Microstrip Yagi-Uda Antenna for IoT Applications

Design of the Wearable Microstrip Yagi-Uda Antenna for IoT Applications

Improve quality of service for the Internet of Things using Blockchain & machine learning algorithms

The quality of service (QoS) parameters in IoT applications plays a prominent role in determining the performance of an application. Considering the significance and popularity of IoT systems, it can be predicted that the number of users and IoT devices are going to increase exponentially shortly. Therefore, it is extremely important to improve the QoS provided by IoT applications to increase their adaptability. Majority of the IoT systems are characterized by their heterogeneous and diverse nature. It is challenging for these systems to provide high-quality access to all the connecting devices with uninterrupted connectivity. Considering their heterogeneity, it is equally difficult to achieve better QoS parameters. Artificial intelligence-based machine learning (ML) tools are considered a potential tool for improving the QoS parameters in IoT applications. This research proposes a novel approach for enhancing QoS parameters in IoT using ML and Blockchain techniques. The IoT network with Blockchain technology is simulated using an NS2 simulator. Different QoS parameters such as delay, throughput, packet delivery ratio, and packet drop are analyzed. The obtained QoS values are classified using different ML models such as Naive Bayes (NB), Decision Tree (DT), and Ensemble, learning techniques. Results show that the Ensemble classifier achieves the highest classification accuracy of 83.74 % compared to NB and DT classifiers.

Enhancing Business Sustainability Through the Internet of Things

In the digital revolution, IoT stands out as a main pillar of technology that make our lives more easier and quicker. IoT is significantly helping several businesses to grow up, yet it's important to note that many IoT applications are just beginning to scratch the surface of their potential. Still there is a lot of scope for business opportunities in IoT which has been discussed in this paper in details. These opportunities also come up with key challenges that often requires extensive integration efforts and skilled professionals to manage. Moreover, IoT introduces new demands, particularly in security and interoperability. Interoperability refers to the ability of different computer systems and software to communicate and work together seamlessly.

Information Communication and Computation Technologies (ICCT) for Agricultural and Environmental Information Systems for Society 5.0

Purpose: This paper aims to discover the dynamic landscape of Information Communication and Computation Technologies (ICCT) within agriculture and environmental information management, elucidating their evolutionary trajectory and the resonance of Society 5.0 principles in fostering innovative solutions. By scrutinizing the core technologies constituting ICCT in these sectors, it endeavours to shed light on the potential for integration within the framework of Society 5.0, contemplating both the possibilities and challenges inherent in this convergence. Methodology: This exploratory chapter delves into the evolving landscape of Society 5.0 and its pivotal emphasis on the integration of Information Communication and Computation Technologies (ICCT) to address the complex challenges in agriculture and environmental information management. Analysis/Results: The paper provides a background on the evolution of Society 5.0 and establishes the rationale for exploring the role of ICCT in advancing Agricultural and Environmental Information Systems within this transformative societal framework. The core technologies in ICCT are explored through IoT applications in precision agriculture, the transformative impact of blockchain in agricultural supply chains, and the utilization of remote sensing and Earth observation systems along with data analytics for environmental insights. The paper further investigates the integration of ICCT in Society 5.0 agricultural and environmental systems, unveiling how these technologies support smart farming practices, citizen engagement in agricultural decision-making, and sustainable resource management. Case studies highlight successful ICCT implementations in agriculture and environmental management, underscoring best practices and lessons learned. Emerging trends in ICCT for agriculture and environmental science are explored, providing insights into future developments. Originality/Value: Through the lens of case studies showcasing successful ICCT implementations, it seeks to distill key insights, while also conducting a forward-looking assessment of emerging trends and applications, thus contributing to a deeper understanding of the evolving role of ICCT in shaping sustainable agricultural and environmental information paradigms within the societal context of the future. Type of Paper: Exploratory analysis.

The effects of 5G network on people and the environment: A machine learning approach to the comprehensive analysis

The progression of telecommunications, starting from the inception of 1G networks in 1979 to the advent of 5G technology in 2019, represents a significant journey of advancement for humanity. As we approach the era of 5G, characterized by heightened machine-to-machine connectivity and transformative applications in AI, IoT, and cloud computing, it becomes imperative to acknowledge and address concerns regarding its potential impacts on health and the environment. Utilizing machine learning algorithms, particularly implemented in Python for this research, provides a potent approach to analyzing intricate datasets concerning 5G signals and their potential correlations with healthcare outcomes. After carefully cleaning and preparing the data and conducting linear regression analysis, uncovered evidence backing the notion that 5G antennas emit greater levels of radiation compared to 4G antennas emerged - a fact often concealed by corporations. Despite relying on a restricted dataset, the results emphasize the necessity for more accurate data to improve model precision. Ongoing research endeavors are vital to alleviate public anxieties regarding 5G technology, thereby fostering trust and bolstering awareness on a wider front.

CUSCO: An Unobtrusive Custom Secure Audio-Visual Recording System for Ambient Assisted Living.

The ubiquity of digital technology has facilitated detailed recording of human behaviour. Ambient technology has been used to capture behaviours in a broad range of applications ranging from healthcare and monitoring to assessment of cooperative work. However, existing systems often face challenges in terms of autonomy, usability, and privacy. This paper presents a portable, easy-to-use and privacy-preserving system for capturing behavioural signals unobtrusively in home or in office settings. The system focuses on the capture of audio, video, and depth imaging. It is based on a device built on a small-factor platform that incorporates ambient sensors which can be integrated with the audio and depth video hardware for multimodal behaviour tracking. The system can be accessed remotely and integrated into a network of sensors. Data are encrypted in real time to ensure safety and privacy. We illustrate uses of the device in two different settings, namely, a healthy-ageing IoT application, where the device is used in conjunction with a range of IoT sensors to monitor an older person's mental well-being at home, and a healthcare communication quality assessment application, where the device is used to capture a patient-clinician interaction for consultation quality appraisal. CUSCO can automatically detect active speakers, extract acoustic features, record video and depth streams, and recognise emotions and cognitive impairment with promising accuracy.

Real-time Multimedia Analytics for IoT Applications: Leveraging Machine Learning for Insights

Real-time Multimedia Analytics for IoT Applications: Leveraging Machine Learning for Insights

RFID tag recognition model for Internet of Things for training room management

With the rapid development of the Internet of Things and intelligent technology, the application of Radio Frequency Identification (RFID) technology in training room management is becoming increasingly widespread. An efficient and accurate RFID system can significantly improve the management efficiency and resource utilization of the training room, thereby improving teaching quality and reducing management costs. Although RFID technology has many advantages, there are still some problems in practical applications, such as label collision and recognition of unknown labels. These issues not only affect the performance of the system but may also cause interference with actual teaching and management. This study proposes a grouping-based bit arbitration query tree algorithm and anti-collision technology to solve label collisions and reduce label recognition time in the technology. A new unknown label recognition algorithm is also proposed to improve the recognition efficiency and accuracy of identifying new unknown labels. Related experiments have shown that the recognition accuracy of the algorithm designed this time is 95.86%. Compared with other algorithms, the number of idle time slots is the smallest. When the number of queries is 1000, the algorithm has 1842 queries, and the communication complexity is the best. When the number of unknown tags is 10,000, the actual accuracy rate is 95.642%. Compared with traditional recognition algorithms, the new unknown label recognition algorithm has a smaller frame length in the same label proportion and good recognition performance. On a theoretical level, the research content on RFID technology helps to improve and develop the basic theories of the Internet of Things and intelligent recognition technology and provides solutions and application technologies for equipment management and IoT applications in training rooms. On a practical level, the research results can provide specific guidance for the management of training rooms, help solve equipment management and safety maintenance problems in practical applications, and improve the management efficiency of training rooms.

Parkinson’s Disease Management Via Wearable Sensors

This study presents an innovative IoT-based system for the real-time management of Parkinson's Disease using a combination of Flex Sensor, Node MCU, SCR Sensor, and ADXL335. The Flex Sensor captures hand tremors, the ADXL335 monitors overall motion, and the SCR Sensor measures physiological stress levels. These components collectively enable continuous monitoring of Parkinson's symptoms. The data is wirelessly transmitted to a centralized server for analysis, allowing healthcare providers to remotely assess patient conditions. Machine learning algorithms offer insights into disease progression and predict potential exacerbations. The system provides timely alerts, enhancing patient care and reducing healthcare costs. This research contributes to the evolving landscape of IoT applications in healthcare for neurodegenerative disorders.

Comprehensive Survey on Applications of Internet of Things, Machine Learning and Artificial Intelligence in Precision Agriculture

A comprehensive, multidisciplinary analysis of the latest developments in digital agriculture is conducted with the use of artificial intelligence (AI), machine learning (ML), and the Internet of Things. By automation and the use of modern, scalable technology solutions that reduce risks, support sustainability, and give farmers predictive advice, traditional agricultural processes are being updated and improved to maximize production. In this paper, the applications of AI, IoT, and ML in agricultural production systems are discussed in detail. The applications that have been explored can be broadly categorized into three areas: soil management, livestock management, and crop management. Weed detection, disease identification, and yield forecasting are some of the applications for crop management. Two applications of livestock management are animal welfare and production. The use of AI, IoT, and ML will make it possible to collect data from agricultural activities for analysis and the extraction of insightful knowledge, facilitating prompt and accurate decision making to increase agricultural productivity. This will result in farming that is more exact and efficient while requiring less labour and producing high-quality produce.

A Design of Four-Port Flexible UWB-MIMO Antenna for Wearable and IoT Applications

A Design of Four-Port Flexible UWB-MIMO Antenna for Wearable and IoT Applications

Smart Healthcare Management Model for Proactive Patient Monitoring

The rapid development of Artificial Intelligence (AI) is leading urban centers to employ AI technologies to improve efficiency and solve urban problems. This research proposes a Centralized System Model for Smart Cities (CSMSC) that centralizes AI-oriented data acquisition, processing, and decision-making. CSMSC uses real-time sensor networks for data collection, sophisticated AI algorithms for nuanced data interpretation, and unified storage for streamlined information management. Additionally, CSMSC integrates AI-based analyzers to autonomously produce alerts, evaluate their urgency, and decide upon suitable responses, enabling quick and targeted city interventions. The paper combines field evidence with theoretical frameworks to highlight the transformative potential of cognitive sensing and machine learning in smart city development. Recent studies have shown that AI on edge is revolutionizing the infrastructure of smart cities by bringing advanced intelligence and real-time analytics closer to the data source. AI on edge enables real-time decision-making, reduces latency, optimizes bandwidth usage, and enhances privacy and security. The potential benefits of using data analytics in smart cities are significant, and future research should focus on developing new algorithms and tools to analyze data and explore new IoT and machine-learning applications.

Reliable edge-to-core optical networks: An optimal algorithm for maximal path diversity

With the emergence of IoT applications, 5G, and edge computing, network resource allocation has shifted toward the edge, bringing services closer to the end users. These applications often require communication with the core network for purposes that include cloud storage, compute offloading, 5G-and-Beyond transport communication between centralized unit (CU), distributed unit (DU) and core network, centralized network monitoring and management, etc. As the number of these services increases, efficient and reliable connectivity between the edge and core networks is of the essence. Wavelength Division Multiplexing (WDM) is a well-suited technology for transferring large amounts of data by simultaneously transmitting several wavelength-multiplexed data streams over each single fiber optics link. WDM is the technology of choice in mid-haul and long-haul transmission networks, including edge-to-core networks, to offer increased transport capacity.Optical networks are prone to failures of components such as network fiber links, sites, and transmission ports. A single network element failure alone can cause significant traffic loss due to the disruption of many active data flows. Thus, fault-tolerant and reliable network designs remain a priority. The architecture called “dual-hub and dual-spoke” is often used in metro area networks (MANs). A dual-hub, or in general a multi-hub network, consists of a set of designated destination nodes (hubs) in which the data traffic from all other nodes (the peripherals) should be directed to the hubs. Multiple hubs offer redundant connectivity to and from the core or wide area network (WAN) through geographical diversity. The routing of the connections (also known as lightpaths) between the peripheral node and the hubs has to be carefully computed to maximize path diversity across the edge-to-core network. This means that whenever possible the established redundant lightpaths must not contain a common Shared Risk Link Group (SRLG).An algorithm is proposed to compute the most reliable set of SRLG disjoint shortest paths from any peripheral to all hubs. The proposed algorithm can also be used to evaluate the overall edge-to-core network reliability quantified through a newly introduced figure of merit.

ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring.

Addressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the "ScalableDigitalHealth" (SDH) framework. The framework integrates smart digital health solutions with latency-aware edge computing autoscaling, providing a novel approach to remote patient monitoring. By leveraging IoT technology and application autoscaling, the "SDH" enables the real-time tracking of critical health parameters, such as ECG, body temperature, blood pressure, and oxygen saturation. These vital metrics are efficiently transmitted in real time to AWS cloud storage through a layered networking architecture. The contributions are two-fold: (1) establishing real-time remote patient monitoring and (2) developing a scalable architecture that features latency-aware horizontal pod autoscaling for containerized healthcare applications. The architecture incorporates a scalable IoT-based architecture and an innovative microservice autoscaling strategy in edge computing, driven by dynamic latency thresholds and enhanced by the integration of custom metrics. This work ensures heightened accessibility, cost-efficiency, and rapid responsiveness to patient needs, marking a significant leap forward in the field. By dynamically adjusting pod numbers based on latency, the system optimizes system responsiveness, particularly in edge computing's proximity-based processing. This innovative fusion of technologies not only revolutionizes remote healthcare delivery but also enhances Kubernetes performance, preventing unresponsiveness during high usage.

A Comparative Study of HTTP and MQTT for IoT Applications in Hydroponics

A Comparative Study of HTTP and MQTT for IoT Applications in Hydroponics

Multipurpose Modular Wireless Sensor for Remote Monitoring and IoT Applications.

Today, maintaining an Internet connection is indispensable; as an example, we can refer to IoT applications that can be found in fields such as environmental monitoring, smart manufacturing, healthcare, smart buildings, smart homes, transportation, energy, and others. The critical elements in IoT applications are both the Wireless Sensor Nodes (WSn) and the Wireless Sensor Networks. It is essential to state that designing an application demands a particular design of a WSn, which represents an important time consumption during the process. In line with this observation, our work describes the development of a modular WSn (MWSn) built with digital processing, wireless communication, and power supply subsystems. Then, we reduce the WSn-implementing process into the design of its modular sensing subsystem. This would allow the development and launching processes of IoT applications across different fields to become faster and easier. Our proposal presents a versatile communication between the sensing modules and the MWSn using one- or two-wired communication protocols, such as I2C. To validate the efficiency and versatility of our proposal, we present two IoT-based remote monitoring applications.