Internet Of Things Applications Research Articles

Advanced Interdisciplinary Approach in Construction Industry: Internet of Things (IOT)

Promoting construction, enhancing safety and multiple functions of IoT. Since the beginning of Fourth Industrial Revolution, digitalization becomes a fundamental function of all the construction project and bring all the project to a brand new practical and efficient world. IoT (Internet of Things), which refers to a large network of connected sensors and devices capable of autonomously exchanging and analysing data in real-time, belongs to a major facilitator of this function. To have an idea of the importance of this technology in the construction field, one must think about it as an instrument to decrease labour cost, reduce project repair time, and save material cost by automating and networking process. Among these could be automated assessment of a construction site to alert about hazards that might affect workers’ lives. IoT alarms and delivered insights reduce risks and keep the working place of the construction workers safe. Overall, it is claimed in the paper that IoT has a significant number of applications in the construction sector- starting from the project management to the quality testing of work. These are just some of the applications of IoT and as the field evolves, more benefits and value-added services would be seen arising. In this regard, IoT will also have a key role in communication and coordination between many stakeholders involved, hence creating collaboration and cooperation for a healthy conductive environment with openness among all. Its integration with latest technologies like digitization of data, data analytics, AI, facilitate predictive maintenance decisions and end up making less mistakes. Although there is a huge potential for IoT to develop in the construction industry, so far, it is not utilised in a large scale. There are some limitations to be reduced like the cybersecurity, interoperability, and workforce readiness among others that need to be addressed or enhanced in due time. Industry participants must join hands to overcome these issues. It would be an understatement to say that IoT has the ability to completely revolutionize the construction industry. In article it illustrates how the Internet of Things is transforming the building sector and offers guidance on how interested parties can take advantage of this technology to raise project sustainability, output, and safety. By adopting innovation and digitization, those involved in the construction industry can take advantage of new moves for efficacy and efficiency in systems performance.

The Role Of Iot In Transforming The Shrimp Industry In Andhra Pradesh

The shrimp industry in Andhra Pradesh, a leading player in India's aquaculture sector, faces numerous challenges including disease management, water quality control, and efficient resource utilization. The integration of Internet of Things (IoT) technologies offers promising solutions to these challenges by enabling real-time monitoring and data-driven decision-making. This research paper explores the application of IoT in the shrimp industry of Andhra Pradesh, highlighting its advantages, limitations, and future potential. By reviewing existing literature, examining current implementations, and discussing practical examples, this paper aims to provide a comprehensive understanding of how IoT can revolutionize shrimp farming practices in the region.

Effective integration of the internet of things and ensemble learning approaches for enhancing sudden topple recognition assisted by cloud computing technology

Falls provide a significant public health hazard globally for the senior population. Untreated Sudden Topple in the elderly leads to functional loss and a notable decline in mobility, autonomy, and quality of life. Early identification of Sudden Topple is essential for a person's well-being or to provide needed care. Telehealth data centers need scalable processing and storing resources to accommodate the increasing number of individuals. Specialized methods that enable the transfer of just pertinent data are necessary. This study presents a Hybrid System composing Cloud Computing and the Internet of Things (IoT) (HS-CC-IoT) to monitor many elderly individuals, identify Sudden Topple, and alert caretakers. The experiments were conducted to reveal the necessary criteria for facilitating the operation of large-scale systems. The research assessed many machine learning algorithms for their appropriateness in detection. The experimental tests to identify sudden topples are in cloud-based data centers and on an Edge IoT gadget with an Ensemble Learning Algorithm. Experiments on the user-to-cloud data transfer showed that a substantial decrease in the quantity of saved and transferred data is possible when conducting Sudden Topple identification on the Edge.

A novel approach to IoT security for intrusion detection system using ensemble network and heuristic-assisted feature fusion

The area of “Internet of Things (IoT)” has gained popularity recently as a method for developing intelligent models. Security and privacy are regarded as the two main issues in every real-world IoT application. The security risks posed by IoT-enabled devices are serious threats to the development of the smart industry. Thus, to reduce the security dangers that emanate from IoT devices and give birth to numerous security concerns, Intrusion Detection Systems (IDSs) specifically tailored for the IoT sectors are important. Conventional intrusion detection systems (IDSs) are unsuitable for use in standard Internet of Things (IoT) networks for a variety of reasons, including memory, battery life, and processing power. Various Intrusion Detection Systems (IDSs) have been proposed in academic research to address these problems. However, while identifying anomalies, various intrusion detection systems (IDSs) have issues with low accuracy and numerous false alarms. To counteract the shortcomings of conventional systems, it is recommended that an ensemble deep learning model be utilized to identify intrusions within the Internet of Things (IoT) domain. The final result is then determined by averaging the scores, which is accomplished by combining them. Divergent measurements are thus used to validate the model. On the other hand, by prohibiting unwanted access, the recommended method not only increases the detection rate but also improves network security.

Comparative Analysis of Embedded Operating Systems: A Criteria-Based Evaluation

Nowadays, the Internet of Things (IoT) encompasses a wide range of applications, including smart home systems, healthcare devices, smart parking systems, and smart transportation solutions. For these types of IoT applications, embedded operating systems play a crucial role by providing excellent real-time performance and reliability. The important purpose of this system is to efficiently perform specific tasks. This paper provides a quantitative and qualitative analysis of embedded operating systems. The study in this paper focused on a selection of systems commonly utilized in industrial and academic environments. We evaluated embedded operating systems like Linux, Android, QNX, FreeRTOS, TinyOS, PalmOS, Ubuntu, LiteOS, VxWorks, and Integrity based on specific criteria. It will help industries and academics select embedded operating systems according to requirements for performing specific tasks.

Balancing the Load and Scheduling the Tasks using Zebra Optimizer in IoT based Cloud Computing for Big-data Applications.

Task scheduling is one of the major problems with Internet of Things (IoT) cloud computing. The need for cloud storage has skyrocketed due to recent advancements in IoT-based technology. Sophisticated planning approaches are needed to load the IoT services onto cloud resources professionally while meeting application necessities. This is significant because, in order to optimise resource utilisation and reduce waiting times, several procedures must be properly configured on various virtual machines. Because of the diverse nature of IoT, scheduling various IoT application activities in a cloud-based computing architecture can be challenging. Fog cloud computing is projected for the integration of fog besides cloud networks to address these expectations, given the proliferation of IoT sensors and the requirement for fast and dependable information access. Given the complexity of job scheduling, it can be difficult to determine the best course of action, particularly for big data systems. The behaviour of zebras in the wild serves as the primary basis of stimulus for the development of the Zebra Optimisation Algorithm (ZOA), a novel bio-inspired metaheuristic procedure presented in this study. ZOA mimics zebras' feeding habits and their defence mechanisms against predators. Various activities are analysed and processed using an optimised scheduling model based on ZOA to minimise energy expenditures and end-to-end delay. To reduce makespan and increase resource consumption, the technique uses a multi-objective strategy. By using a regional exploratory search strategy, the optimisation algorithm may better utilise data and stays out of local optimisation ruts. The analysis revealed that the suggested ZOA outperformed other well-known algorithms. It was advantageous for big data task scheduling scenarios since it converged more quickly than other techniques. It also produced improvements of 18.43% in several outcomes, including resource utilisation, energy consumption, and make span.

Light power resource availability for energy harvesting photovoltaics for self-powered IoT

As the Internet of Things (IoT) expands, the need for energy-efficient, self-powered devices increases and so a better understanding of the available energy resource is necessary. We examine the light power resource availability for energy harvesting photovoltaics (PV) in various environments and its potential for self-powered IoT applications. We analyse light sources, considering spectral distribution, intensity, and temporal variations, and evaluate the impact of location, seasonal variation, and time of day on light power availability. Additionally, we discuss human and building design factors, such as occupancy, room aspect, sensor placement, and décor, which influence light energy availability and therefore power for IoT electronics. We propose a best-case and non-ideal scenario in terms of light resource for energy-harvesting, and using a commercially available organic PV cell, show that the energy yield generated and available to the IoT electronics, can be anywhere between 0.7 mWh and 75 mWh per day, depending on the lighting conditions.

Design and Implementation IOT2ALK cloud Computing Platform for IOT Applications

An integration between the Internet of Things (IoT) and cloud computing can potentially leverage the utilization of both sides. As the IoT-based system is mostly composed of the interconnection of pervasive and constrained devices, it can take advantage of the virtually unlimited resources of cloud entities, i.e., storage and computation services, to store and process its sensed data. In this study, we examine the design and implementation of IoT and cloud computing platforms. The proposed system consists of two main components: hardware and software. Many experiments are used to gather data and upload it to the framework. The software is an IoT2ALK platform that is designed and implementation using front- and back-end techniques that can connect any IoT applications to it. Several experiments are implemented to ensure the effectiveness of the platform. The platform depends on connecting the IoT devices to it and using communication protocols like HTTP to transfer the data from the IoT devices to the platform. Another way to gather data is by uploading a CSV file to the platform after filling it with the required data. The system can collect, store, analyze, and process the data in an efficient manner

A Review Load balancing algorithms in Fog Computing

With the rapid advance of the Internet of Things (IoT), technology has entered a new era. It is changing the way smart devices relate to such fields as healthcare, smart cities, and transport. However, such rapid expansion also challenges data processing, latency, and QoS. This paper aims to consider fog computing as a key solution for addressing these problems, with a special emphasis on the function of load balancing to improve the quality of service in IoT environments. In addition, we study the relationship between IoT devices and fog computing, highlighting why the latter acts as an intermediate layer that can not only reduce delays but also achieve efficient data processing by moving the computational resources closer to where they are needed. Its essence is to analyze various load balancing algorithms and their impact in fog computing environments on the performance of IoT applications. Static and dynamic load balancing strategies and algorithms have been tested in terms of their impact on throughput, energy efficiency, and overall system reliability. Ultimately, dynamic load balancing methods of this sort are better than static ones for managing load in fog computing scenarios since they are sensitive to changing workloads and changes in the system. The paper also discusses the state of the art of load balancing solutions, such as secure and sustainable techniques for Edge Data Centers (EDCs), It manages the allocation of resources for scheduling. We aim to provide a general overview of important recent developments in the literature while also pointing out limitation where improvements might be made. To this end, we set out to better understand and describe load balancing in fog computing and its importance for improving QoS. We thus hope that a better understanding of load balancing technologies can lead us towards more resilient and secure systems.

Trustworthy-Based Authentication Model with Intrusion Detection for IoT-Enabled Networks with Deep Learning Algorithm

In the burgeoning field of the Internet of Things (IoT), ensuring secure and trustworthy communication between devices is paramount. This paper proposes a novel Trustworthy-Based Authentication Model (TBAM) integrated with Intrusion Detection Systems (IDS) leveraging deep learning algorithms to secure IoT-enabled networks. The proposed model addresses the dual challenges of authenticating legitimate devices and detecting malicious intrusions. Specifically, we employ a Convolutional Neural Network (CNN) to analyse network traffic patterns for intrusion detection, leveraging its prowess in feature extraction and classification. Additionally, a Long Short-Term Memory (LSTM) network is utilized for continuous monitoring and anomaly detection, capturing temporal dependencies in data flows that are indicative of potential security threats. The authentication mechanism integrates a trust evaluation system that assigns trust scores to devices based on their behaviour, enhancing the model's capability to distinguish between trusted and malicious entities. Our extensive experiments on real-world IoT datasets demonstrate that the TBAM significantly outperforms traditional security models in terms of detection accuracy, false-positive rate, and computational efficiency. Specifically, our model achieves a detection accuracy of 98.7%, a false-positive rate of 1.2%, and a processing time reduction of 30% compared to baseline models. This work contributes a robust, scalable, and efficient solution to the pressing security concerns in IoT networks, paving the way for more secure and reliable IoT applications.

An optical fluorescence sensor for IoT application in direct visualization of the curing process in polymer matrices

Fluorescent Probe Technique can be applied as the tools in the Internet of Things (IoT), enabling an online preview of ongoing photopolymerization processes in 3D printing using a single sensor and provides unique information about the process.

Lightweight Secure Compression Scheme for Green IoT Applications

The Internet of Things (IoT) is a revolutionary paradigm that has gained significant prominence in recent years. It represents the interconnection of everyday objects, devices and machines to the internet, allowing them to collect, exchange and analyze data. As the number of connected objects grows exponentially, energy consumption and data security become essential challenges to ensure a sustainable development of green IoT. Data compression theory can be used as an enticing key to downsize the large amount of circulated information, since wireless communication is the prime energy consuming component in IoT devices. In addition, data encryption algorithms are crucial to protect sensitive information and ensure the integrity and confidentiality of IoT systems. Therefore, combining compression and encryption can lead to improved overall IoT system performance in terms of both power cost and data privacy. In this paper, a lightweight and secure compression approach is introduced to efficiently manage the energy expenditure and provide end-to-end security for energy constrained devices. This proposed algorithm combines the performance of LTC compression and AES encryption to securely encode collected data in IoT devices. The energy model analysis has shown that the suggested method is effective in terms of processing energy, reduces the volume of transmitted data and guarantees end-to-end information privacy.

Comparative Analysis of Various Feature Extraction Methods on IoT 2023

The Internet of Things (IoT) has emerged as a transformative technology with applications spanning diverse domains, from healthcare to smart cities. Efficient utilization of IoT data is essential for extracting meaningful insights and supporting decision-making processes. This research paper focuses on the critical task of feature extraction from IoT data, with a particular emphasis on the IoT 2023 dataset—a comprehensive and widely-used benchmark dataset in the IoT domain. In this study, exploration and comparison of various feature extraction methods are undertaken to enhance understanding of IoT 2023 dataset characteristics and its potential applications. Traditional statistical techniques, as well as machine learning-based approaches for feature extraction, are investigated. Evaluation of these methods is based on their ability to capture relevant information, reduce dimensionality, and enhance the performance of downstream IoT analytics tasks. Extensive experiments are conducted, and a comprehensive performance analysis is presented to guide the selection of the most suitable feature extraction approach for specific IoT applications. The outcomes of this research contribute to ongoing efforts to harness the potential of IoT data by offering a comprehensive understanding of the impact of feature extraction methods on data quality and predictive accuracy. This aids in paving the way for more informed and effective IoT solutions in 2023 and beyond.

AI-Driven EEC for Healthcare IoT: Security Challenges and Future Research Directions

Emerging edge computing (EEC) has been introduced as an innovative paradigm for the healthcare applications of the Internet of Things (IoT) that aims to distribute the network resources at the network edges to improve security, communication, and decision-making processes. The operation of healthcare IoT applications typically needs the presence of interoperable modules. Despite numerous benefits, these applications face many security challenges at the network edge. In this context, advanced Artificial Intelligence techniques can be used at the network edges for these applications to efficiently utilize the available resources securely. To this end, we aim to present a detailed survey of healthcare IoT applications in the context of AI-enabled EEC technology to identify unresolved security challenges that need attention from the research community and healthcare stakeholders, and then suggest potential research directions to give a clear future insight.

IoT Application for Monitoring and Recording Solar Power Plant Data

The availability of fossil energy sources to produce electricity for the purposes of providing electricity used by industry and society is increasingly limited. Alternative sources of electricity that do not depend on fossil energy sources as an energy source to drive prime mover generators for power plants have been mobilized and developed as alternative sources of electricity, one of which is solar cells. The use of solar cells as a device that can convert sunlight into electrical voltage and then store it in a battery, becomes an alternative source of electrical energy to meet various electrical power needs. Monitoring the current and voltage produced by solar cells is very necessary to find out how much power the solar cells we use produce. We can determine the intensity of light absorbed by solar cells by measuring light intensity. The goal of this project is to monitor and track the electrical power generated by solar power plants using Internet of Things (IoT) technologies. A web server, an ESP32 to process data from the current and voltage sensors mounted on the solar cell and then transfer it to the web server, and a smartphone to monitor the current and voltage produced by the solar cell make up the built Internet of Things system. Smartphones can access the data that is sent from the ESP32 because it is saved on a web server. Data from measurements from the IoT system that we created for monitoring power on solar panels taken on 6 November 2023 until 12 November 2023 Obtained data of average/day 140.2VA and measurements of average light intensity 31,380 lux. This data describes the power produced per day from a place with the intensity of light received by solar cells to produce electrical power as a result of monitoring data from the IoT system that we developed. From the case above, the IoT system that we have developed can monitor initial data for a place where solar panels will be installed to be used as a solar power plant.

Edge-AI: IoT Request Service Provisioning in Federated Edge Computing Using Actor-Critic Reinforcement Learning

Edge computing plays a critical role in the Internet of Things (IoT) environment as it potentially supports the time-critical IoT applications’ resources as well as latency requirements. In the near future, most edge service providers are envisioned to receive revenue from deploying these applications with the expenditures proportional to placing the offloaded requests from IoT devices and allocating the required resources. One way to maximize the edge profit and minimize the response latency is to integrate the edge nodes and form the edge federation. Therefore, edge service providers can have interoperability to distribute the IoT requests on the appropriate edge nodes in the light of providing satisfactory service levels to meet their objectives. Since the edge nodes are volatile and IoT time-critical applications are increasing, the edge nodes are envisioned to face massive traffic from IoT devices. Therefore, exploiting the traditional dynamic requests placement approaches cannot meet the SLA requirement of both IoT devices and edge service providers. In this article, we designed an intelligent reinforcement learning-based request service provisioning system (i.e., here, we call Edge-AI) as part of a smart edge orchestrator in the edge federation. We implement the proposed method, which is called DRL-Dispatcher, and compare it with greedy and random algorithms in edge federation. The experimental results show that the proposed DRL-Dispatcher performs better in terms of profit and low response latency as compared with the baseline approaches.

Active Blended Learning Model for Teaching IoT Application Development Course: A Case Study

Abstract—Traditional University learning approaches in Software Engineering, focusing on students listening to lecturers, then individually performing tasks is an isolating experience, a long way from the inherently collaborative approach in the workforce. Active learning focuses on student-centered learning in a much more authentic experience. This paper focuses on experiences in developing a course in the transnational education context involving a partnership between Indian and Australian universities. It highlights the design of a technical Software Engineering course for Internet of Things (IoT) application development with blended and active learning strategies and focuses on insights gained from implementing this curriculum for two different cohorts at an Indian university. Keywords—Active Learning, Blended Learning, IoT, Student Engagement, Embedded System, Software Engineering.

Extend low-power LoRaWAN coverage device for IoT applications

The advent of Long Range Wide Area Network (LoRaWAN) has significantly enhanced Internet of Things (IoT) applications, particularly in Smart Cities, with its broad coverage and energy efficiency. However, in areas with blind spots and obstacles, the conventional star network has limitations in coverage. To overcome this, researchers conducted a study to explore the potential of LoRaWAN extenders with multiple relays, focusing on coverage, performance, and power consumption. The study involved designing and deploying a relay system aligned with protocol of LoRaWAN to extend the reach of a standard LoRaWAN gateway. Strategically placed relays addressed areas beyond the gateway's coverage. Performance was measured by the longest communication distance achieved by each relay, packet loss ratio, and latency. Power consumption was also monitored. The results demonstrate the practicality and effectiveness of the proposed multiple relay design, expanding the capabilities of LoRaWAN network.

Performance Evaluation of Deep Learning Techniques in The Detection of IOT Malware

Internet of Things (IoT) equipment is rapidly being used in a variety of businesses and for a variety of reasons (for example, sensing and collecting data from the environment in both public and military settings). Because of their expanding involvement in a wide range of applications and their rising computational and processing capabilities, they are a viable attack target for malware tailored to infect specific IoT devices. This study investigates the potential of detecting IoT malware using different deep learning techniques: the classic feedforward neural network (FNN), convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN). The proposed method analyses the execution operation codes of IOT app sequences using modern NLP (natural language processing) methods. The current work utilized an IoT application dataset with 500 malware (collected from the IOTPOT dataset) and 500 goodware samples to train the proposed algorithms. The trained model is tested against 2971 fresh IoT malware and goodware samples. The samples were input into deep learning models, and performance metrics were obtained. The results demonstrate that the RNN model had the best accuracy (99.19%) in detecting fresh malware samples. On the other hand, the results were compared by the time required for training; the CNN model shows that it could achieve high accuracy (98.05%) with less training time. A comparison with various deep learning classifiers demonstrates that the RNN and CNN techniques produce the best results.

SMART-FCD: IOT DATA INTEROPERABILITY USING SENSOR BASED FUZZY LINKED RULES FOR CROSS DOMAIN APPLICATIONS

Internet of Things (IoT) is connected everywhere and enables massive information exchange between objects and people. The unified global IoT must manage a massive amount of data generated by these connected smart which arises the interoperability challenges such as lack of communication protocols, device support, and accepted open standards in smart environments. To combat these issues, a novel Smart-Fuzzy linked rules for Cross Domain application (Smart-FCD) framework is proposed to ensure interoperability by enabling efficient communication and data exchange between multiple platforms and systems. Initially, the heterogenous data from the sensors such as temperature sensors and humidity sensors, and the descriptions are implemented according to the Sensor Measurement List (SenML) language. After composing, semantic modelling will occur which converts the relational data into Resource Description Framework (RDF) format. The Linked Open Vocabulary for the Internet of Things (LOV4IoT) dataset is used to extract interoperable domain knowledge, which is then fed into the related sensor-based fuzzy rules. The IoT preset semantic models which utilise interoperable datasets and domain instances, which are updated via fuzzy association rules. Semantic Web of Things (SWoT) is a technology that assists in creating semantic-based IoT applications, thus IoT developers may use it to create intelligent applications. The sensor data, RDF simulation, evolution time and latency are some of the parameters that are used to evaluate the effectiveness of the proposed Smart-FCD methodology.