Internet Of Things Approach Research Articles

Analysis of Soil Viability Monitoring System for In-House Plantation Growth Using an Internet of Things Approach

Houseplant cultivation has become increasingly popular, allowing individuals to bring nature into their homes. However, successful indoor gardening requires careful monitoring of soil parameters to ensure optimal plant growth. To address this need, sensor technology and Internet of Things (IoT) devices are utilized to monitor soil temperature and moisture levels, which play crucial roles in plant growth. Various soil factors are sensed and collected using an IoT-based microcontroller, with data transmission facilitated by a Message Queue Telemetry Transport (MQTT) broker. Visualization of the data is achieved through the Node-RED programming tool, simplifying dashboard creation for easy monitoring. Furthermore, the collected data is stored in a MySQL server, enabling further analysis through SQL queries. The day is divided into four quarters with six-hour intervals, allowing for soil data collection using temperature and moisture sensors. The resulting information on the dashboard facilitates informed decision-making to enhance soil conditions for optimal indoor plant growth. Experimentation has revealed a reduction in soil temperature of 3°C during daytime due to air conditioning operation, while soil moisture content remains consistently between 60 to 65% during early mornings and late evenings. Additionally, emphasis is placed on remote management using IoT systems, enabling monitoring of plant growth even when access is limited. Overall, monitoring soil factors using IoT technology offers a promising approach to optimizing indoor gardening practices and minimizing environmental resource consumption.

Evaluation of LoRa Network Performance for Water Quality Monitoring Systems

Conserving water resources from scarcity and pollution is the basis of water resource management and water quality monitoring programs. However, due to industrialization and population growth in Malaysia, which have resulted in poor water quality in many areas, this program needs to be improved. A smart water quality monitoring system based on the internet of things (IoT) paradigm was designed to analyze water conditions in real time and enable effective water management. Long-range (LoRa) application of the low-power, wide-area networking concept has become a phenomenon in IoT smart monitoring applications. This study proposes the implementation of a LoRa network in a water quality monitoring system-based IoT approach. The LoRa nodes were embedded with measuring sensors pH, turbidity, temperature, total dissolved solids, and dissolved oxygen, in the designated water stations. They operate at a transmission power of 14 dB and a bandwidth of 125 kHz. The network properties were tested with two different antenna gains of 2.1 dBi and 3 dBi, with three different spread factors of 7, 9, and 12. The water stations were located on the Sungai Pantai and Sungai Anak Air Batu rivers on the Universiti Malaya campus, Malaysia. Following a dashboard display and K-means analysis of the water quality data received by the LoRa gateway, it was determined that both rivers are Class II B rivers. The results from the evaluation of LoRa performance on the received strength signal indicator, signal noise ratio, loss packet, and path loss at best were −83 dBm, 7 dB, <0%, and 64.41 dB, respectively, with a minimum received sensitivity of −129.1 dBm. LoRa has demonstrated its efficiency in an urban environment for smart river monitoring purposes.

Unveiling anomalies: harnessing machine learning for detection and insights

The rise of Internet of Things (IoT) devices has brought about an increase in security risks, emphasizing the need for effective anomaly detection systems. Previous research introduced a dynamic voting classifier to overcome overfitting or inaccurate accuracies caused by dataset imbalance. This article introduces a new method for IoT anomaly detection that employs a hybrid voting classifier, which combines several machine learning models. To solve the overfitting and class weight issues, an adaptive voting classifier is used that adjusts weights according to the highest preference for accuracy. The developing voting system increases the effectiveness of more accurate classifiers, enhancing the group’s overall capability. A proposed combined classifier combines Logistic Regression, AdaBoost, Gradient Boosting, and Multi-Layer Perceptron models using a soft voting method. To develop and assess this method, the CIC-IoT-2023 dataset is utilized, which contains 33 types of IoT attacks across 7 categories. This process includes thorough data preprocessing and feature selection from a pool of 42 available attributes. The performance of this approach is measured against individual classifiers across binary, 8-class, and 34-class classification tasks. The results highlight the effectiveness of the hybrid model. It achieves 98.95% accuracy, 76.72% recall, and 72.01% F1-score in the 34-class problem, surpassing the performance of all individual models. For the 8-class task, the hybrid classifier attains 99.39% accuracy, 90.89% recall, and an 83.01% F1-score. This demonstrates the high potential of the hybrid approach for IoT anomaly detection.

Enabling Seamless Connectivity: Networking Innovations in Wireless Sensor Networks for Industrial Application.

The wide-ranging applications of the Internet of Things (IoT) show that it has the potential to revolutionise industry, improve daily life, and overcome global challenges. This study aims to evaluate the performance scalability of mature industrial wireless sensor networks (IWSNs). A new classification approach for IoT in the industrial sector is proposed based on multiple factors and we introduce the integration of 6LoWPAN (IPv6 over low-power wireless personal area networks), message queuing telemetry transport for sensor networks (MQTT-SN), and ContikiMAC protocols for sensor nodes in an industrial IoT system to improve energy-efficient connectivity. The Contiki COOJA WSN simulator was applied to model and simulate the performance of the protocols in two static and moving scenarios and evaluate the proposed novelty detection system (NDS) for network intrusions in order to identify certain events in real time for realistic dataset analysis. The simulation results show that our method is an essential measure in determining the number of transmissions required to achieve a certain reliability target in an IWSNs. Despite the growing demand for low-power operation, deterministic communication, and end-to-end reliability, our methodology of an innovative sensor design using selective surface activation induced by laser (SSAIL) technology was developed and deployed in the FTMC premises to demonstrate its long-term functionality and reliability. The proposed framework was experimentally validated and tested through simulations to demonstrate the applicability and suitability of the proposed approach. The energy efficiency in the optimised WSN was increased by 50%, battery life was extended by 350%, duplicated packets were reduced by 80%, data collisions were reduced by 80%, and it was shown that the proposed methodology and tools could be used effectively in the development of telemetry node networks in new industrial projects in order to detect events and breaches in IoT networks accurately. The energy consumption of the developed sensor nodes was measured. Overall, this study performed a comprehensive assessment of the challenges of industrial processes, such as the reliability and stability of telemetry channels, the energy efficiency of autonomous nodes, and the minimisation of duplicate information transmission in IWSNs.

Integrating Internet of Things (IoT) Approach to Post-Occupancy Evaluation (POE): An Experimental At-the-Moment Occupant Comfort Control System

This paper describes an empirical experiment of Internet of Things (IoT)’s integration in the Post-Occupancy Evaluation (POE) process. The experiment aimed to trial a novel IoT approach to enabling building user responsiveness to prevalent IEQ for individualised comfort. The purpose is to provide a system that mitigates a common issue of centralised air conditioning that limits occupants’ control over their immediate environment. To achieve this, an IoT platform was developed with smart IEQ monitoring sensors and wearable devices and trialled with PhD researchers in a shared university workspace. The findings provided empirical evidence of IoT’s enhanced benefits to improving user control over their individual comfort and enabling positive energy behaviour in buildings. Specifically, the IoT system provided real-time insight into CO2 concentration data while enabling responsive occupant interaction with their immediate environment and at-the-moment mitigation actions. Outputs of the experiment showed that the perceptions of participants about the stuffiness of the air, productivity, and healthy environment were significantly better after taking the mitigation action compared to before. Also, we found a significant relationship between measured CO2 concentration readings and perceived air stuffiness (p = 0.004) and productivity (p = 0.006) and a non-significant relationship between CO2 concentration readings and perceived healthy environment (p = 0.058). Interestingly, we observed that irrespective of the similarities in recorded CO2 concentration readings being within acceptable ranges (632–712 ppm), the perception of air stuffiness significantly differed (p = 0.018) before and after the mitigation actions. The effectiveness of the developed IoT platform was evidenced as most of the participants found the process very easy to participate in with little interruptions to their work as little time was consumed. The results are useful in modifying approaches to building occupant comfort and energy behaviour in commercial and residential settings.

IoT Enabled Mushroom Farm Automation with Machine Learning

Mushroom farming has gained prominence due to its significant contribution to the global market. One major challenge for mushroom cultivation is maintaining optimal environmental conditions, specifically temperature and humidity. Traditional farming methods, prevalent in many parts of the world, lack precise control over these parameters, often leading to poor yield. This paper presents an innovative approach combining the Internet of Things (IoT) and Machine Learning (ML) for mushroom farm automation. The proposed system employs the ESP8266 microcontroller with specific agricultural sensors for smart monitoring. To regulate the farm's environmental conditions, ML algorithms predict mushroom farm weather states: mild, normal, and hot. The ensemble ML model, comprising five classifiers – Decision Tree, Logistic Regression, K-nearest neighbor, Support Vector Machine, and Random Forest – delivers a commendable accuracy of 100% when combining predictions, surpassing the performance of individual classifiers. This integrated IoT and ML approach promises to revolutionize real-time automation and cultivation practices in the mushroom industry.

Hybrid optimal feature selection approach for internet of things based medical data analysis for prognosis

<p>Healthcare is very important application domain in internet of things (IoT). The aim is to provide a novel combined feature selection (FS) methods like univariate (UV) with tree-based methods (TB), recursive feature elimination (RFE) with least absolute shrinkage selection operator (LASSO), mutual information (MI) with genetic algorithm (GA) and embedded methods (EM) with univariate has been applied to internet of medical things (IoMT)based heart disease dataset. The well-suited machine learning algorithms for IoT medical data are logistic regression (LR) and support vector machine (SVM). Each combined method has been applied to the machine learning algorithms to find the best classifier for prognosis. The various performance metrices has been calculated for all the combined feature selection methods for logistic regression and support vector machine and found that for precise classification could be done using recursive elimination feature selection method with LASSO applied to logistic regression achieved a better performance than all other combined methods with high accuracy, sensitivity and high area under curve. Decision has been taken by data analytics that RFE+LASSO using LR feature selection method will provide an overall better performance for IoT based medical heart disease dataset after comparing all other combined methods with LR and SVM classifiers<em>. </em></p>

Neobots: an open-source platform for a low-cost neonatal incubator with internet of things approach

<div align="center">A baby incubator implements the internet of things (IoT) with an architectural design combining several scientific fields, such as networks, software, and hardware. Furthermore, this research develops an open-source platform called Neobots, including open-source program code to create a baby incubator. Then an overview of the system includes sending sensor data to the IoT Broker with the message queuing telemetry transport (MQTT) protocol and automatically storing data in the database. The results of the comparison value on each temperature sensor with a temperature sensor at the midpoint with an error of less than 0.7°C. Then testing the fuzzy between the Neobots program and the simulation in MATLAB got an error rate of 0-28.27%. In addition, in less than 10 minutes, the system response can adjust the temperature conditions to a setpoint value of 34°C from 29°C, and the average error value is 0.35°C during 1 hour of the Fuzzy implementation on the incubator. Then transfer data from the incubator to the database in a room without noise and full noise to get results for lost data less than 16.41% and 42.14%, delay rates between 0-6 seconds and 0-7 seconds with testing for 1 hour at every 1 second.</div>

Secure and User-Friendly Smart Home Automation: A Mobile-Centric IoT Approach

Home automation systems (HAS), leveraging the power of the Internet of Things (IoT), are gaining significant attention for their potential to reduce costs, labor, and human error. As the demand for smart home technologies grows, effective energy management and cost reduction become increasingly important. This research explores the technical landscape of IoT-based HAS, focusing on remote control and management of home appliances through smartphones and dedicated applications. A comparative analysis of various HAS solutions will be conducted to evaluate their functionality, usability, and energy efficiency. Additionally, the study will examine the security and privacy implications of IoT-based home automation and potential barriers to adoption such as cost and complexity. By addressing these key issues, this research aims to contribute to the development of more effective and accessible smart home solutions.

Deep Learning Based Security Schemes for IoT Applications: A Review

Due to its widespread perception as a crucial element of the Internet of the future, the Internet of Things (IoT) has garnered a lot of attention in recent years. The Internet of Things (IoT) is made up of billions of sentients, communicative "things" that expand the boundaries of the physical and virtual worlds. Every day, such widely used smart gadgets generate enormous amounts of data, creating an urgent need for rapid data analysis across a range of smart mobile devices. Thankfully, current developments in deep learning have made it possible for us to solve the issue tastefully. Deep models may be built to handle large amounts of sensor data and rapidly and effectively learn underlying properties for a variety of Internet of Things applications on smart devices. We review the research on applying deep learning to several Internet of Things applications in this post. Our goal is to provide insights into the many ways in which deep learning techniques may be used to support Internet of Things applications in four typical domains: smart industrial, smart home, smart healthcare, and smart transportation. One of the main goals is to seamlessly integrate deep learning and IoT, leading to a variety of novel ideas in IoT applications, including autonomous driving, manufacture inspection, intelligent control, indoor localization, health monitoring, disease analysis, and home robotics. We also go over a number of problems, difficulties, and potential avenues for future study that make use of deep learning (DL), which is turning out to be one of the most effective and appropriate methods for dealing with various IoT security concerns. The goal of recent research has been to enhance deep learning algorithms for better Internet of Things security. This study examines deep learning-based intrusion detection techniques, evaluates the effectiveness of several deep learning techniques, and determines the most effective approach for deploying intrusion detection in the Internet of Things. This study uses Deep Learning (DL) approaches to better expand intelligence and application skills by using the large quantity of data generated or acquired. The many IoT domains have drawn the attention of several academics, and both DL and IoT approaches have been explored. Because DL was designed to handle a variety of data in huge volumes and required processing in virtually real-time, it was indicated by several studies as a workable method for handling data generated by IoT.

IoT Approach for Ensuring Safety in smart Cities for Energy Saving

The security of city dwellers is of the utmost importance due to the increasing number of smart cities and the speed with which they are being developed. As a whole, smart city safety can be improved through the use of the Internet of Things (IoT), which is the focus of this article. Several safety concerns, such as public safety, emergency management, and disaster preparedness, can be monitored and addressed with the help of the Internet of Things (IoT) framework's network of interconnected devices, sensors, and data analytics. In order to improve situational awareness, reaction times, and overall safety results, this research dives into important Internet of Things (IoT) applications such smart sensors, real-time analytics, and surveillance systems. In addition to looking at potential energy savings, the study analyses the pros and cons of using an Internet of Things (IoT) safety infrastructure in smart cities.

Smart IoT Approach for Renewable Energy Monitoring System

Numerous factories, commercial establishments, and private residences require an uninterrupted power supply. The primary source of this supply is often the grid. Industries, businesses, and residential areas needing 24-hour power inevitably require backup systems to ensure continuous operation of essential electronic devices. Typically, diesel generators are employed as backup power sources, utilizing diesel oil to generate electrical energy. While this approach has been successfully employed for many years, it carries a significant drawback. Diesel oil falls under the category of non-renewable energy sources, unable to be regenerated and requiring an extensive period for renewal. Furthermore, diesel generators emit substantial amounts of carbon dioxide into the atmosphere, contributing to air pollution in the vicinity. To address these challenges, a prototype implementing a smart Internet of Things (IoT) approach has been developed. This study introduces the utilization of an Arduino Nano microcontroller, allowing users to control their power supply source, choosing between the grid, solar panels, or water generation. This control process is facilitated through a smartphone application, granting users the ability to manage it remotely, enhancing monitoring capabilities. The system incorporates two environmentally friendly power sources: solar panels and a mini water turbine, both considered safe for ecological balance. The outcomes of this research demonstrate that users can effectively utilize the generated electricity supply for approximately 12 hours, thereby reducing reliance on the grid and harnessing the potential of renewable energy sources.

Systematic Literature Review on the Role of Big Data in IoT Security

The Internet of Things (IoT) is an interconnected system of physical objects that are embedded with different sensors (for receiving information), chips, software, and other techniques, which allow connecting and transferring of data to other devices via the Internet without human involvement. Since the number of IoT devices is increasing, large amounts of data are being generated from different sources in different formats. This information needs to be used effectively to gain useful insights for enhancing IoT security. Hence, big data techniques are proposed for managing the data to overcome different issues of IoT. Despite the outstanding achievements in IoT security, a systematic literature review (SLR) on implementing big data for securing IoT is lacking. The number of existing related SLRs is limited. Hence, this paper provides a systematic literature review on the use of big data for securing IoT devices. It summarizes the relevant literature produced during the last six years and provides meaningful insights gained by these existing studies. Moreover, it provides a discussion on the sources of IoT big data, the techniques and approaches of big data for securing IoT systems. Current challenges and future research directions are discussed.

Enhancing security mechanisms for robot-fog computing networks

<p>The evolution from conventional Internet usage to the internet of things (IoT) is reshaping communication norms significantly. Cloud computing, while prevalent, faces challenges like limited capacity, high latency, and network failures, especially when handling connected objects, leading to the emergence of fog computing as a more suitable approach for IoT. However, establishing secure connections among heterogeneous IoT entities is complex due to resource disparities and the unsuitability of existing security protocols for resource-constrained devices. This article explores fog computing's architecture, drawing comparisons with cloud computing while emphasizing its significance within the realm of IoT. Moreover, it delves into the practical application of fog computing within the context of the robot teacher project. Subsequently, our exploration introduces an advanced mutual authentication protocol, centered around hashed message authentication code (HMAC), aimed at enhancing the security infrastructure between the robot and the fog computing server.</p>

Behave Differently when Clustering: A Semi-asynchronous Federated Learning Approach for IoT

The Internet of Things (IoT) has revolutionized the connectivity of diverse sensing devices, generating an enormous volume of data. However, applying machine learning algorithms to sensing devices presents substantial challenges due to resource constraints and privacy concerns. Federated learning (FL) emerges as a promising solution allowing for training models in a distributed manner while preserving data privacy on client devices. We contribute SAFI , a semi-asynchronous FL approach based on clustering to achieve a novel in-cluster synchronous and out-cluster asynchronous FL training mode. Specifically, we propose a three-tier architecture to enable IoT data processing on edge devices and design a clustering selection module to effectively group heterogeneous edge devices based on their processing capacities. The performance of SAFI has been extensively evaluated through experiments conducted on a real-world testbed. As the heterogeneity of edge devices increases, SAFI surpasses the baselines in terms of the convergence time, achieving a speedup of approximately × 3 when the heterogeneity ratio is 7:1. Moreover, SAFI demonstrates favorable performance in non-independent and identically distributed settings and requires lower communication cost compared to FedAsync. Notably, SAFI is the first Java-implemented FL approach and holds significant promise to serve as an efficient FL algorithm in IoT environments.

Innovative planning synergies between manufacturing processes and microgrids

Industrial equipment is evolving towards data gathering and communication, following the Internet of things approach. During last years, different management systems and related standards have been developed, in order to raise the performance of manufacturing processes. But a complete optimization of a manufacturing process requires a holistic approach, and adopting microgrid architectures can actually empower the optimization of a manufacturing process. In this paper, synergies about microgrids and manufacturing processes planning will be pointed out. Thus, a suitable approach to MG planning for manufacturing companies from a Knowledge Discovery in Databases (KDD) point of view is described.

A Multi-Agents Deep Reinforcement Learning Autonomous Security Management Approach for Internet of Things

A Multi-Agents Deep Reinforcement Learning Autonomous Security Management Approach for Internet of Things

Multiple Layer Public Blockchain Approach for Internet of Things (IoT) Systems

Multiple Layer Public Blockchain Approach for Internet of Things (IoT) Systems

Secure multi-path routing for Internet of Things based on trust evaluation.

In the realm of the Internet of Things (IoT), ensuring the security of communication links and evaluating the safety of nodes within these links remains a significant challenge. The continuous threat of anomalous links, harboring malicious switch nodes, poses risks to data transmission between edge nodes and between edge nodes and cloud data centers. To address this critical issue, we propose a novel trust evaluation based secure multi-path routing (TESM) approach for IoT. Leveraging the software-defined networking (SDN) architecture in the data transmission process between edge nodes, TESM incorporates a controller comprising a security verification module, a multi-path routing module, and an anomaly handling module. The security verification module ensures the ongoing security validation of data packets, deriving trust scores for nodes. Subsequently, the multi-path routing module employs multi-objective reinforcement learning to dynamically generate secure multiple paths based on node trust scores. The anomaly handling module is tasked with handling malicious switch nodes and anomalous paths. Our proposed solution is validated through simulation using the Ryu controller and P4 switches in an SDN environment constructed with Mininet. The results affirm that TESM excels in achieving secure data forwarding, malicious node localization, and the secure selection and updating of transmission paths. Notably, TESM introduces a minimal 12.4% additional forwarding delay and a 5.46% throughput loss compared to traditional networks, establishing itself as a lightweight yet robust IoT security defense solution.

An Optimized Hierarchal Cluster Formation Approach for Management of Smart Cities

A smart city is a metropolis technology that employs information technology with several internet of things (IoT) devices to enhance the quality of services for citizens, such as the traffic system, energy consumption, and waste collection. In fact, the quality of service (QoS) of these daily routine services are based on an assistive observation system. Wireless sensor networks (WSNs), as the key component of IoT, are used here to gather data into surveillance subsystems for supporting the decision making. To enhance the collected data management of the surveillance subsystems, many clustering techniques are introduced. The low-energy adaptive clustering hierarchy protocol (LEACH) is a key clustering technique of WSN. However, this protocol has deterring limitations, especially in the cluster formation step, which negatively impacts the residual power of many nodes. In fact, a limited number of efforts that try to optimize the clustering formation step represent the main motivation of this work. Considering this problem, the current research proposes an optimized approach to enhance the cluster formation phase of LEACH. The proposed approach depends on the suitability of the residual energy in the nodes to cover the communication energy, with CHs (cluster heads) as a key factor when allocating the node clusters in the first competition. The remaining power and the density of CHs are employed to weigh the accepted CHs and adjust the optimized size of the clusters in the secondary competition. The third competition helps each cluster to select the optimal members from the candidate members according to the impact of each. The advantages and efficiency of the ICSI (intelligent cluster selection approach for IoT) are observed via the ratio of surviving nodes increasing by 21%, residual energy increasing in 32% of the nodes, and a 34% higher network lifetime.