IoT-based Smart Research Articles

Design and Implementation of IoT-Based Smart Laboratory Using ESP32 and Thingsboard to Improve Security and Safety in POLNEP Informatics Engineering Laboratory

The purpose of this research is to design and build an Internet of Things (IoT) based system that can be used to provide early warning when smoke or fire is detected in the building. The system is also expected to be able to provide notifications via mobile devices to technicians and the head of the laboratory when detecting the height of the rainwater overflow that exceeds a certain limit. The urgency of this research is the need for a system by utilizing the latest technology as an effort to improve safety and security for someone who is in the building when there is a fire hazard or for the safety of equipment in the laboratory. The methodology used in this research is to use a product development approach consisting of software and hardware in a project. Broadly speaking, the stages carried out are: (1) Defining project goals; (2) Define project requirements based on project goals; (3) Designing the system architecture; (4) Define User Experience (UX) flow; (5) Identify the parts/stages of development; (6) Assembling hardware and creating program code for each part/stage; (7) Integrating each part/stage; and (8) testing and troubleshooting. The hardware that will be used is the ESP32 microprocessor which is integrated with the IoT Thingsboard platform. The ESP32 microcontroller has been successfully used as an IoT node that is connected to several sensors including environmental sensors (temperature, humidity) DHT22, light intensity sensors (LDR), motion sensors (motion PIR sensors), magnetic switch reed sensors, MQ2 smoke detector sensors, and sensors. water level. On the server side, the Thingsboard platform has been successfully used as an MQTT broker, presenting data in the form of an interactive dashboard, and triggering alarms.

Efficient Top-K Identical Frequent Itemsets Mining without Support Threshold Parameter from Transactional Datasets Produced by IoT-Based Smart Shopping Carts.

Internet of Things (IoT)-backed smart shopping carts are generating an extensive amount of data in shopping markets around the world. This data can be cleaned and utilized for setting business goals and strategies. Artificial intelligence (AI) methods are used to efficiently extract meaningful patterns or insights from such huge amounts of data or big data. One such technique is Association Rule Mining (ARM) which is used to extract strategic information from the data. The crucial step in ARM is Frequent Itemsets Mining (FIM) followed by association rule generation. The FIM process starts by tuning the support threshold parameter from the user to produce the number of required frequent patterns. To perform the FIM process, the user applies hit and trial methods to rerun the aforesaid routine in order to receive the required number of patterns. The research community has shifted its focus towards the development of top-K most frequent patterns not using the support threshold parameter tuned by the user. Top-K most frequent patterns mining is considered a harder task than user-tuned support-threshold-based FIM. One of the reasons why top-K most frequent patterns mining techniques are computationally intensive is the fact that they produce a large number of candidate itemsets. These methods also do not use any explicit pruning mechanism apart from the internally auto-maintained support threshold parameter. Therefore, we propose an efficient TKIFIs Miner algorithm that uses depth-first search strategy for top-K identical frequent patterns mining. The TKIFIs Miner uses specialized one- and two-itemsets-based pruning techniques for topmost patterns mining. Comparative analysis is performed on special benchmark datasets, for example, Retail with 16,469 items, T40I10D100K and T10I4D100K with 1000 items each, etc. The evaluation results have proven that the TKIFIs Miner is at the top of the line, compared to recently available topmost patterns mining methods not using the support threshold parameter.

Neuro-Wavelet Protection Scheme for PV-Wind Energy Source Integrated 6-Terminal Power Network with IoT-Based Smart Environment

Neuro-Wavelet Protection Scheme for PV-Wind Energy Source Integrated 6-Terminal Power Network with IoT-Based Smart Environment

IOT BASED SMART HOME AUTOMATION AND MONITORING SYSTEM

The concept of a smart home is becoming increasingly popular these days. It reduces the amount of physical labor required. In a traditional home, we cannot monitor the electrical appliances, environmental conditions, home security, and cannot control them remotely. Typical residential systems cannot automatically change a systems state in response to a specific scenario. However, this study proposes an IoT-based smart home automation system that works in different modules to automate the entire home. In one module, the system determines the human movement inside the room by using a IR sensor and detects the light level using an LDR sensor. The light level will be adjusted based on the output of the IR sensor. Also, the system operates the fans and heater based on the home temperature and humidity by using an AM2301 sensor. In another module, a flame sensor aids the detection of fire, MQ-135 sensor measures the air quality. In case of fire is detected in the home, the system will also alert the users instantly. To control everything remotely, a mobile application (Blynk App) is used. Converting to a smart home, it can provide security, energy efficiency, and the ability to manage every existing home appliance from a centralized location or remotely. As a result, the system focuses on generating the most excellent possible environmental conditions while requiring the least amount of human effort possible. Beyond everything, it helps in saving time in every manner conceivable.

An IOT Innovation of Smart Solar Energy Consumption Analysis and Control in Micro Grid

Solar energy consumption is a systematic study used to review the design of facilities, services, and equipment in an organization against specifications of solar panel. The solar panel energy consumption analysis is a work that should be done at the beginning of a solar energy. This way, potential changes can be highlighted before they affect the solar energy budget and schedule. The proposed model provides the IOT-based smart solar energy consumption analysis and control model by using solar photovoltaic micro grid. The proposed IOT design must meet product and process requirements. The solar panel energy should properly address important aspects of production processes. This should include risks related to product quality and safety. Finally, unacceptable risks must be minimized by design. In the solar energy consumption analysis process, the deliverables should be evaluated; customers should precheck the proposed design and identify problematic areas, if any. Solar energy consumption analysis reveals whether user requirements and features are sufficient to achieve the desired outcome. Likewise, there should be corrective actions for discrepancies found in design reviews.

Deep malware detection framework for IoT-based smart agriculture

The advancement in smart agriculture through the Internet of Things (IoT) devices has increased the risk of cyber-attacks. Most of the existing malware detection techniques are unable to detect new variants of malware and suffer from poor accuracy. To overcome the challenges of new malware, this research work proposes a novel three-phase Deep Malware Detection (DMD) framework based on the fusion of Discrete Wavelet Transform (DWT) and Generative Adversarial Network (GAN) named as DMD-DWT-GAN, for IoT-based Smart Agriculture (IoT-SA). This work applied DWT for multiresolution analysis by decomposing the image into Approximation coefficients (Ac) and Detail coefficients (Dc). Finally, a lightweight Convolutional Neural Network (CNN) is employed for in-depth analysis of the malware family. The performance of the proposed framework is evaluated using two benchmark datasets such as IoT malware and Malimg. The proposed framework has achieved 99.99% accuracy on both datasets and is better than the state-of-art models.

A Blockchain Based Secure IoT System Using Device Identity Management.

Sharing data securely and efficiently has been identified as an issue in IoT-based smart systems such as smart cities, smart agriculture, smart health, etc. A large number of IoT devices are used in these smart systems and they produce a large amount of data. IoT devices generally have limited storage and processing capabilities, and configuring any security techniques on these devices is a challenge. In this paper, we propose a novel device identity management approach for blockchain-based IoT systems that provides data security in two ways. Firstly, a lightweight time-based identification protocol that uses hub identification for validating data. Secondly, data storage is augmented with an effective blockchain application for providing easy access and immutability for data sharing among multiple parties. Our initial prototype implementation shows that: our identity management approach can be implemented in large scale settings, our system can be effectively implemented in blockchain platforms, and our performance evaluation result shows that the prototype fulfills system requirements adequately.

Dual adaption based evolutionary algorithm for optimized the smart healthcare communication service of the Internet of Things in smart city

The Internet of Things (IoT) is a revolutionary technique of sharing data for smart devices that generates huge amounts of data from smart healthcare systems. Therefore, healthcare systems utilize the convergence power and traffic analysis of sensors that cannot be satisfactorily handled by the IoT. In this article, a novel mutation operator is devised and incorporated with the differential evolution (DE) algorithm. Two tests have been conducted in the validation process. Firstly, the newly dual adaption-based operators incorporated with the differential evolution algorithm are being proposed. The proposed approach provides sufficient diversity and enhances the search speed of nature’s local and global search environments in the problem. The proposed method incorporates the application of IoT-based smart healthcare. Second, an application-based test has been conducted, in which the proposed approach is applied to the application in the smart healthcare system. Therefore, IoT sensor deployment is an optimization problem to minimize service time, delay, and energy loss by considering the communication constraint between sensors(objects). The proposed algorithm is applied in this article to solve this optimization problem. Further, in the experimentation and comparative study, the proposed method is superior to the standard evolutionary algorithms in IoT applications concerning the minimum number of function evaluations and minimization of traffic services. The proposed approach also achieves efficiency in the minimum loss of energy in each service and reduces load and delay.

Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning

The amount of data captured is expanding day by day which leads to the need for a monitoring system that helps in decision making. Current technologies such as cloud, machine learning (ML) and Internet of Things (IoT) provide a better solution for monitoring automation systems efficiently. In this paper, a prediction model that monitors real-time data of sensor nodes in a clinical environment using a machine learning algorithm is proposed. An IoT-based smart hospital environment has been developed that controls and monitors appliances over the Internet using different sensors such as current sensors, a temperature and humidity sensor, air quality sensor, ultrasonic sensor and flame sensor. The IoT-generated sensor data have three important characteristics, namely, real-time, structured and enormous amount. The main purpose of this research is to predict early faults in an IoT environment in order to ensure the integrity, accuracy, reliability and fidelity of IoT-enabled devices. The proposed fault prediction model was evaluated via decision tree, K-nearest neighbor, Gaussian naive Bayes and random forest techniques, but random forest showed the best accuracy over others on the provided dataset. The results proved that the ML techniques applied over IoT-based sensors are well efficient to monitor this hospital automation process, and random forest was considered the best with the highest accuracy of 94.25%. The proposed model could be helpful for the user to make a decision regarding the recommended solution and control unanticipated losses generated due to faults during the automation process.

Propagation Measurements for IQRF Network in an Urban Environment.

Recently, IQRF has emerged as a promising technology for the Internet of Things (IoT), owing to its ability to support short- and medium-range low-power communications. However, real world deployment of IQRF-based wireless sensor networks (WSNs) requires accurate path loss modelling to estimate network coverage and other performances. In the existing literature, extensive research on propagation modelling for IQRF network deployment in urban environments has not been provided yet. Therefore, this study proposes an empirical path loss model for the deployment of IQRF networks in a peer-to-peer configured system where the IQRF sensor nodes operate in the 868 MHz band. For this purpose, extensive measurement campaigns are conducted outdoor in an urban environment for Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) links. Furthermore, in order to evaluate the prediction accuracy of well-known empirical path loss models for urban environments, the measurements are compared with the predicted path loss values. The results show that the COST-231 Walfisch–Ikegami model has higher prediction accuracy and can be used for IQRF network planning in LoS links, while the COST-231 Hata model has better accuracy in NLoS links. On the other hand, the effects of antennas on the performance of IQRF transceivers (TRs) for LoS and NLoS links are also scrutinized. The use of IQRF TRs with a Straight-Line Dipole Antenna (SLDA) antenna is found to offer more stable results when compared to IQRF (TRs) with Meander Line Antenna (MLA) antenna. Therefore, it is believed that the findings presented in this article could offer useful insights for researchers interested in the development of IoT-based smart city applications.

Mobile Cloud IoT for Resource Allocation with Scheduling in Device- Device Communication and Optimization based on 5G Networks

Internet of Things (IoT) is revolutionising technical environment of traditional methods as well as has applications in smart cities, smart industries, etc. Additionally, IoT enabled models' application areas are resource-constrained as well as demand quick answers, low latencies, and high bandwidth, all of which are outside of their capabilities. The above-mentioned issues are addressed by cloud computing (CC), which is viewed as a resource-rich solution. However, excessive latency of CC prevents it from being practical. The performance of IoT-based smart systems suffers from longer delay. CC is an affordable, emergent dispersed computing pattern that features extensive assembly of diverse autonomous methods. This research propose novel technique resource allocation and task scheduling for device-device communication in mobile Cloud IoT environment based on 5G networks. Here the resource allocation has been carried out using virtual machine based markov model infused wavelength division multiplexing. Task scheduling is carried out using meta-heuristic moath flame optimization with chaotic maps. So, by scheduling tasks in a smaller search space, system resources are conserved. We run simulation tests on benchmark issues and real-world situations to confirm the effectiveness of our suggested approach. The parameters measured here are resource utilization of 95%, response time of 89%, computational cost of 35%, power consumption of 38%, QoS of 85%.

Quantitative models for friendly jammer trustworthiness evaluation in IoT networks

Due to the open nature of the wireless communication in an Internet of Things (IoT) network, transmitted messages are easy to eavesdrop. To address the eavesdropping attack, we consider an IoT-based smart home where an IoT device (IoT-D) is selected as a friendly jammer to send artificial noise (AN) to protect transmitted messages from being eavesdropped. However, the selected IoT-D may send partial (or even none) AN, which is called an untrusted jammer. To avoid selecting an untrusted jammer, we aim to design two quantitative models from the perspective of time and space to evaluate the trustworthiness of IoT-Ds. Firstly, we adopt an improved energy detector to better counter signal-to-noise ratio (SNR) fluctuation of AN detection. Secondly, based on the detection results, we adopt two trust models: (1) the spatial-oriented beta trust model, (2) the time-oriented hidden Markov model (HMM)-based trust model. In the first model, direct and indirect trust of an IoT-D are both calculated to obtain an integrated trust value. In the second model, the probability of trusted state is calculated as the trust value, and time intervals between detection results of an IoT-D are taken into consideration. Numerical results are presented to demonstrate the performance of our improved energy detection method and two trust models.

IoT-Based Smart Mask Protection against the Waves of COVID-19.

In the year 2020, the word “pandemic” has become quite popular. A pandemic is a disease that spreads over a wide geographical region. The massive outbreak of coronavirus popularly known as COVID-19 has halted normal life worldwide. On 11th March 2020, the World Health Organization (WHO) quoted the COVID-19 outbreak as a “Pandemic”. The outbreak pattern differs widely across the globe based on the findings discovered so far; however, fever is a common and easily detectable symptom of COVID-19 and the new COVID strain. After the virus outbreak, thermal scanning is done using infrared thermometers in most public places to detect infected persons. It is time-consuming to track the body temperature of each person. Besides, close contact with infected persons can spread the virus from the infected persons to the individual performing the screening or vice-versa. In this research, we propose a device architecture capable of automatically detecting the coronavirus or new COVID strain from thermal images; the proposed architecture comprises a smart mask equipped with a thermal imaging system, which reduces human interactions. The thermal camera technology is integrated with the smart mask powered by the Internet of Things (IoT) to proactively monitor the screening procedure and obtain data based on real-time findings. Besides, the proposed system is fitted with facial recognition technology; therefore, it can also display personal information. It will automatically measure the temperature of each person who came into close contact with the infected humans or humans in public spaces, such as markets or offices. The new design is very useful in healthcare and could offer a solution to preventing the growth of the coronavirus. The presented work hasa key focus on the integration of advanced algorithms for the predictive analytics of parameters required for in-depth evaluations. The proposed work and the results are pretty effectual and performance cognizant for predictive analytics. The manuscript and associated research work integrate the IoT and Internet of Everything (IoE) based analytics with sensor technologies with real-time data so that the overall predictions will be more accurate and integrated with the health sector.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12652-022-04395-7.

Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture.

In recent decades, networked smart devices and cutting-edge technology have been exploited in many applications for the improvement of agriculture. The deployment of smart sensors and intelligent farming techniques supports real-time information gathering for the agriculture sector and decreases the burden on farmers. Many solutions have been presented to automate the agriculture system using IoT networks; however, the identification of redundant data traffic is one of the most significant research problems. Additionally, farmers do not obtain the information they need in time, such as data on water pressure and soil conditions. Thus, these solutions consequently reduce the production rates and increase costs for farmers. Moreover, controlling all agricultural operations in a controlled manner should also be considered in developing intelligent solutions. Therefore, this study proposes a framework for a system that combines fog computing with smart farming and effectively controls network traffic. Firstly, the proposed framework efficiently monitors redundant information and avoids the inefficient use of communication bandwidth. It also controls the number of re-transmissions in the case of malicious actions and efficiently utilizes the network’s resources. Second, a trustworthy chain is built between agricultural sensors by utilizing the fog nodes to address security issues and increase reliability by preventing malicious communication. Through extensive simulation-based experiments, the proposed framework revealed an improved performance for energy efficiency, security, and network connectivity in comparison to other related works.

A Compact In-Band Full Duplexing Antenna With Quasi-Isotropic Radiation Pattern for IoT-Based Smart Home and Intravehicle Wireless Communication Applications

An innovative planner in-band full duplexing antenna with quasi-isotropic radiation pattern is proposed for the first time in this article for Internet of Things (IoT)-based smart home and intravehicle wireless communication applications. The antenna consists of a radiating patch, a fence-strip resonator (FSR), and a ground plane with the same size as the radiating patch. The fence in the FSR located in the center plane of the patch has the function of simultaneously acting as a part of the resonator to improve the isolation between the transmitting port and the receiving port and as an electric current source for radiating a quasi-isotropic pattern. When the FSR resonates, a quasi-TEM field is formed mainly concentrated in the half of the antenna. The magnetic field generates surface electric current on the fence, and the electric field generates surface magnetic current on the open-ended aperture. The surface electric current and the surface magnetic current are not only orthogonal to each other but also have a phase difference of 90°. Theoretical analysis is provided to clarify the mechanism by which such a combination of radiation sources can generate a quasi-isotropic radiation pattern. To verify the proposed concepts, a high port isolation full duplexing antenna with quasi-isotropic radiation pattern is designed and fabricated, which has a compact size of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.28\,\,\lambda _{0} \times 0.28 \lambda _{0}\times 0.042 {\lambda }_{0}$ </tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\lambda }_{0}$ </tex-math></inline-formula> is the free space wavelength at 2.45 GHz). A measured isolation greater than 24 dB is achieved over the operating band from 2.42 to 2.50 GHz. The measurement results also show that the gain variation (GV) of port1 and port2 on the entire virtual radiation spherical surface is less than 6.88 and 6.74 dB, respectively.

An Intelligent Approach of Intrusion Detection in Mobile Crowd Sourcing Systems in the Context of IoT Based SMART City

ABSTRACT The recent era of pervasive computing has evolved with various applications and has ground-breaking realities in mobile crowdsourcing (MCS). Multiple attempts have been devoted to integrating MCS with IoT-based smart cities where crowdsensing has played a crucial role in the recent past. Despite having potential features, MCS devices lack efficiency when security aspects are concerned. The current security approaches exercised in MCS operations imply limited features and are not intelligent enough to deal with different types of attacks in IoT smart cities. On the other hand, as MCS communications involve radio environmental mapping functional blocks from communication, it is an obvious situation that leads to a vulnerable situation of which adversarial modules can take advantage of it. There are different types of active and passive modes of attacks that can degrade the Quality-of-Service (QoS) aspects in IoT-driven smart city operations. This study’s prime aim and the appealing theme is to realize the need for resilient approaches to intelligent intrusion detection in MCS to mitigate different attacks. The study also introduces a theoretical approach of cluster-enabled multi-task (CeMT) based on bio-inspired learning modeling of the genetic approach to identify the maximum possible threats and misbehaving devices in the smart city-based MCS operations. The study also evaluated the model’s performance based on the processing time of identifying malicious events and showed the accuracy of detecting misbehaving working associate (WA) modules.

An IOT-Based System for Agriculture Monitoring

A system is developed for agricultural field monitoring in IoT-based smart farming with the aid of sensors like light, humidity, temperature, soil moisture, etc. Farmers can keep an eye on the state of their fields from anywhere. Smart farming that is IoT-based is significantly more efficient than traditional farming. Agriculture has been practiced for centuries and is regarded as both a science and an art of plant cultivation. In today's world, agriculture must also maintain with the advancement of technology. IoT is crucial to smart agriculture. The necessary data regarding farm areas is provided by Internet of Things (IoT) sensors. The key benefit of IoT is the ability to monitor agriculture through the use of wireless sensor networks, which collect data from numerous sensors put at various locations and transmit it via wireless protocol. NodeMCU is the IoT system that powers smart agriculture. It has a DC motor, moisture sensor, temperature sensor, and humidity sensor. This system begins to measure the level of moisture and humidity. The sensors are used to detect the water level, and the system automatically begins watering if the level is below the range. The sensor works according to the change in temperature level. IoT also displays humidity and moisture level information along with date and time. You can also modify the temperature based on the crops that are grown.

Penerapan Internet of Things Berbasis Teknologi Voice User Interface Untuk Kendali Jarak Jauh Dengan Node MCU 8266 Pada Rumah Pintar

Smart Home is a residence or residence that connects a communication network with electrical equipment that is possible to be controlled, monitored or accessed remotely. Smart home can also increase efficiency, convenience and security by using technology automatically. This study builds an IoT-based Smart Home using voice on Google Assistant. This is needed as a solution for sick people who are in wheelchairs/beds or people with disabilities but can speak or elderly people who cannot reach the switch to turn on/off household appliances. In addition, in order to be able to control home devices from a very long distance. Google Assistant converts voice commands to text. The text is then forwarded from Google Assistant to Webhooks by IFTTT. In testing the system has succeeded in turning on and off electrical equipment with voice commands using Google Assistant. A more effective prototype has been produced based on the results of testing to experts it is obtained "100 %" and to users it is obtained "81,52 %", based on this assessment shows that the prototype is "very feasible" and can control the use of electric switches through the android application.

IoTaaS: Drone-Based Internet of Things as a Service Framework for Smart Cities

The Internet of Things (IoT) offers new services in the context of smart cities through digital devices embedded with sensing, computation, and communication capabilities. The IoT devices enhance the smart city vision by employing advanced communication and computation technologies for smart city administrations. The IoT-based smart city applications require many IoT devices and gateways to be deployed at different city points. Heterogeneous sensing devices, placing smart devices in a constrained or physically inaccessible area, and large urban areas to monitor together make IoT node deployment and sensing management tasks difficult, time-consuming, and expensive. Additionally, certain tasks may require smart devices to be deployed for a very short period of time to sense and report contextual information, making it economically infeasible to purchase the devices. In this regard, we propose a drone-based IoT as a Service (IoTaaS) framework that enables the dynamic provisioning or deployment of IoT devices using drones. IoTaaS allows IoT devices and gateways to be mounted on drones and provides a distributed cloud service by placing the IoT devices in an area according to the requirements specified by a user. We also provide an economic analysis for operating such drone-based IoT services. A proof-of-concept implementation of IoTaaS for smart agriculture and air pollution monitoring applications shows that IoTaaS can reduce setup costs and increase the usage of IoT devices.

IoT-based smart bin allocation and vehicle routing in solid waste management: A case study in South Korea

Increasing waste generation has become a real challenge because of population growth and rapid urbanization. Given this, most waste bins get overfilled easily because of the improper management of waste and the irregular cleaning of waste bins. The internet of things (IoT) is a remarkable modern technology that offers powerful resolutions to modernize traditional systems. In this study, the filling level of waste bins is considered in conjunction with IoT-based waste bins. This paper develops an integrated IoT-based smart bin allocation with a central monitoring system (CMS) and enhanced vehicle routing algorithm in solid waste management. This article proposes the time-dependent penalty concept to waste management authorities if these waste bins are not emptied in time after becoming full. To obtain the solution with faster execution time, an intelligent variable neighborhood search with ant colony optimization method (VNS- ACO) is developed. The proposed model is illustrated with some numerical data, and a sensitivity analysis is established with some parameters. Furthermore, the superiority of our developed VNS-ACO algorithm is established through testing on some traveling salesman problem (TSP) instances in the traveling salesman problem library (TSPLIB). Results have been compared with an advanced version of genetic algorithm (GA) and ACO methods.