Smart Internet Of Things Research Articles

Analyzing and Mitigating Attacks in IoT Smart Home Using a Threat Modeling Approach-Based STRIDE

The Internet of Things (IoT) is a network of interconnected devices that enables data exchange. It is widely used in areas such as healthcare, aviation, agriculture, energy, and home automation. Despite its rapid growth and the massive adoption of connected devices, IoT presents significant security risks. Traditional threat modeling approaches are insufficient to address these risks. Architecture-based modeling is recommended, as it considers the entire system and helps in understanding potential threats. Threat modeling is a systematic technique used to identify and evaluate potential threats that could compromise the security of a system. The main objective is to understand the vulnerabilities of a system in order to design appropriate security measures to mitigate them. This paper aims to analyze and mitigate specific IoT smart home threats using the STRIDE threat modeling framework, which systematically identifies potential vulnerabilities at the development level. By applying STRIDE, which stands for Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege, we focused on addressing key security threats, including denial of service (DoS), phishing, and man-in-the-middle (MitM) attacks. Our findings demonstrate that the proposed mitigation strategies are effective in countering these threats, providing a robust security layer for IoT smart homes. Through this study, we highlight the importance of architecture-based threat modeling to enhance security within the IoT ecosystem and offer practical solutions that strengthen IoT smart home resilience. The outcomes of the STRIDE-based analysis and the effectiveness of the mitigation techniques are detailed, offering empirical evidence to support our approach.

SUSTAINABLE PRECISION AGRICULTURE: INTEGRATING ARTIFICIAL INTELLIGENCE AND IOT FOR OPTIMIZED GREEN FARMING PRACTICES

Agriculture is the backbone of Indian economy. It is evolved in due course of time. The purpose of this study is to assess how well internet of things (IoT) and artificial intelligence (AI) technologies can improve precision farming methods and encourage resource sustainability. The goal of the project is to determine the obstacles farmers have when implementing these innovations and to examine the economic and environmental effects of incorporating these technologies into farming, with an emphasis on yield enhancement and resource conservation. Method: To investigate the connection between AI, IoT, and sustainable farming results, data were collected from a sample of 225 farmers via trials and sensor readings using a quantitative descriptive research technique. Descriptive statistics, independent t-tests, and regression analysis were among the statistical techniques used to evaluate how AI and IoT affected productivity and environmentally friendly agricultural methods. Result: The results reveal a strong positive correlation, with the regression model indicating that IoT and AI play a substantial role in sustainable agriculture and higher production, accounting for around 55.2% of the variation in results. The model's effectiveness is further supported by the ANOVA findings, which show that smart farming, AI, and IoT have a major influence on green agricultural practices (p < 0.001). It is found that AI and IoT have a great deal of contribution towards sustainable agriculture by increasing sustainability and production. However, it is significant to address certain challenges for wider acceptance and scalability across many agricultural groups.

Augmenting Security of Smart Homes

The development of new technology and people's propensity to rely on it more and more each year have led to enormous advancements in human technology. The idea of the Internet of Things (IOT) and later "Smart Homes" was one such enormous step. The surge in the smart home sector is due to the introduction of extremely practical and affordable technologies. However, the expanded use has also created a new set of security and privacy risks for those who rely on smart home technology. This article covers the basic idea of smart homes and IOT devices, as well as any current risks and any previous countermeasures that have been suggested.

Enhancing energy distribution through dynamic multi-hop for heterogeneous WSNs dedicated to IoT-enabled smart grids

This paper examines the impact of hetterogeneous wireless sensor networks (WSNs) on wireless communication systems, with a focus in Internet of Things (IoT) enabled smart grids. It introduces a novel approach for the fair distribution of energy and computational resources among sensor nodes (SNs), which is crucial for extending network lifespan, enhancing performance, and ensuring SG stability. The research highlights the role of initial energy and processing capacities of SNs. Although hierarchical clustering methods are effective in WSNs, finding the ideal routing protocol remains challenging due to extensive deployment. To address energy efficiency and network durability, the study proposes the integration of the heterogeneous dynamic multi-hop (HDM) approach with the low-energy adaptive clustering hierarchy (LEACH) protocol, specifically for IoT smart city applications. The HDM model combines multi-hop communication with dynamic hierarchical clustering, distinguishing between normal and advanced nodes based on energy levels to optimize cluster head selection probabilities. The methodology involves a comparative analysis between the HDM protocol and LEACH in a heterogeneous environment (H-LEACH) in terms of energy conservation, and network lifespan. Results demonstrate that the HDM protocol outperforms H-LEACH. Notably, HDM consumes half of the total energy over 4600 rounds, compared to H-LEACH’s 3000 rounds. These findings have important implications for deploying WSNs in smart grid applications, supporting sustainable and resilient urban IoT ecosystems.

Design and SAR analysis of DGS based deformed microstrip antenna for ON/OFF body smart wearable IoT applications

Abstract This article presents a defected ground structure (DGS)-based deformed microstrip wearable antenna designed for ON/OFF body smart wearable Internet of Things (IoT) applications. The antenna design has certain features such as lightweight, cost-effectiveness, low profile, and flexibility. Fabricated using a Rogers RT/Duroid 5880 substrate with a thickness of 0.508 mm, the proposed antenna includes a deformed rectangular patch and DGS to enhance impedance matching and radiation performance at 2.45 GHz. The results show a low reflection coefficient of approximately -36 dB and a peak gain of 5.61 dBi at 2.45 GHz in a free-space environment without bending. Additionally, the specific absorption rate (SAR) is measured to be 0.249 W/kg for 1g of tissue and 0.137 W/kg for 10g of tissue when the antenna is placed 0.32λ0 away from the body. The study also indicates that SAR values decrease as the distance between the antenna and the body increases. The antenna prototype has been fabricated and experimentally tested under various ON/OFF body conditions and bending scenarios. The measured results closely align with the simulated data, confirming the antenna suitability for ON/OFF body IoT applications. Noteworthy features of the proposed wearable antenna include its conformability and suitability for body-worn applications, making it an excellent candidate for ON/OFF body smart wearable IoT devices.

Design of Car Based on 5G Network Control

The role of smart cars is pivotal, and this project designs and implements a four-wheel vehicle control system leveraging 5G communication technology. The system aims to enhance the portability of smart cars, reduce their costs, enable remote control functionality, and improve mobility to meet the needs of modern Internet of Things (IoT) applications. The system integrates an ESP8266 Wi-Fi module with the Blinker IoT platform to enable remote, real-time control of car movement via a smartphone app. Using Access Point (AP) mode for fast network configuration, users can input Wi-Fi credentials and a Blinker key through a web interface for easy setup. Through the custom app interface, users can send commands to control the car’s forward, backward, turning, and stopping actions, as well as adjust speed and operation delay. Additionally, the system includes Electrically Erasable Programmable Read-Only Memory (EEPROM) data storage to ensure the persistent saving of configuration information, and it features a remote wireless camera for external monitoring of the car’s surroundings. The Android-based remote control design allows users to monitor and control the car’s movement anytime and anywhere. Experimental results show that the system is stable, provides smooth control, operates at low cost and low power consumption, and offers good portability. Therefore, this intelligent car control system offers valuable insights for smart car development and application. It can also be integrated with popular smart homes, IoT, and other emerging technologies, offering broad application potential and promising development prospects.

Artificial intelligence and IoT driven system architecture for municipality waste management in smart cities: A review

Numerous devices, including sensors, RF ID, and other types of smart devices, have been developed as a result of the Artificial Intelligence (AI), and Internet of Things (IoT) revolution. Urban areas can become smart by monitoring and collecting data about their surroundings through the deployment of technologies with powerful computational capabilities and those that are converted into intelligent things. Waste management is among the most significant issues in smart cities, a rise in metropolitan regions and faster increases in population are the main reasons. When it comes to gathering data about waste management, intelligent services can serve as the front line. Waste management with IoT support is a common example of a service offered by smart cities. Various duties, like gathering, processing, and use of waste in appropriate facilities, are included in waste management. The present study proposed an updated waste management system architecture design after reviewing existing artificial intelligence and IoT-based waste management systems and automation in smart cities. The proposed system architecture deals with the automation of municipality trash in smarter urban areas, using IoT technology and sending notification messages based on sensor data relating to the dustbin state, such as full or empty. The notifications are sent simultaneously to the municipality office and the waste carrier vehicle driver, so that waste can be emptied on time. The proposed system architecture represents a scalable and adaptable model for municipalities that aim to transform their waste collection processes and play a key step in minimizing municipality waste in smart cities. By deploying this proposed system architecture with smart sensors and IoT devices, municipalities can monitor waste levels to ensure that bins are emptied when it is necessary. This reduces the frequency of waste collection, lowers fuel consumption, and minimizes operational costs. The Route optimization algorithms further enhance efficiency by determining the most efficient paths for waste collection trucks, so they can reduce travel time and fuel emissions.

Navigating urban congestion: A Comprehensive strategy based on an efficient smart IoT wireless communication for PV powered smart traffic management system.

Egypt faces extreme traffic congestion in its cities, which results in long travel times, large lines of parked cars, and increased safety hazards. Our study suggests a multi-modal approach that combines critical infrastructure improvements with cutting-edge technologies to address the ubiquitous problem of traffic congestion. Assuring vehicles owners of their timely arrival, cutting down on fuel usage, and improving communication using deep learning approach and optimization algorithm within the potential of IoT enabled 5G framework are the main goals. The traffic management system incorporates detection cameras, Raspberry Pi 3 microcontroller, an Android application, cloud connectivity, and traditional traffic lights that are powered using PV modules and batteries to secure the traffic controllers operation in case of grid outage and assure service continuity. The model examines the difficulties associated with Internet of Things (IoT) communication, highlighting possible interference from device-to-device (D2D) devices and cellular user equipment. This all-encompassing strategy aims to reduce fuel consumption, increase road safety and improve traffic efficiency. The model predicts a significant increase in Egypt's urban mobility by utilizing the possibilities of IoT and 5G technologies, which would improve Egypt's towns' livability and efficiency. The goal of this paper is to modernize Egypt's traffic management system and bring it into compliance with global guidelines for intelligent transportation networks.

Agent-Based Trust and Reputation Model in Smart IoT Environments

The Internet of Things (IoT) enables smart devices to connect, share and exchange data with each other through the internet. Since an IoT environment is open and dynamic, IoT participants may need to collaborate with unknown entities with no proven track record. To ensure successful collaboration among these entities, it is important to establish a mechanism that ensures all entities operate in a trustworthy manner. We present a trust and reputation model that can be used to select the best service provider in an IoT environment. Our proposed model, IoT-CADM (Comprehensive Agent-based Decision-making Model for IoT) is an agent-based decentralised trust and reputation model that can be used to select the best service provider for a particular service based on multi-context quality of service. IoT-CADM is developed using a smart multi-agent IoT environment where information about entities is collected and evaluated using a trust and reputation algorithm. We evaluated the performance of the proposed model against some other well-known models in a simulated smart factory supply chain system. Our experimental results showed that the proposed IoT-CADM achieved the best performance.

Internet of Things in Smart Grid: A Comprehensive Review of Opportunities, Trends, and Challenges

The rapid advancement and implementation of Internet of Things (IoT)-based technologies has permitted numerous opportunities for technical innovations in diverse aspects of life. IoT technology has enabled the streamlining of processes in numerous fields, ensuring better efficiency of systems to improve the quality of life. This review paper emphasizes the significant research works that concentrate on the application of IoT to smart grids and summarizes the current work done. The paper also discusses the various challenges and opportunities in smart grids while implementing the IoT, addressing the pioneering methods used in IoT along with their relevant application in different fields. The main outcomes of this paper are to provide 1) identify feasible solutions to overcome the implementation challenges; 2) a clearer understanding of the existing technological advancement in IoT application areas; and 3) spread awareness among researchers and professionals in the ground of smart grids and IoT.

Literature Mapping on IoT Integration and Smart City: Bibliometric Analysis and Socio-Economic Impact

The integration of the Internet of Things (IoT) in smart cities has become a transformative force, revolutionizing urban development and management. This study provides an in-depth analysis of the socio-economic impacts of IoT integration in smart cities, examining its influence on economic growth, job creation, public service optimization, social inequality, privacy, security, and environmental sustainability. Through a comprehensive literature review and bibliometric analysis, we highlight the opportunities and challenges that IoT technologies present in the urban context. While IoT fosters economic growth and improves service efficiency, concerns related to data privacy, security, and the digital divide pose significant challenges. Additionally, IoT's role in promoting environmental sustainability is emphasized, as smart cities increasingly adopt technology to monitor resources and reduce carbon emissions. This study underscores the importance of addressing these socio-economic factors to ensure the equitable and sustainable development of smart cities, thereby maximizing the benefits of IoT technologies while mitigating potential risks.

Utilizing IoT and Cloud Computing for Weather-Health Monitoring Application

In an era defined by increasing climate changes and an increasing importance of accurate weather information, there is a need for a smart IoT- based weather monitoring system. This paper proposes an innovative system that uses Internet of Things (IoT) technology to advance weather monitoring and global data accessibility using Cloud computing. By seamlessly connecting multiple devices, such as sensors, electronic gadgets, and automotive electronics, to the internet, this system offers a complete solution for monitoring environmental conditions such as temperature, relative humidity, pressure, and rain levels. Specialized sensors collect data, which is then transformed into graphical interface using an app, enabling real-time weather information access worldwide. This technology has the ability to rethink how we convey and analyze environmental data with optimal efficiency and reach in industries including agriculture, urban planning, and environmental research.

Proactive strategies to mitigate emerging cybersecurity risks in IoT devices for smart homes

This work proposes strategies to mitigate emerging risks in smart Internet of Things (IoT) devices within homes. With the significant increase in these devices, cybersecurity became a primary concern. Therefore, this study focused on identifying potential risks and vulnerabilities, using surveys to assess current security practices and knowledge about protective measures among other aspects related to cybersecurity. Based on these results, recommendations were made for users, to strengthen security in their IoT devices and promote good practices to create a safer and more protected home environment against possible cyber threats.

HARNESSING THE POWER OF IOT AND FOG COMPUTING FOR ENHANCED PATIENT MONITORING IN CRITICAL CARE UNITS

In the realm of critical care, the need for precise and continuous monitoring of patients is paramount. This paper explores the development of an advanced system that leverages the capabilities of the Internet of Things (IoT) to facilitate the meticulous tracking of a patients vital signs in Intensive Care Units (ICUs). The system is engineered to minimize the potential for human error by providing uninterrupted monitoring of various health indicators such as body temperature, blood oxygen saturation (SpO2), heart rate, blood pressure, and electrocardiogram (ECG) readings. Additionally, it can analyze patient fluids for glucose, lactate, blood circulation, red and white blood cell counts, as well as calcium and potassium levels. The system employs fog nodes as a means to store and process the data collected from patients. These nodes generate comprehensive health reports based on the collected data and store them in a cloud-based platform. This setup allows healthcare professionals, including doctors and ICU staff, to access real-time patient data and reports from any location at any time. The system is designed to alert the medical team in case of any detected anomalies in the patients health parameters. This paper also provides a comparative analysis of previous research conducted on smart IoT devices used in ICU patient monitoring systems, highlighting the unique advantages offered by the proposed system. The paper concludes with a discussion on potential future enhancements, such as bolstering data security measures and incorporating machine learning techniques for improved system performance.

IoT Smart Gardening on Herbal Plant and Analytic Virtualization Platform System

The Internet of Things (IoT) is an emerging technology developing recently. The technology of IoT allows users to communicate with controllers and sensors, which are widely used in the agricultural sector, such as farming and weather forecasts. Recently, it has been identified that traditional herbal home gardens are difficult to monitor where herbal plants are sensitive and need a proper monitor. This research has developed an IoT smart gardening for herbal plant systems that helps to analyze four herbal plant types: Aloe Barbadensis miller, Salvia Rosmarimus, Clinacanthus nutans, and Curcuma. The system was designed using a NodeMCU ESP32 controller, soil moisture sensor, and DHT11 temperature and humidity sensor. The collected data was monitored in real-time on the Blynk platform via mobile phones and web browsers on sunny and rainy days. The result shows that the highest temperature on a sunny day was captured at 30°C, and the lowest was 26°C. However, during a rainy day, the highest temperature captured is 28°C and the lowest was 24°C. The highest humidity percentage on a sunny day was 80%, and the lowest was 40%, while the highest humidity on a rainy day was 98%, and the lowest was 68%. The soil moisture data that captured the water content of the soil of each herbal plant on a sunny day has been lost due to high temperatures. However, each herbal plant barely lost its water content due to low temperature and high humidity percentage conditions. The comparison showed that temperature, humidity, and weather could affect the water content in the soil. This research has significantly improved the agriculture sector in monitoring herbal plants and moving from traditional planting with an IoT system concept.

The Intersection of the Internet of Things and Smart Cities: A Tertiary Study

Since the transition from an agricultural to an industrial economy, cities have attracted large masses of people in search of their facilities. Cities are symbols of progress and opportunities. However, urbanization also brings with it several problems and challenges. Smart Cities (SC) offer a way to address these challenges by using technology to make cities more efficient, sustainable, and livable. There are numerous technologies that enable the concept of smart cities, including the Internet of Things (IoT). The IoT provides the fundamental sensing infrastructure that allows connecting and virtualizing the physical world, extracting environmental variables that serve as initial inputs for decision-making processes. Such processes are provided by software systems whose construction and execution need to deal with the dynamism, heterogeneity and often serendipitous nature that permeate both the domains of smart cities and IoT. As the integration of IoT and Smart Cities paradigms is still at an early stage, and there are not yet holistic solutions to explore the full potential of such a synergy, we carried out a literature review on the topic. In particular, the objective of this article is to describe the results of a structured literature review to identify general concepts about quality attributes, applications, technologies, and challenges of IoT solutions applied to the SC domain. Our main goal is to assist in understanding the basic concepts of the research area through the search for secondary studies. This review is a tertiary study covering 17 reviews and aims to promote a high-level discussion on the identified characteristics and provide an overview of the area to promote a better perception of current development needs and opportunities.

TL-ABKS: Traceable and lightweight attribute-based keyword search in edge–cloud assisted IoT environment

Edge–cloud coordination offers the chance to mitigate the enormous storage and processing load brought on by a massive increase in traffic at the network’s edge. Though this paradigm has benefits on a large scale, outsourcing the sensitive data from the smart devices deployed in an Internet of Things (IoT) application may lead to privacy leakage. With an attribute-based keyword search (ABKS), the search over ciphertext can be achieved; this reduces the risk of sensitive data explosion. However, ABKS has several issues, like huge computational overhead to perform multi-keyword searches and tracing malicious users. To address these issues and enhance the performance of ABKS, we propose a novel traceable and lightweight attribute-based keyword search technique in an Edge–cloud-assisted IoT, named TL-ABKS, using edge–cloud coordination. With TL-ABKS, it is possible to do effective multi-keyword searches and implement fine-grained access control. Further, TL-ABKS outsources the encryption and decryption computation to edge nodes to enable its usage to resource-limited IoT smart devices. In addition, TL-ABKS achieves tracing user identity who misuse their secret keys. TL-ABKS is secure against modified secret keys, chosen plaintext, and chosen keyword attacks. By comparing the proposed TL-ABKS with the current state-of-the-art schemes, and conducting a theoretical and experimental evaluation of its performance and credibility, TL-ABKS is efficient.

ỨNG DỤNG PHƯƠNG PHÁP HỌC MÁY ĐỂ DỰ ĐOÁN CÁC YẾU TỐ MÔI TRƯỜNG TRONG THIẾT KẾ HỆ THỐNG THỦY CANH IOT

In IoT hydroponics, the integration of Internet of Things (IoT) and Machine Learning (ML) has opened up great opportunities to increase the productivity and smart system’s management. With the collected data from sensors in the environment, the machine learning model would analyze and predict the trend of the environmental factors, this combination not only improves the outcome quality but also helps saving the resources. Integrating machine learning into an IoT hydroponics system could not only create a smart, autonomous and adaptable system to changing conditions of environment in real-time but also optimize resources for a cost-effective and productive hydroponic system. In this study, a novel method was presented for predicting environmental factors using Machine Learning algorithm for smart IoT hydroponic systems. By applying this method, an IoT hydroponic system can predict the trends of environmental factors which affects the plants such as temperature, moisture, pH levels…. The experiment results show that the accuracy of the predicted data is reliable, it reached 94.2% for a day and 92.6% for a week. These results could help users take proactive measures to improve the cultivation quality.

Smart IoT SCADA System for Hybrid Power Monitoring in Remote Natural Gas Pipeline Control Stations

A pipeline network is the most efficient and rapid way to transmit natural gas from source to destination. The smooth operation of natural gas pipeline control stations depends on electrical equipment such as data loggers, control systems, surveillance, and communication devices. Besides having a reliable and consistent power source, such control stations must also have cost-effective and intelligent monitoring and control systems. Distributed processes are monitored and controlled using supervisory control and data acquisition (SCADA) technology. This paper presents an Internet of Things (IoT)-based, open-source SCADA architecture designed to monitor a Hybrid Power System (HPS) at a remote natural gas pipeline control station, addressing the limitations of existing proprietary and non-configurable SCADA architectures. The proposed system comprises voltage and current sensors acting as Field Instrumentation Devices for required data collection, an ESP32-WROOM-32E microcontroller that functions as the Remote Terminal Unit (RTU) for processing sensor data, a Blynk IoT-based cloud server functioning as the Master Terminal Unit (MTU) for historical data storage and human–machine interactions (HMI), and a GSM SIM800L module and a local WiFi router for data communication between the RTU and MTU. Considering the remote locations of such control stations and the potential lack of 3G, 4G, or Wi-Fi networks, two configurations that use the GSM SIM800L and a local Wi-Fi router are proposed for hardware integration. The proposed system exhibited a low power consumption of 3.9 W and incurred an overall cost of 40.1 CAD, making it an extremely cost-effective solution for remote natural gas pipeline control stations.

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.