Internet Of Things Platform Research Articles

User preference-aware content caching strategy for video delivery in cache-enabled IoT networks

The escalating growth of content-dependent services and applications within the Internet of Things (IoT) platform has led to a surge in traffic, necessitating real-time data processing. Content caching has emerged as an effective solution to counteract this traffic upswing. Caching not only improves network efficiency but also enhances user service quality. Critical to the development of an optimal caching algorithm is the accurate prediction of future content popularity. This prediction hinges on the ability to anticipate users' content preferences, which is a pivotal method for assessing content popularity. In this study, we introduce a novel caching strategy termed User Preference-aware content Caching Strategy (UPCS) tailored for an IoT platform, where users access multimedia services offered by remote Content Providers (CPs). The UPCS encompasses three key algorithms: a content popularity prediction algorithm that utilizes Variational Autoencoders (VAE) to forecast users' future content preferences based on their prior requests, an online algorithm for dynamic cached content replacement, and a cooperative caching algorithm to augment caching efficiency. The proposed content caching strategy outperforms alternative methods, exhibiting superior cache hit rates and reduced Content Retrieval Delays (CRD).

ChamelIoT: a tightly- and loosely-coupled hardware-assisted OS framework for low-end IoT devices

The evergrowing Internet of Things (IoT) ecosystem continues to impose new requirements and constraints on every device. At the edge, low-end devices are getting pressured by increasing workloads and stricter timing deadlines while simultaneously are desired to minimize their power consumption, form factor, and memory footprint. Field-Programmable Gate Arrays (FPGAs) emerge as a possible solution for the increasing demands of the IoT. Reconfigurable IoT platforms enable the offloading of software tasks to hardware, enhancing their performance and determinism. This paper presents ChamelIoT, an agnostic hardware operating systems (OSes) framework for reconfigurable IoT devices. The framework provides hardware acceleration for kernel services of different IoT OSes by leveraging the RISC-V open-source instruction set architecture (ISA). The ChamelIoT hardware accelerator can be deployed in a tightly- or loosely-coupled approach and implements the following kernel services: thread management, scheduling, synchronization mechanisms, and inter-process communication (IPC). ChamelIoT allows developers to run unmodified applications of three well-established OSes, RIOT, Zephyr, and FreeRTOS. The experiments conducted on both coupling approaches consisted of microbenchmarks to measure the API latency, the Thread Metric benchmark suite to evaluated the system performance, and tests to the FPGA resource consumption. The results show that the latency can be reduced up to 92.65% and 89.14% for the tightly- and loosely-coupled approaches, respectively, the jitter removed, and the execution performance increased by 199.49% and 184.85% for both approaches.

RANCANG BANGUN ALAT PENDETEKSI DINI PONTENSI KEBAKARAN SECARA OTOMATIS pada RUANGAN TERINTGRASI dengan INTERNET of THINGS

In an effort to enhance safety and early detection of potential fires in indoor spaces, this research designs and constructs an automatic early fire detection device integrated with the Internet of Things (IoT). The device incorporates a flame sensor, MQ2 sensor, DHT11 temperature and humidity sensor, buzzer, LCD 16x2, and Node MCU to provide an effective and efficient solution. The flame sensor is utilized to detect the presence of fire, while the MQ2 sensor is employed to detect potentially hazardous gases associated with fires. The DHT11 sensor provides temperature and humidity information in the surrounding environment, aiding in identifying potential conditions conducive to fires. The Node MCU serves as the central processing unit of the system, collecting data from the sensors and transmitting it to the IoT platform via Wi-Fi connectivity. This data can be accessed and monitored in real-time through applications or online platforms. Upon detecting a potential fire, the buzzer issues an audible alert, and the LCD 16x2 displays detection information for the user. The primary advantage of this device lies in its ability to provide prompt and efficient detection information, accessible remotely through internet connectivity. With the integration of IoT, the device offers a smarter and more responsive solution for addressing potential fires in indoor spaces. The implementation of this device is anticipated to enhance safety and provide better protection against the risk of fires.

SISTEM IDENTIFIKASI SAMPAH ANORGANIK BERBASIS IOT (Internet of Things) MENGGUNAKAN METODE SAW (Simple Additive Weighting)

Inorganic waste is one of the environmental issues that needs to be effectively managed. An Internet of Things (IoT)-based system using the Simple Additive Weighting (SAW) method is implemented to provide a solution for the more efficient management of inorganic waste. This system utilizes sensors connected to the IoT network to detect and identify the types of inorganic waste entering a specific area. The SAW method is employed to assign weights to each criterion applied in the identification process of inorganic waste, such as type, weight, and physical condition. The system assesses based on the predefined criteria weights, enabling high-accuracy categorization of inorganic waste. Identification results can be accessed in real-time through the IoT platform Thingspeak, allowing for quick monitoring and analysis. Implementing this system is expected to positively contribute to the management of inorganic waste, facilitating more efficient collection, categorization, and overall waste handling. Moreover, adopting IoT technology and the SAW method in this system is envisioned to provide sustainable solutions in waste management efforts to support environmental sustainability.

SISTEM MONITORING KUMBUNG JAMUR TIRAM OTOMATIS BERBASIS IoT (Internet of Things) MENGGUNAKAN METODE K-NEAREST NEIGHBOUR

In response to the growing market demand for oyster mushrooms, effective monitoring of the growth environment becomes crucial. This research aims to develop an automated Internet of Things (IoT)-based monitoring system for oyster mushroom cultivation, utilizing Wemos D1 R1 hardware, DHT22 temperature and humidity sensor, LM35 temperature sensor, and the K-Nearest Neighbour (KNN) method for data analysis. The IoT platform employed in this study is Antares. The system is designed to monitor real-time temperature and humidity within the mushroom cultivation chamber. The DHT22 sensor is employed for simultaneous measurement of air temperature and humidity, while the LM35 sensor gauges the soil temperature in the mushroom growth substrate. Data collected by these sensors are automatically transmitted to the Antares platform through Wemos D1 R1 via WiFi connectivity. The K-Nearest Neighbour (KNN) method is applied to analyze the accumulated temperature and humidity data. KNN provides the capability to identify patterns and trends in oyster mushroom growth based on environmental conditions. The results of this analysis offer valuable insights for oyster mushroom farmers to optimize growth conditions and enhance harvest yields. Through the implementation of this system, it is anticipated that efficiency and productivity in oyster mushroom cultivation will be improved. This IoT-based automated monitoring system provides an effective and practical solution for real-time monitoring of oyster mushroom growth conditions, offering farmers the opportunity to take prompt and informed actions to enhance their harvest yields.

A categorized information fusion model for reliable services delivery in smart cities

Wireless Sensor Network serves as the core source of data in the Internet of Things (IoT) environment. The WSN nodes sense, aggregate, and relay sensed data for the different services in the IoT. The reliability of the aggregated and transmitted information is crucial for the effectiveness of IoT services. The data from multiple sensors at different timestamps need to be fused for efficient data transmission, reducing complexity and improving the reliability of the data being transmitted, improving the quality of IoT services. Therefore, data fusion is an essential aspect of WSN, where multiple sensors collect and combine data to produce a single, more comprehensive data for transmission. This data fusion reduces the amount of data transmission and network energy consumption and increases the accuracy of the result. This paper introduces a Categorised Information Fusion Model (CIFM) to minimise the replication in handling the aggregation of multiple instances of sensor data by categorising the information. The proposed CIFM employs federated learning for distributed verification of different aggregation time frames. This learning identifies the replication of sensor data from different sensors based on time frames and aggregation instances. In the multi-timed aggregation level, the sensed information's interfering distribution is diminished by controlling the relaying instance. The recurrent learning instances diminish the multi-timed information based on the occurrence factor. This recurrent learning improves delivery precision by controlling computation complexity and fusion time. The CIFM model also reduces the communication overhead by reducing the amount of replicated information that needs to be transmitted and processed and providing a consistent delivery ratio. The use of federated learning also allows for privacy-preserving data processing as the raw data is only processed locally, and aggregated results are shared. The performance of the proposed CIFM model is evaluated through simulations and compared with other existing fusion methods. The results show that the proposed model outperforms existing accuracy and communication efficiency methods. This approach helps to simplify the process of aggregating and relaying the data in the WSN to improve the performance and efficiency of IoT services. The goal of CIFM is to provide a more effective way to process and utilise the information from the sensors to deliver accurate and reliable data to the IoT platform. The CIFM improves the fusion rate by 7.77 % and minimize the fusion time up to 13.03 % compared to other methods.

SleepSmart: an IoT-enabled continual learning algorithm for intelligent sleep enhancement

AbstractSleep is an essential physiological process that is crucial for human health and well-being. However, with the rise of technology and increasing work demands, people are experiencing more and more disrupted sleep patterns. Poor sleep quality and quantity can lead to a wide range of negative health outcomes, including obesity, diabetes, and cardiovascular disease. This research paper proposes a smart sleeping enhancement system, named SleepSmart, based on the Internet of Things (IoT) and continual learning using bio-signals. The proposed system utilizes wearable biosensors to collect physiological data during sleep, which is then processed and analyzed by an IoT platform to provide personalized recommendations for sleep optimization. Continual learning techniques are employed to improve the accuracy of the system's recommendations over time. A pilot study with human subjects was conducted to evaluate the system's performance, and the results show that SleepSmart can significantly improve sleep quality and reduce sleep disturbance. The proposed system has the potential to provide a practical solution for sleep-related issues and enhance overall health and well-being. With the increasing prevalence of sleep problems, SleepSmart can be an effective tool for individuals to monitor and improve their sleep quality.

IoT-digital twin-inspired smart irrigation approach for optimal water utilization

Agriculture industry faces the challenge of increasing productivity by 50% from 2012 to 2050 while reducing water usage, given that it currently consumes 69% of the world’s freshwater. To achieve this goal, smart technologies such as Artificial Intelligence (AI), Digital Twins (DT), and Internet of Things (IoT) are being increasingly utilized. However, the use of DT in agriculture is still in its early stages. This study proposes a smart irrigation framework inspired by digital twins in an application domain. The irrigation framework’s sensors and actuators are linked to their virtual counterparts to create a digital twin. The IoT platform collects, aggregates, and processes data to determine daily irrigation requirements, and the behavior of the irrigation system is simulated. The proposed framework has two main advantages: evaluating the behavior of the digital twin and IoT platform in the context of agriculture before integrating them into the field and comparing various irrigation methods with current farming methods. By providing farmers with information about soil, weather, and crops, the system has the potential to improve farm operations and reduce water consumption.

Development of LoRaWAN-based IoT system for water quality monitoring in rural areas

This article delineates the design and deployment of an innovative real-time water quality monitoring system tailored for rural regions, focusing on monitoring the water resource quality parameters. We propose a solar-powered, waterproof, portable, and Internet of Things (IoT)-enabled solution that leverages Long Range Wide Area Network (LoRaWAN) technology. Central to this system is a sophisticated LoRa node outfitted with an array of sensors for capturing key water parameters, such as pH, total dissolved solids, turbidity and temperature. A conjunction of an Arduino microcontroller-based board and a LoRa shield facilitates real-time data capture and transmission to a LoRaWAN gateway. The acquired data is transmitted to The Things Network server, which is seamlessly integrated with a ThingSpeak web-based IoT server and ThingView mobile applications. We incorporate a solar cell with a solar shield to ensure sustainable energy provision for powering the entire system through a rechargeable battery. This allows users to access vital water quality information online simultaneously and continuously in real-time. As a testament to its robustness, the system was empirically tested at Gambang Lake to demonstrate its effectiveness, functionality, buoyancy, and waterproof capabilities. We further validated the results by comparing them with laboratory sample analysis findings. Experimental evaluations confirmed the system's reliability, as evidenced by the strong agreement between the water conditions measured using our solution and those obtained from laboratory instruments. Moreover, our system efficiently and remotely updated data across multiple IoT platforms using the LoRa radio interface over the LoRaWAN gateway.

Autonomous open-source electric wheelchair platform with internet-of-things and proportional-integral-derivative control

<span lang="EN-US">This study aims to improve the working model of autonomous wheelchair navigation for disabled patients using the internet of things (IoT). A proportional-integral-derivative (PID) control algorithm is applied to the autonomous wheelchair to control movement based on position coordinates and orientation provided by the global positioning system (GPS) and digital compass sensor. This system is controlled through the IoT system, which can be operated from a web browser. Autonomous wheelchairs are handled using a waypoint algorithm; ESP8266 is used as a microcontroller unit that acts as a bridge for transmitting data obtained by sensors and controlling the direct current (DC) motors as actuators. The proposed system and the autonomous wheelchair performance gave satisfactory results with a longitude and latitude error of 1.1 meters to 4.5 meters. This error is obtained because of the limitations of GPS with the type of Ublox Neo-M8N. As a starting point for further research, a mathematical model of a wheelchair was created, and pure pursuit control algorithm was used to simulate the movement. An open-source autonomous IoT platform for electric wheelchairs has been successfully created; this platform can help nurses and caretakers.</span>

Seamless Wireless Communication Platform for Internet of Things Applications

The rapid growth of the Internet of Things (IoT) devices resulted in the proliferation of wireless technologies to cater to their increasing data rate requirements and support multiple applications. However, such ever-increasing wireless technologies present numerous challenges such as incompatible wireless standards, increased energy consumption, and insecure communication. The traditional gateways proposed in the literature suffers from limitations such as computational complexity, resource requirements, increased cost, and device size. We vision an era of seamless wireless communication to alleviate the aforementioned challenges in IoT applications. through three inter-dependent functionalities namely detection and identification of wireless technologies, energy-efficient transmit power control, and secure end-to-end communication. To prove the concept, a new gateway is proposed to achieve these three functionalities with only physical layer measurements so that the different communication protocols in the higher layers can be avoided. Novel schemes are conceptualized for resource-limited seamless IoT applications. Moreover, the conceptual seamless IoT platform is validated through software-based computer simulation and software-defined radio-based testbed implementation. The preliminary analysis demonstrates that the proposed platform has great potential in advancing seamless IoT applications.

Design and Development of Iot Based Apparatus For Measuring Thermal Conductivity of Insulating Slabs

Thermal conductivity is the amount of heat that can be passed through or conducted by any material. Evaluating the thermal conductivity of insulators is critical for many applications. The available thermal conductivity apparatuses are expensive, time-consuming, and tedious to operate. With the advent of nanotechnology, there is a tremendous increase in the research and development of new insulating materials to be used for various applications. So, there is an urgent need to develop a portable, simple, and cost-effective apparatus that can be used in research laboratories to measure the thermal conductivity of newly developed insulating materials. In this regard, a portable and cost-effective thermal conductivity apparatus has been indigenously developed in the laboratory. The apparatus has been used to measure the thermal conductivity of insulating slabs. The values obtained from the apparatus have been verified with known material and found to be in broad agreement with the value available in the literature. The results obtained have been deployed onto the cloud in real-time using the Internet of Things platform. A plot of the thermal conductivity of the insulating slab at different temperatures has been displayed in the cloud. It can be concluded that the developed apparatus can be used to measure the thermal conductivity of any insulating slab with reasonable accuracy. Furthermore, this apparatus gives the same accuracy for measuring the thermal conductivity of metals as long as their dimensions are known, such as metallic bars or slabs. The thermal conductivity of the metals is compared to the standard values and this can determine their purity and if composites such as nanomaterials have been added. It is very useful for the mining industry as well, to measure the thermal conductivity of metals and assess their purity.

Continuous agile cyber–physical systems architectures based on digital twins

Modern cyber-physical systems, for the most part, are large-scale multilevel heterogeneous distributed systems that integrate subsystems of different kinds and are built on the Internet of Things platforms, where system structure and behavior are not constant. Managing such systems and keeping them in working condition throughout their lifetime is a difficult task. The proposed article discusses one of the possible approaches to solving this problem, based on the use of well-known continuous and agile architecture paradigms. However, there are currently no effective mechanisms for implementing these paradigms. The proposed article suggests a new approach to implementing continuous agile architectures by utilizing digital twins and proposes a reference architecture for a run-time dynamic digital twin. This method is unique because it builds a series of dynamic digital twins that model the system in real time, utilizing data about system events. Build the first models using the models used in earlier stages of the system lifecycle. This gives the following opportunities: i) a way to use dynamic digital twins to implement the continuous agile architecture paradigm; ii) a generalized three-level model of the life cycle of the continuous agile architecture; iii) a reference architecture for dynamic digital twins; and iv) a set of models that are all about using dynamic digital twins. The suggested approach enables the management of heterogeneous multilevel cyber-physical systems with variable structure and behavior variability.

Blynk App-Based Plant Monitoring System Design

Plantations represent a vital economic sector in Indonesia. However, farmers who rely on plantations for their livelihood often encounter challenges related to air and soil quality, which can significantly impact plant growth. Each type of plant requires specific air and soil conditions to thrive optimally, including factors such as air temperature, humidity, soil moisture, and pH levels. To address these issues, a solution has been developed in the form of a plantation monitoring system that can be accessed remotely and in real-time through cellular and desktop applications. This monitoring system utilizes various sensors, including DHT11 sensors, resistive soil moisture sensors, and soil pH sensors, all of which are meticulously calibrated with digital soil sensors. The collected data can be conveniently monitored and managed through an Internet of Things platform, such as Blynk.Based on the case study, the measurement system that is displayed on Blynk has a range of 0-100 °C for air temperature, 0-100% for air humidity, 0-1000 for soil moisture, and 0-14 for soil pH.

IoT based sensor enabled vehicle parking system

Along with the development of society and the country, the development of cities also occurs. People use technology to facilitate the work of the community. Some of them are the Internet of Things (IoT). This technical concept solves some of the issues like traffic system, parking lots, and operating electronic devices at home when the user is away. The Internet of things provides wireless access to this system and allows users to maintain one-track parking space availability in the crowded area and on the busy lanes, with the increase in Vehicle population, road congestion in big cities is one of the major consequences. The system majorly focuses on the concept of a parking space detection mechanism that uses sensors in combination with the use of Internet of Things. i.e., it sends parking status to the internet web page. This allows users to see which parking spaces are free and where to park. This is achieved by sending the sensor's data to an IoT platform via the ESP8266 WiFi module, which displays data using HTTP. These types of systems make the user find parking slots easier than creating a mess this process saves the user time as well as improves the easy being by tracking the charges of the vehicle.

Internet of things (IoT)-based on real-time and remote boiler fuel monitoring system: a case of Raha Beverages Company Limited, Arusha-Tanzania

RAHA Beverages Company (RABEC) is one of the banana wine production companies that utilizes fuel in steam production in Arusha-Tanzania, where fuel data conditions such as temperature, pressure, discharge, fuel level, and gas leakage with humidity were a challenge to monitor, which provoked boiler malfunction and plant breakdown. Today, RABEC manually uses a dropping stick into the fuel tank to monitor fuel data conditions which is time-consuming and gives inaccurate readings, inefficiency, fuel economy discrepancy, and accidents. This study aimed to design and develop an IoT-based fuel monitoring system. The flow meter, ultrasonic level, thermistor fuel temperature, humidity, and pressure sensors were used to gather fuel information where the GSM module was employed to send fuel data messages to the operator’s phone. An AT mega 328 microcontroller was used to process and analyse the fuel data and send them to the Thing Speak IoT platform using Wi-Fi connectivity. The results showed that when the fuel level was less than the threshold value, an operator was alerted by a refilling message via GSM technology. A pressure of 0.1psi, a fuel temperature of 120 ℃, and 80% of humidity, the system notifies the operator by an alert message to check the injector pressure and if the fuel–air mixture is perfect. In addition, these data were observed on the LCD and ThingSpeak webpage. To conclude, the developed system proved the best performance with a 99.98% of success rate with high accuracy, security, and efficiency rate compared to the current monitoring system.

AICropCAM: Deploying classification, segmentation, detection, and counting deep-learning models for crop monitoring on the edge

Precision Agriculture (PA) promises to meet the future demands for food, feed, fiber, and fuel while keeping their production sustainable and environmentally friendly. PA relies heavily on sensing technologies to inform site-specific decision supports for planting, irrigation, fertilization, spraying, and harvesting. Traditional point-based sensors enjoy small data sizes but are limited in their capacity to measure plant and canopy parameters. On the other hand, imaging sensors can be powerful in measuring a wide range of these parameters, especially when coupled with Artificial Intelligence. The challenge, however, is the lack of computing, electric power, and connectivity infrastructure in agricultural fields, preventing the full utilization of imaging sensors. This paper reported AICropCAM, a field-deployable imaging framework that integrated edge image processing, Internet of Things (IoT), and LoRaWAN for low-power, long-range communication. The core component of AICropCAM is a stack of four Deep Convolutional Neural Networks (DCNN) models running sequentially: CropClassiNet for crop type classification, CanopySegNet for canopy cover quantification, PlantCountNet for plant and weed counting, and InsectNet for insect identification. These DCNN models were trained and tested with >43,000 field crop images collected offline. AICropCAM was embodied on a distributed wireless sensor network with its sensor node consisting of an RGB camera for image acquisition, a Raspberry Pi 4B single-board computer for edge image processing, and an Arduino MKR1310 for LoRa communication and power management. Our testing showed that the time to run the DCNN models ranged from 0.20 s for InsectNet to 20.20 s for CanopySegNet, and power consumption ranged from 3.68 W for InsectNet to 5.83 W for CanopySegNet. The classification model CropClassiNet reported 94.5 % accuracy, and the segmentation model CanopySegNet reported 92.83 % accuracy. The two object detection models PlantCountNet and InsectNet reported mean average precision of 0.69 and 0.02 for the test images. Predictions from the DCNN models were transmitted to the ThingSpeak IoT platform for visualization and analytics. We concluded that AICropCAM successfully implemented image processing on the edge, drastically reduced the amount of data being transmitted, and could satisfy the real-time need for decision-making in PA. AICropCAM can be deployed on moving platforms such as center pivots or drones to increase its spatial coverage and resolution to support crop monitoring and field operations.

Internet of things for smart manufacturing based on advanced encryption standard (AES) algorithm with chaotic system

Smart manufacturing using the Internet of Things (IoT) ensures uninterrupted and human intervention-less automation in industries for precision outcomes. As the smart manufacturing encloses chaotic systems the point of security is always demandable due to external threats. For mitigating the authorization issues in chaotic systems, a Smart Reviving Authorization Model using Advanced Encryption Standard (SRAM-AES) is designed in this article. This model is selective for chaotic systems for reviving their conventional operation cycles and preventing failures. A machine/controller’s performance is monitored for its point of instability through differential access. The malicious access and its cause for controller unstableness are verified using IoT elements (remotely) and deep recurrent learning algorithms. Such identified instances are recovered by providing alternate controller recommendations from the IoT platform. In the recurrent learning process, the unstable to stable point possibilities are verified; the passing controllers are equipped with AES mitigating the previous authorizations. For a stable-functioning controller, the AES deficiency in authorization is verified in its completion cycles for consecutive production instances. Thus this model stands reliable for preventing unauthorized access, controller downtime reduction, and production failures.

Machine Learning for Fault Detection and Diagnosis of Large Photovoltaic Plants Through Internet of Things Platform

Machine Learning for Fault Detection and Diagnosis of Large Photovoltaic Plants Through Internet of Things Platform

IoT Applications, Platforms, Systems, And Framework based on Blockchain

The Internet of Things (IoT) has lately evolved as a new technology capable of providing real-time and cutting-edge sensing capabilities to numerous industries such as healthcare, agriculture, smart cities, smart homes, and supply chain. Because of this technology's inherent promise, it has already seen exponential growth in a wide range of use-cases across numerous application domains. As academics around the world continue to examine its capabilities, there is widespread consensus that in order to get the most out of this technology and fully realise its potential, IoT must be built on a flexible network architecture with strong support for security, privacy, and trust. Blockchain (BC) technology, on the other hand, has lately emerged as a breakthrough technology with the promise to give several beneficial qualities such as robustness, support for integrity, anonymity, decentralisation, and autonomous control. Several BC systems are offered, which may be appropriate for various use-cases, including IoT applications. As a result, the integration of IoT with BC technology is seen as a potential solution to some critical concerns. To do this, a good grasp of the requirements of various IoT applications and the viability of a BC platform for a specific application satisfying its underlying requirements is required. This project explains many ways such as the gateway process and sensor device. By addressing the present blockchain concerns, IoT may enable a variety of security services, all of which are described in detail. Various authors present some common facts on the use of blockchain in IoT, which aids in a thorough understanding of the concept. Blockchain improves security and privacy in IoT platforms. In this project, an extra immutable ledger is created using all of the resources and information mentioned in the existing procedure. [1].