Development Of IoT Applications Research Articles

Remote Controlled Agricultural Robot

Abstract: The Agri Bot project presents a pioneering endeavor in the realm of autonomous agriculture by seamlessly integrating robotics, IoT, and mobile application development. Utilizing an Arduino UNO microcontroller, ESP32 camera module, servo motors, and various sensors, the system excels in executing precise agricultural tasks such as digging, filling, seed sowing, and fertilizer dispensing. The accompanying mobile application, developed through MIT App Inventor and utilizing Bluetooth communication, provides an intuitive interface for users to remotely oversee and manage the Agri Bot's operations. Beyond its successful hardware and software integration, the project holds transformative potential for traditional farming practices, evident in the incorporation of advanced technologies such as obstacle detection sensors and machine learning for crop recognition. The modular design and collaborative potential of swarm robotics hint at scalability and adaptability for evolving agricultural needs. As the project lays the groundwork for autonomous agriculture, future prospects include autonomous navigation, sustainable power solutions, data analytics for decision support, and enhanced remote monitoring. Collaboration with agricultural experts and ongoing refinements based on real-world feedback are crucial for optimal deployment in diverse farming environments. The Agri Bot project represents a significant milestone in the convergence of technology and agriculture, heralding a future where autonomous systems play a central role in shaping the agricultural landscape.

A Testbed Platform to Support an IoT City Lab

This paper describes the development activity that has been carried out for a living laboratory for the city of Cagliari aimed at functioning as a learning center for local SMEs willing to improve their skills in IoT and create applications that will be integrated in an open innovation ecosystem. The many users belonging to the various SMEs involved in the project required an ICT laboratory with a platform that could manage them and provide a multi-tenant environment for the development of IoT applications. The architecture also had to be scalable and interoperable, and the resulting platform had to collect many kinds of data from sensors or other data sources, elaborate them, and show georeferenced information on a 3D satellite interactive view along with statistics on side panels. This work was based on a platform already developed by CRS4 for a previous project. Preserving the concept of the decision-making tool for Smart Cities, almost every component was redesigned, and, in this paper, we describe the new solutions that have been implemented. Starting from the former structure, further features were added in a novel way in order to offer an enhanced framework that can deal with the activities of the laboratory, exploiting the scalability of the open-source systems involved, their robustness and flexibility, and leveraging domain standards. In this article, the main challenges involved in the development of the platform are described, as well as the solutions that have been implemented so far.

Lightweight and Verifiable Secure Aggregation for Multi-dimensional Data in Edge-enhanced IoT

The development of IoT applications promotes the generation of large-scale data. In this situation, more and more organizations or institutions are interested in collecting and analyzing these data to obtain high-quality information to serve people’s lives. Nevertheless, existing secure aggregation solutions are mostly proposed under the honest-but-curious threat model, without considering malicious aggregators. Actually, the aggregation node may return tampered or forged aggregation results for vulnerabilities or ulterior motives. It is more challenging to devise efficient verifiable data aggregation due to the characteristics of IoT devices. In this paper, we propose a Lightweight and Verifiable Secure Aggregation Scheme for Multi-dimensional Data in Edge-enhanced IoT (LVSA-MD), which allows the secure aggregation of multi-dimensional data, and the requester can verify the correctness and integrity of the aggregation results. In LVSA-MD, we combine Chinese remainder theorem with secret sharing technique to protect the individual data privacy, as well as reducing the communication overhead. Moreover, by involving homomorphic MAC, we achieve a lightweight verifiable mechanism to ensure the integrity of result. We additionally employ identity-based signature to achieve the source authentication. Detailed security analysis demonstrates that the proposed LVSA-MD achieves the goal of protecting privacy, integrity and authentication. Moreover, extensive theoretical analyses and experimental evaluations show that our LVSA-MD performs efficiently with respect to computation and communication while retaining more desired properties.

Global Models of Smart Cities and Potential IoT Applications: A Review

As the world becomes increasingly urbanized, the development of smart cities and the deployment of IoT applications will play an essential role in addressing urban challenges and shaping sustainable and resilient urban environments. However, there are also challenges to overcome, including privacy and security concerns, and interoperability issues. Addressing these challenges requires collaboration between governments, industry stakeholders, and citizens to ensure the responsible and equitable implementation of IoT technologies in smart cities. The IoT offers a vast array of possibilities for smart city applications, enabling the integration of various devices, sensors, and networks to collect and analyze data in real time. These applications span across different sectors, including transportation, energy management, waste management, public safety, healthcare, and more. By leveraging IoT technologies, cities can optimize their infrastructure, enhance resource allocation, and improve the quality of life for their citizens. In this paper, eight smart city global models have been proposed to guide the development and implementation of IoT applications in smart cities. These models provide frameworks and standards for city planners and stakeholders to design and deploy IoT solutions effectively. We provide a detailed evaluation of these models based on nine smart city evaluation metrics. The challenges to implement smart cities have been mentioned, and recommendations have been stated to overcome these challenges.

Microservice Application Scheduling in Multi-Tiered Fog-Computing-Enabled IoT.

Fog computing extends mobile cloud computing facilities at the network edge, yielding low-latency application execution. To supplement cloud services, computationally intensive applications can be distributed on resource-constrained mobile devices by leveraging underutilized nearby resources to meet the latency and bandwidth requirements of application execution. Building upon this premise, it is necessary to investigate idle or underutilized resources that are present at the edge of the network. The utilization of a microservice architecture in IoT application development, with its increased granularity in service breakdown, provides opportunities for improved scalability, maintainability, and extensibility. In this research, the proposed schedule tackles the latency requirements of applications by identifying suitable upward migration of microservices within a multi-tiered fog computing infrastructure. This approach enables optimal utilization of network edge resources. Experimental validation is performed using the iFogSim2 simulator and the results are compared with existing baselines. The results demonstrate that compared to the edgewards approach, our proposed technique significantly improves the latency requirements of application execution, network usage, and energy consumption by 66.92%, 69.83%, and 4.16%, respectively.

Smart Insoles for Gait Analysis Based on Meshless Conductive Rubber Sensors and Neural Networks

In recent years, advances in wearable medical devices have been driven by the development of flexible sensors and wireless IoT technologies, with research and applications in this field rapidly increasing. Plantar pressure analysis is a gait method that can be used to analyze foot pressure during human movement to identify potential health problems, but most of the devices based on this method are currently single-functional, poorly portable, and expensive. This work proposes a smart insole using meshless conductive rubber sensors, as well as a multi-channel data acquisition and transmission system based on these sensors, deep learning networks, and Bluetooth technology. The smart insole sensing unit and circuit board were made of flexible materials and powered by rechargeable lithium batteries. The system can be used to collect plantar pressure data from the sensors in real time, analyze the pressure in different regions of the foot through a neural network, and infer the gait cycle and abnormal gait of the user. The effective communication range can be up to 70 meters under barrier-free conditions. This smart insole has a bright future thanks to the pressure detection range, fast response time, and good durability.

The Growing Function of Understanding Wireless Network Technologies for Upcoming Generation Digital Stream Processing

With the development of the fifth generation (5G) and sixth generation (6G) networks, wireless networks are beginning accomplish persistent large scale obtainment, and communication. Modern cellular technology and the developing new era are both seen favourably for the smart grid. The 5G and 6G networks are being developed and prepared for deployment by the mobile industry. The development of IoT and other intelligent automation applications is being significantly fueled by the growing wireless networks, which are becoming more widely accessible. Network densification, fast throughput, precise location, and energy efficiency criteria will be increasingly demanding in future wireless communications. One of the core areas of wireless networking research in the future will be how to increase productivity while reducing expenses. Approaching this goal in a way that allows for the ability to learn from experience is crucial. Transfer learning (TL) promotes new activities and domains to pick up knowledge from more seasoned tasks and domains so that new tasks may be completed more quickly and effectively. The connection and similarity information between various jobs in several domains of wireless communications can assist TL conserve energy and increase efficiency. Applying TL to upcoming 6G communications is thus a very important subject.

Architecture for IoT applications based on reactive microservices: A performance evaluation

The Internet has evolved from a network interconnecting computers to a complex ecosystem integrating devices of the most varied types, and enabling the amalgamation of the physical and virtual worlds. Integrating these heterogeneous devices fosters novel services and applications that generate value-added information and actionable knowledge for the end-user. Several challenges are involved in the design and building of IoT ecosystems, which have fostered research in the field, in search of patterns, guidelines, methods and tools that support such activities. In terms of architectural patterns, the microservice architectural style has been increasingly adopted in the development of IoT applications and services. Its adoption promotes some essential properties in IoT, such as scalability and extensibility. As there are always tradeoffs involved in every architectural decision, it is important to analyze whether the benefits brought by the use of microservices do not come at the expense of some loss or degradation of application performance. Recent research has analyzed the performance interference of microservices based on edge computing applications. However, a comprehensive assessment of the performance impact of characteristics inherent to the use of reactive microservices on IoT applications is still missing in the literature. In this paper, we present an experimental evaluation of the performance of IoT applications that make use of an architecture based on reactive microservices. The architecture was proposed by our group in a previous work and was tailored for reliable IoT applications running at the edge of the network. The experiments presented in this paper analyze the application performance based on various benchmark scenarios. In addition, we performed load and scalability testing of an IoT application that adopts the architecture components in a hybrid scenario (real devices and emulated devices). The results obtained were promising. The architecture had a good response to the increase in the workload, not presenting errors, crashes or instabilities due to the increase in IoT data traffic. Moreover, analyzing the overhead generated by the architecture components, there was no performance reduction or service unavailability. Such results point to the fact that the adoption of microservices in the construction of IoT systems can bring effective benefits without jeopardizing their performance due to eventually generated overheads.

New Instances in IOT Technology

Abstract: Future of the Internet of Things with enabled Artificial Intelligence, block chain technology, Big data technology, cloud AWS - Azure endpoints with little coding or no coding to manager enamors powerful device management. Few of the application of Internet of Things like B2C sector of summers like smart homes, smart appliances and elderly care. In infrastructure area in internet of things like smart city, smart energy & utilities and environmental monitoring. In the Commerce area in the Internet of Things like Medical & healthcare, transportation and building accommodations. Coming to Industry and farming area in the Internet of Things like smart manufacturing and smart agricultures and more. Coming to IT Datacenters need to use Internet of Things and IoT enabled ship, airplanes and trains exceptional application going use in multiple manners. IoT application development services enables on all IoT devices / machines connect virtually with global wireless standard using fifth generation 5G mobile network (ultra low latency internet via embedded IoT devices). In Asia-Pacific zone, 5G adoption is going to hit 4 billion in 2025 as per Ericson mobility report.

Quantum Inspired Differential Evolution with Explainable Artificial Intelligence-Based COVID-19 Detection

Recent advancements in the Internet of Things (Io), 5G networks, and cloud computing (CC) have led to the development of Human-centric IoT (HIoT) applications that transform human physical monitoring based on machine monitoring. The HIoT systems find use in several applications such as smart cities, healthcare, transportation, etc. Besides, the HIoT system and explainable artificial intelligence (XAI) tools can be deployed in the healthcare sector for effective decision-making. The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage. This article presents a new quantum-inspired differential evolution with explainable artificial intelligence based COVID-19 Detection and Classification (QIDEXAI-CDC) model for HIoT systems. The QIDEXAI-CDC model aims to identify the occurrence of COVID-19 using the XAI tools on HIoT systems. The QIDEXAI-CDC model primarily uses bilateral filtering (BF) as a preprocessing tool to eradicate the noise. In addition, RetinaNet is applied for the generation of useful feature vectors from radiological images. For COVID-19 detection and classification, quantum-inspired differential evolution (QIDE) with kernel extreme learning machine (KELM) model is utilized. The utilization of the QIDE algorithm helps to appropriately choose the weight and bias values of the KELM model. In order to report the enhanced COVID-19 detection outcomes of the QIDEXAI-CDC model, a wide range of simulations was carried out. Extensive comparative studies reported the supremacy of the QIDEXAI-CDC model over the recent approaches.

IoFT-FIS: Internet of farm things based prediction for crop pest infestation using optimized fuzzy inference system

Advanced farming techniques help to know the appropriate environmental conditions, soil quality, water and fertilizer needs, and crop monitoring during each plant’s growth phase, resulting in higher yields. Also, IoT-compatible crop monitoring and data collection systems can help identify crop diseases and the breeding status of pathogenic pests. Extracting data from samples collected from the complete crop growth cycle acknowledged a plausible relationship between weather parameters, crop yield, and insect reproduction. Data analysis done on the data collected through an IoT monitoring system helped determine the environmental factors supporting the higher pest breeding conditions. The proposed fuzzy inference system’s knowledge base is designed using these weather parameters. The multi-objective evolutionary algorithm uses fuzzy rules to find suitable cropping window and low pest breeding conditions. This proposal identifies crop-sowing windows based on fuzzy logic with maximum crop yield and minimum pest growth by deploying IEEE 802.15.4 wireless IoT-enabled sensor network monitoring infrastructure in medium grass vegetation. Experiments are being done on rice and Sugarcane crops. The experiments were conducted in the agriculture field of Gwalior, Madhya Pradesh, India. The soil moisture, rainfall, temperature, etc., data were collected using the wireless sensor network deployed in the field. Fuzzy logic-based identification of appropriate planting seasons by IoT application development services helps farmers prevent the development of pests and proactively take precautions to achieve maximum yields.

Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT

The high-speed development of mobile broadband networks and IoT applications has brought about massive data transmission and data processing, and severe traffic congestion has adversely affected the fast-growing networks and industries. To better allocate network resources and ensure the smooth operation of communications, predicting network traffic becomes an important tool. We investigate in detail the impact of variable sampling rate on traffic prediction and propose a high-speed traffic prediction method using machine learning and recurrent neural networks. We first investigate a VSR-NLMS adaptive prediction method to perform time series prediction dataset transformation. Then, we propose a VSR-LSTM algorithm for real-time prediction of network traffic. Finally, compared with the traditional traffic prediction algorithm based on fixed sampling rate (FSR-LSTM), we simulate the prediction accuracy of the VSR-LSTM algorithm based on the variable sampling rate proposed. The experiment shows that VSR-LSTM has higher traffic prediction accuracy because its sampling rate varies with the traffic.

Edge-Centric Programming for IoT Applications With Automatic Code Partitioning

IoT application development usually involves separate programming at the device side and server side. While separate programming style is sufficient for many simple applications, it is not suitable for many complex applications that involve complex interactions and intensive data processing. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EdgeProg</i> , an edge-centric programming approach to simplify IoT application programming, motivated by the increasing popularity of edge computing. With EdgeProg, users could write application logic in a centralized manner with an augmented If-This-Then-That (IFTTT) syntax and virtual sensor mechanism. The program can be processed at the edge server, which can automatically generate the actual application code and intelligently partition the code into device code and server code, for achieving the optimal latency. EdgeProg employs dynamic linking and loading to deploy the device code on a variety of IoT devices, which do not run any application-specific codes at the start. Results show that EdgeProg achieves an average reduction of 20.96%, 27.8% and 79.41% in terms of execution latency, energy consumption, and lines of code compared with state-of-the-art approaches.

IoT applied to irrigation systems in agriculture: A usability analysis

The Internet of Things favors using technological tools in rural environments thanks to the ability to connect to the Internet between devices that facilitate daily tasks. The research aims to evaluate the usability of the decision support system for irrigation in agriculture, AgroRIEGO, through the development of an IoT-based device. The sponsors of this project were the Ministry of Information and Communication Technologies and the Center of Excellence in the Internet of Things Appropriation (CEA-IoT) in Colombia. Among the methods used is the use of the heuristic evaluation technique, structured into 15 categories and 62 subcategories of assessment. This analysis was complemented by the contribution of a group of experts in the design and development of IoT applications and devices and agriculture to assess the system's attributes.

Toward Long-Term Effective and Robust Device-Free Indoor Localization via Channel State Information

With the rapid development of location-based IoT applications in recent years, indoor device-free passive localization based on Wi-Fi channel state information (CSI) has attracted considerable attention. In this article, we propose a long-term effective, robust, and accurate device-free passive fingerprinting localization scheme LTLoc, which only requires a single communication link. It takes the amplitudes extracted from the CSI along with the calibrated phases as fingerprints and trains a deep neural network (DNN) regression model to estimate the target location. Since Wi-Fi signals are susceptible to various environmental factors, CSI fingerprints also change over time, making the performance of the localization model built with the fingerprint database drop dramatically over a long period, and recalibrating the entire positioning area is laborious and time-consuming. To address this problem, we design an adaptive DNN (AdaptDNN) based on meta-networks by combining deep learning and domain adaptive methods. It can use meta-network learning to determine which layers and features of the DNN need to be transferred to automatically adapt to CSI fingerprints change. Extensive evaluations in an indoor environment with significantly different CSI fingerprints over six days have shown that LTLoc’s effectiveness in coping with changing CSI fingerprints over a long period is significantly superior to existing work in terms of localization and adaptability.

IoT-Enabled IEEE 802.15.4 WSN Monitoring Infrastructure-Driven Fuzzy-Logic-Based Crop Pest Prediction

Precision agriculture, as the future of farming technology, addresses challenges faced by farmers by data mining of information collected through IoT-enabled crop monitoring infrastructures. The identification of crop disease is one of the widely studied challenges. Crop diseases cannot be accurately predicted by merely analyzing individual disease causes. This proposal presents a fuzzy-logic-based pest prediction mechanism assisting in beforehand preparedness for potential pest prevention. The experiments are performed for pests in rice and millet crops. The data mining over samples collected in a cropping cycle revealed the plausible correlation between temperature, relative humidity, and rainfall with pest breeding. The data collected through IoT monitoring infrastructure is analyzed for the ambient breeding condition of the pest. These conditions are then employed to design the knowledge base of the fuzzy system. More specifically, the linguistic variables of the fuzzy membership function are optimized using a genetic algorithm for close prediction of pest breeding in given environmental conditions. The proposal verified that the weather factors have a strong impact on the occurrence of pests and diseases, and the fuzzy-logic-based pest prediction through IoT application development services will help farmers to take precautionary measures beforehand.

Sense, Transform & Send for the Internet of Things (STS4IoT): UML profile for data-centric IoT applications

The Internet of Things is currently one of the most representative sources of Big Data. It can acquire real-time data from multiple spatially distributed points, allowing for the extraction of valuable insights. However, an appropriate integration, processing, and analysis of these data depends on several factors starting from the correct definition of the information systems. This paper introduces STS4IoT, a UML profile and automatic code-generation tool for model-driven IoT, to address this issue. STS4IoT allows designing and implementing an IoT application from the required data only, bridging the gaps between the IoT and database design worlds. The IoT data design includes both different in-network transformations and the join of streams from multiple sources. Besides, it follows the Model-Driven Architecture (MDA) guidelines to provide abstraction levels oriented to the different roles participating in the application design. The STS4IoT validation shows it has an excellent structure and is highly understandable. Its instance models are well-formed, highly abstract and readable. And the automatic implementation tool can generate complete code for complex real-world applications involving multiple IoT devices. Then, STS4IoT simplifies the definition and development of IoT applications and their integration into other information systems, such as stream data warehouses.

Smart healthcare IoT applications based on fog computing: architecture, applications and challenges

The history of human development has proven that medical and healthcare applications for humanity always are the main driving force behind the development of science and technology. The advent of Cloud technology for the first time allows providing systems infrastructure as a service, platform as a service and software as a service. Cloud technology has dominated healthcare information systems for decades now. However, one limitation of cloud-based applications is the high service response time. In some emergency scenarios, the control and monitoring of patient status, decision-making with related resources are limited such as hospital, ambulance, doctor, medical conditions in seconds and has a direct impact on the life of patients. To solve these challenges, optimal computing technologies have been proposed such as cloud computing, edge computing, and fog computing technologies. In this article, we make a comparison between computing technologies. Then, we present a common architectural framework based on fog computing for Internet of Health Things (Fog-IoHT) applications. Besides, we also indicate possible applications and challenges in integrating fog computing into IoT Healthcare applications. The analysis results indicated that there is huge potential for IoHT applications based on fog computing. We hope, this study will be an important guide for the future development of fog-based Healthcare IoT applications.

IoT Monitoring System for Applications with Renewable Energy Generation and Electric Drives

nternet of Things IoT developed for monitoring, control, and management of sectors such as Smart Cities, Energy, Environment, Transport, Manufacture, Industrial Automation, Maritime, Healthcare, Education, etc, by interconnecting devices over internet. New sectors emerged in renewable energy systems, industrial motion drives, sensors and actuators. This article presents the design, and development of specific IoT applications for wind energy generating units, and electric drives. IoT technologies in the control systems of electric machines, mainly in applications of motor drives, and wind energy generating systems, contribute to improved monitoring, and management of performance, and to possible savings of energy. The experimental configurations upgrade laboratory infrastructure and offer new teaching and research perspectives to engineering education.

IPDs Help to Simplify IoT Applications

Chipset specific Integrated Passive Devices (IPDs) are intended to simplify the development of next gen wireless IoT applications, as New Electronics discovers