IoT Operating Research Articles

Investigation on optimization strategy of IoT operation mode based on blockchain

Under the call of the national scientific and technological power, the research in the field of sensor technology has made rapid progress. Many smart devices integrating various technical heights have entered the homes of ordinary people. Making these smart devices increasingly intelligent has greatly improved people’s lifestyles and even changed their lives. With the development of blockchain technology and the development of 5G communication equipment and applications, the industrial model of Blockchain and the Internet of Things can also become an important guide for the development of the Internet of Things. Therefore, this paper studies the optimization strategy of the IoT operation mode based on Blockchain. The research results have shown that the IoT operation mode based on Blockchain is superior to the traditional IoT operation mode in terms of node energy consumption, life cycle, and flux performance. Under the IoT operation mode of the Blockchain, the node energy consumption is reduced by about 1.19J on average, and the flux performance is increased by about 21 on average. This showed that the IoT operation model based on Blockchain is feasible.

Enhancing quality-driven iot operations for smart cities: A comprehensive approach for routing, security, and optimization

This study introduces a tailored data acquisition and communication framework for IoT smart applications, focusing on enhancing efficiency and system performance. The proposed Quality-Driven IoT Routing (EQR-SC) for smart cities utilizes IoT-enabled wireless sensor networks. Additionally, a noteworthy contribution is the introduction of the Chaotic Firefly Optimization (CFOA) algorithm for IoT sensor cluster formation, potentially optimizing the organization and efficiency of IoT sensor networks in smart cities. Trust-based cluster Head Selection is enhanced by employing the Weighted Clustering Algorithm (WCA), which assigns weights to nodes based on trustworthiness and relevant metrics to select reliable cluster heads. The proposal of a lightweight data encryption technique enhances data security among IoT sensors, ensuring the privacy and integrity of transmitted information. To optimize pathfinding within the IoT platform, the research employs the Bellman-Ford algorithm, ensuring efficient data routing while accommodating negative edge weights when necessary. Finally, a thorough performance analysis, conducted through network simulation (NS2), provides insights into the effectiveness of the proposed OQR-SC technique, allowing for valuable comparisons with existing state-of-the-art methods.

Agents for automatic control of sensors using Multi-Agent Systems and Ontologies: A scalable IoT architecture

Research efforts focused on Smart Building (SB) development have concentrated on the automation of resources within intelligent environments towards an enhanced experience for occupants. The process converts everyday manual activities into automatic actions, such as turning on lights upon entering a room, activating air conditioning on hot days, and switching off a television when there are no viewers. This article addresses a case study on context-aware monitoring conducted at the Laboratory of Distributed Systems and Concurrent Programming (LaSDPC) of the University of São Paulo. The focus is on a Fog layer of the IoT operating closer to sensors and reducing communication delays. Concepts of context-aware systems, multi-agent systems, ontology, and MQTT protocol were considered for the implementation of the intelligent system on-site. The programming languages used were Java and Python, leveraging libraries and frameworks dedicated to such technologies. A scalable system that does not compromise computational resource use and maintains responsiveness with low data exchange latency is proposed and tests checked the intelligent behavior of the laboratory under specific conditions using temperature, luminosity, and presence of a person as parameters.

The Danger of Minimum Exposures: Understanding Cross-App Information Leaks on iOS through Multi-Side-Channel Learning.

Research on side-channel leaks has long been focusing on the information exposure from a single channel (memory, network traffic, power, etc.). Less studied is the risk of learning from multiple side channels related to a target activity (e.g., website visits) even when individual channels are not informative enough for an effective attack. Although the prior research made the first step on this direction, inferring the operations of foreground apps on iOS from a set of global statistics, still less clear are how to determine the maximum information leaks from all target-related side channels on a system, what can be learnt about the target from such leaks and most importantly, how to control information leaks from the whole system, not just from an individual channel. To answer these fundamental questions, we performed the first systematic study on multi-channel inference, focusing on iOS as the first step. Our research is based upon a novel attack technique, called Mischief, which given a set of potential side channels related to a target activity (e.g., foreground apps), utilizes probabilistic search to approximate an optimal subset of the channels exposing most information, as measured by Merit Score, a metric for correlation-based feature selection. On such an optimal subset, an inference attack is modeled as a multivariate time series classification problem, so the state-of-the-art deep-learning based solution, InceptionTime in particular, can be applied to achieve the best possible outcome. Mischief is found to work effectively on today's iOS (16.2), identifying foreground apps, website visits, sensitive IoT operations (e.g., opening the door) with a high confidence, even in an open-world scenario, which demonstrates that the protection Apple puts in place against the known attack is inadequate. Also importantly, this new understanding enables us to develop more comprehensive protection, which could elevate today's side-channel research from suppressing leaks from individual channels to controlling information exposure across the whole system.

Urban Farming Prototype Design and Development with IoT System using STEMSEL and Runlinc IDE

Urban farming technology is getting more acceptable with the increase in population. Running urban farming with IoT becomes more important as urban farming has many constraints. This project aimed to design and develop an automated urban farming system operated with an IoT system using Science Technology Engineering Maths Social Enterprise Learning Artificial Intelligence (STEMSEL AI), and Runlinc Integrated Development Environment (IDE) to ease urban farming. The prototype consists of three major components: the structural part, the water piping system, and the IoT operating system. Idea generation was applied to develop various conceptual designs in the structural component. Concept evaluation was performed to choose the final design. The IoT operation system was designed and developed using the STEMSEL module and Runlinc IDE. A few challenges were identified throughout the design process. Overall, STEMSEL is much simpler and easier to set up than other controllers.

DESIGN AND ANALYSIS OF IOT-BASED TWO-SEATER SOLAR BOAT

: Solar energy has been supposed as the most suitable renewable energy coffers to substitute the part of energy in the ship’s application. In this exploration the two-seater solar boat is Fabricated and Tested; this is more effective than another solar boat system that is presently available in the request. It's a combination of energy storehouse and IOT operation with detectors used to cover its parameters. The outgrowth of this exploration is to run the boat 4 hours a day with the help of direct solar radiation collected on the solar PV Panels and it can run up to 3 hours at night with the help of a battery and another important parameter like power. To gain the overall electrical power claim, originally, the length of the boat was designed as 8 bases and the beam width is 4 bases, and the height of the boat was 3.5 bases. also grounded on the carrying capacity for 2 travelers, relegation of water (910 kg), and boat speed 6.07 km/ h (3.28 knots) were reckoned. Eventually designed the power of dc motor 5kW to drive the boat up to 3000 rpm and the necklace needed to drive the motor is11872.95 NM. These are the parameter that will help to increase the effectiveness of the boat. This exploration shows the overall system used as solar-operated water boats useful for water superiority and volume dimension. Development of a further provident, safe and precise system for chancing depth standards and water superiority indicators of water bodies with the support of a solar-based water boat.

Burgler Alarm Using IOT

Abstract: IoT refers to the structure of connected physical bias which is growing at a rapid-fire rate as a huge number of biases and objects are getting associated with the Internet. The Internet of effects can prove that technology has fleetly evolved. currently, the operation of the internet had been extensively used around the world. still, as technology advanced the need for home security systems using the Internet of effects had come more pivotal. Home security is a veritably useful operation of IoT and we're using it to produce an affordable security system for homes as well as artificial use. Besides that, the current home security system on the request was too precious due to the complexity of the bias which occasionally used a precious microcontroller or microprocessor. The Purpose of this trouble was to develop all of the Burglar Alarm system factors which were preliminarily linked and specified as a result of the system design phase conducted earlier . After a brief preface, an executive summary of the results of the development effort is presented . this is followed in a complete description of the overall Burglary Alarm System (BAS), It’s installation , operation, and the tackle details of each of the Individual Subsystems. The test proves that this system is dependable with low- cost, so it can be extensively applied to ultramodern places.

Design of an IoT platform for data analytics based fault detection and classification in solar PV power plants using CFKC and ODENN

New policies are commenced all over the globe to diminish the use of fossil fuels, which gives rise to the augmented utilization of solar energy (SE). The photovoltaic (PV) system’s performance is extremely environmental variables reliant. Long-range transmission of SE is incompetent as well as complex to carry in the PV system. It can be affected by disparate sorts of faults, which cause severe energy loss all through the system operation. Thus, it is vital to incessantly monitor the solar PV (SPV) system to detect as well classify the faults by preventing energy losses. The IoT applications in SE production engage sensor devices that are fixed to the generation, and transmission, together with distribution equipment. These devices assist in monitoring the operation of the SPV power plant (SPVPP) system remotely in real-time. Presenting a new algorithm that can perform fault detection and classification in a PV system to a higher level of accuracy is the major contribution of this work. Thus, this work designs as well as develops an IoT platform for carrying out analytical tasks that can analyze data generated as of IoT operating systems to detect as well as classify faults in the SPVPP. Initially, the data collected from the dataset is pre-processed in which data duplication is performed using Hadoop distributed file system (HDFS) and then the fault is detected from the pre-processed data using the cosine function based k-means clustering (CFKC) technique in the SPV system. Finally, the obtained fault data is fed into the optimized deep learning centered ENN (ODENN) method which classifies the faults. The proposed techniques detect as well as classify the faults effectively that are experimentally proved by means of comparing them with the prevailing techniques, namely ENN, ANN and SVM, along with KNN in terms of some quality measures. The obtained results for ODENN showed an accuracy of 98.99%, specificity of 97.6%, and a sensitivity of 97.02%.

Smart IoT lysimetry system by weighing with automatic cloud data storage

Water is an essential resource for human life and for agricultural food production, climate change and the increased demand for water resources, has already impacted the availability of this resource in the world, in view of future forecasts of scarcity its rational use will be increasingly demanded. Thus, more efficient irrigation systems that optimize water use and reduce water loss will be important tools to help agriculture in the future. This work aims to develop and calibrate an intelligent and autonomous system for determining reference evapotranspiration (ETo), and precise irrigation control optimized for IoT operation, with data storage in a cloud database. Intelligent system integrates: mechanical weighing lysimeter; sensors and actuators commanded by a digital controller based on a microcontroller platform, connected to the internet through a Wi-Fi communication interface to cloud computing services models: platform as a service (PaaS) and software as a service (SaaS). The operation test results reveal that the system is capable of accurately measuring ET O values, consistent with the values estimated by the FAO-56 standard method, with high sensitivity and precision for determining mass variations, with an R² Correlation coefficient = 0.999321, and coefficient of determination of adjusted R² of 0.999321. Being a viable option for application in precision agriculture.

Obtaining a broadband magneto-mechano-electric generator with large power for IoT operation

The strong interaction between the MME generator and MFC resulted in a wide bandwidth of 57–61.5 Hz while maintaining a large power under different magnetic fields.

ML Based Authentication Scheme for Data Storage in Cloud Based IoT

Today, organizations are using IoT devices to accurately collect real data and make better business decisions to increase customer satisfaction. The data collected should be stored and stored in a well-designed storage system, which encourages companies to review their data storage infrastructure. The company needs to store data created by the Internet of Things, and that data grows exponentially, forcing IoT to think about cloud storage for data storage. Security issues are a major concern when handling and processing data in DI and cloud environments. Secure integration of IoT and cloud computing, and introduced a model to ensure this integration. The secure database of any IoT operating system was suffers from poorly protected read and write functions, which limits data storage on any IoT platform. In addition, clouds can provide space to store a wide variety of data that plays an important role in the world of cyber security. However, large centralized systems operating in the cloud are also very vulnerable due to their power, so they can be transformed into a kind of double-edged sword. In this paper, we propose a novel secure lightweight authentication scheme for data storage (SLA-DS) in IoT and cloud server. The SLA-DS integrates IoT and cloud technology combination which mainly focuses on security issues.

IOT in Improving the Standards of Food and Farming

Information & communication technology give tremendous services to all the sectors including food technologies. On the other hand, this world fighting against hunger, food wastes, health and nutrition-less food. There are way further technologies increase not only the profitability, but also quality and product rate. IoT technologies use by the food technologies in numerous countries for food safety, transportation, packaging, temperature monitoring, nutrition analysis, and find the imperfect foods using IoT bias and operations via smart phones and computers in real time. This studies concentrated on IoT in food technologies in terms of the food product, security issues and possible results for those issues. For that, a methodical literature review was conducted and anatomized using qualitative and quantitative system. Findings have verified that, IoT and applicable technologies appreciatively give its full support to increase the demand and quality of food product process. Meanwhile, The data sequestration issues and give immediate specialized results were the major security issues faced by those IoT bias and operation. In addition to that, neither of these papers plant conducted in Sri Lanka related to this study and none of the studies plant related to IoT, Food- destroying blankets. Piecemeal from these all, this study suggested developing IoT system and operation grounded on pall computing to drive down from the food- destroying blankets. Eventually, it's planned to conduct a statistical analysis to find the prospects of food scientists, food product industrialists, and growers. This paper purely grounded on exploration workshop from notorious Journals and conferences.

Ethical Decision Making in Iot Data Driven Research: A Case Study of a Large-Scale Pilot.

IoT technologies generate intelligence and connectivity and develop knowledge to be used in the decision-making process. However, research that uses big data through global interconnected infrastructures, such as the ‘Internet of Things’ (IoT) for Active and Healthy Ageing (AHA), is fraught with several ethical concerns. A large-scale application of IoT operating in diverse piloting contexts and case studies needs to be orchestrated by a robust framework to guide ethical and sustainable decision making in respect to data management of AHA and IoT based solutions. The main objective of the current article is to present the successful completion of a collaborative multiscale research work, which addressed the complicated exercise of ethical decision making in IoT smart ecosystems for older adults. Our results reveal that among the strong enablers of the proposed ethical decision support model were the participatory and deliberative procedures complemented by a set of regulatory and non-regulatory tools to operationalize core ethical values such as transparency, trust, and fairness in real care settings for older adults and their caregivers.

Anomaly Detection and Performance Analysis by Using Big Data Filtering Techniques For Healthcare on IoT Edges

The IoT is a sensors world that detects countless physical events in our environment and transforms them into data, and transfers this data to different environments or digital systems. The usage areas of Internet of things-based technologies are constantly increasing and technologies are being developed to support the IoT infrastructure. But, in order to effectively manage the large number of big-data generate in the detection layer, it should be pre-processed and done in accordance with big-data standards. For the effective management of big data, it is imperative to improving the standards of the data set, and filtering methods are being developed for a higher quality data set. For instance, using data cleaning methods is a preprocessing method that facilitates data mining operations. In this way, more manageable data is obtained by preventing the formation of interference and big data can be managed more effectively. In this study, we investigate the efficient operation of IoT and big data originating from the internet of things. Additionally, real-time anomalous data filtering is performed on IoT edges with a data set consisting of six different data produced in real- time. Furthermore, the speed and accuracy performances of classifiers are compared, and machine learning algorithms such as the random cut forest-RCF, logistic regression-LR, naive bayes-NB, and neural network-NN classifiers are used for comparison. According to the accuracy performance values, the RCF and LR classifiers are very close, but considering the speed values, it is seen that the LR classifier is more successful in IoT systems.

Performance Improvement of SIMD Processor for High-Speed end Devices in IoT Operation Based on Reversible Logic with Hybrid Adder Configuration

Performance Improvement of SIMD Processor for High-Speed end Devices in IoT Operation Based on Reversible Logic with Hybrid Adder Configuration

Dynamic Monitoring Method of Abnormal Intrusion of Distribution Terminals for Power Internet of Things

At this stage, the security protection system of power IOT distribution terminals lacks effective anomaly intrusion detection methods, and the distribution terminals of power IOT have the characteristics of large number and wide range, which plays an important role in maintaining the normal operation of power IOT. Once the distribution terminal encounters illegal intrusion, it can lead to the damage of the important infrastructure of the distribution terminal of the power Internet of things, and pose a great security threat to the stable operation of the power Internet of things. This paper analyzes the content of building the network flow order of distribution terminals for the power Internet of things. From the aspects of basic attribute selection, data processing, building the network flow order, attribute classification, abnormal feature extraction, and realizing abnormal monitoring, it deeply discusses the abnormal intrusion dynamic monitoring method of the network flow order for the power Internet of things, in order to accurately monitor the abnormal intrusion dynamic data and enhance the abnormal intrusion dynamic monitoring effect, Improve the level of abnormal intrusion dynamic monitoring technology, and effectively ensure the safe and stable operation of power distribution terminals of the Internet of things.

IoT Based Image Processing Filters

Internet of things (IoT) becomes the backbone of the advanced countries and it has a real contribute to exchange the traditional style or way of practical life, even personal life into smart style, with (IoT) technology the life become more and more easy and professional. internet of things achieves various applications coordinate with sensors and standard protocols to apply what is called machine -to- machine connection (M2M), in this paper we will talk more about the concept of (M2M), the main component of internet of things and finally the common protocols that is used in network, in addition to that this work present an IOT operation with processing system using camera for capturing image and Xilinx system generator(XSG)models for designing image processing algorithms and the result of the processing is an image with black and white for edge detection and Thresholding models and gray color image for gray enhancement model.

IoT Based Image Processing Filters

Internet of things (IoT) becomes the backbone of the advanced countries and it has a real contribute to exchange the traditional style or way of practical life, even personal life into smart style, with (IoT) technology the life become more and more easy and professional. internet of things achieves various applications coordinate with sensors and standard protocols to apply what is called machine -to- machine connection (M2M), in this paper we will talk more about the concept of (M2M), the main component of internet of things and finally the common protocols that is used in network, in addition to that this work present an IOT operation with processing system using camera for capturing image and Xilinx system generator(XSG)models for designing image processing algorithms and the result of the processing is an image with black and white for edge detection and Thresholding models and gray color image for gray enhancement model.

Incentive mechanism for collaborative distributed learning in Artificial Intelligence of Things

Artificial Intelligence of Things (AIoT) is an emerging trend that integrates artificial intelligence into the Internet of Things, which enables intelligent IoT operations and smart industrial applications. AIoT can generate a large amount of data from the network edge. Due to the concerns of bandwidth and privacy, it is often impractical to move the collected data to the cloud. To address this issue, a collaborative distributed learning has been proposed to let the clients collaboratively train a machine learning model together with their local data in a distributed manner. In this paper, we study the free market incentive mechanism for collaborative distributed learning, where multiple parameter servers (PSs) compete with each other to motivate clients to contribute model training. Specifically, the Stackelberg game with multiple leaders and multiple followers has been designed to analyze the incentive mechanism. Different experiments have been performed to illustrate the efficiency of the proposed approach. In particular, compared with the state-of-the-art under the same budget constraint, the final average utility of the PSs can be increased by at least 94.3%.

CNN Intrusion Detection for Threat Analysis of a Network

The technological advancement realized in the discovery and embrace of both IoT and IIoT is totally indispensable. Many systems and subsystems both robust and miniaturized have made their existence into the technical arena due to IoT. It goes without saying that IoT has brought into light very diverse benefits that cut across universal applications.However, the pre-requisite of a network channel existence for an IoT operation to be successful is the only pitfall that this essentially unique system possesses. There is a significant amount of danger associated with transmission networks. They have very substantial susceptibility to both online and offline threats by malicious cyber intentions.This paper focuses on the analyses of the threats posed to these IoT networks through Artificial Neural Networks. Specifically, a model is trained through recurrent and convolutional neural network to do intensive analysis on the threat intensity, type and threat source for data logging purposes. The Intruder detection system (IDS) explored in this paper registers a success rate of 99% based on the empirical data posed to the model.