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An Optimal Load Balancing Framework for Fog-Assisted Smart Grid Applications
The growth of the Internet of Things (IoT) causes a significant amount of data to come in from physical devices and sensors, which adds to the latency and processing delays in smart grid applications. The pay-per-model method of transmitting gathered data that cloud computing offers improves scalability and functionality for end devices, which increases smart grid efficiency. Milliseconds matter in the crucial realms of load balancing, resource usage, and distribution systems, where any latency or jitter is unacceptable. By strategically positioning processing, networking, storage, and communication capabilities at the network edge, fog computing, an outgrowth of cloud technology, successfully addresses current issues in service groups. This paper introduces a unique hybrid framework on a highly virtualized platform and proposes three potential load balancing algorithms: throttled, Round Robin, and a novel Equilibrium Optimizer with Simulated Annealing (EO-SA). The article provides a comprehensive investigation on several load balancing techniques for obtaining optimized services in a smart grid environment thereby focusing on better utilization of network resources and reduction of costs. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An online signature method using DNA based bio-hash for positive identification and non-repudiation
This work focuses on using biological data as a unique feature to generate e-Signature. DNA, the blue print of life is of unique nature. The signature created using biological data will be difficult to repudiate in the scenario of a legal dispute. Applications of human DNA are not limited to molecular biology, with the advents of fast growing technologies it is possible to inject DNA into e-Signature for positive identification. The proposed methodology uses Signature DNA as a unique biological feature for the registrant. This work has various phases, the first phase includes creating the Signature DNA using hybridized unique DNA segments of the individual (Registrant) which is the unique identification of the user and difficult to duplicate and repudiate. It generates a Bio-Hash of the Signature DNA. The DNA-Hash generated serves for positive identification of the user which computed with the hash of the e-Document and a random value serve as a Bio-Sign (e-Signature) for the e-Document in the second phase. Bio-Sign converted into QR code with a link to the e-Sign service providers website will ensure usability for verification. In the verification phase the verifier scans the QR code which connects to the e-Service provider's web link. The service provider computes and verifies the document and ensures the e-Signature is valid or not to the verifier. If the signer repudiates the signature, positive identification using DNA helps to achieve Non-Repudiation, the last phase. In the scenario of a legal dispute, the registrant cannot repudiate as the authorities can provide positive identification using the DNA Signature for greater assurance. The proposed technique ensures authentication, integrity and Non-Repudiation with Zero knowledge scenario to the verifier. 2017 IEEE. -
An objective function based technique for devignetting fundus imagery using MST
Fundus photography is a powerful imaging modality that is utilized for detecting macular degeneration, retinal neoplasms, choroid disturbances, glaucoma and diabetic retinopathy. As the illumination source in fundus imaging is situated at the center of the fundus camera, the illumination at the peripheral regions of the images would be relatively less than the center, which is termed vignetting. Vignetting adversely affects the performance of computerized methods for analyzing fundus imagery. A devignetting method for fundus imagery based on the Modified Sigmoid Transform (MST) is proposed in this paper. Gain (A) and centering parameter (?) of MST have a crucial influence on its performance. For low values of the gain, local contrast is penalized, and the overall dynamic range is compressed. When the value of gain is very high, the images after the illumination correction will have a washed out appearance. The optimum value of gain is determined in this paper from an objective method based on two statistical indices, Average Gradient of Illumination Component (AGIC) and Error of Enhancement (EME). MST with gain value defined via objective methods is able to correct the uneven illumination in fundus images without penalizing the local contrast. The proposed method is compared with illumination equalization model, homomorphic filtering and Adaptive Gamma Correction (AGC) and was found to be superior in terms of naturality uniformity of background illumination, and computational speed. 2018 -
An Objective Evaluation of Harris Corner and FAST Feature Extraction Techniques for 3D Reconstruction of Face in Forensic Investigation
3d reconstructed face images are the volumetric data from two dimensions, it can provide geometric information, which is very helpful for different application like facial recognition, forensic analysis, animation. Reconstructed face images can provide better visualization, than a two dimensional image can provide. For a proper 3d reconstruction one of primary step is feature extraction. The objective of this study is to conduct a comprehensive evaluation of two prominent traditional feature extraction techniques, namely Harris Corner and FAST (Features from Accelerated Segment Test), for the purpose of 3D reconstruction of face images in forensic analysis. In this research paper feature extraction was carried out using the Harris corner detection and FAST Feature technique. 3D reconstruction is completed using the retrieved features. In this study a comparative analysis was conducted assessing the aspect ratio, depth resolution. The results of the assessment provide valuable insights into the strengths and limitations of both techniques, aiding researchers and practitioners in selecting the most suitable method for 3D face image reconstruction applications. 2023, Ismail Saritas. All rights reserved. -
An Novel Cutting Edge ANN Machine Learning Algorithm for Sepsis Early Prediction and Diagnosis
Early detection and diagnosis of sepsis can significantly improve patient outcomes, but current diagnostic methods are limited. The problem addressed in this paper is the early detection and diagnosis of sepsis using machine learning algorithms. Sepsis is a life-threatening condition that can rapidly progress and cause organ failure, leading to increased mortality rates. Early detection and treatment of sepsis are critical for improving patient outcomes and reducing healthcare costs. However, sepsis can be challenging to diagnose, and existing methods have limitations in terms of accuracy and timeliness This research proposes a new cutting-edge Optimized Artificial Neural Network machine learning algorithm for sepsis early prediction and diagnosis. The proposed algorithm combines different data sources, including patient vital signs, laboratory results, and clinical notes, to predict the likelihood of sepsis development. The algorithm was evaluated on a large dataset of patient records and achieved promising results in terms of accuracy, Precision and Recall. The proposed algorithm can potentially serve as a valuable tool for clinicians in the early detection and diagnosis of sepsis, leading to better patient outcomes. 2023 American Institute of Physics Inc.. All rights reserved. -
An MoS2/PEDOT:PSS-based flexible NIR-responsive soft actuator
The development of sophisticated smart devices heavily relies on flexible soft actuators combined with near infrared (NIR) light responsive two-dimensional (2D) materials. Soft robots provide a number of benefits, such as flexibility, high sensitivity, compliance and security. Amidst many manufacturing and driving approaches, light has surfaced as a facilitator, aiding in the fabrication of soft actuators. Using few-layered molybdenum disulphide (MoS2) and poly(3,4-ethylenedioxythiophene)/poly(styrene sulfonate) (PEDOT:PSS), the current work aims to introduce a polymer nanocomposite film for soft actuator applications under NIR light exposure. The actuation behavior was impacted by PEDOT:PSS under NIR light exposure. In order to incorporate controllable deformation of the actuator, the photothermal properties of the composite film were investigated. In situ Raman spectroscopy and the density functional theory (DFT) calculation explain the structural change and energy optimization of PEDOT:PSS. A soft insect was further designed based on this photothermal property, which can deform under light exposure. Therefore, such flexible design has huge potential for soft robotics applications in modern technologies. 2025 RSC. -
An IoT-based tracking application to monitor goods carrying vehicle for public distribution system in India
Designing a secured transportation system to handover food items to various fair price shops is one of the objectives of smart city development in India. In this paper, an IoT-based tracking solution for moving goods carrying vehicle is proposed. A hardware prototype model is developed using different sensors with GPS/GPRS tracking module and is attached to the vehicle. An alarm is raised to make decision in case of trouble or malfunction. The data generated by the model during the movement of vehicle is encrypted using RSA algorithm and sent to cloud for monitoring by an application developed using PHP and analysis using MapReduce programming model. Experiments are conducted to study the feasibility of the developed model during deployment. From the experiment it is observed that, the developed hardware model and the application meet the objective of monitoring vehicle, safer recovery in case of malfunction and secured delivery of items. Copyright 2021 Inderscience Enterprises Ltd. -
An IoT-Based System for Fault Detection and Diagnosis in Solar PV Panels
This abstract describes an IoT-based system for fault detection and diagnosis in solar PV panels. The proposed Fuzzy logic-based fault detection algorithms aims to improve the performance and reliability of solar PV panels, which can be affected by various faults such as shading, soiling, degradation, and electrical faults. The system includes wireless sensor nodes that are deployed on the panels to collect data on their electrical parameters and environmental conditions, such as temperature, irradiance, and humidity. The collected data is then transmitted to a central server for processing and analysis using machine learning algorithms. The system can detect and diagnose faults in real-time, and provide alerts and recommendations to maintenance personnel to take appropriate actions to prevent further damage or downtime. The system has several advantages over traditional manual inspection and maintenance methods, including reduced downtime, lower maintenance costs, and improved energy efficiency. The proposed system has been validated through experimental tests, and the results show that it can accurately detect and diagnose faults in solar PV panels with high reliability and efficiency. 2023 EDP Sciences. All rights reserved. -
An IoT-Based Model for Pothole Detection
Maintenance of the good roads plays a very important role in the growth of the country. Poorly maintained roads can lead to potholes which causes severe accidents. To overcome the damage caused by poor roads, the pothole detection model has been proposed in this paper. In recent days, the Internet of Things (IoT)-embedded models are developed in different applications. The main objective of the proposed work is to design the IoT prototype to collect data which can be used to detect potholes and humps. This prototype is embedded with three sensors, namely accelerometer, ultrasonic sensor, and GPS. The data from these sensors is collected by the controller and transmitted by Wi-Fi module to store in the cloud. The collected data can be downloaded as a spreadsheet from the cloud and can be used for different data analysis applications like pothole notifier application. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An iot-based fog computing approach for retrieval of patient vitals
Internet of Things (IoT) has been an interminable technology for providing real-time services to end users and has also been connected to various other technologies for an efficient use. Cloud computing has been a greater part in Internet of Things, since all the data from the sensors are stored in the cloud for later retrieval or comparison. To retrieve time-sensitive data to end users within a needed time, fog computing plays a vital role. Due to the necessity of fast retrieval of real-time data to end users, fog computing is coming into action. In this paper, a real-time data retrieval process has been done with minimal time delay using fog computing. The performance of data retrieval process using fog computing has been compared with that of cloud computing in terms of retrieval latency using parameters such as temperature, humidity, and heartbeat. With this experiment, it has been proved that fog computing performs better than cloud computing in terms of retrieval latency. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
AN IOT-BASED COMPUTATIONAL INTELLIGENCE MODEL TO PERFORM GENE ANALYTICS IN PATERNITY TESTING AND COMPARISON FOR HEALTH 4.0
Parental comparison and parenthood testing are essential in various legal and medical scenarios. The accuracy and reliability of these tests heavily rely on the gene analysis algorithms used. However, analyzing the quality of succession data are quite challenging due to the presence of detrimental characteristics. To address this issue, we propose using machine learning-based algorithms such as clustering (Correlation-based) and Classification (Modified Naive Bayesian) to separate these characteristics from the parent-child gene array. This progression helps to identify, validate, and select tools, techniques for scrutinizing indecent sequences, leading to accurate and reliable results. In this paper, we present an IoT-based intelligence tool for parental comparison that uses a secure gene analysis algorithm. The model employs multiple sensors and devices to collect genetic data, which is then securely processed and analyzed using contemporary algorithms. The suggested model uses advanced techniques such as encryption and decryption to ensure the privacy and confidentiality of the genetic information. Our experimental consequences reveal that the proposed model is reliable, secure, and provides accurate results. The model has the potential to be used in various legal and medical contexts where the security and reliability of genetic data are critical. 2023 Little Lion Scientific. -
An IoT-based agriculture maintenance using pervasive computing with machine learning technique
Purpose: In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc. In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset. Design/methodology/approach: In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis. Findings: In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like Threshold segmentation and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained. Originality/value: The implemented machine learning design is outperformance methodology, and they are proving good application detection rate. 2021, Emerald Publishing Limited. -
An IOT enabled seats management system of moving bus transportation and method thereof /
Patent Number: 202111041762, Applicant: Mr. Chandan Choubey.
The present invention discloses an IoT Enabled vacant seats management system of moving bus transportation and method thereof. The system and method include, but not limited to, a handheld device with a user interface to view the desired bus on the route available for commuting and checking the vacant seat information in real-time; a GPS unit provided with the handheld device and a communication device, which is installed at bus stand; an infrared sensor to detect further movement and number of the commuters entered the bus in conjunction with a camera device. -
An iot based wearable device for healthcare monitoring
Nowadays IoT (Internet of Things) devices are popularly used to monitor humans remotely in the healthcare sector. There are many IoT devices that are being introduced to collect data from human beings in a different scenario. These devices are embedded with sensors and controllers in them to collect data. These devices help to support many applications like a simple counting step to an advanced rehabilitation for athletes. In this research work, a mini wearable device is designed with multiple sensors and a controller. The sensors sense the environment and the controller collects data from all the sensors and sends them to the cloud in order to do the analysis related to the application. The implemented wearable device is a pair of footwear, that consists of five force sensors, one gyroscope, and one accelerometer in each leg. This prototype is built using a Wi-Fi enabled controller to send the data remotely to the cloud. The collected data can be downloaded as xlsx file from the cloud and can be used for different analyses related to the applications. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
An IOT based system to track feasible business model /
Patent Number: 202241051204, Applicant: Amrita Chaurasia.
E-commerce that spans international borders makes it possible for smaller businesses to more swiftly penetrate many international marketplaces. The purpose of this article is to investigate the ways in which effective market creation influences the international performance of small and medium-sized businesses (SMEs) that are involved in international e-commerce. Using the effectuation theory as a foundation, we propose that businesses can generate demand in foreign markets by developing innovative new ways for customers to interact and engage with them in the digital world. -
An IOT based system for antitheft security detection in a cloud environment /
Patent Number: 202241007566, Applicant: Dr. S. Brinthakumari.
Internet of Things (IOT) is the connection of things / objects through networks, in which things or objects can interact with each other without or minimal human intervention. Now-a-days, Security has grown to be the maximum tough task. Everyone wishes protection however in present scenario, not anything is secure now no longer even of their very own houses. In this work, we are proposing an IoT based system for Antitheft Security Detection in a Cloud Environment. In this system we used PIR sensor, ultrasonic sensor and Vibration sensor. -
An IOT based self phased analysis of adverse effects in covid recovered patients /
Patent Number: 2021101599, Applicant: Jasmine Selvakumari Jeya I. -
An IOT based self phased analysis of adverse effects in covid recovered patients /
Patent Number: 2021101599, Applicant: Jasmine Selvakumari Jeya I. -
An IOT based remote solar monitoring system design /
Patent Number: 202141010711, Applicant: Sathishkumar V E.
The rapid development of technologies, cost of renewable energy equipment is reducing worldwide and encouraging Solar Power Plants installations in large quantity. The Solar Power Plants generates the Solar Energy, converts sun light into electricity by Photovoltaic cells, mirrors, and lenses. The commercial way of producing the energy from the sun is the viable way of renewable energy. The large scale of the solar power plants are installing in the locations which are not accessible to monitor everyday. The remote real time solar monitoring system is required for each individual solar panel in the solar energy system to know the performance in the form of power output levels. -
An Iot Application to Monitor the Variation in Pressure to Prevent the Risk of Pressure Ulcers in Elderly
Pressure sores are a common form of skin problem which occurs with patients who are bedridden or immobile. It is believed that the occurrence of ulcers due to pressure can be prevented. Making best use of resources available and providing comfort to the patient, it is very much important to identify people at risk and provide preventive measures. This work is associated with a method to analyze pressure from pressure points on bedridden patients. A system is presented in this work that continuously monitors the pressure from pressure points using force sensors and sends an alarm to the nurses or caretakers if there is a variation in the pressure exerted on a specific area. 2018 IEEE.