This paper presents a solar photovoltaic (PV) array positioning and tracking system that integrates with the Internet of Things (IoT). The proposed system is designed to optimize the energy output of a solar PV system by adjusting the position and orientation of the PV array in real-time based on weather and solar irradiance data. The system consists of a microcontroller-based control unit, sensors to measure weather and solar irradiance, and a stepper motor to adjust the position of the PV array. The control unit uses the data from the sensors to determine the optimal position of the PV array and sends commands to the stepper motor to adjust its position accordingly. The IoT integration allows the system to communicate with other devices and services, enabling remote monitoring and control of the PV array. The system's performance was evaluated through simulations and experiments, demonstrating significant improvements in energy output compared to a fixed-position PV array. The proposed system has potential applications in both residential and commercial solar PV systems, providing a cost-effective and efficient solution for maximizing energy generation

IoT Based Single Axis Solar Tracker

Driver interruption incredibly affects the security of individuals &it is a pertinent theme for various applications, from independent driving help to insurance agency &examinations. We address the issue of programmed acknowledgment of driver interruptions by taking advantage of profound learning & convolutional brain organizations. Assessment has been done on the State Ranch dataset, which comprises of 10 dis-tinct activities performed by 26 subjects, for example, ordinary driving, messaging, chatting on the telephone, working the radio, drinking, arriving at behind, &so forth. One of the most difficult points inside the discipline of canny transportation structures is the programmed understanding of the driver's direct. This examination researches diverted driver pose acknowledgment as a feature of the human development acknowledgment system. Various fender benders were accounted for that were as a consequence of occupied drivers. Our goal became to move along the exhibition of identifying drivers' diverted developments. The occupied thought process force ways of behaving include messaging, talk me at the phone, working the radio, consuming, achieving toward the rear of, fixing hair &cosmetics, &talk me to the traveler.

Distracted Driver Posture Recognition

Cloud computing has revolutionized IT delivery by offering scalable on-demand internet services encompassing software, platforms, and infrastructure. However, cloud services face significant performance challenges due to their susceptibility to failures given their vast operational scale. Implementing fault tolerance in dynamic cloud services is a key challenge, with complex configurations and dependencies complicating deployment. This paper introduces an innovative approach that combines double deep Q-learning (DDQL) with a dynamic fault-tolerant real-time scheduling algorithm (DFTRTSA) to enhance fault tolerance in real-time systems. DDQL, an extension of deep Q-learning, optimizes the fault-tolerance decision-making process. The algorithm adjusts scheduling strategies dynamically based on system conditions and errors. The fusion of DDQL and DFTRTSA aims to create a resilient and adaptive fault-tolerant mechanism, ensuring uninterrupted operation while meeting real-time requirements. This adaptive approach efficiently manages resources, meets deadlines, and gracefully handles errors, as demonstrated through experiments. Our DDQL-DFTRTSA method outperforms conventional fault-tolerant mechanisms in defect tolerance, energy efficiency, downtime reduction, and system dependability. It proves to be an ideal solution for real-time systems in dynamic and unpredictable environments.

Enhancing fault tolerance: dual Q-learning with dynamic scheduling

Data privacy is always a concern when it comes to any kind of personal data. In medical domain, the medical data of a patient needs to be more protected and also be accessible. The medical industry is facing the problem of storing and sharing medical data with multiple parties while promising security at the same time. The hospitals don’t share their patient data with other hospitals or organizations because of a lack of security in the infrastructure used to transfer this sensitive data. With the introduction of a blockchain based Electronic Medical Records (EMR) may help to solve the issue to a certain extent. From literature it is found that the current blockchain EMR schemas focus on patient-owned models. In this paper, design and development of a web-based software to store patient electronic medical records in a block chain-based back end that is supplied over the internet using Amazon Web Services (AWS) Managed Blockchain is proposed. The major goal is to ensure that the model fits the Confidentiality, Availability, and Integrity (CIA) trinity and performs effectively in benchmarks. In comparison to literature, the proposed block chain demonstrated up to a 65% increase in read throughput, 260%-400% improvement in write throughput on certain work loads, in addition to being exceptionally dependable and secure. The underlying model can be upgraded using cutting-edge algorithms and approaches to lower the model’s maintenance costs and raise its efficiency. The proposed model has been compared with already proposed method in literature and tends to give significant results.

Data Address focused Implementation of Low Latency Secure Blockchain for EMRs

Emerging Research Trends in Data Deduplication: A Bibliometric Analysis from 2010 to 2023

Cloud computing these days, may be a rudimentary correction happening inside the space of information innovation. It plays a crucial part in terms of data putting away and diminishing the esteem to business visionaries. In any case, information security proceeds to be one in all the most elevated issues for cloud computing. To deliver the secure information capacity and recovery, a few procedures are anticipated be that as it may the bulk of them confront a few downsides that reduce the common sense of Cloud Computing. This work scrutinizes a half breed cloud approach for proficient information capacity and security. The aim is to propose a model to identify duplication and prevent it thereby using the cloud storage efficiently.

A Hybrid Cloud Approach for Efficient Data Storage and Security

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A Hybrid Cloud Approach for Efficient Data Storage and Security