Advancement of IoT in Blockchain Technology and its Applications

Hybrid Mechanism of Secure Multi-Party Computation and Blockchain for Medical IoT

Sai Sukeerthi Yarlagaada — 2024-03-01

The convergence of the Internet of Things (IoT) with medical applications presents unprecedented opportunities for healthcare advancement but also introduces significant security and privacy challenges. This paper proposes a novel hybrid mechanism that combines Secure Multi-Party Computation (SMPC) and Blockchain technologies to address these challenges effectively. The proposed hybrid mechanism aims to ensure the confidentiality, integrity, and availability of sensitive medical data collected from IoT devices while maintaining patient privacy and data ownership. By leveraging SMPC, the system enables multiple parties, including patients, healthcare providers, and medical researchers, to collaboratively compute functions over their distributed data without exposing individual inputs. This facilitates secure data sharing and analysis without compromising confidentiality. The integration of Blockchain technology provides a tamper-resistant and immutable ledger for recording transactions and data provenance. Smart contracts deployed on the Blockchain automate the execution of predefined rules and policies, enhancing transparency and auditability in the system. The decentralized nature of Blockchain ensures resilience against single points of failure and malicious attacks. Through the proposed hybrid mechanism, medical IoT devices can securely transmit data to authorized parties for analysis and decision-making while preserving patient privacy and trust. Real-world implementation of the proposed system demonstrates its effectiveness in ensuring data security, privacy, and integrity in medical IoT environments.

Enhancing ATM Security: A Smart Approach

Md. Hussain Ansari — 2024-03-05

Typically, in ATM transactions, unauthorized individuals can easily withdraw cash merely by obtaining someone's PIN. To bolster the security of transactions and ensure they occur under the account holder's knowledge and consent, we've implemented RF readers for authentication. Our system is equipped with a centralized database containing the RFID of each individual, accompanied by their photograph for verification purposes. When a person presents their RF tag to the RF reader, it activates a camera, capturing the cardholder's image. Subsequently, the system conducts a comparison with the database to verify the ID and match it with the individual's facial features. If the facial recognition fails to align with the original card account holder, the system promptly dispatches an image along with an OTP via email to the account holder, signalling an attempted unauthorized use of their ATM. Access to cash withdrawal is granted only upon successful entry of the OTP, thereby thwarting potential fraud. Conversely, if the account holder initiates a transaction, the system recognizes their face from its database and authorizes direct access to withdraw cash from the ATM without necessitating further verification steps. This comprehensive security protocol significantly mitigates the risk of fraudulent ATM transactions, providing account holders with heightened assurance regarding the safeguarding of their funds and personal information.

IoT System for Real-Time Water Monitoring

Kaustubh G. Pise — 2024-03-07

Water is one of the vital parameters in any living organism's life. Nature consists of five elements of which water is the most valued element. As it is very important, the composition of water or water bodies consists of parameters such as pH, temperature, TDS, turbidity etc. which have their specified values for specified types of reservoirs. To use the water bodies effectively, one should have adequate knowledge of water parameters. So, to depict these parameters there were lots of methodologies from the past few years. Every time technical evolution hits the traditional systems also, that made changes from traditional measuring techniques to new innovative and time-saving methods with the help of engaging technologies like IoT. The swift advancement of IoT technology offers a fresh method for acquiring, transmitting, and processing real-time data. Utilizing this technology, individuals can remotely access continuous updates on water quality from distant locations.

FIRE-NET: Fire Detection and Assistance System Using Machine Learning

Manasa Sandeep — 2024-03-07

An advanced technology-driven approach to augment fire detection and emergency response mechanisms is presented in this paper. The integration of the Internet of Things (IoT) and Machine Learning (ML) is explored for the development of an intelligent fire detection and emergency alert system. Within the proposed system, IoT encompasses a fire sensor and a GPS sensor to detect potential fire incidents and capture precise location data, respectively. Machine Learning algorithms, utilizing a curated dataset from Kaggle, are employed to analyze fire data, enhancing the accuracy of detection by minimizing false alarms. Upon confirmation of a legitimate fire incident, the system autonomously triggers an emergency response mechanism, promptly transmitting location details to multiple systems within the fire department. This seamless and automated communication channel facilitates timely intervention, minimizing widespread damage, and enhancing emergency service efficiency. The survey delves into the current state-of-the-art technologies, methodologies, and challenges associated with the integration of IoT and ML in fire detection and emergency response systems, contributing to the development of intelligent solutions for public safety improvement and the mitigation of fire-related incidents' impact on communities

A Novel Advanced IoT System for Predictive Maintenance Systems and Supply Chain Optimization Solutions for Manufacturing Industries

P. Siva Satya Prasad — 2024-04-23

This paper presents a pioneering approach to revolutionize manufacturing industries through the development of a novel advanced Internet of Things (IoT) system tailored for predictive maintenance and supply chain optimization. Leveraging the vast array of data generated within manufacturing environments, the proposed system integrates cutting-edge IoT technologies with advanced predictive analytics to enable proactive identification of equipment failures and optimization of supply chain operations. The research encompasses a comprehensive dataset comprising sensor data, maintenance records, and supply chain information sourced from industrial pumps, facilitating the creation of robust predictive maintenance models and insightful supply chain optimization strategies. Experimental procedures involve data pre-processing, feature engineering, and model development, followed by rigorous evaluation against ground truth data. The results showcase significant enhancements in equipment up-time; maintenance cost reduction, and supply chain responsiveness, demonstrating the efficacy and scalability of the IoT-based solutions. Through this innovative approach, manufacturing industries can achieve unprecedented levels of operational efficiency, cost savings, and competitive advantage, paving the way for transformative advancements in predictive maintenance and supply chain management paradigms.