Deepika Rani Sona

Biometric-based access control systems are gaining popularity over traditional security methods such as keys orpersonal identification numbers (PINs), offering stronger protection for homes and organizations as a safer and smarter alternative. This research presents a sequential fingerprint authentication framework in which multiple biometric inputs must be verified in a predefined order to grant access. Unlike conventional single-fingerprint authentication systems, the proposed design transforms biometric authentication into a biometric passcode mechanism, requiring both correct identity and correct order of fingerprints. This additional layer significantly increases resistance to unauthorized access attempts while maintaining a low-cost embedded implementation suitable for practical access control applications. This solution is flexible and affordable, and it can be used in residential, commercial and industrial environments. By combining fingerprint authentication with sequential verification, the proposed system provides a robust, scalable and user-friendly security solution. The integration with Arduino and relay control further enhances its flexibility, making it a practical choice for modern smart security systems.

The rapid expansion of 5G networks offers ultra‐reliable low‐latency communication (URLLC), enhanced mobile broadband (eMBB), and massive machine type communication (mMTC) demands for intelligent and adaptive optimization strategies. This is particularly critical in ultra‐dense urban environments, where interference, traffic variability, and energy inefficiencies pose major challenges. This study offers a next‐generation optimization framework driven by machine learning. It combines random forest classifiers, long short‐term memory (LSTM) networks, and gradient boosting regressors. Together, these models improve key performance indicators, such as the signal‐to‐interference‐plus‐noise ratio (SINR), reference signal received power (RSRP), latency, throughput, and energy efficiency. The proposed system demonstrated a strong predictive performance. It achieved an R² score of 0.89, mean squared error (MSE) of 0.25, and mean absolute percentage error (MAPE) of 5.20%. Under high load and dense interference conditions, the framework delivered a 22.4% increase in average throughput, a 17.8% reduction in packet loss, and a 19.5% drop in end‐toto‐end latency. The LSTM model effectively responded to changes in signal strength caused by user mobility. The random forest classifier detects interference with 93.4% accuracy, enabling quick mitigating action. Moreover, the energy efficiency of the proposed system is improved by 18.7%, making it both optimized and cost‐effective for better performance evaluation. These outcomes show that future 5G infrastructure depends on artificial intelligence‐driven self‐organizing, self‐optimizing networks. This framework provides a reliable path toward scalable, resilient, and adaptive service delivery in smart cities.

In disaster‐affected areas where traditional communication infrastructure is often damaged or unavailable, maintaining reliable wireless connectivity is critical for effective emergency response. This paper presents an optimized Unmanned Aerial Vehicle (UAV)‐based relay algorithm designed to enhance emergency wireless coverage through dynamic positioning and energy‐efficient routing. The proposed system employs UAV‐mounted sensors to assess disaster zones in real time, identify communication blackspots, and guide UAV deployment based on key parameters such as altitude, battery life, and coverage radius. A communication protocol incorporating error correction mechanisms ensures robust data exchange between UAVs and ground users. Two core algorithms are introduced: one for optimal UAV deployment using population and terrain data, and another for maximizing device‐to‐device (D2D) communication capacity while minimizing interference. Simulation results validate the proposed approach, demonstrating substantial improvements in key performance metrics including path loss reduction, lower packet loss probability, and enhanced D2D capacity. Additionally, the system achieves over 93% communication efficiency while maintaining low energy consumption. This work highlights the practical viability of UAV relays for post‐disaster communication and provides a scalable, adaptive framework for extending coverage in emergency scenarios, ultimately supporting timely rescue coordination and resource allocation in crisis environments.

Fuel theft at gas stations has arisen as a unique and deceitful means of defrauding regular Indian consumers, with unscrupulous pump operators selling less petrol than promised at the same price. The objective of this investigation is to create a system that can detect such fraudulent activity and warn the user in a timely manner. The suggested system checks fuel levels during refilling and warns the user if any irregularities are discovered. It also has a capability that detects incoming phone calls, allowing the user to identify calls without relying on Bluetooth connectivity. The system execution is composed of combining an ultrasonic sensor to correctly detect fuel levels, a Node MCU microcontroller for data processing and wireless communication, an LCD display for visual feedback, a buzzer for aural alarms, and an LM358AN which is a low-power dual op-amp for signal conditioning. The outcome of the work uses a telegram bot to take user input, such as projected fuel volumes and uses real-time data to detect and alert users to potential fraud. Furthermore, the system regularly uploads data to the Thing Speak cloud platform, which uses powerful analytics to monitor and detect any irregularities that may occur. The device has an additional feature to improve user experience that detects any incoming calls or messages to the driver’s mobile phone so that the driver does not miss any vital alerts. Overall, all this research aims to aid in detecting unethical fuel pump operators and assist regular individuals in getting the right amount of propellant they have paid for, therefore lessening the ubiquitous issue of fuel theft.

The purpose of this work is to design of an artificial pancreas for precise blood glucose insulin regularization for a diabetes patient. The author proposed a continuous Glucose monitoring system. Where Insulin will be injected externally into the patient using an insulin pump known as the artificial pancreas. Bergman's simple model is used in a closed-loop system that employs a PID controller and MATLAB/SIMULINK. This regularization system determines the required amount of external Insulin infusion through the control actions involved according to the meal disorders and other factors involved. The artificial pancreas simulation model includes a continuous glucose monitor, an insulin injection pump, and a potential PID controller. This study uses a combinational mathematical model to provide a simple glucose-insulin monitoring and regularisation method for type 1 diabetes patients. The findings of the SIMULINK simulation have demonstrated that when the intake of glucose during meals is balanced, the body's insulin sensitivity can be stabilized and the hypoglycemia condition effectively regulated. The results show that this model is reliable and useful, and it may be utilized to lay out information for research on blood glucose regulation.