Journal of Instrumentation and Innovation Sciences

Energy-efficient Smart Home Monitoring using IoT

D. Geethamani — 2026-04-04

The rapid increase in residential energy consumption has become a critical concern due to growing urbanization, rising electricity costs, and increasing environmental impact. Traditional home energy management systems rely largely on manual control and static configurations, which often lead to inefficient energy usage and unnecessary power wastage. In recent years, the internet of things (IoT) has emerged as a promising technology for enabling intelligent, automated, and data-driven energy management in residential environments. This study presents an IoT-based energy-efficient smart home monitoring and management system designed to continuously observe energy consumption patterns and environmental conditions in real time. The proposed system integrates smart energy meters, environmental sensors, intelligent controllers, and cloud-based analytics to optimize energy usage while maintaining occupant comfort. By analyzing appliance-level energy consumption alongside parameters such as temperature, humidity, lighting conditions, and occupancy, the system performs automated control actions to minimize energy waste. The proposed approach enhances user awareness, reduces electricity costs, and contributes to sustainable residential living.

Smart Highway Accident Detection and Emergency Alert System

P. Karuppasamy — 2026-03-31

Road accidents continue to be a major global concern, often leading to severe injuries and fatalities, especially when emergency response is delayed. To address this issue, this paper proposes a smart highway accident detection and emergency alert system that enables automatic detection and rapid communication during accident events. The system utilises an ESP32 microcontroller integrated with GPS and GSM modules to facilitate real-time monitoring and instant transmission of location-based alerts. To improve detection accuracy, the YOLOv8 deep learning model is employed to analyse traffic conditions and identify genuine accident scenarios while minimising false alarms caused by minor disturbances. Once an accident is detected, the system immediately sends precise location details and alert messages to nearby hospitals, emergency responders, traffic authorities, and registered contacts, ensuring faster medical assistance. Additionally, the system incorporates IoT capabilities, allowing accident data to be stored and monitored on a cloud platform for further analysis, such as identifying accident-prone areas and improving traffic management strategies. Experimental evaluations demonstrate that the system performs reliably under various environmental and traffic conditions, making it a scalable, efficient, and cost-effective solution for enhancing road safety and emergency response on highways.

Design and Development of an Arduino-based Smart Braille Learning System

Ashish Patil — 2026-03-17

The smart Braille learning device using Arduino is developed to assist visually impaired individuals in learning the Braille alphabet and numbers interactively and affordably. Learning Braille using traditional embossed charts and books can be difficult for beginners due to limited feedback and a lack of interactivity. To address this challenge, the proposed system uses an Arduino Uno microcontroller integrated with a keyboard, push button, LED indicators, buzzer, and a 16×2 LCD to visually and functionally represent Braille characters. Each English alphabet and numeral is mapped to its corresponding six-dot Braille pattern, which is displayed using LEDs arranged in a Braille matrix. When a user presses a key, the system identifies the character, activates the appropriate LED combination, and displays the same character on the LCD for verification. A mode-selection button allows switching between alphabet and numeric modes, enhancing usability. The system was designed and tested using the Tinkercad simulation platform, ensuring accurate functionality without physical hardware implementation. Simulation results confirm reliable input detection, correct Braille representation, and real-time display output. The proposed device offers a low-cost, easy-to-use learning solution suitable for educational institutions and training centers for visually impaired learners. The system can be further enhanced with voice output, haptic feedback, and wireless connectivity to improve learning accessibility and user experience.

Power Optimized FPGA Implementation of High-Speed Test Pattern Generator for BIST

Y. Rama Krishna — 2026-04-03

Pseudorandom Built-In Self-Test (BIST) schemes are essential for the efficient testing of Integrated Circuits (ICs), ensuring high-quality manufacturing yield. As modern integrated circuit complexity increases, test vector generation time and power consumption during testing become critical concerns. This paper presents an FPGA implementation of a BIST architecture featuring a Flip-Flop and AND gate (FF+AND) based clock-gating technique, an accumulator-based 3-weight Test Pattern Generator (TPG) using a Full Adder array, a 4-state finite state machine (FSM) BIST controller, and a combinational response analyser for fault detection. The clock gating is implemented through the Flipflop_Clk module, which combines a positive-edge-triggered D Flip-Flop (dff. v) with an AND gate to produce a glitch-free gated clock. The TPG employs ten FullAdder instances forming an accumulator with 3-weight masking via set_mask and reset_mask inputs, generating effective fault-covering patterns. The Circuit Under Test (CUT) implements a mixed logic network with AND, OR, XOR, and NAND gates with 10 inputs and 3 outputs. Implementation on Xilinx Artix-7 FPGA using Vivado achieves a total on-chip power of 0.59W, maximum delay of 5.496 ns, with only 46 Slice LUTs and 25 Slice Registers. The system correctly detects SA0 (stuck-at-0) faults on node N11, with the extension module (Extension.v) showing approximately 41.7% power reduction compared to the ungated Top.v baseline.

AI-Driven Autonomous Drone Ecosystem for Smart Cities: Applications in Emergency Response, Road Monitoring, and Healthcare

Soudarrajan M. — 2026-03-28

Artificial Intelligence (AI) has been rapidly developing as one of the leading technologies in infrastructure surveillance and healthcare frameworks, with pavement distress recognition, pothole detection, and the provision of medical services being prominent. The desire to have the proper power of this or that road network monitoring and effective healthcare support devices has prompted the use of innovative digital technologies, and specifically drone technology. Safety, precision, and efficiency in the construction setting and medical practice are increasingly becoming a critical issue. The latest advancements in computer vision which have been facilitated by the large-scale image and video analytics have made a major contribution to automated monitoring processes. Nonetheless, the shortcomings of on-ground data collection solutions have promoted the application of UAVs in an extensive coverage of the area, real-time monitoring, and extensive data gathering. This literature review explores the aspect of the application of AI integrated drone systems in smart monitoring in a systematic manner. It commences by looking at image acquiring instruments and sensors technologies, their functionality benefits, and technical limitations. The paper then examines the weaknesses and problems of pavement condition assessment methods that employ computer vision applications. New research methods for determining surface texture changes, structural cracks, potholes, wear and tear of joints, thermal effects, and rutting are discussed. Besides that, the growing use of AI-driven drones in healthcare is discussed, and their involvement in medical monitoring, logistics, and service delivery is mentioned. The future directions of research are the establishment of real-time distress detection applications on busy highways and city roads and intelligent cost-saving models to improve healthcare systems efficiency and affordability.