Sasidevi S.

Sasidevi S.

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Sasidevi S.

Assistant Professor, Department of ECE, Knowledge Institute of Technology

Research Focus & Metrics

Academic Career Length

3Years (2024-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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15 May 2024 in Plasmonics
Verified
Journal Article
Closed access
PDF file
SSSasidevi S
Sasidevi S
KSKumarganesh S
Kumarganesh S
SSSaranya S
Saranya S
TBThiyaneswaran Balashanmugam
Thiyaneswaran Balashanmugam
SMShree K V M
Shree K V M
DPDIGVIJAY PANDEY
DIGVIJAY PANDEY

10 July 2024 in Journal of ISMAC
Verified
Journal Article
Closed access
SSSasidevi S.
Sasidevi S.
VMVasanthi M.
Vasanthi M.
GJGaneshkumar J.
Ganeshkumar J.
KVKamalesh V.
Kamalesh V.
NGNaveenkumar G.
Naveenkumar G.
ASAjithkumar S.
Ajithkumar S.
The research aims to develop an intelligent fault detection and control system for street lights using an STM32 microcontroller and Internet of Things (IoT) technologies. The system uses various sensors and communication methods to manage lighting more effectively and maintain infrastructure better. Light-Dependent Resistors (LDRs), which turn lights on automatically at nightfall and off at dawn, are essential to the system's operation. Moreover, motion is detected by the infrared (IR) sensors on the street, allowing the lighting to save energy by dimming during times of low traffic and brightening when movement is detected. By using control logic and sensor readings, a relay functions as an electronically controlled switch to turn the lights on and off. To process sensor data, the STM32 microcontroller is used in the main functionality. Energy savings are facilitated by the automated on/off switching based on ambient light levels, enabled by the LDR sensor data. In addition, the device includes a GPS module for precise street light location tracking. An IoT platform transmits this location data as well as operational status and real-time sensor readings. Maintenance staff can access regional data on light quality and sensor readings through an LCD display. This extensive data enables maintenance team to focus better, use resources efficiently, and enable remote monitoring and fault identification (e.g., burned-out bulbs). This Internet-of-Things (IoT) system provides an economical and environmentally friendly approach to efficient street light management by integrating automation, remote control, and real-time monitoring.