D. Siva Varma ¹, Abraham Sudharson Ponraj ¹

Affiliations
1 School of Electronics Engineering, VIT University, Chennai - 600127, Tamil Nadu, IN

Abstract

Objectives: This paper aims at providing a feasible solution for monitoring the environment as well as to control the traffic using vehicular network. Methods/Statistical Analysis: The MQ-2 gas sensor and the GPS Module are interfaced with the Raspberry Pi to facilitate the vehicular network. The well integrated module helps in making the routine life of getting into traffic avoidable. Findings: This work effectively gives a solution to control the traffic as well as to monitor the environmental pollution. It helps to reduce the pollution along with the traffic. This can be implemented on the vehicles to provide a better communication between the vehicles. Application/Improvement: This lead to the implementation of a network which works on the grounds of the vehicle communication and brings out a better throughput.

Keywords

Artificially Intelligent, Automobile, GPS Module, MQ-2 Gas Sensor, Raspberry Pi¸ Traffic Congestion.

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Gundavaram Raghu ¹, Abraham Sudharson Ponraj ¹

Affiliations
1 School of Electrical Engineering, Vellore Institute of Technology (VIT) University, Vandalur - Kelambakkam Road Chennai – 600 127, Tamil Nadu, IN

Abstract

Objectives: With the increase in the aging population, the health of the elderly has been a widespread concern. There rises a need to develop a system which does remote monitoring and safety assistance. Methods: In this proposed work, heart Beats per Minute (BPM), temperature, precise location of the patient, seizure detection and fall detection of the elderly individual is monitored continuously. The precise location of the aged individuals is achieved by means of Radio-Frequency Identification (RFID) technology. Seizure detection uses accelerometer sensor. Fall detection is identified using gyro sensor. Temperature and hearts beat per minute are calculated using heart beat sensor and lm35 sensor. Findings: The algorithms developed for the seizure and fall detection are tested on different conditions and the results are satisfactory. Fall detection algorithm completely differentiates between the fall and bend of the individual. Always a doctor or care takers cannot be present for monitoring. Hence there rises a need to send alert messages when the fall or seizure is detected. Alerts are sending to caretakers or doctors using internet. Improvement/Applications: The work can be further extended by monitoring several individuals at the same time and developing a mobile app for remote monitoring and assisting.

Keywords

Internet, IOT, RFID, Remote Monitoring, Seizure.

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