Final Year IEEE Embedded Projects in Chennai

Abstract :

In this paper, we are proposing a surveillance solution, composed of a Wireless Sensor Network (WSN) to detect and track trespassers. Wireless Sensor Networks uses lightweight UAVs like quadcopters or drones to interact with WSN to improve surveillance capabilities. This can work for various scenarios such as detecting and investigating network failures, maintaining the sensor network, tracking trespassers, transmitting real-time intrusion videos, and responding to hostage situations. For optimizing the tracking mission, a heuristic-based scheduling algorithm is proposed. This can increase the rate of trespassers spotted by the quadcopters and drones. We had developed techniques to localize terrestrial sensors using RFID technology accurately. It can compute the optimal positions for new sensors to drop and relay data between sensors for tracking intruders. This developed VTail prototype for drones and quadcopters is tested for providing valid and accurate parameters in various simulations.
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Abstract :

Accurate and quantifiable measurement of light is essential in creating many practical day-to-day applications, such as Traffic lighting systems, Gardening, Museum lighting systems, Poultry Industry at emergency exits, etc. So, Light measurement and analysis are essential steps for ensuring safety. Many industries are burdened with a limited number of resources and a shortage of experts in this field; real-time remote sensing presents an effective solution that in turn minimizes their efforts and expenditures to achieve desired results within time. This introduces a real-time remote Light intensity monitoring system with Raspberry Pi for tracking light systems remotely. Raspberry pi is a low-cost ARM-powered Linux-based computer that can act as a server, and it can communicate with clients Via a LAN network or external Wi-Fi modules. The key feature of this system is the light intensity being monitored instantly and its data stored in the database for future use and shown in the form. Which empowers experts to make the right decisions to get desired results.

Abstract :

In recent years technological advancements are developed in rapid paces and it has enabled significant evolution and deployment of Wireless Sensor Networks (WSN). These networks are an essential player in the Internet of Things, generating a vast and increasing amount of varied data. There are only a few documented works to tackle these challenges for collecting, manipulating, and exploiting the data generated. This paper has a proposal for integrating BigData tools to gather, store, and analyze data generated by a WSN that monitors air pollution levels in a city. We can provide a proof of concept that combines Storm and Hadoop for data processing, storage, and analysis, and usage of Arduino-based kits for constructing sensor prototypes. There is a significant evolution in the deployment of Wireless Sensor Networks (WSN). These networks are an essential player in the Internet of Things, generating a vast and increasing amount of varied data. There are only a few documented works to tackle these challenges for collecting, manipulating, and exploiting the data generated. This paper has a proposal for integrating BigData tools to gather, store, and analyze data generated by a WSN that monitors air pollution levels in a city. We can provide a proof of concept that combines Storm and Hadoop for data processing, storage, and analysis, and usage of Arduino-based kits for constructing sensor prototypes.

Abstract :

Automobiles and computing technologies are creating a new level for the data services to vehicles. The Automobile Black Box has functions that are similar to an airplane Blackbox. The black box is used to analyze the cause of vehicular accidents. It can prevent life and property losses arising because of vehicle accidents. This paper introduces a prototype for an Automobile Black Box System for vehicles. This Black box system aims to achieve accident analysis by tracking what event had occurred in the vehicle at the time of the incident. The Black box system can also enhance security by preventing and tampering with the Black Box data. The Black Box can send alert messages to pre-recorded mobile numbers via Short Message Service (SMS) if an accident occurs. The proposed system uses 12 sensors to record the various driving data parameters. The Raspberry Pi (RPi) and Arduino controllers are used for regulating these sensors. The data received from the sensors can be stored on an SD card and mounted on RPi for retrieving after an accident occurs. This Black Box system uses external sensors like a camera and GPS for collecting video and location data.

Abstract :

With the breakthroughs in semiconductor and wireless technology, the size of RFID devices reduced. Thus RFID devices could be redesigned as various forms or behaviors to adapt. The more added values provided better RFID characteristics, the RFID applications will compete with the bar-code system in the future. In this paper, the RFID system has applied in-vehicle information communication safety using wireless communications to provide passenger information and services. In this paper, vehicular safety with a mobile RFID system is introduced, which consists of five functions. They are the small tags that are embedded around the vehicle, scanners, Geographic Information systems, mobile devices, and Electronic Product Code middleware. The middleware function is to drive the reader and connect the server. This program for system development has ensured passenger safety and vehicle safety in real-time.