Nanodust Network for Tactical Border Surveillance System to Detect, Classify and Track Enemy Intrusion Guide Students Mr. M.Manimaran, M.Tech K.S.K.Arun.

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Presentation transcript:

Nanodust Network for Tactical Border Surveillance System to Detect, Classify and Track Enemy Intrusion Guide Students Mr. M.Manimaran, M.Tech K.S.K.Arun prasad 921711112004 Assistant Professor S.Mohammed Khalid 921711112017 ICE T.Mohammed Salman 921711112018

Overview Abstract Existing Method Proposed Method Hardware & Software Block Diagram Advantages Applications Result Conclusion Future enhancement

Abstract The greatest threat to national security is “Terrorism” infiltrating through borders. In critical border areas such as Kashmir and Bangladesh regular forces or even satellites cannot monitor these intruding terrorists as the area monitored is quite large and quite complex. This project provides an innovative and effective solution to this problem. The motes can form a network on its own among them, are small in size, rapidly deployable, have wireless connection to outside world. The dust motes communicate with central parent node using wireless radio network. The system process the sensor readings, classify the targets and the tracking history can be viewed in the Graphics LCD display attached in the central monitoring unit.

Existing system Multi-target tracking (MTT) is a process of estimating the trajectories and velocities of mobile targets. Collaborative target tracking uses a multi-sensor scheme to improve the tracking accuracy compared with single-sensor tracking. Physical limitations of sensor nodes in terms of battery supplied energy, processing capability, communication bandwidth . The storage have driven the desire for sensor and sampling interval selections to improve the energy efficiency.

Proposed system The project aim is to design a next generation intelligent ultra small dust like wireless sensor motes which has multiple onboard sensors. A processor, which has the ability to detect an enemy intrusion across borders and battlefields. Thousands of these smart dust motes can be deployed within a large area in a few hours by one or two men. The system process the sensor readings, classify the targets and the tracking history can be viewed in the Graphics LCD display attached in the central monitoring unit. No need for human persons to maintain these. Once deployed they will run for many years.

Hardware requirements Microcontroller(ARM Cortex-M3) Graphics LCD IEEE 802.15.4 [MiWi] Enhanced PIR sensor 3-axis MEMS Accelerometer MEMS Microphone ARM Magnetic sensor

Software requirements Programming Language: Embedded C Development Tool: LPCXpresso IDE (Eclipse based) Embedded Protocols Used: SPI[Serial peripheral interface] ADC[Analog to digital converter]

Block Diagram: Monitoring unit

Block diagram

Advantages We can form the wireless sensor network communication in the low cost. It can understand the human intrusion in the easy way. The dust mote is small. Cost is very low when comparing soldiers cost. No need for human persons to maintain these. Once deployed they will run for many years. Low power 32-bit ARM Cortex-M3 microcontroller enables the mote to be operated for years from battery power.

Applications The project provides a effective solution to the “Border Terrorism” problem. Variety of onboard sensors is used to make the mote to identify any kind of intrusion, human or machines or vehicles. The intrusion path can be tracked using wireless communication between different motes. Low power 32-bit ARM Cortex-M3 microcontroller enables the mote to be operated for years from battery power. A portable monitoring node with graphics LCD is used for easy UI where the intrusion path will be plotted graphically and alarm will be raised

Result Mote-1 Thermal Sign: Enhanced PIR sensor is used to identify humans via the heat emitted from their body. Vibration Sign: 3-axis MEMS Accelerometer is used to sense the physical vibration caused by the intruder

Mote-2 Acoustic Sign: MEMS Microphone is used to sense the acoustic sound signals. Magnetic Sign: Intruders carrying weapons and moving in vehicles can be identified using their magnetic signature in this AMR Magnetic sensor.

Display Display: Graphics LCD used to show the intrusion type and tracking history.

Conclusion Next generation intelligent ultra small dust like wireless sensor motes which has multiple onboard sensors and a processor was designed. It has the ability to detect an enemy intrusion across borders and battlefields. If thousands of these smart dust motes are deployed within a large area, they will detect the intrusion and classify it into vehicles or individuals and groups. The hardware include a variety of sensors for vibration/seismic, magnetic, acoustic and thermal signature. A microcontroller for processing these sensor values and a radio transceiver for communication over a wireless network.

Future enhancement In future it can be installed in appartments so can be survived a vast area with low labour cost. It can be survived in border to protect from enemies. It can be installed in areas where satellite communication is not possible. It can be installed in remote areas where power supply is not possible.

REFERENCE Toward the World Smallest Wireless Sensor Nodes With Ultralow Power Consumption (IEEE SENSORS JOURNAL, VOL. 14, NO. 6, JUNE 2014)- Jian Lu, Hironao Okada, Toshihiro Itoh, Takeshi Harada, and Ryutaro Maeda. K. Martinez, J. K. Hart, and R. Ong, “Environmental sensor networks,” Computer, vol. 37, no. 8, pp. 50–56, Aug. 2004. H. Alemdar and C. Ersoy, “Wireless sensor networks for healthcare: A survey,” Comput. Netw., vol. 54, no. 15, pp. 2688–2710, 2010. S. Amini, R. Verhoeven, J. Lukkien, and S. D. Chen, “Toward a security model for a body sensor platform,” in Proc. IEEE ICCE, Las Vegas, NV, USA, Feb. 2011, pp. 143–144. T. Itoh, T. Masuda, and K. Tsukamoto, “Development of a sensor system for animal watching to keep human health and food safety,” Synthesiology, vol. 3, no. 3, pp. 231–240, 2010. Santoshkumar, C. Kelvin, and C. Chavhan, “Development of wireless sensor node to monitor poultry farm,” in Proc. Commun. Comput. Inf. Sci., vol. 296, 2013, pp. 27–32.

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