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DOTNET 2013 IEEE MOBILECOMPUTING PROJECT Target tracking and mobile sensor navigation in wireless sensor networks
DOTNET 2013 IEEE MOBILECOMPUTING PROJECT Target tracking and mobile sensor navigation in wireless sensor networks
DOTNET 2013 IEEE MOBILECOMPUTING PROJECT Target tracking and mobile sensor navigation in wireless sensor networks
DOTNET 2013 IEEE MOBILECOMPUTING PROJECT Target tracking and mobile sensor navigation in wireless sensor networks
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DOTNET 2013 IEEE MOBILECOMPUTING PROJECT Target tracking and mobile sensor navigation in wireless sensor networks

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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org

To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org

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  • 1. Target Tracking and Mobile Sensor Navigation in Wireless Sensor Networks ABSTRACT: This work studies the problem of tracking signal-emitting mobile targets using navigated mobile sensors based on signal reception. Since the mobile target’s maneuver is unknown, the mobile sensor controller utilizes the measurement collected by a wireless sensor network in terms of the mobile target signal’s time of arrival (TOA). The mobile sensor controller acquires the TOA measurement information from both the mobile target and the mobile sensor for estimating their locations before directing the mobile sensor’s movement to follow the target. We propose a min-max approximation approach to estimate the location for tracking which can be efficiently solved via semi definite programming (SDP) relaxation, and apply a cubic function for mobile sensor navigation. We estimate the location of the mobile sensor and target jointly to improve the tracking accuracy. To further improve the system performance, we propose a weighted tracking algorithm by using the measurement information more efficiently. Our results demonstrate that the proposed algorithm provides good tracking performance and can quickly direct the mobile sensor to follow the mobile target. EXISTING SYSTEM: GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com
  • 2. There exist a number target localization approaches-based various measurement models such as received signal strength (RSS), time of arrival (TOA), time difference of arrival (TDOA), signal angle of arrival (AOA), and their combinations. For target tracking, Kalman filter was proposed, where a geometric-assisted predictive location tracking algorithm can be effective even without sufficient signal sources. Li et al.investigated the use of extended Kalman filter in TOA measurement model for target tracking. Particle filtering has also been applied with RSS measurement model under correlated noise to achieve high accuracy. In addition to the use of stationary sensors, several other works focused on mobility management and control of sensors for better target tracking and location estimation. Zou and Chakrabarty studied a distributed mobility management scheme for target tracking, where sensor node movement decisions were made by considering the tradeoff among target tracking quality improvement, energy consumption, loss of connectivity, and coverage. Rao and Kesidis further considered the cost of node communications and movement as part of the performance tradeoff DISADVANTAGES OF EXISTING SYSTEM: The mobile target’s maneuver is unknown PROPOSED SYSTEM: In this work, we consider the joint problem of mobile sensor navigation and mobile target tracking based on a TOA measurement model. Our chief contributions include a more general TOA measurement model that accounts for the measurement noise due to multipath propagation and sensing error. Based on the model, we propose a min-max approximation approach to estimate the location for tracking that can be efficiently and effectively solved by means of semidefinite programming (SDP) relaxation. We apply the cubic function for navigating the movements of mobile sensors. In addition, we also investigate the simultaneous localization of the mobile sensor and the target to improve the tracking accuracy. We present a weighted tracking algorithm in order to exploit the measurement information more efficiently. The numerical result shows that the proposed tracking approach works well ADVANTAGES OF PROPOSED SYSTEM:
  • 3.  TOA measurements are easy to acquire, as each sensor only needs to identify a special signal feature such as a known signal preamble to record its arrival time.  Our particular use of TOA is a more practical model because we do not need the sensors to know the transmission start time of the signal a priori. As a result, our TOA model enables us to directly estimate the source location by processing the TOA measurement data.  The mobile sensor navigation control depends on the estimated location results, more accurate localization algorithm from TOA measurements leads to better navigation control. HARDWARE & SOFTWARE REQUIREMENTS: HARDWARE REQUIREMENTS: System : Pentium IV 2.4 GHz. Hard Disk : 40 GB. Floppy Drive : 1.44 Mb. Monitor : 15 VGA Color. Mouse : Logitech. Ram : 512 MB. SOFTWARE REQUIREMENTS: Operating system : Windows XP Professional. Coding Language : C#.NET
  • 4. REFERENCE: Enyang Xu, Zhi Ding,Fellow, IEEE, and Soura Dasgupta, Fellow, IEEE “Target Tracking and Mobile Sensor Navigation in Wireless Sensor Networks” - IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 12, NO. 1, JANUARY 2013.

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