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Logic Mind Technologies
Vijayangar (Near Maruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Local Area Prediction-Based Mobile Target
Tracking in Wireless Sensor Networks
Abstract
Tracking mobile targets in wireless sensor networks (WSNs) has many important applications.
As it is often the case in prior work that the quality of tracking (QoT) heavily depends on high
accuracy in localization or distance estimation, which is never perfect in practice. These bring a
cumulative effect on tracking, e.g., target missing. Recovering from the effect and also frequent
interactions between nodes and a central server result in high energy consumption. We design a
tracking scheme, named t-Tracking, aiming to achieve two major objectives: high QoT and high
energy efficiency of the WSN. We propose a set of fully distributed tracking algorithms, which
answer queries like whether a target remains in a โ€œspecific areaโ€ (called a โ€œfaceโ€ in localized
geographic routing, defined in terms of radio connectivity and local interactions of nodes). When
a target moves across a face, the nodes of the face that are close to its estimated movements
compute the sequence of the targetโ€™s movements and predict when the target moves to another
face. The nodes answer queries from a mobile sink called the โ€œtrackerโ€, which follows the target
along with the sequence. t-Tracking has advantages over prior work as it reduces the dependency
on requiring high accuracy in localization and the frequency of interactions. It also timely solves
the target missing problem caused by node failures, obstacles, etc., making the tracking robust in
a highly dynamic environment. We validate its effectiveness considering the objectives in
extensive simulations and in a proof-of-concept system implementation.
SYSTEM SPECIFICATION
Software Specification
Operating system : Linux (Ubuntu)
Software : Network Simulator 2.35
Programming languages : Tool Command Language, AWK, C++
Hardware Specification
Main processor : Dual Core
Hard disk capacity : 80GB
Cache memory : 2 GB
PROJECT FLOW:
First Review:
Literature Survey
Paper Explanation
Design of Project
Project Enhancement explanation
Second Review:
Implementing 40% of Base Paper
Third Review
Implementing Remaining 60% of Base Paper with Future Enhancement (Modification)
For More Details please contact
Logic Mind Technologies
Vijayangar (NearMaruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Mail: logicmindtech@gmail.com

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  • 1. Logic Mind Technologies Vijayangar (Near Maruthi Medicals), Bangalore-40 Ph: 8123668124 // 8123668066 Local Area Prediction-Based Mobile Target Tracking in Wireless Sensor Networks Abstract Tracking mobile targets in wireless sensor networks (WSNs) has many important applications. As it is often the case in prior work that the quality of tracking (QoT) heavily depends on high accuracy in localization or distance estimation, which is never perfect in practice. These bring a cumulative effect on tracking, e.g., target missing. Recovering from the effect and also frequent interactions between nodes and a central server result in high energy consumption. We design a tracking scheme, named t-Tracking, aiming to achieve two major objectives: high QoT and high energy efficiency of the WSN. We propose a set of fully distributed tracking algorithms, which answer queries like whether a target remains in a โ€œspecific areaโ€ (called a โ€œfaceโ€ in localized geographic routing, defined in terms of radio connectivity and local interactions of nodes). When a target moves across a face, the nodes of the face that are close to its estimated movements compute the sequence of the targetโ€™s movements and predict when the target moves to another face. The nodes answer queries from a mobile sink called the โ€œtrackerโ€, which follows the target along with the sequence. t-Tracking has advantages over prior work as it reduces the dependency on requiring high accuracy in localization and the frequency of interactions. It also timely solves the target missing problem caused by node failures, obstacles, etc., making the tracking robust in a highly dynamic environment. We validate its effectiveness considering the objectives in extensive simulations and in a proof-of-concept system implementation.
  • 2. SYSTEM SPECIFICATION Software Specification Operating system : Linux (Ubuntu) Software : Network Simulator 2.35 Programming languages : Tool Command Language, AWK, C++ Hardware Specification Main processor : Dual Core Hard disk capacity : 80GB Cache memory : 2 GB PROJECT FLOW: First Review: Literature Survey Paper Explanation Design of Project Project Enhancement explanation Second Review: Implementing 40% of Base Paper Third Review Implementing Remaining 60% of Base Paper with Future Enhancement (Modification) For More Details please contact Logic Mind Technologies Vijayangar (NearMaruthi Medicals), Bangalore-40 Ph: 8123668124 // 8123668066 Mail: logicmindtech@gmail.com