incremental clustering based object tracking in wireless sensor networks
TRANSCRIPT
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Incremental Clustering-based Object Tracking in
Wireless Sensor Networks
Presented by - M S SACHIN
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INTRODUCTION
• Amongst most of moving object tracking methods clustering based object tracking has shown significant results,which in term provides the network to be scalable and energy efficient for large-scale WSNs
• static cluster based object tracking is the most common approach for large-scale Wireless Sensor Network (WSN). However, as static clusters are restricted to share information globally, tracking can be lost at the boundary region of static clusters.
• The main objective of this paper is an Incremental Clustering Algorithm is proposed in conjunction with Static Clustering Technique to track an object consistently throughout the network solving boundary problem.
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• Wireless Sensor Networks (WSNs) and their applications have remarkable prospective in both military and commercial places.
• Tracking objects throughout the established network is one of the most concerned areas in WSNs. Battlefield surveillance, emergency rescue, patient monitoring
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Object tracking in WSNs involves two steps
Step 1: Sensor nodes need to detect the presence of a moving object within an area of interest, estimate its location, and forward the location information to the base station.
Step 2: it needs to control the tracking process to monitor or capture the moving object.
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The WSN Tracking and Localization Problem
• static clusters are bound to share information within cluster, so tracking inconsistency may happen at the boundary during object movement in boundary regions.
• energy cost gets higher for frequent cluster creation and deletion during dynamic clustering.
• distance-based calculation in dynamic clustering has reduced the reliability and accuracy in object localization and tracking.
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Proposed technique for moving object tracking techniques in WSNs.
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Incremental Clustering-based Object Tracking in Wireless Sensor Networks
• The main contribution of this paper is to propose an incremental clustering algorithm for consistent object tracking at the boundary region of static clusters
Principal Contributions :• A complete and consistent algorithm for robust object tracking and
accurate localization in WSNs• An energy efficient Incremental Clustering Algorithm for object
tracking at the boundary region of static clusters.
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• Fig 2a: sensor nodes are partitioned into several static clusters to continue energy-efficient tracking task with a low-cost
• Fig 2b: clusters are represented as circular shape, solid lines indicate active clusters and dashed lines indicate dismissal of clusters.
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• Fig 2c : When an object exits from the static cluster another temporary cluster will be created and the process will continue dismissing the previously created one.
• Fig 2d: Incremental clustering algorithm can create clusters incrementally at the boundary region This technique can not only create clusters, but also can retain recently created clusters which in turn increase energy-efficiency.
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A . Object Tracking and Localization in WSNs Algorithm
• 1: Initialize Network with Static Cluster• 2: Boundary Node Formation• 3: while (object detect) do• 4: if tracking is in safety region then• 5: Tracking Continue with Static Cluster Representative• 6: else (object is in boundary region) if Observation Trajectory
Satisfies Membership Condition of Incremental Clusters then
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• 7: Update Clusters (Incrementally created) with new Observation Trajectory
• 8: else Create a new Cluster using Incremental Clustering Algorithm• 9: Tracking Continue with Increment Cluster Representative• 10: end if• 11: RSSI Analysis to Calculate Anchor Node from Selected Cluster• 12: Localization using Trilateration Algorithm• 13: end while
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B. Incremental Clustering Algorithm -
1. cluster can grows incrementally. Network can retain previously learned information which can solve stability-plasticity dilemma.
• object tracking can continue smoothly in the border region with energy-efficiency
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2. Clusters in a network follow Gaussian distribution As sensors are deployed in 2D space Gaussian distribution can be used.
A. Experimental Setup:• Using Matlab Simulator, 100 nodes are deployed
randomly into the area of 100 x 100 m2 for moving object monitoring• Considering an application of radio transmission of
energy dissipation during transmit and receive, assuming
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(a) Deployment of Sensor Network and Static Clustering based Object Detection.
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B. Experimental Results -
• tracking sequences considering both Static Clustering (LEACH protocol) and Incremental Clustering based object tracking.
• indicate incremental clusters are formed when object is in the boundary region of static clusters to continue the task of tracking.
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(b) Object Tracking Inside static Cluster using LEACH protocol.
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(c) Incremental Clustering basedObject Tracking at the BoundaryRegion of Static Clusters.
(d) Object Tracking Inside staticCluster using LEACH protocol
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Incremental Clustering vs. Dynamic Clustering
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Series 2Series 1
Incremental Clustering
Dynamic Clustering
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• that network is more stable and energy-efficient for Incremental Clustering based tracking
• for choosing Dynamic Clustering average of 40 nodes goes down
• only 12 nodes dies for choosing Incremental Clustering at the boundary region of static clusters
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CONCLUSIONS
• we have developed an approach for real-time incremental learning of continuous flow of object’s tracking
• The clusters are updated automatically
• In response to new information, the incremental learning algorithm adapts to add this information
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Thank you