An Adaptive Dynamic Cluster-Based Protocol for Target Tracking in Wireless Sensor Networks

Lou

International Journal of Distributed Sensor Networks

et al. 2013

Target tracking is a typical and important application of wireless sensor networks (WSNs). In consideration of the network scalability and energy efficiency for target tracking in large-scale WSNs, it has been employed as an effective solution by organizing the WSNs into clusters. However, tracking a moving target in cluster-based WSNs suffers a boundary problem when the target moves across or along the boundaries of clusters, as the static cluster membership prevents sensors in different clusters from sharing information. In this paper, we propose a novel mobility management protocol, called hybrid cluster-based target tracking (HCTT), which integrates on-demand dynamic clustering into a cluster-based WSN for target tracking. By constructing on-demand dynamic clusters at boundary regions, nodes from different static clusters that detect the target can temporarily share information, and the tracking task can be handed over smoothly from one static cluster to another. As the target moves, static clusters and on-demand dynamic clusters alternately manage the target tracking task. Simulation results show that the proposed protocol performs better in tracking the moving target when compared with other typical target tracking protocols.

Section: International Journal Of Distributed Sensor Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Cluster-Based Target Tracking Protocol for Wireless Sensor Networks

Lou

International Journal of Distributed Sensor Networks

et al. 2013

“…So that the node can be a member of different clusters at different time and it minimizes the localization error [1]. Unless static clustering, dynamically it forms the cluster with sensors.…”

Section: Dynamic Cluster Based Target Trackingmentioning

confidence: 99%

Analysis of Cluster based Target Tracking in Wireless Sensor Networks

Gopal¹,

Krishnamoorthy²

2013

IJCA

Wireless Sensor Networks (WSNs), is one of the most emerging technologies in recent years. Signal processing between sensor nodes to the base station is most important task for these networks. Target tracking in WSN mainly focuses on localization and object tracking. This paper addresses various cluster based target tracking techniques in wireless sensor networks. To support reliability, energy efficiency and efficient routing, nodes are often grouped into clusters. The clusters can be formed before the intrusion enters the network which is called the static method or clusters are created by using signal strength from target which is called the dynamic method. This paper gives a various cluster based target tracking methods for efficient target tracking in WSN.

2009 24th International Symposium on Computer and Information Sciences

“…Target tracking applications can be cluster based [1]- [8] spanning tree based [9], and prediction based [10], [11]. In spanning tree based algorithms, nodes which detect the target select a root and construct a spanning tree.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of cluster-based target tracking protocols for wireless sensor networks

Alaybeyoǧlu

Dağdeviren

Erciyes

et al. 2009

Abstract-Target tracking is an important application type for wireless sensor networks (WSN).In this paper we provide simulations of static and dynamic clustering algorithms against various mobility models and target speeds. The mobility models that we applied are Random Waypoint Model, Random Direct Model and Gauss Markov Model. We provide metrics to measure the tracking performance of both approaches. We show that the dynamic clustering is favorable in terms of tracking accuracy whereas the energy consumption of static clustering is significantly smaller. We also show that the target moving with Gauss Markov Model can be tracked more accurately than the other models.