Mobile object tracking in wireless sensor networks

Tsai, Hua-Wen; Chu, Chih‐Ping; Chen, Tzung-Shi

doi:10.1016/j.comcom.2007.02.018

Cited by 153 publications

(89 citation statements)

References 21 publications

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“…While in these works, mobile nodes are assumed that can either move by themselves or move with the field (e.g., buoys moved by ocean currents), our work assumes a network of stationary nodes (or cars whose movement is predetermined by the road network) and aims to estimate the motion from the time series of sensor measurements collected by the nodes. Further, there is a significant amount of work on object tracking with GSNs, i.e., generating information on the trajectory of a mobile object without necessarily following it, such as [21]. However, to the best of our knowledge, the problem of estimating field-(not object-) motion with a GSN has not been tackled so far.…”

Section: Related Workmentioning

confidence: 96%

Field Motion Estimation with a Geosensor Network

Fitzner

Sester

2016

IJGI

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Physical environmental processes, such as the evolution of precipitation or the diffusion of chemical clouds in the atmosphere, can be approximated by numerical models based on the underlying physics, e.g., for the purpose of prediction. As the modeling process is often very complex and resource demanding, such models are sometimes replaced by those that use historic and current data for calibration. For atmospheric (e.g., precipitation) or oceanographic (e.g., sea surface temperature) fields, the data-driven methods often concern the horizontal displacement driven by transport processes (called advection). These methods rely on flow fields estimated from images of the phenomenon by computer vision techniques, such as optical flow (OF). In this work, an algorithm is proposed for estimating the motion of spatio-temporal fields with the nodes of a geosensor network (GSN) deployed in situ when images are not available. The approach adapts a well-known raster-based OF algorithm to the specifics of GSNs, especially to the spatial irregularity of data. In this paper, the previously introduced approach has been further developed by introducing an error model that derives probabilistic error measures based on spatial node configuration. Further, a more generic motion model is provided, as well as comprehensive simulations that illustrate the performance of the algorithm in different conditions (fields, motion behaviors, node densities and deployments) for the two error measures of motion direction and motion speed. Finally, the algorithm is applied to data sampled from weather radar images, and the algorithm performance is compared to that of a state-of-the-art OF algorithm applied to the weather radar images directly, as often done in nowcasting.

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Section: Related Workmentioning

confidence: 96%

Field Motion Estimation with a Geosensor Network

Fitzner

Sester

2016

IJGI

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“…Most tracking protocols suggested up to now [1,2,3,4] have not noticed the type of target mobility model sufficiently. In other words, is the efficiency of a tracking protocol the same when tracking a tank or a human?…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Wireless sensor networks are expected serve as a key infrastructure for a broad range of applications including precision agriculture, surveillance highway systems, emergent disaster response and recovery. One of the important application issues for sensor networks is utilized to track mobile object [1]. Researches about target tracking with continuous monitoring mechanism can be divided into three categories: structure-less scheme, tree-based scheme, and cluster-based scheme.…”

Section: Introductionmentioning

confidence: 99%

A New Adaptive Method for Target Tracking in Wireless Sensor Networks

Ahmadi¹,

Sabaei²,

Ahmadi³

2011

IJCA

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These days mobile target tracking is considered as one of the important applications of wireless sensor networks. In this regard, the clustering structure is one of the most applicable network structures. In this paper, we suggested a new method for target tracking that makes adaptation with target mobility model. This method utilizes two tools to create adaptability which are changing the size and shape of clusters according to target mobility model. Also by combining static and dynamic clustering a semi dynamic clustering structure has been developed to implement these tools. Simulation results show that our suggested method decreases both energy consumption by decreasing clusters size when the target moves uniformly, and tracking error by changing the size and the shape of clusters according to target mobility when the target moves unpredictably. General TermsSemi-Dynamic clustering, target tracking, tracking error.

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“…First, the sensor nodes are deployed in the network then the network is arranged in the form of polygons. Node has three states [15]: active (node participates in tracking), inactive (node is in sleep mode), awakening (node awakes for a short period). In first step, some sensor node detects the target.…”

Section: Figurementioning

confidence: 99%

Energy Efficient Target Tracking Scheme for Wireless Sensor Networks

2016

IJSR

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Target tracking in WSN has a lot of applications in military fields and surveillance purpose. Target tracking in WSN is more challenging because WSNs have issues such as limited battery power, unpredictable environments, high mobility of nodes as well as targets and failure of sensor nodes at runtime etc. This paper proposes a new polygon based target-tracking scheme, which is a predictive, and cluster based scheme. In this scheme, the clusters are polygon shaped, so the whole network is arranged in the form of polygons. The sensor nodes are interconnected with their neighboring nodes to form the edges of the polygons. Proposed scheme is more energy efficient than the existing schemes because only selected nodes of the polygon are kept active during the tracking process rather than keeping all the nodes active. Edge detection algorithm is used to send the message to the sensors of the next polygon and activate them before target's arrival in that polygon. A-star algorithm is used to choose the optimal nodes and to find the optimal routing path from source node to the sink. This routing scheme improves the energy efficiency as well as tracking accuracy of WSN by forwarding the target-tracking message via the optimal shortest path.

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Mobile object tracking in wireless sensor networks

Cited by 153 publications

References 21 publications

Field Motion Estimation with a Geosensor Network

Field Motion Estimation with a Geosensor Network

A New Adaptive Method for Target Tracking in Wireless Sensor Networks

Energy Efficient Target Tracking Scheme for Wireless Sensor Networks

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