A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks

Sajida Imran,Young-Bae Ko

doi:10.3390/s17020361

Abstract

In wireless sensor networks, detection and tracking of continuous natured objects is more challenging owing to their unique characteristics such as uneven expansion and contraction. A continuous object is usually spread over a large area, and, therefore, a substantial number of sensor nodes are needed to detect the object. Nodes communicate with each other as well as with the sink to exchange control messages and report their detection status. The sink performs computations on the received data to estimate the object boundary. For accurate boundary estimation, nodes at the phenomenon boundary need to be carefully selected. Failure of one or multiple boundary nodes (BNs) can significantly affect the object detection and boundary estimation accuracy at the sink. We develop an efficient failure-prone object detection approach that not only detects and recovers from BN failures but also reduces the number and size of transmissions without compromising the boundary estimation accuracy. The proposed approach utilizes the spatial and temporal features of sensor nodes to detect object BNs. A Voronoi diagram-based network clustering, and failure detection and recovery scheme is used to increase boundary estimation accuracy. Simulation results show the significance of our approach in terms of energy efficiency, communication overhead, and boundary accuracy.

Highlights

Continuous object tracking is a useful application area of sensor networks for detecting and monitoring of roaming paths of continuous natured objects like forest fire, oil spills, flow of volcanic disposals, and hazardous biochemical diffusions [1,2]
We develop an efficient failure-prone object detection approach that detects and recovers from boundary nodes (BNs) failures and reduces the number and size of transmissions without compromising the boundary estimation accuracy
Failure of one or multiple boundary nodes (BNs) can significantly affect the quality and reliability of boundary data received at the sink and, may reduce boundary estimation accuracy

Summary

Introduction

Continuous object tracking is a useful application area of sensor networks for detecting and monitoring of roaming paths of continuous natured objects like forest fire, oil spills, flow of volcanic disposals, and hazardous biochemical diffusions [1,2]. A more efficient way is to let the nodes, present at the phenomenon boundary (BNs), send their detection status to the sink, which processes these boundary data to extract useful information [4]. This can greatly save traffic and communication overhead. A more efficient way is to allow only the nodes present at the phenomenon boundary to send sensing information to the sink for boundary estimation. Detection of BNs for a continuously changing object is very challenging for a number of reasons: (1) The rapid changes in phenomenon shape change the selected BNs. In one time slot, certain nodes may be at the phenomenon boundary, but in the time slot, they may be inside the phenomena or no longer sensing the phenomena. Sending minor changes in the phenomenon shape wastes the sparse resources of the sensing modules and reduces the lifetime of the sensor network

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Results

Conclusion