Abstract
It is a challenging task for all Harbors or Naval Administration to restrict and monitor the movement of defense or commercial ships. Most commonly used techniques of monitoring are radars and satellite images. These techniques are not reliable as radars can be turned off voluntarily and receptions of images are affected by adverse climatic conditions. This paper proposes a reliable ship intruder detection algorithm that classifies different types of objects approaching the model system in and out of phase with the ocean waves. The proposed technique also takes care of superimposition of temporal and spatial values of nodes that are presumably deployed in the sea surface up to a certain distance. Simulation results prove that the proposed algorithm detects and classifies objects efficiently even when 50% of the nodes reporting the tracking phenomenon are tampered.
Highlights
Object tracking is one of the most practical applications of Wireless sensor network (WSN)
Our contribution is as follows: We develop a classification algorithm for identifying various objects approaching a moving ship
Objects like other ships moving in parallel and in opposite direction is successfully detected with our approach
Summary
Object tracking is one of the most practical applications of WSN. Wireless network is itself vulnerable and when combined with sensors the other errors that add upon to it are sensor failures, localization errors, prediction, and detection errors. It uses spatial and temporal correlations of the amplitude data collected by these senor nodes to detect an approaching object by the help of the wave pattern generated by it. The wireless sensor network needs the intelligence of classifying objects by identifying the pattern of waves generated by them.
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