PROJECT TITLE :

Low-Overhead and High-Precision Prediction Model for Content-Based Sensor Search in the Internet of Things

ABSTRACT:

A growing variety of Net-connected sensors have already promoted the advance of sensor search service. Accessing all accessible objects to find the sought sensor results in huge communication overhead, thus a coffee-overhead and high-precision prediction model (LHPM) is proposed to enhance the sensor search potency. We tend to design the approximation method to lower the reporting energy price. Then a multistep prediction technique is proposed to accurately estimate the sensor state. Furthermore, a sensor ranking method is presented to assess the matching possibilities of sensors, therefore on effectively reduce the communication overhead of the search process. Simulation results demonstrate the validity of the proposed prediction model in the area of content-based mostly sensor search.


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