Doubly Cognitive Architecture Based Cognitive Wireless Sensor Network

TL;DR

This paper introduces a Doubly Cognitive Wireless Sensor Network that intelligently allocates sensing resources using neural networks and SVMs, significantly saving energy and reducing sensing time to address spectrum scarcity.

Contribution

It proposes a novel Doubly Cognitive WSN architecture that enhances spectrum utilization and energy efficiency through progressive sensing strategies based on AI techniques.

Findings

Significant energy savings in sensor nodes.

Reduced sensing time for spectrum detection.

Improved spectrum utilization efficiency.

Abstract

Nowadays scarcity of spectrum availability is increasing highly. Adding cognition to the existing Wireless Sensor Network (WSN) infrastructure will help in this situation. As sensor nodes in WSN are limited with some constrains like power, efforts are required to increase the lifetime and other performance measures of the network. In this paper we propose the idea of Doubly Cognitive WSN. The basic idea is to progressively allocate the sensing resources only to the most promising areas of the spectrum. This work is based on Artificial Neural Network as well as on Support Vector Machine (SVM) concept. As the load of sensing resource is reduced significantly, this approach will save the energy of the nodes, and also reduce the sensing time dramatically. The proposed work can be enhanced by doing the pattern analysis thing after a sufficiently long time again and again to review the strategy of sensing. Thus Doubly Cognitive WSN will enable current WSN to overcome the spectrum scarcity as well as save the energy of the sensor nodes.