Restricted Boltzmann Machine Based Energy Efficient Cognitive Network

Current network technology is statically configured and it is difficult to self-adjust changes on demand. Existing protocols react for situations but it cannot take intelligent decisions. Emerging cognitive network plays a key role in networking environment because of its unique features namely reasoning and decision making. Energy efficiency is highly desirable for effective data communication network. In this paper, energy aware routing protocols and trust based metrics for improving energy efficiency is addressed. The proposed method uses Restricted Boltzmann Machine to stabilize the energy level of the network during routing. RBM based routing is comparatively better than conventional Boltzmann Machine based routing in terms of self-learning the trust metrics. The performance graph shows that the proposed RBM based routing has achieved lesser energy level consumption and higher trust values which ensures effective cognitive approach.

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