Synaptic Connection Autonomic Networks

Inhalt:
This paper proposes a novel approach to form weighted peer-to-peer networks in a self-organising and decentralised way, termed Synaptic Connection Autonomic Networks (SCAN). Distributed peers in SCAN establish and update their connections or associations based on resource sharing results by following Hebbian learning in a related manner to that in biological neural systems. The strengths of peer associations reflect the utility of one peer to another and continuously adapt over time. In operation SCAN constructs resilient peer-to-peer networks in real time. The result is a more efficient and effective resource sharing mechanism between distributed peers. Simulated experiments verified that SCAN successfully formed P2P networks with correct peer associations and the resource search based on it was continuously improving as the networks were correctly formed.