Employing the flocking behavior of birds for controlling congestion in autonomous decentralized networks

Recently a great emphasis has been given on autonomous decentralized networks (ADNs) wherein constituent nodes carry out specific tasks collectively. Their dynamic and constrained nature along with the emerging need for offering quality of service (QoS) assurances drive the necessity for effective network control mechanisms. This study focuses on designing a robust and self-adaptable congestion control mechanism which aims to be simple to implement at the individual node, and involve minimal information exchange, while maximizing network lifetime and providing QoS assurances. Our approach combats congestion by mimicking the collective behavior of bird flocks having global self-* properties achieved collectively without explicitly programming them into individual nodes. The main idea is to dasiaguidepsila packets (birds) to form flocks and flow towards the sink (global attractor), whilst trying to avoid congestion regions (obstacles). Unlike the bio-swarm approach of Couzin, which is formulated on a metrical space, our approach is reformulated on to a topological space (graph of nodes), while repulsion/attraction forces manipulate the direction of motion of packets. Our approach provides sink direction discovery, congestion detection and traffic management in ADNs with emphasis on Wireless Sensor Networks (WSNs). Performance evaluations show the effectiveness of our self-adaptable mechanism in balancing the offered load and in providing graceful performance degradation under high load scenarios compared to typical conventional approaches.