Paper Info
Title
Employing the flocking behavior of birds for controlling congestion in autonomous decentralized networks
Abstract
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 'guide' packets (birds) to form flocks and flow towards the sink (global attractor), whilst trying to avoid congestion regions (obstacles). Unlike the bioswarm 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.
Year
DOI
Venue
2009
10.1109/CEC.2009.4983153
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
qos assurance,congestion region,typical conventional approach,autonomous decentralized network,self-adaptable congestion control mechanism,approach combats congestion,congestion detection,individual node,direction discovery,bioswarm approach,wireless communication,collective behavior,particle swarm optimization,congestion control,convergence,displays,force,genetic algorithms,information exchange,simulated annealing,traffic management,wireless sensor network,space exploration,routing,quality of service,wireless sensor networks,metric space,topological space
Simulated annealing,Computer science,Network packet,Information exchange,Flocking (behavior),Quality of service,Computer network,Offered load,Network congestion,Wireless sensor network,Distributed computing
Conference
Citations 
PageRank 
References 
12
0.67
10
Authors
4
Name
Order
Citations
PageRank
Pavlos Antoniou135317.04
Andreas Pitsillides299895.02
Tim Blackwell334727.87
Andries Engelbrecht4694.36