[1] B. Hull, K. Jamieson and H. Balakrishnan, “Mitigating Congestion in Wireless Sensor Networks,” Proceedings of the 2nd International Conference on

Embedded Networked Sensor Systems, ACM SenSys 2004, November 2004, pp. 134-147.[2] C.-Y. Wan, S. B. Eisenman and A. T. Campbell, “CODA: Congestion Detection and Avoidance in Sensor Networks,” Proceedings of the 1st International

Conference on Embedded Networked Sensor Systems, ACM SenSys 2003, November 2003, pp. 266-279.[3] C. Wang, K. Sohraby, and B. Li., “SenTCP: A hop-by-hop congestion control protocol for wireless sensor networks,” IEEE INFOCOM 2005, March 2005.[4] M. Bundgaard, T. C. Damgaard, F. Dacara, J. W. Winther and K. J. Christoffersen, “Ant Routing System – A routing algorithm based on ant algorithms

applied to a simulated network”, Report, University of Copenhagen, 2002. [5] F. Dressler, B. Kruger, G. Fuchs and R. German, “Self-organisation in Sensor Networks using Bio-Inspired Mechanisms,” Proceedings of 18th

ACM/GI/ITG International Conference on Architecture of Computing Systems, March 2005, pp. 139-144.

• Natural and Biological Systems can provide strong research framework beyond classic mathematical (analytical) models.

• Network control models and techniques intended for WSNs need to possess the properties arisen from the aforementioned systems: - Self-* properties: self-organization, self-adaptation, self-optimization, self-healing, etc. - Robustness and Resilience (tolerance against failures or attacks) - Decentralized operation.

• Develop techniques that extract hypotheses about interaction networks apply them for the control of stressful congestion conditions in challenging sensor environment.

• Complex systems can draw inspiration from natural and biological processes to develop techniques and tools for building robust, self-adaptable and self-organizing network information systems.

• Study of Nature/Biologically-Inspired Systems relies on: - Swarm Intelligence (ants, bees, birds, etc.)

- Artificial Immune system - Evolutionary (genetic) algorithms - Cell and Molecular Biology

• Global properties (self-organization, robustness, etc.) are achieved without explicitly programming them into individual nodes. These properties are obtained through emergent behavior even under unforeseen scenarios, environmental variations or deviant nodes.

• Wireless Sensor Networks (WSNs) consist of tiny low-cost, low-power unsophisticated sensor nodes.

• Fundamental aim: Produce globally meaningful information from raw local data obtained by individual sensor nodes based on 2 goals:

save energy, maximize network lifetime,

maintain connectivity.

• Constraints: Computation capability, memory space, communication bandwidth and energy supply.

• Congestion in WSNs:

aggregated incoming traffic flow > outgoing channel capacity,

channel contention and interference in shared communication medium.

• Consequences of congestion in WSNs: energy waste, throughput reduction, information loss lower QoS / network lifetime.

Congestion Control mechanisms goals:prolong network lifetime + provide adequate QoS levels

Wireless Sensor Network Control: Drawing Inspiration from Complex Systems

Pavlos Antoniou and Andreas PitsillidesNetworks Research Laboratory, Computer Science Department, University of Cyprus

E-mail: paul.antoniou@cs.ucy.ac.cy, andreas.pitsillides@ucy.ac.cy

LOGO

INTRODUCTION

• Protocols and implementation in WSNs infer congestion based on methodologies known from the Internet:

Fusion [1]: queue length, channel contention.

CODA [2]: present/past channel conditions, buffer occupancy.

SenTCP [3]: local inter-arrival packet time, service time, buffer occupancy.

• Modern information systems are complex: sheer size, large number of nodes/users, heterogeneous devices, complex interactions among components difficult to deploy, manage, keep functioning correctly through traditional techniques.

• Need for: robust, self-organized, self-adaptable, self-repairing, decentralized networked systems Complex Systems Science

• Complex Systems Science studies how elements of a system give rise to collectivebehaviors of the system, andhow the system interacts withenvironment.

• Focus on: - elements (nodes), - wholes (networks), and - relationships (links, information dissemination).

LOGO

RELATED WORK

COMPLEX SYSTEMS IN GENERAL

NATURE & BIOLOGICALLY-INSPIRED SYSTEMS

• Complex System Science represents a radical shift from traditional algorithmic techniques.

• Complex Natural and Biological Systems can provide efficient solutions to a wide variety of problems in a sensor environment Promise for the Future.

• Nature-inspired and bio-inspired techniques such as ant colony algorithms [4] and cell biology-based approaches [5] respectively have achieved remarkable success in computer science problems of search and optimization.

• Our Aim: Capture successful natural/biological mechanisms and exploit their properties to control the complexity of stressful congestion conditions in Wireless Sensor Networks.

CONCLUSIONS AND FUTURE WORK

OUR DIRECTION

Collective ant foraging for routing [4] (Ant Colony Algorithms in Swarm Intelligence)

Blood pressure regulation for the control of information flow [5] (Cell Biology)

REFERENCES