1. [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 Pitsillides Networks Research Laboratory, Computer Science Department, University of Cyprus E-mail: paul.antoniou@cs.ucy.ac.cy, andreas.pitsillides@ucy.ac.cypaul.antoniou@cs.ucy.ac.cyandreas.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 collective behaviors of the system, and how the system interacts with environment. 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