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Wireless Sensor Network Control:
Drawing Inspiration from Complex Systems
LOGO
LOGO
Pavlos Antoniou and Andreas Pitsillides
Networks Research Laboratory, Computer Science Department, University of Cyprus
E-mail: [email protected], [email protected]
INTRODUCTION
NATURE & BIOLOGICALLY-INSPIRED SYSTEMS
• Wireless Sensor Networks (WSNs) consist of tiny low-cost, lowpower unsophisticated sensor nodes.
• Complex systems can draw inspiration from natural and biological
processes to develop techniques and tools for building robust, selfadaptable and self-organizing network information systems.
• Fundamental aim: Produce globally meaningful information from raw
local data obtained by individual sensor nodes based on 2 goals:
• Study of Nature/Biologically-Inspired Systems relies on:
- Swarm Intelligence (ants, bees, birds, etc.)
 save energy, maximize network lifetime,
Collective ant foraging for
routing [4] (Ant Colony
Algorithms in Swarm
Intelligence)
 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
RELATED WORK
• 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.
COMPLEX SYSTEMS IN GENERAL
• 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).
- Artificial Immune system
- Evolutionary (genetic) algorithms
- Cell and Molecular Biology
Blood pressure
regulation for the
control of
information flow
[5] (Cell 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.
OUR DIRECTION
• 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, selfoptimization, 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.
CONCLUSIONS AND FUTURE WORK
• 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.
REFERENCES
[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.