TOSSIM: accurate and scalable simulation of entire TinyOS applications
Proceedings of the 1st international conference on Embedded networked sensor systems
A wireless sensor network For structural monitoring
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Simulating the power consumption of large-scale sensor network applications
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Adaptive design optimization of wireless sensor networks using genetic algorithms
Computer Networks: The International Journal of Computer and Telecommunications Networking
PRESTO: feedback-driven data management in sensor networks
NSDI'06 Proceedings of the 3rd conference on Networked Systems Design & Implementation - Volume 3
Protocols and Architectures for Wireless Sensor Networks
Protocols and Architectures for Wireless Sensor Networks
Self-organisation of sensor networks using genetic algorithms
International Journal of Sensor Networks
Genetic Algorithm Based Wireless Sensor Network Localization
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
Computers & Mathematics with Applications
Quality-aware sensor data collection
International Journal of Sensor Networks
Prototyping home automation wireless sensor networks with ASSL
Proceedings of the 7th international conference on Autonomic computing
Developing intelligent sensor networks: a technological convergence approach
Proceedings of the 2010 ICSE Workshop on Software Engineering for Sensor Network Applications
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Bio-inspired Learning and Intelligent Systems
Modeling and optimization of UWB communication networks through a flexible cost function
IEEE Journal on Selected Areas in Communications
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Wireless sensor networks (WSNs) possess inherent tradeoffs among conflicting performance objectives such as data yield, data fidelity and power consumption. In order to address this challenge, this paper proposes a biologically-inspired application framework for WSNs. The proposed framework, called El Niño, models an application as a decentralized group of software agents. This is analogous to a bee colony (application) consisting of bees (agents). Agents collect sensor data on individual nodes and carry the data to base stations. They perform this data collection functionality by autonomously sensing their local network conditions and adaptively invoking biological behaviors such as pheromone emission, swarming, reproduction and migration. Each agent carries its own operational parameters, as genes, which govern its behavior invocation and configure its underlying sensor nodes. El Niño allows agents to evolve and adapt their operational parameters to network dynamics and disruptions by seeking the optimal tradeoffs among conflicting performance objectives. This evolution process is augmented by a notion of accelerated evolution. It allows agents to evolve their operational parameters by learning dynamic network conditions in the network and approximating their performance under the conditions. This is intended to expedite agent evolution to adapt to network dynamics and disruptions.