QoS Management for Wireless Sensor Networks with a Mobile Sink
EWSN '09 Proceedings of the 6th European Conference on Wireless Sensor Networks
Proceedings of the 4th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
Collaborative signal and information processing in wireless sensor networks: a review
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
ICCOM'10 Proceedings of the 14th WSEAS international conference on Communications
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An intelligent information security mechanism for the network layer of WSN: BIOSARP
CISIS'11 Proceedings of the 4th international conference on Computational intelligence in security for information systems
ACS'11 Proceedings of the 11th WSEAS international conference on Applied computer science
Constraint-based self-adaptation of wireless sensor networks
Proceedings of the 2nd International Workshop on Adaptive Services for the Future Internet and 6th International Workshop on Web APIs and Service Mashups
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Wireless sensor applications (WSNs) are often required to simultaneously satisfy conflicting operational objectives (e.g., latency and power consumption). Based on an obser- vation that various biological systems have developed the mechanisms to overcome this issue, this paper proposes a biologically-inspired adaptation mechanism, called MON- SOON. MONSOON is designed to support data collection applications, event detection applications and hybrid appli- cations. Each application is implemented as a decentralized group of software agents, analogous to a bee colony (appli- cation) consisting of bees (agents). Agents collect sensor data and /or detect an event (a significant change in sen- sor reading) on individual nodes, and carry sensor data to base stations. They perform these data collection and event detection functionalities by sensing their surrounding en- vironment conditions and adaptively invoking biologically- inspired behaviors such as pheromone emission, reproduc- tion and migration. Each agent has its own behavior pol- icy, as a gene, which defines how to invoke its behaviors. MONSOON allows agents to evolve their behavior policies (genes) and adapt their operations to given objectives. Sim- ulation results show that MONSOON allows agents (WSN applications) to simultaneously satisfy conflicting objec- tives by adapting to dynamics of physical operational envi- ronments and network environments (e.g., sensor readings and node /link failures) through evolution.