TY - GEN
T1 - Evolutionary constraint-based multiobjective adaptation for self-organizing wireless sensor networks
AU - Boonma, Pruet
AU - Suzuki, Junichi
PY - 2007
Y1 - 2007
N2 - Wireless sensor applications (WSNs) are often required to simultaneously satisfy conflicting operational objectives (e.g., latency and power consumption). Based on an observation that various biological systems have developed the mechanisms to overcome this issue, this paper proposes a biologicallyinspired adaptation mechanism, called MONSOON. With MONSOON, each application is designed 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 and surrounding environment conditions and adaptively invoking biological behaviors such as pheromone emission, replication, reproduction and migration. Each agent has its own behavior policy, as a gene, which defines how to invoke its behaviors. MONSOON allows agents to evolve their behavior policies (i.e., genes) and simultaneously adapt to conflicting objectives. In addition to consider multiple objectives equally, MONSOON also allows agents to evolve in a constraint-based (or intentionally-biased) manner. A constraint is defined as an upper or lower bound for each objective. Simulation results show that MONSOON allows agents (WSN applications) to adapt to dynamics of the network (e.g., node/link failures) through evolution and simultaneously satisfy conflicting objectives in a self-organizing manner.
AB - Wireless sensor applications (WSNs) are often required to simultaneously satisfy conflicting operational objectives (e.g., latency and power consumption). Based on an observation that various biological systems have developed the mechanisms to overcome this issue, this paper proposes a biologicallyinspired adaptation mechanism, called MONSOON. With MONSOON, each application is designed 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 and surrounding environment conditions and adaptively invoking biological behaviors such as pheromone emission, replication, reproduction and migration. Each agent has its own behavior policy, as a gene, which defines how to invoke its behaviors. MONSOON allows agents to evolve their behavior policies (i.e., genes) and simultaneously adapt to conflicting objectives. In addition to consider multiple objectives equally, MONSOON also allows agents to evolve in a constraint-based (or intentionally-biased) manner. A constraint is defined as an upper or lower bound for each objective. Simulation results show that MONSOON allows agents (WSN applications) to adapt to dynamics of the network (e.g., node/link failures) through evolution and simultaneously satisfy conflicting objectives in a self-organizing manner.
KW - Biologically-inspired networking
KW - Evolutionary and adaptive sensor networks
KW - Self-organizing sensor networks
UR - https://www.scopus.com/pages/publications/53149132041
UR - https://www.scopus.com/pages/publications/53149132041#tab=citedBy
U2 - 10.1109/BIMNICS.2007.4610095
DO - 10.1109/BIMNICS.2007.4610095
M3 - Conference contribution
AN - SCOPUS:53149132041
SN - 9789639799059
T3 - Proceedings of the Bio-Inspired Models of Network, Information, and Computing Systems, Bionetics 2007
SP - 111
EP - 119
BT - Proceedings of the Bio-Inspired Models of Network, Information, and Computing Systems, Bionetics 2007
T2 - 2nd International Conference on Bio-Inspired Models of Network, Information, and Computing Systems, BIONETICS 2007
Y2 - 10 December 2007 through 12 December 2007
ER -