Multi-Objective Evolutionary Optimizations of a Space-Based Reconfigurable Sensor Network under Hard Constraints Export

@inproceedings{Yang2007MultiObjective, abstract = {Wireless sensor networks have emerged as a promising way to develop high security systems. This paper presents the optimizations of a space-based reconfigurable sensor network under hard constraints by employing an efficient multi-objective evolutionary algorithm (MOEA). First, a system model is proposed for cluster-based space wireless sensor networks. Second, the statement of multi-objective optimization problems is mathematically formulated under multiple constraints. Third, the MOEA is used to find multi- criteria solutions in the sense of Pareto optimizations. Finally, simulation results are provided to illustrate the effectiveness of applying the MOEA to the multi-objective evolutionary optimizations of a space-based reconfigurable sensor network under hard constraints.}, author = {Yang, Erfu and Erdogan, A. T. and Arslan, T. and Barton, N.}, booktitle = {Bio-inspired, Learning, and Intelligent Systems for Security, 2007. BLISS 2007. ECSIS Symposium on}, citeulike-article-id = {3403906}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/BLISS.2007.20}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4290942}, citeulike-linkout-2 = {http://dx.doi.org/http://dx.doi.org/10.1109/BLISS.2007.20}, citeulike-linkout-3 = {http://dx.doi.org/10.1109/BLISS.2007.20}, comment = {Using the clustered picosat model from espacenet, they optimise for various parameters. Assume a picosat with only a battery, cluster head with (probably) solar power too. Multi-objective evolutionary optimisation of * Energy consumption * System life time * Maximize coverage * Minimize number of participating satellites in cluster Mathematical expressions for all of the above, GA, then show that they can be optimized. And that's it.}, doi = {10.1109/BLISS.2007.20}, journal = {Bio-inspired, Learning, and Intelligent Systems for Security, 2007. BLISS 2007. ECSIS Symposium on}, keywords = {cluster-topology, espacenet, multi-objective-evolotionary-algorithm, satellite, wsn}, pages = {72--75}, posted-at = {2008-10-14 11:14:53}, priority = {0}, title = {Multi-Objective Evolutionary Optimizations of a Space-Based Reconfigurable Sensor Network under Hard Constraints}, url = {http://dx.doi.org/10.1109/BLISS.2007.20}, year = {2007} }

TY - CONF ID - Yang2007MultiObjective L3 - citeulike-article-id:3403906 N2 - Wireless sensor networks have emerged as a promising way to develop high security systems. This paper presents the optimizations of a space-based reconfigurable sensor network under hard constraints by employing an efficient multi-objective evolutionary algorithm (MOEA). First, a system model is proposed for cluster-based space wireless sensor networks. Second, the statement of multi-objective optimization problems is mathematically formulated under multiple constraints. Third, the MOEA is used to find multi- criteria solutions in the sense of Pareto optimizations. Finally, simulation results are provided to illustrate the effectiveness of applying the MOEA to the multi-objective evolutionary optimizations of a space-based reconfigurable sensor network under hard constraints. JF - Bio-inspired, Learning, and Intelligent Systems for Security, 2007. BLISS 2007. ECSIS Symposium on EP - 75 TI - Multi-Objective Evolutionary Optimizations of a Space-Based Reconfigurable Sensor Network under Hard Constraints T2 - Bio-inspired, Learning, and Intelligent Systems for Security, 2007. BLISS 2007. ECSIS Symposium on SP - 72 KW - cluster-topology KW - espacenet KW - multi-objective-evolotionary-algorithm KW - satellite KW - wsn N1 - Using the clustered picosat model from espacenet, they optimise for various parameters. Assume a picosat with only a battery, cluster head with (probably) solar power too. Multi-objective evolutionary optimisation of * Energy consumption * System life time * Maximize coverage * Minimize number of participating satellites in cluster Mathematical expressions for all of the above, GA, then show that they can be optimized. And that's it. AU - Yang, Erfu AU - Erdogan, AT AU - Arslan, T AU - Barton, N PY - 2007/// UR - http://dx.doi.org/10.1109/BLISS.2007.20 ER -