Details

Title

Minimizing sensor movement in target coverage problem: A hybrid approach using Voronoi partition and swarm intelligence

Journal title

Bulletin of the Polish Academy of Sciences: Technical Sciences

Yearbook

2017

Numer

No 2

Publication authors

Divisions of PAS

Nauki Techniczne

Publisher

Polish Academy of Sciences

Date

2017

Identifier

ISSN 0239-7528, eISSN 2300-1917

References

Bai (2006), Deploying wireless sensors to achieve both coverage and connectivity th Mobile Ad, Proc ACM Int Symp Hoc Netw Comput, 7, 131. ; Karaboga (2007), A powerful and efficient algorithm for numerical function optimization : Artificial bee colony ( ABC ) algorithm, Journal of Global Optimization, 39, 459, doi.org/10.1007/s10898-007-9149-x ; Liao (2015), Minimizing movement for target coverage and network connectivity in mobile sensor networks Transactions on Parallel and Distributed, IEEE Systems, 26, 1971. ; Mini (2014), Sensor deployment and scheduling for target coverage problem in wireless sensor network, IEEE Sensors Journal, 14, 636, doi.org/10.1109/JSEN.2013.2286332 ; Sonmez (2011), Artificial bee colony algorithm for optimization of truss structures, Applied Soft Computing, 11, 2406, doi.org/10.1016/j.asoc.2010.09.003 ; Dhillon (2002), Sensor placement for grid coverage under imprecise detections International Conference on Information Fusion, Proc, 1581. ; Keshavarzian (2006), Wakeup scheduling in wireless sensor networks th Mobile Ad, Proc ACM Int Symp Hoc Netw Comput, 7, 322. ; Omkar (2011), Artificial bee colony ( ABC ) for multi - objective design optimization of composite structures, Applied Soft Computing, 11, 489, doi.org/10.1016/j.asoc.2009.12.008 ; Liu (2005), Randomized coverage - preserving scheduling schemes for wireless sensor networks, Proc NETWORKING, 956. ; Imanian (2014), Velocity based artificial bee colony algorithm for high dimensional continuous optimization problems Applications of Artificial, Engineering Intelligence, 36, 148. ; Makhoul (2009), Dynamic scheduling of cover - sets in randomly deployed wireless video sensor networks for surveillance applications nd IFIP Wireless Days, Proc Conf, 2, 73. ; Karaboga (2008), On the performance of artificial bee colony ( ABC ) algorithm, Applied Soft Computing, 8, 687, doi.org/10.1016/j.asoc.2007.05.007 ; Luo (2012), Mobile sensor node deployment and asynchronous power management for wireless sensor networks on, IEEE Transactions Industrial Electronics, 59, 2377, doi.org/10.1109/TIE.2011.2167889 ; Dhillon (2003), Sensor placement for effective coverage and surveillance in distributed sensor networks IEEE Conference on Wireless Communications and Networking, Proc, 1609. ; Liu (2012), Sensor deployment of wireless sensor networks based on ant colony optimization with three classes of ant transitions, IEEE Communications Letters, 16, 1604, doi.org/10.1109/LCOMM.2012.090312.120977 ; Gu (2007), Target coverage with QoS requirements in wireless sensor networks, Proc Intell Pervas Comput, 35. ; Liu (2013), Dynamic coverage of mobile sensor networks Transactions on Parallel and Distributed, IEEE Systems, 24, 301. ; Akay (2013), A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding, Applied Soft Computing, 13, 3066, doi.org/10.1016/j.asoc.2012.03.072 ; Yildiz (2013), A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing, Applied Soft Computing, 13, 2906, doi.org/10.1016/j.asoc.2012.04.013 ; Tan (2010), Exploiting reactive mobility for collaborative target detection in wireless sensor networks, IEEE Trans Mobile Comput, 9, 317, doi.org/10.1109/TMC.2009.125 ; Parsopoulos (2002), Recent approaches to global optimization problems through particle swarm optimization Natural Computing : An, International Journal, 1, 235. ; Clerc (2002), The particle swarm - explosion stability and convergence in a multidimensional complex space, IEEE Trans Evolutionary Computation, 6, 58, doi.org/10.1109/4235.985692 ; Lu (2005), Energy - efficient connected coverage of discrete targets in wireless sensor networks Networking and Mobile, Computing, 3619. ; Chong (2003), Sensor networks : Evolution opportunities and challenges of the, Proc IEEE, 91, 1247, doi.org/10.1109/JPROC.2003.814918 ; Chang (2008), Energy - aware node placement topology control and MAC scheduling for wireless sensor networks, Computer Networks, 52, 2189, doi.org/10.1016/j.comnet.2008.02.028 ; Ozturk (2011), Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm, Sensors, 11, 6056, doi.org/10.3390/s110606056 ; Develi (2015), Artificial bee colony optimization for modelling of indoor PLC channels : A case study from Turkey, Electric Power Systems Research, 127. ; Yen (2009), Range - based sleep scheduling ( RBSS ) for wireless sensor networks, Wireless Pers Commun, 48, 411, doi.org/10.1007/s11277-008-9530-1 ; Chaudhary (2009), coverage problem in wireless sensor networks, Proc Int Conf Distrib Comput Netw, 325. ; Yildiz (2013), Optimization of cutting parameters in multi - pass turning using artificial bee colony - based approach, Information Sciences, 220. ; Ke (2007), Constructing a wireless sensor network to fully cover critical grids by deploying minimum sensors on grid points is NP - Complete, IEEE Trans Computers, 56, 710, doi.org/10.1109/TC.2007.1019

DOI

10.1515/bpasts-2017-0030

×