A Data Collecting Strategy for Farmland WSNs using a Mobile Sink
Keywords:
farmland WSNs, data collection, virtual grid, mobile sinkAbstract
To the characteristics of large number of sensor nodes, wide area and unbalanced energy consumption in farmland Wireless Sensor Networks, an efficient data collection strategy (GCMS) based on grid clustering and a mobile sink is proposed. Firstly, cluster is divided based on virtual grid, and the cluster head is selected by considering node position and residual energy. Then, an optimal mobile path and residence time allocation mechanism for mobile sink are proposed. Finally, GCMS is simulated and compared with LEACH and GRDG. Simulation results show that GCMS can significantly prolong the network lifetime and increase the amount of data collection, especially suitable for large-scale farmland Wireless Sensor Networks.
References
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[2] Dong, M.; Ota, K.; Liu, A. (2016). RMER: Reliable and energy-efficient data collection for largescale wireless sensor networks. IEEE Internet of Things Journal, 3(4), 511-519, 2016. https://doi.org/10.1109/JIOT.2016.2517405
[3] Dutta, P.K.; Banerjee, S. (2019). Monitoring of aerosol and other particulate matter in air using aerial monitored sensors and real time data monitoring and processing, Journal of System and Management Sciences, 9(2), 104-113.
[4] Fan, P.F.; Shang, Z. (2019). Application of wireless sensor network in monitoring of weapon and equipment production. Instrumentation Mesure Metrologie, 18(1), 37-41, 2019. https://doi.org/10.18280/i2m.180106
[5] Ha, I.; Djuraev, M.; Ahn, B. (2017). An optimal data gathering method for mobile sinks in WSNs. Wireless Personal Communications, 97(1), 1401-1417, 2017. https://doi.org/10.1007/s11277-017-4579-3
[6] Hu, W.; Yao, W.; Hu, Y.; Li, H. (2019). Selection of Cluster Heads for Wireless Sensor Network in Ubiquitous Power Internet of Things. International Journal of Computers Communications & Control, 14(3), 344-358, 2019. https://doi.org/10.15837/ijccc.2019.3.3573
[7] Hu, W.; Li, H.H.; Yao, W.H.; Hu, Y.W. (2019). Energy Optimization for WSN in Ubiquitous Power Internet of Things. International Journal of Computers Communications & Control, 14(4), 503-517, 2019. https://doi.org/10.15837/ijccc.2019.4.3572
[8] Javaid, N.; Rasheed, M.B.; Imran, M.; Guizani, M.; Khan, Z.A.; Alghamdi, T.A.; Ilahi, M. (2015). An energy-efficient distributed clustering algorithm for heterogeneous WSNs. EURASIP Journal on Wireless communications and Networking, 2015(1), 151, 2015. https://doi.org/10.1186/s13638-015-0376-4
[9] Khan, A.W.; Abdullah, A.H.; Anisi, M.H.; Bangash, J.I. (2014). A comprehensive study of data collection schemes using mobile sinks in wireless sensor networks. Sensors, 14(2), 2510-2548, 2014. https://doi.org/10.3390/s140202510
[10] Khan, A.W.; Abdullah, A.H.; Razzaque, M.A.; Bangash, J.I.; Altameem, A. (2015). VGDD: A virtual grid based data dissemination scheme for wireless sensor networks with mobile sink. International Journal of Distributed Sensor Networks, 11(2), 890348, 2015. https://doi.org/10.1155/2015/890348
[11] Kinalis, A.; Nikoletseas, S.; Patroumpa, D.; Rolim, J. (2014). Biased sink mobility with adaptive stop times for low latency data collection in sensor networks. Information Fusion, 15, 56-63, 2014. https://doi.org/10.1016/j.inffus.2012.04.003
[12] Kumar, A.K.; Sivalingam, K.M.; Kumar, A. (2013). On reducing delay in mobile data collection based wireless sensor networks. Wireless Networks, 19(3), 285-299, 2013. https://doi.org/10.1007/s11276-012-0466-8
[13] Kumar, I.; Sachan, V.; Shankar, R.; Mishra, R.K. (2018). An investigation of wireless S-DF hybrid satellite terrestrial relaying network over time selective fading channel. Traitement du Signal, 35(2), 103-120, 2018. https://doi.org/10.3166/ts.35.103-120
[14] Kumar, R.V.K.; Naik, G.M.; Murali, G. (2019). Wireless nano senor network (WNSN) for trace detection of explosives: The case of RDX and TNT. Instrumentation Mesure Metrologie, 18(2), 153-158, 2019. https://doi.org/10.18280/i2m.180209
[15] Lee, K.; Kim, Y.H.; Kim, H.J.; Han, S. (2014). A myopic mobile sink migration strategy for maximizing lifetime of wireless sensor networks. Wireless Networks, 20(2), 303-318, 2014. https://doi.org/10.1007/s11276-013-0606-9
[16] Lee, K.; Kim, Y.H.; Kim, H.J.; Han, S. (2014). A myopic mobile sink migration strategy for maximizing lifetime of wireless sensor networks. Wireless Networks, 20(2), 303-318, 2014. https://doi.org/10.1007/s11276-013-0606-9
[17] Lin, T.; Wu, P.; Gao, F.M.; Wang, L.H. (2019). A secure query protocol for multi-layer wireless sensor networks based on internet of things. Revue d'Intelligence Artificielle, 33(2), 145-149, 2019. https://doi.org/10.18280/ria.330210
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[20] Misbahuddin, M.; Ratna, A.A.P.; Sari, R.F. (2018). Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs. International Journal of Computers Communications & Control, 13(1), 99-116, 2018. https://doi.org/10.15837/ijccc.2018.1.3088
[21] Ren, J.; Huang, S.Y.; Song, W.; Han, J. (2019). A novel indoor positioning algorithm for wireless sensor network based on received signal strength indicator filtering and improved Taylor series expansion. Traitement du Signal, 36(1), 103-108, 2019. https://doi.org/10.18280/ts.360113
[22] Salarian, H.; Chin, K.W.; Naghdy, F. (2013). An energy-efficient mobile-sink path selection strategy for wireless sensor networks. IEEE Transactions on Vehicular Technology, 63(5), 2407-2419, 2013. https://doi.org/10.1109/TVT.2013.2291811
[23] Salarian, H.; Chin, K.W.; Naghdy, F. (2013). An energy-efficient mobile-sink path selection strategy for wireless sensor networks. IEEE Transactions on Vehicular Technology, 63(5), 2407-2419, 2013. https://doi.org/10.1109/TVT.2013.2291811
[24] Srbinovski, B.; Magno, M.; O'Flynn, B.; Pakrashi, V.; Popovici, E. (2015). Energy aware adaptive sampling algorithm for energy harvesting wireless sensor networks. 2015 IEEE Sensors Applications Symposium (SAS), 1-6, 2015. https://doi.org/10.1109/SAS.2015.7133582
[25] Talmale, R.; Bhat, M.N.; Thakare, N. (2019). Energy attentive pre-fault detection mechanism with multilevel transmission for distributed wireless sensor network. Revue d'Intelligence Artificielle, 33(2), 97-103, 2019. https://doi.org/10.18280/ria.330203
[26] Wang, F.F.; Hu, H.F. (2019). An improved energy-efficient cluster routing protocol for wireless sensor network. Ingénierie des Systí¨mes d'Information, 24(4), 419-424, 2019. https://doi.org/10.18280/isi.240409
[27] Wen, W.; Zhao, S.; Shang, C.; Chang, C.Y. (2017). EAPC: Energy-aware path construction for data collection using mobile sink in wireless sensor networks. IEEE Sensors Journal, 18(2), 890-901, 2017. https://doi.org/10.1109/JSEN.2017.2773119
[28] Zhang, R.; Pan, J.; Xie, D.; Wang, F. (2015). NDCMC: A hybrid data collection approach for large-scale WSNs using mobile element and hierarchical clustering. IEEE Internet of Things Journal, 3(4), 533-543, 2015. https://doi.org/10.1109/JIOT.2015.2490162
[29] Zhou, H.; Yu, K.M. (2019). A novel wireless sensor network data aggregation algorithm based on self-organizing feature mapping neutral network, Ingenierie des Systemes d'Information, 24(1), 119-123, 2019. https://doi.org/10.18280/isi.240118
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2020-09-25
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