Optimized Cluster-Based Dynamic Energy-Aware Routing Protocol for Wireless Sensor Networks in Agriculture Precision

[EN] Wireless sensor networks (WSNs) are becoming one of the demanding platforms, where sensor nodes are sensing and monitoring the physical or environmental conditions and transmit the data to the base station via multihop routing. Agriculture sector also adopted these networks to promote innovations for environmental friendly farming methods, lower the management cost, and achieve scientific cultivation. Due to limited capabilities, the sensor nodes have suffered with energy issues and complex routing processes and lead to data transmission failure and delay in the sensor-based agriculture fields. Due to these limitations, the sensor nodes near the base station are always relaying on it and cause extra burden on base station or going into useless state. To address these issues, this study proposes a Gateway Clustering Energy-Efficient Centroid- (GCEEC-) based routing protocol where cluster head is selected from the centroid position and gateway nodes are selected from each cluster. Gateway node reduces the data load from cluster head nodes and forwards the data towards the base station. Simulation has performed to evaluate the proposed protocol with state-of-the-art protocols. The experimental results indicated the better performance of proposed protocol and provide more feasible WSN-based monitoring for temperature, humidity, and illumination in agriculture sector.This work has also been partially supported by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR.Qureshi, KN.; Bashir, MU.; Lloret, J.; León Fernández, A. (2020). Optimized Cluster-Based Dynamic Energy-Aware Routing Protocol for Wireless Sensor Networks in Agriculture Precision. Journal of Sensors. 2020:1-19. https://doi.org/10.1155/2020/9040395S1192020Sneha, K., Kamath, R., Balachandra, M., & Prabhu, S. (2019). New Gossiping Protocol for Routing Data in Sensor Networks for Precision Agriculture. Soft Computing and Signal Processing, 139-152. doi:10.1007/978-981-13-3393-4_15Qureshi, K. N., Abdullah, A. H., Bashir, F., Iqbal, S., & Awan, K. M. (2018). Cluster-based data dissemination, cluster head formation under sparse, and dense traffic conditions for vehicular ad hoc networks. International Journal of Communication Systems, 31(8), e3533. doi:10.1002/dac.3533Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67, 104-122. doi:10.1016/j.comnet.2014.03.027Feng, X., Zhang, J., Ren, C., & Guan, T. (2018). An Unequal Clustering Algorithm Concerned With Time-Delay for Internet of Things. IEEE Access, 6, 33895-33909. doi:10.1109/access.2018.2847036Savaglio, C., Pace, P., Aloi, G., Liotta, A., & Fortino, G. (2019). Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks. IEEE Access, 7, 29355-29364. doi:10.1109/access.2019.2902371Srbinovska, M., Gavrovski, C., Dimcev, V., Krkoleva, A., & Borozan, V. (2015). Environmental parameters monitoring in precision agriculture using wireless sensor networks. Journal of Cleaner Production, 88, 297-307. doi:10.1016/j.jclepro.2014.04.036Lloret, J., Garcia, M., Bri, D., & Diaz, J. (2009). A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks. Sensors, 9(12), 10513-10544. doi:10.3390/s91210513Qureshi, K. N., Din, S., Jeon, G., & Piccialli, F. (2020). Link quality and energy utilization based preferable next hop selection routing for wireless body area networks. Computer Communications, 149, 382-392. doi:10.1016/j.comcom.2019.10.030Kumar, S. A., & Ilango, P. (2017). The Impact of Wireless Sensor Network in the Field of Precision Agriculture: A Review. Wireless Personal Communications, 98(1), 685-698. doi:10.1007/s11277-017-4890-zAnisi, M. H., Abdul-Salaam, G., & Abdullah, A. H. (2014). A survey of wireless sensor network approaches and their energy consumption for monitoring farm fields in precision agriculture. Precision Agriculture, 16(2), 216-238. doi:10.1007/s11119-014-9371-8Long, D. S., & McCallum, J. D. (2015). On-combine, multi-sensor data collection for post-harvest assessment of environmental stress in wheat. Precision Agriculture, 16(5), 492-504. doi:10.1007/s11119-015-9391-zFu, X., Fortino, G., Li, W., Pace, P., & Yang, Y. (2019). WSNs-assisted opportunistic network for low-latency message forwarding in sparse settings. Future Generation Computer Systems, 91, 223-237. doi:10.1016/j.future.2018.08.031Mehmood, A., Khan, S., Shams, B., & Lloret, J. (2013). Energy-efficient multi-level and distance-aware clustering mechanism for WSNs. International Journal of Communication Systems, 28(5), 972-989. doi:10.1002/dac.2720Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey. IEEE Communications Surveys & Tutorials, 15(2), 551-591. doi:10.1109/surv.2012.062612.00084De Farias, C. M., Pirmez, L., Fortino, G., & Guerrieri, A. (2019). A multi-sensor data fusion technique using data correlations among multiple applications. Future Generation Computer Systems, 92, 109-118. doi:10.1016/j.future.2018.09.034Rao, P. C. S., Jana, P. K., & Banka, H. (2016). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Networks, 23(7), 2005-2020. doi:10.1007/s11276-016-1270-7Fu, X., Fortino, G., Pace, P., Aloi, G., & Li, W. (2020). Environment-fusion multipath routing protocol for wireless sensor networks. Information Fusion, 53, 4-19. doi:10.1016/j.inffus.2019.06.001Liu, X. (2015). Atypical Hierarchical Routing Protocols for Wireless Sensor Networks: A Review. IEEE Sensors Journal, 15(10), 5372-5383. doi:10.1109/jsen.2015.2445796Jan, N., Javaid, N., Javaid, Q., Alrajeh, N., Alam, M., Khan, Z. A., & Niaz, I. A. (2017). A Balanced Energy-Consuming and Hole-Alleviating Algorithm for Wireless Sensor Networks. IEEE Access, 5, 6134-6150. doi:10.1109/access.2017.2676004Gupta, G. P., Misra, M., & Garg, K. (2014). Energy and trust aware mobile agent migration protocol for data aggregation in wireless sensor networks. Journal of Network and Computer Applications, 41, 300-311. doi:10.1016/j.jnca.2014.01.003Safa, H., Karam, M., & Moussa, B. (2014). PHAODV: Power aware heterogeneous routing protocol for MANETs. Journal of Network and Computer Applications, 46, 60-71. doi:10.1016/j.jnca.2014.07.035Liu, X. (2015). An Optimal-Distance-Based Transmission Strategy for Lifetime Maximization of Wireless Sensor Networks. IEEE Sensors Journal, 15(6), 3484-3491. doi:10.1109/jsen.2014.2372340Brar, G. S., Rani, S., Chopra, V., Malhotra, R., Song, H., & Ahmed, S. H. (2016). Energy Efficient Direction-Based PDORP Routing Protocol for WSN. IEEE Access, 4, 3182-3194. doi:10.1109/access.2016.2576475Abo-Zahhad, M., Ahmed, S. M., Sabor, N., & Sasaki, S. (2015). Mobile Sink-Based Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks. IEEE Sensors Journal, 15(8), 4576-4586. doi:10.1109/jsen.2015.2424296Huynh, T.-T., Dinh-Duc, A.-V., & Tran, C.-H. (2016). Delay-constrained energy-efficient cluster-based multi-hop routing in wireless sensor networks. Journal of Communications and Networks, 18(4), 580-588. doi:10.1109/jcn.2016.000081Shen, J., Wang, A., Wang, C., Hung, P. C. K., & Lai, C.-F. (2017). An Efficient Centroid-Based Routing Protocol for Energy Management in WSN-Assisted IoT. IEEE Access, 5, 18469-18479. doi:10.1109/access.2017.2749606Awan, K. M., Shah, P. A., Iqbal, K., Gillani, S., Ahmad, W., & Nam, Y. (2019). Underwater Wireless Sensor Networks: A Review of Recent Issues and Challenges. Wireless Communications and Mobile Computing, 2019, 1-20. doi:10.1155/2019/6470359Sajwan, M., Gosain, D., & Sharma, A. K. (2018). CAMP: cluster aided multi-path routing protocol for wireless sensor networks. Wireless Networks, 25(5), 2603-2620. doi:10.1007/s11276-018-1689-0Varga, A. (2010). OMNeT++. Modeling and Tools for Network Simulation, 35-59. doi:10.1007/978-3-642-12331-3_3Lartillot, O., Toiviainen, P., & Eerola, T. (2008). A Matlab Toolbox for Music Information Retrieval. Studies in Classification, Data Analysis, and Knowledge Organization, 261-268. doi:10.1007/978-3-540-78246-9_31Mathur, P., Nielsen, R. H., Prasad, N. R., & Prasad, R. (2016). Data collection using miniature aerial vehicles in wireless sensor networks. IET Wireless Sensor Systems, 6(1), 17-25. doi:10.1049/iet-wss.2014.0120Zou, T., Lin, S., Feng, Q., & Chen, Y. (2016). Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks. Sensors, 16(1), 53. doi:10.3390/s16010053Song, Y., Ma, J., Zhang, X., & Feng, Y. (2012). Design of Wireless Sensor Network-Based Greenhouse Environment Monitoring and Automatic Control System. Journal of Networks, 7(5). doi:10.4304/jnw.7.5.838-844Nikolidakis, S., Kandris, D., Vergados, D., & Douligeris, C. (2013). Energy Efficient Routing in Wireless Sensor Networks Through Balanced Clustering. Algorithms, 6(1), 29-42. doi:10.3390/a6010029Ndzi, D. L., Harun, A., Ramli, F. M., Kamarudin, M. L., Zakaria, A., Shakaff, A. Y. M., … Farook, R. S. (2014). Wireless sensor network coverage measurement and planning in mixed crop farming. Computers and Electronics in Agriculture, 105, 83-94. doi:10.1016/j.compag.2014.04.01

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