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• Bar-Noy, A., & Baumer, B. (2015). Average case network lifetime on an interval with adjustable sensing ranges. Algorithmica, 72(1), 148-166. https://doi.org/10.1007/s00453-013-9853-5

• Cerulli, R., De Donato, R., & Raiconi, A. (2012). Exact and heuristic methods to maximize network lifetime in wireless sensor networks with adjustable sensing ranges. European Journal of Operational Research, 220(1), 58-66. https://doi.org/10.1016/j.ejor.2012.01.046

• Cheng, S., Cai, Z., & Li, J. (2017). Approximate sensory data collection: A survey. Sensors, 17(3), Article 564. https://doi.org/10.3390/s17030564

• Debnath, S., Hossain, A., Chowdhury, S. M., & Singh, A. K. (2018). Effective sensing radius (ESR) and performance analysis of static and mobile sensor networks. Telecommunication Systems, 68(1), 115-127. https://doi.org/10.1007/s11235-017-0379-z

• Dhawan, A., Vu, C. T., Zelikovsky, A., Li, Y., & Prasad, S. K. (2006). Maximum lifetime of sensor networks with adjustable sensing range. In Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD’06) (pp. 285-289). IEEE Publishing. https://doi.org/10.1109/snpd-sawn.2006.46

• Dolas, P., & Ghosh, D. (2018). Compressed Sensing Based Network Lifetime Enhancement in Wireless Sensor Networks. In V. Janyani, M. Tiwari, G. Singh & P. Minzioni (Eds.), Optical and Wireless Technologies (pp. 465-471). Springer. https://doi.org/10.1007/978-981-10-7395-3_52

• Dong, Z., Shang, C., Chang, C. Y., & Roy, D. S. (2020). Barrier coverage mechanism using adaptive sensing range for renewable WSNs. IEEE Access, 8, 86065-86080. https://doi.org/10.1109/access.2020.2992867

• Hao, J., Zhang, B., Jiao, Z., & Mao, S. (2015). Adaptive compressive sensing based sample scheduling mechanism for wireless sensor networks. Pervasive and Mobile Computing, 22, 113-125. https://doi.org/10.1016/j.pmcj.2015.02.002

• Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660-670. https://doi.org/10.1109/twc.2002.804190

• Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences (pp. 1-10). IEEE Publishing. https://doi.org/10.1109/hicss.2000.926982

• Liu, S., Gao, S., Bao, T., & Zhang, Y. (2017). A hybrid approach to maximize the lifetime of directional sensor networks with smoothly varying sensing ranges. Chinese Journal of Electronics, 26(4), 703-709. https://doi.org/10.1049/cje.2017.06.001

• Liu, X. (2016). A novel transmission range adjustment strategy for energy hole avoiding in wireless sensor networks. Journal of Network and Computer Applications, 67, 43-52. https://doi.org/10.1016/j.jnca.2016.02.018

• Nayak, A. K., Misra, B. B., & Rai, S. C. (2011). Energy efficient adaptive sensing range for sensor network. In 2011 International Conference on Energy, Automation and Signal (pp. 1-6). IEEE Publishing. https://doi.org/10.1109/iceas.2011.6147143

• Nkomo, M., Hancke, G. P., Abu-Mahfouz, A. M., Sinha, S., & Onumanyi, A. J. (2018). Overlay virtualized wireless sensor networks for application in industrial internet of things: A review. Sensors, 18(10), Article 3215. https://doi.org/10.3390/s18103215

• Puneeth, D., & Kulkarni, M. (2020). Data aggregation using compressive sensing for energy efficient routing strategy. Procedia Computer Science, 171, 2242-2251. https://doi.org/10.1016/j.procs.2020.04.242

• Raza, U., Bogliolo, A., Freschi, V., Lattanzi, E., & Murphy, A. L. (2016). A two-prong approach to energy-efficient WSNs: Wake-up receivers plus dedicated, model-based sensing. Ad Hoc Networks, 45, 1-12. https://doi.org/10.1016/j.adhoc.2016.03.005

• Rossi, A., Singh, A., & Sevaux, M. (2012). An exact approach for maximizing the lifetime of sensor networks with adjustable sensing ranges. Computers & Operations Research, 39(12), 3166-3176. https://doi.org/10.1016/j.cor.2012.04.001

• Seah, W. K., Eu, Z. A., & Tan, H. P. (2009). Wireless sensor networks powered by ambient energy harvesting (WSN-HEAP)-Survey and challenges. In 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology (pp. 1-5). IEEE Publishing. https://doi.org/10.1109/wirelessvitae.2009.5172411

• Singh, N., Kumar, S., Kanaujia, B. K., Choi, H. C., & Kim, K. W. (2019). Energy-efficient system design for internet of things (IoT) devices. In M. Mittal, S. Tanwar, B. Agarwal, & L. M. Goyal, (Eds.), Energy Conservation for IoT Devices (pp. 49-74). Springer. https://doi.org/10.1007/978-981-13-7399-2_3

• Walker, C., Sivakumar, S., & Al-Anbuky, A. (2015). Data flow and management for an IoT based WSN. In 2015 IEEE International Conference on Data Science and Data Intensive Systems (pp. 624-631). IEEE Publishing. https://doi.org/10.1109/dsdis.2015.29

• Wannachai, A., & Champrasert, P. (2015). Adaptive transmission range based on event detection for WSNs. In 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) (pp. 1-6). IEEE Publishing. https://doi.org/10.1109/issnip.2015.7106944

• Yang, O., & Heinzelman, W. (2009). A better choice for sensor sleeping. In European Conference on Wireless Sensor Networks (pp. 134-149). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-00224-3_9

• Zhang, H., Li, L., Yan, X. F., & Li, X. (2011). A load-balancing clustering algorithm of WSN for data gathering. In 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC) (pp. 915-918). IEEE Publishing. https://doi.org/10.1109/aimsec.2011.6010559