Pertanika Journal of Science & Technology
Pertanika Journal Home Facebook
Pertanika · Universiti Putra Malaysia Press

Pertanika Journal of Science & Technology

Official journal of Universiti Putra Malaysia for scholarly work across science, engineering and related technologies.

e-ISSN 2231-8526 ISSN 0128-7680
Research article

Adaptive Density Control Based on Random Sensing Range for Energy Efficiency in IoT Sensor Networks

Fuad Bajaber

https://doi.org/10.47836/pjst.31.4.14
KeywordsDensity control, energy efficiency, IoT, sensing range, wireless sensor network
Article content

Abstract

IoT sensor networks enable long-term environmental monitoring. Most environmental applications require sensor node data gathering to satisfy application objectives. Therefore, sensing range optimization is a significant element in prolonging the lifetime of IoT sensor networks and saving energy. This study proposes an adaptive density control based on random sensing range (ADCR). It can reduce data redundancy by selecting several active and hybrid nodes in a sensing field. Thus, reducing redundancy power consumption will maximize the network lifetime. The simulation results demonstrate the effectiveness of density control based on the random sensing range.
Supporting literature

References

  1. Al-Shalabi, M., Anbar, M., Wan, T.C., & Khasawneh, A. (2018). Variants of the low-energy adaptive clustering hierarchy protocol: Survey, issues and challenges. Electronics, 7(8), Article 136. https://doi.org/10.3390/electronics7080136
  2. 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
  3. 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
  4. Cheng, S., Cai, Z., & Li, J. (2017). Approximate sensory data collection: A survey. Sensors, 17(3), Article 564. https://doi.org/10.3390/s17030564
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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