Skip to main content

Advertisement

Log in

Directional sensor placement in vegetable greenhouse for maximizing target coverage without occlusion

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Wireless sensor network (WSN) is the key sensing resource for the internet of things (IoT) in vegetable greenhouse. The coverage control ensures that WSN can obtain enough effective information. However, the current coverage researches ignore the object size and lack of attention to the occlusion between targets. There are many leaves and fruits in vegetables, which can easily cause blind area and low utilization of directional sensors. Based on the geometric relationship between the directional sensors and targets, this paper studies a non-occlusion coverage scheme for the greenhouse IoT. Firstly, combined with the traditional coverage theory, a directional coverage model without occlusion is constructed by analysing the multivariate relationship between the sensor nodes and monitored targets. An objective function is then established to maximize the effective coverage. Based on the directional coverage model, this paper studies a hierarchical cooperative particle swarm optimization algorithm, which decomposes the global effective coverage problem into the utilization optimization of each sensor and finally get the orientation angle set. The experimental results show that the studied model and algorithm can avoid occlusion between covered objects while improving sensor utilization to a certain degree.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Chi, T., & Chen, M. (2019). A frequency hopping method for spatial RFID/WiFi/Bluetooth scheduling in agricultural IoT. Wireless Networks,25(2), 805–817.

    Article  Google Scholar 

  2. Mekonnen, Y., Namuduri, S., Burton, L., et al. (2019). Review—Machine learning techniques in wireless sensor network based precision agriculture. Journal of the Electrochemical Society,167(3), 037522.

    Article  Google Scholar 

  3. Bako, B., & Božek, P. (2016). Trends in simulation and planning of manufacturing companies. In Proceeding of international conference on manufacturing engineering and materials (ICMEM) (Vol. 149, pp. 571–575).

  4. Rubanga, D. P., Hatanaka, K., & Shimada, S. (2019). Development of a simplified smart agriculture system for small-scale greenhouse farming. Sensors and Materials,31(3), 831–843.

    Article  Google Scholar 

  5. Zhang, R. B., Ren, Z. W., Sun, J., et al. (2017). Method for monitoring the cotton plant vigor based on the WSN technology. Computers and Electronics in Agriculture,133, 68–79.

    Article  Google Scholar 

  6. González-Amarillo, C. A., Corrales-Muñoz, J. C., Mendoza-Moreno, M. A., et al. (2018). An IoT-based traceability system for greenhouse seedling crops. IEEE Access,6, 67528–67535.

    Article  Google Scholar 

  7. Shafi, U., Mumtaz, R., Garcia-Nieto, J., et al. (2019). Precision agriculture techniques and practices: from considerations to applications. Sensors,19(17), 3796.

    Article  Google Scholar 

  8. Hanel, T., Jarmer, T., & Aschenbruck, N. (2019). Using distributed compressed sensing to derive continuous hyperspectral imaging from a wireless sensor network. Computers and Electronics in Agriculture,166, 104974.

    Article  Google Scholar 

  9. Oppenheim, D., Shani, G., Erlich, O., et al. (2019). Using deep learning for image-based potato tuber disease detection. Phytopathology,109(6), 1083–1087.

    Article  Google Scholar 

  10. Kochhar, A., & Kumar, N. (2019). Wireless sensor networks for greenhouses: An end-to-end review. Computers and Electronics in Agriculture,163, 104877.

    Article  Google Scholar 

  11. Belfkih, A., Duvallet, C., & Sadeg, B. (2019). A survey on wireless sensor network databases. Wireless Networks,25(8), 4921–4946.

    Article  Google Scholar 

  12. Kaushik, A., Indu, S., & Gupta, D. (2019). Grey wolf optimization based algorithm for optimum camera placement. Wireless Personal Communications,105(3), 1143–1167.

    Article  Google Scholar 

  13. Zhao, J., Yoshida, R., Cheung, S. C. S., et al. (2013). Approximate techniques in solving optimal camera placement problems. International Journal of Distributed Sensor Networks,10, 10. https://doi.org/10.1155/2013/241913.

    Article  Google Scholar 

  14. Al Hasan, M., Ramachandran, K. K., & Mitchell, J. E. (2008). Optimal placement of stereo sensors. Optimization Letters,2(1), 99–111.

    Article  MathSciNet  MATH  Google Scholar 

  15. Altahir, A. A., Asirvadam, V. S., Hamid, N. H., et al. (2017). Modeling multicamera coverage for placement optimization. IEEE Sensors Letters,1(6), 1–4.

    Article  Google Scholar 

  16. Xiong, Y. H., Li, J., & Lu, M. J. (2019). Critical location spatial-temporal coverage optimization in visual sensor network. Sensors,19(19), 4106.

    Article  Google Scholar 

  17. Peng, S., & Xiong, Y. (2019). An area coverage and energy consumption optimization approach based on improved adaptive particle swarm optimization for directional sensor networks. Sensors,19(5), 1192.

    Article  Google Scholar 

  18. Fu, Y., Zhou, J., & Deng, L. (2014). Surveillance of a 2D plane area with 3D deployed cameras. Sensors,14(2), 1988–2011.

    Article  Google Scholar 

  19. Panag, T. S., & Dhillon, J. S. (2019). Maximal coverage hybrid search algorithm for deployment in wireless sensor networks. Wireless Networks,25(2), 637–652.

    Article  Google Scholar 

  20. Xenakis, A., Foukalas, F., & Stamoulis, G. (2017). Topology control with coverage and lifetime optimization of wireless sensor networks with unequal energy distribution. Computers and Electrical Engineering,64, 182–199.

    Article  Google Scholar 

  21. Altahir, A. A., Asirvadam, V. S., Hamid, N. H., et al. (2017). Optimizing visual surveillance sensor coverage using dynamic programming. IEEE Sensors Journal,17(11), 3398–3405.

    Article  Google Scholar 

  22. Altahir, A. A., Asirvadam, V. S., Hamid, N. H., et al. (2018). Optimizing visual sensor coverage overlaps for multiview surveillance systems. IEEE Sensors Journal,18(11), 4544–4552.

    Article  Google Scholar 

  23. Tao, D., & Wu, T. Y. (2015). A survey on barrier coverage problem in directional sensor networks. IEEE Sensors Journal,15(2), 876–885.

    Article  Google Scholar 

  24. Chang, C. Y., Hsiao, C. Y., & Chang, C. T. (2018). QoS guaranteed surveillance algorithms for directional wireless sensor networks. Ad Hoc Networks,81, 71–85.

    Article  Google Scholar 

  25. Chang, J., Shen, X., Bai, W., et al. (2019). Hierarchy graph based barrier coverage strategy with a minimum number of sensors for underwater sensor networks. Sensors,19(11), 2546.

    Article  Google Scholar 

  26. Rout, M., & Roy, R. (2016). Self-deployment of mobile sensors to achieve target coverage in the presence of obstacles. IEEE Sensors Journal,16(14), 1.

    Article  Google Scholar 

  27. Yu, J., Wan, S., Cheng, X., et al. (2017). Coverage contribution area based k-coverage for wireless sensor networks. IEEE Transactions on Vehicular Technology,66(9), 8510–8523.

    Article  Google Scholar 

  28. Halder, S., & Ghosal, A. (2015). A location-wise predetermined deployment for optimizing lifetime in visual sensor networks. IEEE Transactions on Circuits and Systems for Video Technology,26(6), 1131–1145.

    Article  Google Scholar 

  29. Wang, S., Yang, X., Wang, X., et al. (2019). A virtual force algorithm-levy-embedded grey wolf optimization algorithm for wireless sensor network coverage optimization. Sensors,19(12), 2735.

    Article  Google Scholar 

  30. Sharmin, S., Nur, F. N., Razzasque, M. A., et al. (2017). Tradeoff between sensing quality and network lifetime for heterogeneous target coverage using directional sensor nodes. IEEE Access,5, 15490–15504.

    Article  Google Scholar 

  31. Jun, S., Chang, T. W., Jeong, H., et al. (2017). Camera placement in smart cities for maximizing weighted coverage with budget limit. IEEE Sensors Journal,17(23), 7694–7703.

    Article  Google Scholar 

  32. Cheng, B., Cui, L., Jia, W., et al. (2016). Multiple region of interest coverage in camera sensor networks for tele-intensive care units. IEEE Transactions on Industrial Informatics,12(6), 2331–2341.

    Article  Google Scholar 

  33. Tseng, Y. C., Chen, P. Y., & Chen, W. T. (2012). K-angle object coverage problem in a wireless sensor network. IEEE Sensors Journal,12(12), 3408–3416.

    Article  Google Scholar 

  34. He, S. B., Shin, D. H., Zhang, J. S., et al. (2016). Full-view area coverage in camera sensor networks: Dimension reduction and near-optimal solutions. IEEE Transactions on Vehicular Technology,65(9), 7448–7461.

    Article  Google Scholar 

  35. Lin, Y. T., Saluja, K. K., & Megerian, S. (2011). Adaptive cost efficient deployment strategy for homogeneous wireless camera sensors. Ad Hoc Network,9(5), 713–726.

    Article  Google Scholar 

  36. Karakaya, M., & Qi, H. (2011). Distributed target localization using a progressive certainty map in visual sensor networks. Ad Hoc Network,9(4), 576–590.

    Article  Google Scholar 

  37. Yang, X. T., Wen, Y. Y., Yuan, D. N., et al. (2019). Coverage degree-coverage model in wireless visual sensor networks. IEEE Wireless Communications Letters,8(3), 817–820.

    Article  MathSciNet  Google Scholar 

  38. Karakaya, M., & Qi, H. R. (2012). Coverage estimation for crowded targets in visual sensor networks. ACM Transactions on Sensor Networks,8(3), 1–22.

    Article  Google Scholar 

  39. Yap, F. G. H., & Yen, H. H. (2017). Novel visual sensor deployment algorithm in occluded wireless visual sensor networks. IEEE Systems Journal,11(4), 2512–2523.

    Article  Google Scholar 

  40. Saeed, A., Abdelkader, A., Khan, M., et al. (2019). On realistic target coverage by autonomous drones. ACM Transactions on Sensor Networks,15(3), 1–33.

    Article  Google Scholar 

  41. Zhang, S. H., Li, X., He, H., et al. (2018). A next best view method based on self-occlusion information in depth images for moving object. Multimedia Tools and Applications,77(8), 9753–9777.

    Article  Google Scholar 

  42. Zhang, S. H., Miao, Y. X., Li, X., et al. (2017). Determining next best view based on occlusion information in a single depth image of visual object. International Journal of Advanced Robotic Systems. https://doi.org/10.1177/1729881416685672.

    Article  Google Scholar 

  43. Jun, S., Chang, T. W., & Yoon, H. J. (2012). Placing visual sensors using heuristic algorithms for bridge surveillance. Applied Sciences-Basel,8(1), 70.

    Article  Google Scholar 

  44. Brown, T., Wang, Z., Shan, T., et al. (2017). Obstacle-aware wireless video sensor network deployment for 3D indoor monitoring. In Proceedings of globecom 20172017 IEEE global communications conference, Singapore, Singapore.

Download references

Acknowledgements

This research was funded by National Natural Science Foundation of China, Grant Number 61871041, Technical System of National Bulk Vegetable Industry, Grant Number CARS-23-C06, and National Key Research and Development Program of China, Grant Number 2019YFD1101105.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qingxue Li.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, H., Li, Q., Zhu, H. et al. Directional sensor placement in vegetable greenhouse for maximizing target coverage without occlusion. Wireless Netw 26, 4677–4687 (2020). https://doi.org/10.1007/s11276-020-02370-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-020-02370-8

Keywords

Navigation