Overcoming Terrain Challenges with Edge Computing Solutions: Optimizing WSN Deployments Over Obstacle Clad-Irregular Terrains

Authors

  • Shekhar Tyagi Department of Computer Science and Engineering, Indian Institute of Technology, Indore, India
  • Abhishek Srivastava Department of Computer Science and Engineering, Indian Institute of Technology, Indore, India

DOI:

https://doi.org/10.13052/jwe1540-9589.2384

Keywords:

Wireless sensors, irregular-terrain, obstacles, sensor deployment

Abstract

Wireless sensor networks (WSNs) are primarily used for real time data collection and monitoring, especially in environments where direct human involvement is challenging due to harsh conditions. Optimized deployment of WSN nodes is a long standing issue and several ideas have been proposed to address this. Existing deployment strategies are mostly based on the assumption that the terrain for deployment of nodes is perfectly regular. This is an impractical assumption and in this paper we address this gap by proposing a deployment strategy for WSN nodes over irregular terrains. Such terrains comprise uneven elevations, morphology and vegetation based obstacles, rocky obstacles, and so on. Our approach comprises extraction of satellite images of the region of interest (RoI) from Google Earth and generating a KML file (Keyhole Markup Language) for the RoI containing the latitude, longitude, and elevation values of each and every point in the RoI. These points are used to generate a contour map of the RoI containing detailed terrain morphology. A radio frequency path loss model in combination with an advanced inverse distance weighted (IDW)-interpolation technique is proposed to ensure connectivity and coverage in such irregular terrains with varying nature of obstacles. The technique effectively detects occlusions and enables effective deployment. This edge computing approach involves real-time decision-making at the network edge (the sensor nodes) leading to a deterministic deployment of motes in diverse terrain conditions with various obstacles. The approach is compared with existing deployment techniques and the results validate its efficacy. To demonstrate the practicality of our approach, we have also implemented a deployment in real-world environmental conditions, validating our approach in challenging terrains.

Downloads

Download data is not yet available.

Author Biographies

Shekhar Tyagi, Department of Computer Science and Engineering, Indian Institute of Technology, Indore, India

Shekhar Tyagi holds Bachelor’s and Master’s degrees in Computer Science and Engineering. He is currently pursuing his Doctoral degree in Computer Science and Engineering at the Indian Institute of Technology Indore. His research interests include edge computing, IoT security in resource-constrained environments, and machine learning.

Abhishek Srivastava, Department of Computer Science and Engineering, Indian Institute of Technology, Indore, India

Abhishek Srivastava is a Professor in the Discipline of Computer Science and Engineering at the Indian Institute of Technology Indore. He completed his Ph.D. in 2011 from the University of Alberta, Canada. Abhishek’s group at IIT Indore has been involved in research on service-oriented systems most commonly realized through web services. More recently, the group has been interested in applying these ideas in the realm of Internet of Things. The ideas explored include coming up with technology agnostic solutions for seamlessly linking heterogeneous IoT deployments across domains. Further, the group is also delving into utilizing machine learning adapted for constrained environments to effectively make sense of the huge amounts of data that emanate from the vast network of IoT deployments.

References

Wikipedia, WSN, https://en.wikipedia.org/wiki/Wireless_sensor_network, Last accessed on 2024/01/02.

James Kennedy and Russell Eberhart. Particle swarm optimization. In Proceedings of ICNN’95 - International Conference on Neural Networks, volume 4, pages 1942–1948. IEEE, 1995.

Raghavendra V Kulkarni and Ganesh Kumar Venayagamoorthy. Particle swarm optimization in wireless-sensor networks: A brief survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(2):262–267, 2010.

Dina S. Deif and Yasser Gadallah. An ant colony optimization approach for the deployment of reliable wireless sensor networks. IEEE Access, 5:10744–10756, 2017.

Shan You, Guanglin Zhang, and Demin Li. Coverage improvement strategy based on Voronoi for directional sensor networks. In Machine Learning and Intelligent Communications: First International Conference, MLICOM 2016, Shanghai, China, August 27-28, 2016, Revised Selected Papers 1, pages 247–256. Springer, 2017.

Yifeng Tang, Dechang Huang, Rong Li, and Zhaodi Huang. A Non-Dominated Sorting Genetic Algorithm Based on Voronoi Diagram for Deployment of Wireless Sensor Networks on 3-D Terrains. Electronics, 11(19):3024, 2022. MDPI.

Chun-Hsien Wu, Kuo-Chuan Lee, and Yeh-Ching Chung. A Delaunay triangulation based method for wireless sensor network deployment. Computer Communications, 30(14-15):2744–2752, 2007. Elsevier.

Bo Yan. Node Deployment Algorithm Based on Improved Steiner Tree. International Journal of Multimedia and Ubiquitous Engineering, 10(7):329–338, 2015.

Wenli Li. PSO based wireless sensor networks coverage optimization on DEMs. In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence: 7th International Conference, ICIC 2011, Zhengzhou, China, August 11-14, 2011, Revised Selected Papers 7, pages 371–378. Springer, 2012.

Siba K. Udgata, Samrat L. Sabat, and S. Mini. Sensor deployment in irregular terrain using artificial bee colony algorithm. In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), pages 1309–1314. IEEE, 2009.

Wendi Fu, Yan Yang, Guoqi Hong, and Jing Hou. WSN deployment strategy for real 3D terrain coverage based on greedy algorithm with DEM probability coverage model. Electronics, 10(16):2028, 2021. MDPI.

Chandan Kr Bhattacharyya, Swapan Bhattacharya, and others. LDM (layered deployment model): A novel framework to deploy sensors in an irregular terrain. Wireless Sensor Network, 3(06):189, 2011. Scientific Research Publishing.

Chien-Fu Cheng and Chu-Chiao Hsu. The deterministic sensor deployment problem for barrier coverage in WSNs with irregular shape areas. IEEE Sensors Journal, 22(3):2899–2911, 2021. IEEE.

Yanzhi Du. Method for the optimal sensor deployment of WSNs in 3D terrain based on the DPSOVF algorithm. IEEE Access, 8:140806–140821, 2020. IEEE.

Adda Boualem, Youcef Dahmani, Cyril De Runz, Marwane Ayaida, “Spiderweb strategy: application for area coverage with mobile sensor nodes in 3D wireless sensor network,” International Journal of Sensor Networks, vol. 29, no. 2, pp. 121–133, 2019. Inderscience Publishers (IEL).

Fu Xiao, Xiekun Yang, Meng Yang, Lijuan Sun, Ruchuan Wang, Panlong Yang, “Surface coverage algorithm in directional sensor networks for three-dimensional complex terrains,” Tsinghua Science and Technology, vol. 21, no. 4, pp. 397–406, 2016. TUP.

Liang Liu, Huadong Ma, “On coverage of wireless sensor networks for rolling terrains,” IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 1, pp. 118–125, 2011. IEEE.

Tyagi, S., Srivastava, A.: Addressing WSN Deployments Over Obstacle Clad-Irregular Terrains. In the 4th International Workshop on Big Data Driven Edge Cloud Services (BECS 2024) Co-located with the 24th International Conference on Web Engineering (ICWE 2024), June 17–20, 2024, Tampere, Finland.

Google. Google Earth 7.3. Available online: https://earth.google.com. Last accessed on 2024/01/02.

TCX 2.0. TCX Converter. Available online: https://tcx-converter.software.informer.com/2.0. Last accessed on 2024/01/09.

GPS Visualizer. GPS Visualizer. Available online: https://www.gpsvisualizer.com/. Last accessed on 2024/01/06.

John Coulthard. Quick Grid 5.4.4. Available online: https://www.galiander.ca/quikgrid/. Last accessed on 2024/01/08.

Tajudeen O. Olasupo, Abdulaziz Alsayyari, Carlos E. Otero, Kehinde O. Olasupo, and Ivica Kostanic. Empirical path loss models for low power wireless sensor nodes deployed on the ground in different terrains. In 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pages 1–8. IEEE, 2017.

Tajudeen O. Olasupo, Carlos E. Otero, Kehinde O. Olasupo, and Ivica Kostanic. Empirical path loss models for wireless sensor network deployments in short and tall natural grass environments. IEEE Transactions on Antennas and Propagation, 64(9):4012–4021, 2016. IEEE.

Tajudeen Olawale Olasupo and Carlos E. Otero. The impacts of node orientation on radio propagation models for airborne-deployed sensor networks in large-scale tree vegetation terrains. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(1):256–269, 2017. IEEE.

Tajudeen Olawale Olasupo, Carlos Enrique Otero, Luis Daniel Otero, Kehinde Olumide Olasupo, and Ivica Kostanic. Path loss models for low-power, low-data rate sensor nodes for smart car parking systems. IEEE Transactions on Intelligent Transportation Systems, 19(6):1774–1783, 2017. IEEE.

Mina Khoshrangbaf, Vahid Khalilpour Akram, and Moharram Challenger. Ant colony based coverage optimization in wireless sensor networks. In Annals of Computer Science and Information Systems, volume 32, pages 155–159, 2022.

Yuhui Shi and Russell Eberhart, A modified particle swarm optimizer, in 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360), pages 69–73, IEEE, 1998.

Jie Jia, Jian Chen, Guiran Chang, and Zhenhua Tan. Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm. Computers & Mathematics with Applications, 57(11-12):1756–1766, 2009. Elsevier.

Published

2025-02-07

How to Cite

Tyagi, S. ., & Srivastava, A. . (2025). Overcoming Terrain Challenges with Edge Computing Solutions: Optimizing WSN Deployments Over Obstacle Clad-Irregular Terrains. Journal of Web Engineering, 23(08), 1127–1154. https://doi.org/10.13052/jwe1540-9589.2384

Issue

Section

Articles