A Study and Analysis of a New Hybrid Approach for Localization in Wireless Sensor Networks


  • Rupendra Pratap Singh Hada Department of Computer Science and Engineering, Indian Institute of Technology Indore, India
  • Uttkarsh Aggarwal 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




Localization, random forest, multilateration


Accurate localization of nodes in a wireless sensor network (WSN) is imperative for several important applications. The use of global positioning systems (GPS) for localization is the natural approach in most domains. In WSNs, however, the use of GPS is challenging because of the constrained nature of deployed nodes as well as the often inaccessible sites of WSN nodes deployment. Several approaches for localization without the use of GPS and harnessing the capabilities of the received signal strength indicator (RSSI) exist in literature, but each of these makes the simplifying assumption that all the WSN nodes are within the communication range of every other node. In this paper, we go beyond this assumption and propose a hybrid technique for node localization in large WSN deployments. The hybrid technique comprises a loose combination of a machine learning (ML) based approach for localization involving random forest and a multilateration approach. This hybrid approach takes advantage of the accuracy of ML localization and the iterative capabilities of multilateration. We demonstrate the efficacy of the proposed approach through experiments on a simulated set-up and follow it up with a feasibility demonstration through a prototypical implementation in the real world.


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Author Biographies

Rupendra Pratap Singh Hada, Department of Computer Science and Engineering, Indian Institute of Technology Indore, India

Rupendra Pratap Singh Hada is a PhD Research Scholar in Indian Institute of Technology (IIT), Indore, India. Previously, he worked as an assistant professor at the BCST, Indore, India. He received his first degree in computer science engineering from the RGPV University, Bhopal, India in 2015, and he is a postgraduate in Computer Engineering from the Shri Govindram Seksaria Institute of Technology and Science, Indore, India in 2019. His PhD research is focused on the Applications of Machine Learning in WSNs.

Uttkarsh Aggarwal, Department of Computer Science and Engineering, Indian Institute of Technology Indore, India

Uttkarsh Aggarwal was a former M.S. Candidate in the Department of Computer Science Engineering at the Indian Institute of Technology Indore. He received his B.Tech degree in Information Technology from The NorthCap University (formerly ITM University), Gurgaon, India in 2017. His research interests are data mining, machine learning, computer vision, embedded systems and computer networks.

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 PhD 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 the 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.


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How to Cite

Hada, R. P. S. ., Aggarwal, U. ., & Srivastava, A. . (2023). A Study and Analysis of a New Hybrid Approach for Localization in Wireless Sensor Networks. Journal of Web Engineering, 22(02), 279–302. https://doi.org/10.13052/jwe1540-9589.2224



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