A soft computing approach to localization in wireless sensor networks

Sukhyun Yun, Jaehun Lee, Wooyong Chung, Euntai Kim, Soohan Kim

Research output: Contribution to journalArticlepeer-review

160 Citations (Scopus)


In this paper, we propose two intelligent localization schemes for wireless sensor networks (WSNs). The two schemes introduced in this paper exhibit range-free localization, which utilize the received signal strength (RSS) from the anchor nodes. Soft computing plays a crucial role in both schemes. In the first scheme, we consider the edge weight of each anchor node separately and combine them to compute the location of sensor nodes. The edge weights are modeled by the fuzzy logic system (FLS) and optimized by the genetic algorithm (GA). In the second scheme, we consider the localization as a single problem and approximate the entire sensor location mapping from the anchor node signals by a neural network (NN). The simulation and experimental results demonstrate the effectiveness of the proposed schemes by comparing them with the previous methods.

Original languageEnglish
Pages (from-to)7552-7561
Number of pages10
JournalExpert Systems with Applications
Issue number4
Publication statusPublished - 2009 May

Bibliographical note

Funding Information:
This work was supported by the Ministry of Commerce, Industry and Energy of Korea (HISP).

All Science Journal Classification (ASJC) codes

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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