Received-Signal-Strength-Based Localization in Wireless Sensor Networks

Ruixin Niu, Aditya Vempaty, Pramod Kumar Varshney

Research output: Contribution to journalArticle

34 Scopus citations


In this paper, an overview of recent developments in received-signal-strength (RSS)-based localization in wireless sensor networks is presented. Several important practical issues and their solutions are discussed. To save communication bandwidth and sensor energy, a maximum-likelihood estimator based on quantized data is presented along with its corresponding Cramér.Rao lower bound (CRLB) and optimal quantizer design schemes. For further system resource savings, an iterative sensor selection approach is presented to activate only the most informative sensors, by maximizing the mutual information or minimizing the posterior CRLB at each iteration. For a resource constrained WSN with imperfect wireless channels, channel-aware target localization is described, where the channel model is incorporated into the localization scheme itself, thereby improving performance without increasing communication overhead. Another practical issue involving the presence of malicious sensors called Byzantines is discussed and mitigation schemes are provided. A recent coding-theory-based approach which is both computationally inexpensive and robust to such malicious attacks is also discussed.

Original languageEnglish (US)
JournalProceedings of the IEEE
StateAccepted/In press - Jun 5 2018


  • Byzantine attacks
  • channel-aware estimation
  • Communication system security
  • Cramér-Rao lower bound (CRLB)
  • Fingerprint recognition
  • localization
  • Maximum likelihood estimation
  • quantization
  • received signal strength (RSS)
  • Security
  • security
  • sensor selection
  • Wireless communication
  • Wireless sensor networks
  • wireless sensor networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Received-Signal-Strength-Based Localization in Wireless Sensor Networks'. Together they form a unique fingerprint.

  • Cite this