Decision Fusion for IoT-Based Wireless Sensor Networks

Mohammad A. Al-Jarrah, Maysa A. Yaseen, Arafat Al-Dweik, Octavia A. Dobre, Emad Alsusa

Research output: Contribution to journalArticlepeer-review

74 Scopus citations

Abstract

This article presents a novel decision fusion algorithm for Internet-of-Things-based wireless sensor networks, where multiple sensors transmit their decisions about a certain phenomenon to a remote fusion center (FC) over a wide area network. The proposed algorithm denoted as the individual likelihood approximation (ILA) can significantly reduce the decision fusion error probability performance while maintaining the low computational complexity of other state-of-the-art fusion algorithms. The performance of the ILA rule is evaluated in terms of the global fusion probability of error, and an efficient analytical expression is derived in terms of a single integral. The analytical results corroborated by Monte Carlo simulation show that the ILA significantly outperforms all other considered rules, such as the Chair-Varshney (CV) and MaxLog rules. Moreover, the impact of the link from the cluster head to the FC, which is modeled as a binary symmetric channel with unknown transition probabilities, has been investigated. It is shown that the probability of error over such links should not exceed 10-3 to avoid severe performance degradation. Furthermore, we derive a closed-form expression for the system fusion error probability of the CV rule for the most general system parameters.

Original languageBritish English
Article number8907365
Pages (from-to)1313-1326
Number of pages14
JournalIEEE Internet of Things Journal
Volume7
Issue number2
DOIs
StatePublished - Feb 2020

Keywords

  • Data fusion
  • global connectivity
  • Internet of Things (IoT)
  • long-range wide area network (LoRaWAN)
  • narrowband IoT (NB-IoT)
  • wireless sensor network (WSN)

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