Collision Recognition in Multihop IEEE 802.15.4-Compliant Wireless Sensor Networks

Minyue Wu, Xiaoya Hu, Rongqing Zhang, Liuqing Yang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Collisions caused by the hidden terminal effects may result in severe packet corruption and performance degradation in multihop IEEE 802.15.4-compliant wireless sensor networks (WSNs). In order to avoid such collisions through scheduling protocols, it is important to first recognize these collisions by distinguishing them from some other noncollision cases (e.g., path loss, multipath fading, shadow fading, and IEEE 802.11 interference), which may also lead to similar consequences. In this paper, we focus on the collision recognition problem in multihop IEEE 802.15.4-compliant WSNs. First, through a series of measurements of the error properties in various collision and noncollision scenarios, we investigate the statistical behaviors of error patterns including the bit error rate and error position distribution, which reveal obvious differences between collision and noncollision cases in terms of bit- and symbol-level error position distribution. Based on these observations, we further propose a machine learning-based collision recognition mechanism by inserting some redundant blocks in a data frame. The inserted blocks are known to both the sender and receiver, thereby it enables the receiver to recognize the error patterns only according to the redundant sequences. Moreover, a mutual information-guided byte selection technique is also provided to effectively improve the recognition accuracy. Finally, the proposed mechanism is verified under three different transmission environments. The experimental results show that the proposed mechanism achieves good recognition accuracy over 90% with 94% coding efficiency.

Original languageEnglish (US)
Article number8726112
Pages (from-to)8542-8552
Number of pages11
JournalIEEE Internet of Things Journal
Issue number5
StatePublished - Oct 2019
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received February 15, 2019; revised April 24, 2019; accepted May 16, 2019. Date of publication May 30, 2019; date of current version October 8, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61471177 and Grant 61572210, and in part by the National Key Research and Development Project under Grant 2017YFE 0119300. (Corresponding author: Xiaoya Hu.) M. Wu is with the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China (e-mail:

Publisher Copyright:
© 2014 IEEE.


  • Collision recognition
  • IEEE 802.15.4
  • error pattern
  • machine learning
  • mutual information (MI)


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