Abstract

The existing deterministic neighbor discovery algorithms in WSNs ensure that successful discovery can be obtained within a certain time, but the average discovery delay is long. It is difficult to meet the need for rapid discovery in mobile low duty cycle environments. This paper proposes a group-based fast neighbor discovery algorithm (GBFA). By carrying neighbor information in beacon packet, GBFA knows in advance some potential neighbors and select more energy efficient potential neighbors and actively make nodes wake up to verify whether these potential neighbors are true neighbors, thereby speeding up neighbor discovery and improving energy utilization efficiency. The evaluation results indicate the mean delay and extra energy consumption of the GBFA algorithm are lower than Group-based method up to \(10.58\%\) and \(18.88\%\) respectively.

Highlights

  • With the quick development of Internet of Things (IoT), one of its important supporting technologies, i.e., wireless sensor networks (WSNs), gets more attention [1]

  • We introduce the concept of energy efficiency to fairly and conveniently compare the performances of all related methods

  • With the rapid development of IoT, existing neighbor discovery methods in WSNs fail to satisfy some requirements in IoT-oriented WSNs

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Summary

Introduction

With the quick development of Internet of Things (IoT), one of its important supporting technologies, i.e., wireless sensor networks (WSNs), gets more attention [1]. WSN is self-organizing network which contains lots of sensors with very limited energy. Sensed data are forwarded in a multi-hop manner to sink node or base station. WSNs are indispensable for IoT, especially in the cases where sensed data volume becomes more huge with the development of IoT. These data can be processed in sink node firstly and only a fraction of data is sent to Cloud, the network communication load can be reduced significantly. In the cases where a large number of Sensors 2018, 18, 3319; doi:10.3390/s18103319 www.mdpi.com/journal/sensors

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