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Simple random sampling-based probe station selection for fault detection in wireless sensor networks.

Rimao Huang¹, Xuesong Qiu, Lanlan Rui

¹State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China. tianshuihrm@sina.com

Sensors (Basel, Switzerland)

|December 14, 2011

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This study introduces a new method for fault detection in wireless sensor networks (WSNs). By dynamically selecting nodes and adjusting probing frequency, network lifetime is extended without sacrificing fault detection accuracy.

Area of Science:

Wireless Sensor Networks (WSNs)
Network Fault Detection
Distributed Systems

Background:

Existing fault detection in WSNs relies on static manager nodes, leading to network imbalance and premature failure.
Fixed-frequency probing generates excessive traffic, wasting limited network energy.
Traditional probing node selection algorithms are too complex for energy-constrained WSNs.

Purpose of the Study:

To investigate the distribution characteristics of fault nodes in WSNs.
To develop a dynamic fault detection strategy that enhances network lifetime and efficiency.
To reduce energy consumption and network traffic associated with fault detection.

Main Methods:

Validated the Pareto principle, showing most faults cluster in a few network segments.

Keywords:

Pareto principle fault detection probe station probing frequency simple random sampling wireless sensor network

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Developed a Simple Random Sampling-based algorithm for dynamic selection of probe stations.

Proposed a dynamic rule for adjusting probing frequency to minimize redundant packets.

Main Results:

The proposed dynamic algorithm effectively balances the load across the network.
Reduced unnecessary probing packets, leading to significant energy savings.
Simulation experiments confirmed prolonged network lifetime without compromising fault detection rates.

Conclusions:

Dynamic node selection and adaptive probing frequency are crucial for efficient WSN fault detection.
The presented approach offers a practical solution for energy-critical wireless sensor networks.
This method significantly improves the longevity and performance of WSNs.