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

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.
• 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.