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A Comparison of Alternative Distributed Dynamic Cluster Formation Techniques for Industrial Wireless Sensor Networks.

Mohammad Gholami¹, Robert W Brennan²

¹Department of Mechanical and Manufacturing Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada. gholamim@ucalgary.ca.

Sensors (Basel, Switzerland)

|January 12, 2016

Summary

This study compares pre-defined and ad hoc wireless sensor node clustering for industrial tracking. Ad hoc clusters offer better adaptability to environmental changes but are less efficient overall.

Keywords:

clustering distributed adaptive systems reconfigurability wireless sensor networks

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Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Wireless sensor networks (WSNs) are crucial for industrial monitoring and tracking.
Existing clustering techniques face challenges in dynamic industrial environments.
Effective cluster management is vital for WSN performance and resource utilization.

Purpose of the Study:

To develop a novel distributed management approach for tracking mobile nodes in industrial WSNs.
To objectively compare pre-defined and ad hoc clustering approaches for WSNs.
To analyze the trade-offs between cost, effectiveness, and reconfigurability in cluster management.

Main Methods:

Development of a new distributed management strategy for mobile node tracking.
Comparative analysis of pre-defined versus ad hoc clustering methods.
Introduction and application of three novel metrics: cost/efficiency, performance, and resource consumption.

Main Results:

Ad hoc clusters demonstrate superior adaptability to environmental changes in industrial WSNs.
Pre-defined clusters exhibit higher overall efficiency compared to ad hoc clusters.
A clear trade-off exists between adaptability and efficiency in WSN cluster management.

Conclusions:

Ad hoc clustering is more responsive to dynamic industrial sensing environments.
The choice between pre-defined and ad hoc clustering depends on the specific application's priorities (adaptability vs. efficiency).
The proposed metrics provide a robust framework for evaluating WSN clustering strategies.