RANKING OPTIMAL SENSOR NODES IN WSN USING SIMPLE ADDITIVE WEIGHTING (SAW) APPROACH

Subject Area: COMMUNICATION SYSTEM ENGINEERING

Wireless Sensor Networks (WSNs) are crucial for various applications, and ranking sensor nodes within these networks is a fundamental challenge. The Simple Additive Weighting (SAW) approach, a Multi-Criteria Decision-Making (MCDM) method, offers a systematic solution to this challenge. It considers diverse criteria, including energy, communication range, and processing power, enabling the selection of optimal sensor nodes. Recent research has extended and improved the SAW method in various ways. For instance, fuzzy logic has been applied to address uncertainty in sensor node attributes, dynamic weight adaptation has enhanced adaptability, and entropy weight assignment has improved ranking accuracy. These modifications make the SAW approach even more efficient and practical. The SAW method's theoretical foundation involves creating a pair-wise comparison matrix to assign weights to criteria. The weighted scores are used to construct a decision matrix for sensor nodes, and the final ranking is determined through the weighted sum of these scores. A case study illustrates the practical application of the SAW approach, where sensor A3 is identified as the best choice for a routing operation. This outcome demonstrates the SAW approach's effectiveness in selecting optimal sensor nodes to ensure optimal performance in WSNs