Latest revision as of 13:07, 14 September 2026

Abstract

In recent years, localization has emerged as a significant aspect of Wireless Sensor Networks (WSNs). The Distance Vector-Hop (DV-HOP) algorithm is a widely used localization approach for WSN due to its costeffectiveness and simplicity. However, it suffers from accuracy issues due to the random deployment of sensor nodes, and thus, the calculation errors in hop distance and the minimum number of hops. To address these challenges, this paper proposes Multi-Layer Perceptron-based DV-HOP with Received Signal Strength Indicator (MLPDV-RSSI), a novel intelligent algorithm that enhances localization by integrating DV-Hop with Multilayer Perceptron (MLP) and Received Signal Strength Indicator (RSSI). The proposed algorithm employs the RSSI to determine the location of sensors within a defined hop from the anchor nodes (ANs). Meanwhile, the DV-HOP method effectively localizes sensor nodes over multiple hops from ANs. Combining DV-HOP with the RSSI in the proposed model enhances the accuracy, particularly in dynamic indoor environments where measurements fluctuate. Feeding the RSSI measurements, hop count, and average hop distance values into the MLP for training while adjusting the weights reduces the error in the average hop distance and further improves accuracy. The simulation results show that the proposed method applied in a static environment achieves a significant accuracy improvement by up to 42% compared to the benchmarks. In a dynamic environment under 40% mobile nodes with non-Gaussian interference, a significant performance improvement by up to 48.9% is achieved compared to DV-HOP.

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