Wireless Sensor Networks (WSN) are essentially distributed sensor systems designed to sense and monitor the external environment. They represent a core technology in the Internet of Things (IoT), enabling real-time data collection and analysis. In WSNs, positioning technology plays a crucial role, especially in distance-based methods where ranging accuracy directly affects overall positioning precision.
Traditional RSSI (Received Signal Strength Indicator) based ranging techniques face several challenges, including complex path loss models, signal fluctuations due to environmental changes, and significant ranging errors. To address these issues, this paper introduces an improved RSSI ranging method that leverages the attenuation factor of the anchor node’s signal. This approach eliminates the need for a detailed path loss model while maintaining high accuracy by correlating the signal strength with the measured attenuation.
In practical applications, wireless sensor networks rely on nodes providing both location and sensing data to ensure effective monitoring. Accurate node localization is essential for tasks such as tracking, object recognition, and trajectory prediction. The general problem of node localization involves using known anchor nodes to determine the positions of unknown nodes through distance measurements or region estimation.
RSSI-based ranging measures received signal power and uses it to estimate distance via a theoretical or empirical path loss model. However, this method is susceptible to multipath fading and non-line-of-sight conditions, leading to signal strength fluctuations and inaccurate distance estimates. To overcome these limitations, this study proposes a novel method that utilizes the attenuation characteristics of signals from anchor nodes.
The proposed method calculates the signal attenuation factor between two anchor nodes and applies it to estimate distances between known and unknown nodes. This technique reduces dependency on complex models and improves adaptability to varying environments. Simulations conducted on the NS2 platform demonstrate the effectiveness of the algorithm, showing reduced ranging errors and more stable signal behavior over different distances.
By integrating the concept of signal attenuation factors, this approach enhances the reliability and precision of RSSI-based ranging in WSNs. It provides a promising solution for improving the performance of wireless positioning systems in dynamic and unpredictable environments. Overall, the method offers a practical and efficient way to enhance the accuracy of node localization in wireless sensor networks.
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