Wireless Sensor Networks (WSN) are essentially distributed systems composed of numerous small, low-power sensor nodes that can sense and monitor their surroundings. As a key technology in the Internet of Things (IoT), WSNs enable real-time data collection and transmission, making them essential for applications such as environmental monitoring, smart cities, and industrial automation.
One of the most critical technologies within WSNs is positioning, which plays a vital role in determining node locations for tasks like tracking, navigation, and target localization. Among various positioning techniques, range-based methods are widely used, where accurate distance estimation between nodes is crucial for reliable location determination. The Received Signal Strength Indicator (RSSI) is one of the most commonly used ranging techniques due to its low cost and simplicity. However, traditional RSSI-based methods suffer from several limitations, including complex path loss models, signal fluctuations caused by environmental changes, and high ranging errors—often reaching up to ±50%.
To address these issues, this paper proposes an improved RSSI ranging method that leverages the attenuation factor of the anchor node's signal. Unlike conventional approaches, this method does not require the establishment of a detailed path loss model. Instead, it calculates the signal attenuation factor based on the known distances between two anchor nodes. This factor is then used to estimate the distance between a known node and an unknown node, significantly reducing the impact of environmental interference and signal intensity fluctuations.
The proposed method improves the accuracy and stability of RSSI-based ranging by taking advantage of the consistent relationship between signal attenuation and distance in the same environment. It also enhances adaptability to different conditions without requiring additional hardware or complex calibration processes. Through simulation experiments conducted using the NS2 platform, the effectiveness of the algorithm was validated, demonstrating a more stable and accurate relationship between signal strength and distance compared to traditional RSSI methods.
In conclusion, this study introduces a novel approach to RSSI-based ranging by incorporating the signal attenuation factor of anchor nodes. By eliminating the need for complicated path loss models and reducing the influence of external factors, the proposed method significantly improves the positioning accuracy of wireless sensor networks. This advancement makes RSSI-based solutions more practical for real-world applications where environmental variability and signal instability are common challenges.
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