Commonly Used Localization Algorithms in Wireless Sensor Networks

Localization algorithms in wireless sensor networks are primarily used to determine the position information of individual nodes within the network, with wide applications in environmental monitoring, target tracking, and other fields. Common localization algorithms include methods based on Received Signal Strength Indicator (RSSI) and Distance Vector-Hop (DV-Hop).

RSSI Localization Algorithm The RSSI (Received Signal Strength Indicator) algorithm estimates distances between nodes using signal attenuation models. As signal strength decreases with increasing distance, by measuring RSSI values from multiple anchor nodes, the position of unknown nodes can be estimated using triangulation algorithms. The RSSI algorithm is straightforward to implement but is susceptible to multipath effects and environmental interference, resulting in relatively large positioning errors. In code implementation, this typically involves configuring RF modules to measure signal strength, applying logarithmic distance path loss models, and solving equations using least squares methods.

DV-Hop Localization Algorithm DV-Hop (Distance Vector-Hop) is a hop-count-based localization method that operates in three phases: Anchor nodes broadcast their position and hop count information through network flooding protocols. Unknown nodes calculate the average distance per hop to anchor nodes using distance accumulation and hop count division. Position estimation is performed using multilateral positioning methods like trilateration. DV-Hop performs well in uniformly distributed networks but exhibits larger errors in non-uniform network topologies. Implementation typically requires designing message propagation protocols and implementing matrix operations for coordinate calculation.

Error Comparison In MATLAB simulations, the RSSI algorithm typically shows higher errors due to environmental noise, while DV-Hop demonstrates more stable performance in moderately dense network configurations. Through optimization of node distribution patterns and parameter tuning (such as transmission power adjustment and calibration coefficients), localization accuracy can be further improved. Simulation code often involves statistical error analysis functions and visualization tools for performance comparison.