Wireless Sensor Network RSSI-Based Localization Algorithm Simulation

In confined one-dimensional environments such as mines, localization technology for Wireless Sensor Networks (WSN) is critically important. RSSI (Received Signal Strength Indicator)-based localization algorithms have become the ideal choice for such scenarios due to their low cost and ease of deployment.

RSSI localization algorithms estimate distances between nodes by measuring signal strength attenuation. In linear environments like mines, signal propagation is constrained by tunnel structures, where multipath effects and signal attenuation become particularly significant. Therefore, simulations must specifically consider the following factors:

Path Loss Model: In one-dimensional mine environments, signal attenuation models may differ from traditional free-space or log-distance models. Parameter adjustments should incorporate realistic environmental conditions, where implementation typically involves configuring path loss exponents and reference distances in the simulation code.

Noise Suppression: Due to interference from metal structures and mechanical equipment in mines, RSSI measurements are susceptible to noise. Accuracy can be improved through filtering algorithms such as Kalman filters, which require implementing state prediction and measurement update cycles in the simulation code.

Anchor Node Deployment: In narrow one-dimensional spaces, strategic placement of anchor nodes (reference nodes with known positions) significantly impacts localization accuracy. Common deployment strategies include equidistant spacing or key position placement, which can be programmed using array-based coordinate initialization and optimization algorithms.

Simulations enable validation of algorithm adaptability in mine environments, such as analyzing localization errors under different path loss exponents or evaluating the impact of anchor node density on performance. Such simulations provide reliable theoretical foundations for practical deployments while optimizing the balance between system cost and accuracy through parameter tuning and Monte Carlo error analysis in the code implementation.