Wireless sensors now work with degraded signal data, slashing communication costs
Researchers have cracked a stubborn problem in wireless sensor networks: locating devices accurately when signal measurements are severely compressed or corrupted. The breakthrough cuts the data these networks must transmit to coordinate location fixes—a major win for cost-conscious deployments in factories, logistics, and infrastructure monitoring where bandwidth is expensive.
Originaltitel: Cooperative localization based on severely quantized RSS measurements in wireless sensor network
<p>We study severely quantized received signal strength (RSS)-based cooperative localization in wireless sensor networks. We adopt the well-known sum-product algorithm over a wireless network (SPAWN) framework in our study. To address the challenge brought by severely quantized measurements, we adopt the principle of importance sampling and design appropriate proposal distributions. Moreover, we propose a parametric SPAWN in order to reduce both the communication overhead and the computational complexity. Experiments with real data corroborate that the proposed algorithms can achieve satisfactory localization accuracy for severely quantized RSS measurements. In particular, the proposed parametric SPAWN outperforms its competitors by far in terms of communication cost. We further demonstrate that knowledge about non-connected sensors can further improve the localization accuracy of the proposed algorithms.</p>