Localization of Energy Harvesting Empowered Underwater Optical Wireless Sensor Networks

TLDR

Energy limitation due to limited battery power and difficulty of replacement is a major problem in underwater optical wireless sensor networks. This paper proposes a received‑signal‑strength based localization framework for energy‑harvesting underwater optical wireless sensor networks, addressing optical noise and seawater channel impairments that challenge range estimation. The framework harvests ambient energy to activate nodes, measures RSS to construct block kernel matrices, reduces shortest‑path estimation error, and applies a closed‑form localization algorithm benchmarked by an analytical Cramer‑Rao lower bound. Simulations demonstrate that the proposed framework outperforms well‑known network localization techniques.

Abstract

This paper proposes a received signal strength (RSS)-based localization framework for energy harvesting underwater optical wireless sensor networks (EH-UOWSNs), where the optical noise sources and channel impairments of seawater pose significant challenges on range estimation. In UOWSNs, energy limitation is another major problem due to the limited battery power and difficulty to replace or recharge the battery of an underwater sensor node. In the proposed framework, sensor nodes with insufficient battery harvest ambient energy and start communicating once they have sufficient storage of energy. Network localization is carried out by measuring the RSSs of active nodes, which are modeled based on the underwater optical communication channel characteristics. Thereafter, block kernel matrices are computed for the RSS-based range measurements. Unlike the traditional shortest-path approach, the proposed technique reduces the estimation error of the shortest path for each block kernel matrix. Once the complete block kernel matrices are available, a closed form localization technique is developed to find the location of every optical sensor node in the network. An analytical expression for the Cramer-Rao lower bound is also derived as a benchmark to evaluate the localization performance of the developed technique. The extensive simulations show that the proposed framework outperforms the well-known network localization techniques.