Efficient WSN Node Placement by Coupling KNN Machine Learning for Signal Estimations and I-HBIA Metaheuristic Algorithm for Node Position Optimization.

Wireless sensor network (WSN) deployment is an intensive field of research. In this paper, we propose a novel approach based on machine learning (ML) and metaheuristics (MH) for supporting decision-makers during the deployment process. We suggest optimizing node positions by introducing a new hybridized version of the "Hitchcock bird-inspired algorithm" (HBIA) metaheuristic algorithm that we named "Intensified-Hitchcock bird-inspired algorithm" (I-HBIA). During the optimization process, our fitness function focuses on received signal maximization between nodes and antennas. Signal estimations are provided by the machine learning "K Nearest Neighbors" (KNN) algorithm working with real measured data. To highlight our contribution, we compare the performances of the canonical HBIA algorithm and our I-HBIA algorithm on classical optimization benchmarks. We then evaluate the accuracy of signal predictions by the KNN algorithm on different maps. Finally, we couple KNN and I-HBIA to provide efficient deployment propositions according to actual measured signal on areas of interest.

A Wireless Autonomous Real-Time Underwater Acoustic Positioning System.

Recent acoustic telemetry positioning systems are able to reconstruct the positions and trajectories of organisms at a scale of a few centimeters to a few meters. However, they present several logistical constraints including receiver maintenance, calibration procedures and limited access to real-time data. We present here a novel, easy-to-deploy, energy self-sufficient underwater positioning system based on the time difference of arrival (TDOA) algorithm and the Global System for Mobile (GSM) communication technology, capable of locating tagged marine organisms in real time. We provide an illustration of the application of this system with empirical examples using continuous and coded tags in fish and benthic invertebrates. In situ experimental tests of the operational system demonstrated similar performances to currently available acoustic positioning systems, with a global positioning error of 7.13 ± 5.80 m (mean ± SD) and one-third of the pings can be localized within 278 m of the farthest buoy. Despite some required improvements, this prototype is designed to be autonomous and can be deployed from the surface in various environments (rivers, lakes, and oceans). It was proven to be useful to monitor a wide variety of species (benthic and pelagic) in real time. Its real-time property can be used to rapidly detect system failure, optimize deployment design, or for ecological or conservation applications.