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.

Active tracking of Maja squinado in the Mediterranean sea with wireless acoustic sensors: method, results and prospectives.

The Sustainable TEchnologies for LittoraL Aquaculture and MArine REsearch (STELLA MARE) platform has as an objective to provide data for the management of the sea in relation with the fishing industry. In this paper, we introduce the first experiment on the active tracking of a crab species, Maja squinado, symbolic of the deregulation of fishing activity. This paper introduces the method used for monitoring Maja squinado and the first collected data on the behavior of this little-known species.

Performance of a protected wireless sensor network in a fire. Analysis of fire spread and data transmission.

The paper deals with a Wireless Sensor Network (WSN) as a reliable solution for capturing the kinematics of a fire front spreading over a fuel bed. To provide reliable information in fire studies and support fire fighting strategies, a Wireless Sensor Network must be able to perform three sequential actions: 1) sensing thermal data in the open as the gas temperature; 2) detecting a fire i.e., the spatial position of a flame; 3) tracking the fire spread during its spatial and temporal evolution. One of the great challenges in performing fire front tracking with a WSN is to avoid the destruction of motes by the fire. This paper therefore shows the performance of Wireless Sensor Network when the motes are protected with a thermal insulation dedicated to track a fire spreading across vegetative fuels on a field scale. The resulting experimental WSN is then used in series of wildfire experiments performed in the open in vegetation areas ranging in size from 50 to 1,000 m(2).