Underwater Electromagnetic Sensor Networks, Part II: Localization and Network Simulations

Abstract

In the first part of the paper, we modeled and characterized the underwater radio channel in shallow waters. In the second part, we analyze the application requirements for an underwater wireless sensor network (U-WSN) operating in the same environment and perform detailed simulations. We consider two localization applications, namely self-localization and navigation aid, and propose algorithms that work well under the specific constraints associated with U-WSN, namely low connectivity, low data rates and high packet loss probability. We propose an algorithm where the sensor nodes collaboratively estimate their unknown positions in the network using a low number of anchor nodes and distance measurements from the underwater channel. Once the network has been self-located, we consider a node estimating its position for underwater navigation communicating with neighboring nodes. We also propose a communication system and simulate the whole electromagnetic U-WSN in the Castalia simulator to evaluate the network performance, including propagation impairments (e.g., noise, interference), radio parameters (e.g., modulation scheme, bandwidth, transmit power), hardware limitations (e.g., clock drift, transmission buffer) and complete MAC and routing protocols. We also explain the changes that have to be done to Castalia in order to perform the simulations. In addition, we propose a parametric model of the communication channel that matches well with the results from the first part of this paper. Finally, we provide simulation results for some illustrative scenarios.

Using a Deep Learning Algorithm to Improve the Results Obtained in the Recognition of Vessels Size and Trajectory Patterns in Shallow Areas Based on Magnetic Field Measurements Using Fluxgate Sensors

“…When the sensors are deployed on the seabed and are included within the network, they execute an algorithm that allows them to know the distance they are from the nodes next to them [18]. These distances are established between 20 and 25 meters maximum.…”

Software-In-Loop Simulation of an Underwater Wireless Sensor Network for Monitoring Seawater Quality: Parameter Selection and Performance Validation

“…The results of this investigation led to the fit of a parametric path loss model, allowing for narrow-band communications while keeping a reduced amount of noise [ 13 ]. Moreover, it was established that the existing communication framework could be used for self-localisation techniques by means of specifically tailored algorithms.…”

“…Therefore, the maximum distance between nodes and pipe has been set to 5 m as a design criterion. In order to guarantee connectivity between nodes, the distance between two neighbour nodes is restricted to a maximum of 15 m, in accordance with the measurements and simulations previously performed and described in [ 10 , 12 , 13 ].…”

Model Free Localization with Deep Neural Architectures by Means of an Underwater WSN

“…Even though anchor nodes are common [7], they introduce a deployment cost. One solution to this problem is the use of self-localization algorithms [8]. Another solution is the use of an anchor-less localization mechanism that relies on fingerprinting [9], but this method needs to model the environment.…”