An active sonar system consists of a source emitting a sound pulse (ping) and a receiver listening to the reflection of the wave on a target, known as the echo. Such a system is further divided into two distinct confi...
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An active sonar system consists of a source emitting a sound pulse (ping) and a receiver listening to the reflection of the wave on a target, known as the echo. Such a system is further divided into two distinct configurations. The first one, named monostatic, is made up of a collocated source and receiver, while the second one, referred to as bistatic, is based on a non-collocated source and receiver. To this extent, a Multistatic Sonar Network (MSN) is thus comprised of a set of sources and receivers deployed across a given area of Interest (AoI), which, taken pairwise, form sonar systems in monostatic and/or bistatic configuration. In this paper, we therefore propose an efficient two-phase greedy heuristic to solve the areacoverage (AC) problem in the scope of MSNs, a special case of Wireless Sensor Networks (WSNs), while taking into account existing coastlines. For this problem, the objective is to determine the optimal spatial layout of the MSN, i.e. the one that maximize the overall coverage of the AoI with regard to a limited number of sensors and a given probabilistic detection model. Furthermore, we use a Mixed Integer Linear Program (MILP) from the literature as a reference for the numerical experiments conducted on a dataset of diversified instances. The latter were specifically derived from Digital Elevation Models (DEMs) of AoIs selected throughout the globe and in such a way as to encompass a wide spectrum of peculiar geometric situations.
The dynamic deployment of sensors in AoI (area of Interest) plays an important role in ensuring the quality of services (QoS) of Wireless Sensor Networks (WSNs) by optimising the coverage and lifetime of the network. ...
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The dynamic deployment of sensors in AoI (area of Interest) plays an important role in ensuring the quality of services (QoS) of Wireless Sensor Networks (WSNs) by optimising the coverage and lifetime of the network. In this paper, a new dynamic deployment approach using metaheuristics based on Electromagnetism-Like (EM-L) algorithm and Elfes Probabilistic Detection Model (EPDM) was proposed to optimise the coverage of WSNs based on the sensor coverageproblem, and coverage analysis of AoI was performed. A variable threshold detection probability (TDP) was defined instead of defining a fixed TDP as in the literature. Thus, a more realistic modelling environment was created by considering the signal-to-noise ratio (SNR) in the coverage calculation. The simulation results show that the sensors are always effectively deployed in different scenarios with variable TDPs by the proposed approach compared to a random distribution.
The work undertaken in this paper pertains to the optimal spatial configuration of a heterogeneous Wireless Sensor Network (WSN) for the areacoverage (AC) problem. Specifically, this research falls under the heading ...
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The work undertaken in this paper pertains to the optimal spatial configuration of a heterogeneous Wireless Sensor Network (WSN) for the areacoverage (AC) problem. Specifically, this research falls under the heading of Anti -Submarine Warfare (ASW) with an emphasis on active sonar systems and, more pointedly still, on a specific type of sensor: sonobuoys (portmanteau word formed by "sonar"and "buoy"). These buoys are further divided into three main categories: transmitter-only (Tx), receiver-only (Rx) and transmitter-receiver (TxRx). In this paper, we will therefore try to determine the geographical location of the different buoys comprising a Multistatic Sonar Network (MSN), special case of WSN, so as to maximize the overall surface area covered. To do this, we discretize an area of Interest (AoI) into regular cells using bathymetric and altimetric data, and place a deployment position and a fictitious target at the center of each cell so that we can evaluate the network's performance. More precisely, we are taking into account a limited number of sensors (buoys) with possible pairwise incompatibilities, variable performances, probabilistic detection models, an adverse masking effect (direct blast) as well as coastlines features. Finally, in order to solve this problem, we have developed several efficient Mixed-Integer Linear Programs (MILPs), all of which have been thoroughly tried-and-tested on a benchmark set of 100 instances derived from real elevation data. This has led us to identify an ideal model, i.e. one that is significantly better than all the others in the statistical sense.
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