Underwater Wireless Sensor Networks: Estimation of Acoustic Channel in Shallow Water

Abstract

Underwater sensor networks (UWSN) include a large number of devices and sensors which are positioned in a specific area to carry out monitoring in cooperation with each other as well as data collection. In this paper it has been studied and simulated the performance of an extremely important parameter for communication in UWSN such as the acoustic channel capacity as function of water temperature and salinity arise. The performance’s knowledge on acoustic channel may be improved with a deep study of its dependence by season, weather conditions or environmental parameters variation. If an accurate estimation of the acoustic communication capacity utilization for a given area is required, we must consider also the bottom materials of this area. The simulation results presented in this study through an improved algorithm, will help to understand better the underwater acoustic channel performance as a function of all these factors. This is of particular importance for acoustic modems designing, in order to implement suitable functionalities able to adapt the data transmission capacity of the acoustic link to the structure of the oceanic bottom and its component material.

Energy-Efficient Packet Forwarding Scheme Based on Fuzzy Decision-Making in Underwater Sensor Networks

“…It can be expressed in two ways, in cylindrical ( ) for shallow waters (depth less than 100 m) and spherical spreading ( ) for oceanic waters [ 16 ], respectively, as where r indicates the hop distance (m) and f is frequency. The speed of sound in underwater is given by c (m/s) [ 17 ] where T is temperature in Celsius scale, S is salinity in parts per thousand, and d is depth in meters. Ambient noise is a contribution of at least four factors: turbulence noise ( , shipping noise , wave and other surface noise , and thermal noise .…”

“…Ambient noise is a contribution of at least four factors: turbulence noise ( , shipping noise , wave and other surface noise , and thermal noise . The frequency dependence of every ambient noise component, is given by [ 17 ] N ( f ) = N t ( f ) + N s ( f ) + N w ( f ) + N t h ( f ), 10 log N t ( f ) = 17 − 30 log ( f ), 10 log N s ( f ) = 40 + 20 ( s − 0.5) + 26 log f − 60 log ( f + 0.03), 10 log N t h ( f ) = −15 + 20 log f . …”

“…where r indicates the hop distance (m) and f is frequency. The speed of sound in underwater is given by c (m/s) [17] c = 1449.2 + 4.6 T − 0.055 T 2 + 0.00029…”

“…Ambient noise is a contribution of at least four factors: turbulence noise (N t ), shipping noise (N s ), wave and other surface noise (N w ), and thermal noise (N th ). The frequency dependence of every ambient noise component, is given by [17]…”

Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms

“…Underwater wireless sensor networks (UWSNs) consist of many underwater wireless sensor nodes distributed within the marine environment, which support a wide variety of applications such as surveillance, navigation, data acquisition, resource exploration, and disaster prevention [1][2][3]. Each sensor node of UWSNs is equipped with an acoustic modem because it uses acoustic signals to communicate with each other [4].…”