Linking Acoustic Communications and Network Performance: Integration and Experimentation of an Underwater Acoustic Network

Abstract

Abstract-Underwater acoustic networks (UANs) are an emerging technology for a number of oceanic applications, ranging from oceanographic data collection to surveillance applications. However, their reliable usage in the field is still an open research problem, due to the challenges posed by the oceanic environment. The UAN project, a European-Union-funded initiative, moved along these lines, and it was one of the first cases of successful deployment of a mobile underwater sensor network integrated within a wide-area network, which included above water and underwater sensors. This contribution, together with a description of the underwater network, aims at evaluating the communication performance, and correlating the variation of the acoustic channel to the behavior of the entire network stack. Results are given based on the data collected during the UAN11 (May 2011, Trondheim Fjord area, Norway) sea trial. During the experimental activities, the network was in operation for five continuous days and was composed of up to four Fixed NOdes (FNOs), two autonomous underwater vehicles (AUVs), and one mobile node mounted on the supporting research vessel. Results from the experimentation at sea are reported in terms of channel impulse response (CIR) and signal-to-interference-plus-noise ratio (SINR) as measured by the acoustic modems during the sea tests. The performance of the upper network levels is measured in terms of round trip time (RTT) and probability of packet loss (PL). The analysis shows how the communication performance was dominated by variations in signal-to-noise ratio, and how this impacted the behavior of the whole network. Qualitative explanation of communication performance variations can be accounted, at least in the UAN11 experiment, by standard computation of the CIR and transmission loss estimate.

Balancing between robustness and energy consumption in underwater acoustic sensor networks

“…Underwater acoustic networks (UANs) project reports one of the successful deployment of mobile UWSNs [8]. In the UANs project, four fixed nodes, two autonomous underwater vehicles (AUVs), and one sensor node mounted on the surface ship (acting as a mobile node) are deployed in seawater by aiming to evaluate the performance of UWSNs and the variation of the underwater acoustic channel.…”

“…The experimental data collect from the sea trial in Trondheim Fjord area, Norway, in 2011. Their experimental results indicate that the chosen acoustic modems [8] are able to provide robust communication against the multipath effect. However, the underwater communication capability degrades quickly in low SNR conditions.…”

UComms: A Conference and Workshop on Underwater Communications, Channel Modeling, and Validation

“…It is correspondingly difficult to successfully field a full network for at-sea validation. Caiti et al [12] report one of the first examples of successful deployment of a mobile underwater sensor network integrated within a wide area network, including above water and UW sensors, under the European Union (EU) Undersea Acoustic Network (UAN) project. In this case, a simple characterization based on the SNR at the receiver was adequate to characterize the overall system.…”

A mechanism of topology optimization for underwater acoustic sensor networks based on autonomous underwater vehicles

“…1,[25][26][27][28][29][30] The quality of communication among the sensor nodes and the reliability of topology of UASNs have been seriously affected due to 1 the factors of complex marine environment, such as the high latency, low bandwidth, and easy attenuation of signal and multipath effects, seriously affecting. [15][16][17][18][19][20][21][22][23] Currently, optimization for terrestrial sensor networks has some achievements. Fu and Han 2 solve the sensor node-distributed optimization problems of the terrestrial sensor network by quantum genetic algorithm; Aziz et al 3 consider comprehensively the battery power of sensor and other factors, then employing the distributed topology control technology to optimize the energy consumption of wireless sensor networks; Wang et al 4 present a parallel particle swarm optimization (PSO) strategies based on the mobility of sensor node, so that distribution of the node in sensor networks reaches local optimum; Mi and Zhou 5 proposed a terrestrial wireless sensor network routing algorithm multi-objective optimization.…”