Underwater Acoustic Sensor Networks (UW-ASNs)





Research Projects 

Project Description
Underwater Acoustic Sensor Network Architecture

Since underwater monitoring missions can be extremely expensive due to the high cost involved in underwater devices, it is important that the deployed network be highly reliable, so as to avoid failure of monitoring missions due to failure of single or multiple devices. For example, it is crucial to avoid designing the network topology with single points of failure that could compromise the overall functioning of the network.

The network capacity is also influenced by the network topology. Since the capacity of the underwater channel is severely limited, it is very important to organize the network topology such a way that no  communication bottlenecks are introduced.

There are several different architectures for Underwater Acoustic Sensor Networks, depending on the application:

    • Two-dimensional UW-ASNs for ocean bottom monitoring. These are constituted by sensor nodes that are anchored to the bottom of the ocean. Typical applications may be environmental monitoring, or monitoring of underwater plates in tectonics. 
    • Three-dimensional UW-ASNs for ocean column monitoring. These include networks of sensors whose depth can be controlled, and may be used forsurveillance applications or monitoring of ocean phenomena (ocean bio-geo-chemical processes, water streams, pollution, etc). 
    • Three-dimensional networks of Autonomous Underwater Vehicles (AUVs). These networks include fixed portions composed of anchored sensors and mobile portions constituted by autonomous vehicles.

Architecture for 2D Underwater Sensor Networks

    Fig. 1 - Two-dimensional Underwater Sensor Networks

    A reference architecture for two-dimensional underwater networks is shown in the figure above. A group of sensor nodes are anchored to the bottom of the ocean with deep ocean anchors. By means of wireless acoustic links, underwater sensor nodes  are interconnected to one or more underwater sinks (uw-sinks), which are network devices in charge of relaying data from the ocean bottom network to a surface station. To achieve this objective, uw-sinks are equipped with two acoustic transceivers, namely a vertical and a  horizontal transceiver. The horizontal transceiver is used by the uw-sink to communicate with the sensor nodes in order to: i) send commands and configuration data to the sensors (uw-sink to sensors); ii) collect monitored data (sensors to uw-sink). The vertical link is used by the uw-sinks to relay data to a surface station. Vertical transceivers must be long range transceivers for deep water applications as the ocean can be as deep as 10 km. The surface station is equipped with an acoustic transceiver that is able to handle multiple parallel communications with the deployed uw-sinks. It is also endowed with a long range RF and/or satellite transmitter to communicate with the onshore sink (os-sink) and/or to a  surface sink (s-sink).

    Sensors can be connected to uw-sinks via direct links or through multi-hop paths. In the former case, each sensor directly sends the gathered data to the selected uw-sink. This is the simplest way to network sensors, but it may not be the most energy efficient, since the sink may be far from the node and the power necessary to transmit may decay with powers greater than two of the distance. Furthermore, direct links are very likely to reduce the network throughput because of increased acoustic interference due to high transmission power. In case of multi-hop paths, as in terrestrial sensor networks, the data produced by a source sensor is relayed by intermediate sensors until it reaches the uw-sink. This results in energy savings and increased network capacity, but increases the complexity of the routing functionality as well. In fact, every network device usually takes part in a collaborative process whose objective is to diffuse topology information such that efficient and loop free routing decisions can be made at each intermediate node. This process involves signaling and computation. Since, as discussed above, energy and capacity are precious resources in underwater environments, in UW-ASNs the objective is to deliver event features by exploiting multi-hop paths and minimizing the signaling overhead necessary to construct underwater paths at the same time.

Architeture for 3D Underwater Sensor Networks

Fig. 2 - Three-dimensional Underwater Sensor Networks

Three dimensional underwater networks are used to detect and observe phenomena that can not be adequately observed by means of ocean bottom sensor nodes, i.e., to perform  cooperative sampling of the 3D ocean environment. In three-dimensional underwater networks, sensor nodes float at different depths in order to observe a given
phenomenon. One possible solution would be to attach each uw-sensor node to a surface buoy, by means of wires whose length can be regulated so as to adjust the depth of each sensor node. However, although this solution allows easy and quick deployment
of the sensor network, multiple floating buoys may obstruct ships navigating on the surface, or they can be easily detected and deactivated by enemies in military settings.

For these reasons, a different approach can be to anchor sensor devices to the bottom of the ocean. In this architecture, depicted in the figure above, each sensor is anchored to the ocean bottom and equipped with a floating buoy that can be inflated by a pump. The buoy pushes the sensor towards the ocean surface. The depth of the sensor can then be regulated by adjusting the length of the wire that connects the sensor to the anchor, by means of an electronically controlled engine that resides on the sensor.

Many challenges arise with such an architecture, that need to be solved in order to enable 3D monitoring, including:
  • Sensing coverage. Sensors should collaboratively regulate their depth in order to achieve full column coverage, according to their sensing ranges. Hence, it must be possible to obtain sampling of the desired phenomenon at all depths. 
  • Communication coverage. Since in 3D underwater networks there is no notion of uw-sink, sensors should be able to relay information to the surface station via multi-hop paths. Thus, network devices should coordinate their depths such a way that the network topology is always connected, i.e., at least one path from every sensor to the surface station always exists.
Sensor Networks with Autonomous Underwater Vehicles

AUVs can function without tethers, cables, or remote control, and thus have a multitude of applications in oceanography, environmental monitoring, and underwater resource study. Previous experimental work has shown the feasibility of relatively inexpensive AUV submarines equipped with multiple underwater sensors that can reach any depth in the ocean  Hence, they can be used to enhance the capabilities of underwater sensor networks in many ways. The integration and enhancement of fixed sensor networks with AUVs is an almost unexplored research area which requires new network coordination algorithms, such as:

  • Adaptive sampling. This includes control strategies to command the mobile vehicles to places where their data will be most useful. This approach is also known as adaptive sampling and has been proposed in pioneering monitoring missions. For example, the density of sensor nodes can be adaptively increased in a given area when a higher sampling rate is needed for a given monitored phenomenon. 
  • Self-Configuration. This includes control procedures to automatically detect connectivity holes due to node failures and request the intervention of an AUV. AUVs can either be used to deploy new sensors or as relay nodes to restore connectivity.
Below, we show some pictures of existing AUVs.

Caribou, by Bluefin Robotics Corporation, is equipped with state-of-the-art sensors (side-scan sonar and sub-bottom profiler), and can collect high-quality data for archaeological remote sensing, multi-static acoustic modeling, fisheries resource studies and development of concurrent mapping and localization techniques.

Different sensors are integrated in the Bluefin AUV:  Doppler Velocity Logs, Acoustic Doppler, Current Profilers, Conductivity and Temperature, Fluorometer, Li-Cor PAR sensor, Inertial Navigation Systems, Attitude Heading Reference, Marine Global Positioning Systems, Depth Gauges

Advanced Manufacturing for a Mine Countermeasures. AUV Designed for Lockheed Martin by MIT Sea Grant AUV Lab.CETUS is a new low-cost UUV for underwater intervention designed for transit/search and hovering/inspection Operable as AUV or ROV (Fiber-optic Tether).

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Differences with Terrestrial Sensor Networks

The main differences between terrestrial and underwater sensor networks are as follows:

  • Cost. While terrestrial sensor nodes are expected to become increasingly inexpensive, underwater sensors are expensive devices. This is especially due to the more complex underwater transceivers and to the hardware protection needed in the extreme underwater environment. 
  • Deployment. While terrestrial sensor networks are densely deployed, in underwater the deployment is deemed to be more sparse, due to the cost involved and to the challenges associated to the deployment itself in the underwater environment.
  • Power. The power needed for underwater communications is higher than in terrestrial radio communications due to higher distances and to more complex signal processing at the receivers.
  • Memory. While terrestrial sensor nodes have very limited storage capacity, uw-sensors may need to be able to do some data caching as the underwater channel may be intermittent.
  • Spatial Correlation. While the readings from terrestrial sensors are often correlated, this is more unlikely to happen in underwater networks due to the higher distance among sensors.

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Application Scenarios

  The above described features enable a broad range of applications for underwater acoustic sensor networks:

  • Ocean Sampling Networks. Networks of sensors and AUVs, such as the Odyssey-class AUVs, can perform synoptic, cooperative adaptive sampling of the 3D coastal ocean environment Experiments such as the Monterey Bay field experiment in August 2003  demonstrated the advantages of bringing together sophisticated new robotic vehicles with advanced ocean models to improve our ability to observe and predict the characteristics of the oceanic environment.
  • Environmental Monitoring such as pollution monitoring (chemical, biological, etc.), monitoring of ocean currents and winds, improved weather forecast, detecting climate change, understanding and predicting the effect of human activities on marine ecosystems, etc.
  • Disaster Prevention. Sensor networks that measure seismic activity from remote locations and provide tsunami warnings to coastal areas. 
  • Assisted Navigation. Sensors can be used to locate dangerous rocks or shoals in shallow waters, mooring positions, submerged wrecks, etc. 
  • Distributed Tactical Surveillance. AUVs and fixed underwater sensors can collaboratively monitor areas for surveillancereconnaissance,  targeting and intrusion detection systems. 
  • Mine Reconnaissance. The simultaneous operation of multiple AUVs with acoustic and optical sensors can be used to perform rapid environmental assessment and detect mine like objects.
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Basics of Acoustic Propagation

Underwater acoustic communications are mainly influenced by path loss, noise, multi-path, Doppler spread, and high and variable propagation delay. All these factors determine the temporal and spatial variability of the acoustic channel, and make the available bandwidth of the UnderWater Acoustic channel (UW-A) limited and dramatically dependent on both range and frequency. Long-range systems that operate over several tens of kilometers may have a bandwidth of only a few kHz, while a short-range system operating over several tens of meters may have more than a hundred kHz bandwidth. In both cases these factors lead to low bit rate.

Underwater acoustic communication links can be classified according to their range as very long, long, medium, short, and very short links. Acoustic links are also roughly classified as vertical and horizontal, according to the direction of the sound ray. As shown after, their propagation characteristics differ consistently, especially with respect to time dispersion,multi-path spreads, and delay variance. In the following, as usually done in oceanic literature, shallow water refers to water with depth lower than 100 m, while deep water is used for deeper oceans.

Hereafter we analyze the factors that influence acoustic communications in order to state the challenges posed by the underwater channels for underwater sensor networking. These include:

  • Path loss
  1. Attenuation. Is mainly provoked by absorption due to conversion of acoustic energy into heat, which increases with distance and frequency. It is also caused by scattering an reverberation (on rough ocean surface and bottom), refraction, and dispersion (due to the displacement of the reflection point caused by wind on the surface). Water depth plays a key role in determining the attenuation.
  2. Geometric Spreading. This refers to the spreading of sound energy as a result of the expansion of the wavefronts. It increases with the propagation distance and is independent of frequency. There are two common kinds of geometric spreading:  spherical (omni-directional point source), and cylindrical (horizontal radiation only).
  • Noise
  1. Man made noise. This is mainly caused by machinery noise (pumps, reduction gears, power plants, etc.), and shipping activity (hull fouling, animal life on hull, cavitation), especially in areas encumbered with heavy vessel traffic. 
  2. Ambient Noise. Is related to hydrodynamics (movement of water including tides, current, storms, wind, rain, etc.), seismic and biological phenomena.
  • Multi-path
  1. Multi-path propagation may be responsible for severe degradation of the acoustic communication signal, since it generates Inter-Symbol Interference (ISI). 
  2. The multi-path geometry depends on the link configuration. Vertical channels are characterized by little time dispersion, whereas horizontal channels may have extremely long multi-path spreads. 
  3. The extent of the spreading is a strong function of depth and the distance between transmitter and receiver.
  • High delay and delay variance
  1. The propagation speed in the UW-A channel is five orders of magnitude lower than in the radio channel. This large propagation delay (0.67 s/km) can reduce the throughput of the system considerably. 
  2. The very high delay variance is even more harmful for efficient protocol design, as it prevents from accurately estimating the round trip time (RTT), which is the key parameter for many common communication protocols.
  • Doppler spread
  1. The Doppler frequency spread can be significant in UW-A channels, causing a degradation in the performance of digital communications: transmissions at a high data rate cause many adjacent symbols to interfere at thereceiver, requiring sophisticated signal processing to deal with the generated ISI.  
  2. The Doppler spreading generates: i) a simple frequency translation, which is relatively easy for a receiver to compensate for; ii) a continuous spreading of frequencies, which constitutes a non-shifted signal, which is more difficult for a receiver to compensate for.
  3. If a channel has a Doppler spread with bandwidth B and a signal has symbol duration T, then there are approximately BT uncorrelated samples of its complex envelope. When BT is much less than unity, the channel is said to be underspread and the effects of the Doppler fading can be ignored, while, if greater than unity, it is overspread.

Most of the described factors are caused by the chemical-physical properties of the water medium such as temperature, salinity and density, and by their spatio-temporal variations. These variations, together with the wave guide nature of the channel,cause the acoustic channel to be temporally and spatially variable. In particular, the horizontal channel is by far more rapidly varying than the vertical channel, in both deep and shallow water.

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A Cross Layered Protocol Stack

    A protocol stack for uw-sensors should combine power awareness and  management, and promote cooperation among the sensor nodes. It should consist of physical layerdata link layer, network layer, transport layer, and  application layer functionalities.
    The protocol stack should also include a power management plane, a coordination plane, and a localization plane. The power management plane is responsible for network functionalities aimed at minimizing the energy consumption (e.g., sleep modes, power control, etc.). The coordination plane is responsible for all functionalities that require coordination among sensors, (e.g., coordination of the sleep modes, data aggregation, 3D topology optimization, etc.). The localization plane is responsible for providing absolute or relative localization information to the sensor node, when needed by the protocol stack or by the application.

    It is worth pointing out that while all the research on underwater networking so far has followed the traditional layered approach for network design, it is an increasingly accepted opinion in the wireless networking community that the improved network efficiency, especially in critical environments, can be obtained with cross-layer design approaches. These techniques will entail a joint design of different network functionalities, from modem design to MAC and routing, from channel coding and modulation to source compression and transport layer, with the objective to overcome the shortcomings of a layered approach that lacks of information sharing across protocol layers, forcing the network to operate in a suboptimal mode. For this reason, while for the sake of clarity in the following sections we present the challenges associated with underwater sensor networks following the traditional layered approach, we believe that the underwater environment particularly requires for cross-layer design solutions that allow a more efficient use of the scarce available resources.
    However, although we advocate integrating functionalities to improve network performance and to avoid duplication of functions by means of cross-layer design, it is important to keep an eye on ease of design by following a modular design approach. This also allows improving and upgrading particular functionalities without the need to re-design the entire communication system.

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