seminar report akhil 25 10 2013.docx

SEMINAR REPORT OCTOBER 2013 ADAPTIVE ACOUSTIC MODEM FOR UNDERWATER

SENSOR NETWORKS

ABSTRACT

There is a growing interest in using underwater networked systems for oceanographic

applications. These networks often rely on acoustic communication, which poses a number of

challenges for reliable data transmission. The underwater acoustic channel is highly variable;

each link can experience a vastly conditions, which change according to environmental factors as

well as the locations of the communicating nodes. This makes it difficult to ensure reliable

communication.

The main challenge is how to build a system that provides reliable and energy efficient

communication in underwater sensor networks. To this end, an adaptive underwater acoustic

modem is proposed in the seminar which changes its parameters according to the situation. The

seminar discusses about the design of such a modem and provides supporting results from

simulations and experiments.

ECE Department | SBCE 1


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Chapter 1

INTRODUCTION

Underwater sensor networks have wide range of oceanographic applications including

marine exploration, environmental monitoring and coastal surveillance. The preferred mode of

wireless communication in these networks is based on acoustic signals. This is due to the fact

that radio frequencies suffer high attenuation underwater. Optical communication is possible but

only in clear water at relatively short distances. Unfortunately, acoustic communication is

challenging due to large and variable multipath delay spread, Doppler shifts and long

propagation delays.

Underwater networks are envisioned to consist of tens to hundreds of nodes that are

deployed in 3-dimensional space, in different configurations. A concrete example is a sensor

network consisting of freely floating autonomous drifters for underwater exploration. In such

scenarios, the acoustic channel can vary considerably between different transmitter receiver

pairs. This is due to the significant variation in the nodes’ positions, the extent of motion

between them, and the topography of the ocean environment. For example, in moored

oceanographic applications, where nodes are deployed at different depths in a static

configuration, the channel experienced by nodes closer to the bottom of the ocean is different

from those near the surface due to variations in signal reflection and refraction across different

water layers. In networks consisting of mobile vehicles, additional variability is introduced due

to the dynamics of the ocean environment and the relative motion between the vehicles.

To ensure reliable communication, we must choose the modem parameters for worst case

channel conditions. Unfortunately, this often leads to communicating at lower data rates than are

practically possible. Moreover, due to the high transmit power of acoustic modems (often tens of

watts), a lower data rate results in a substantial increase in the energy consumed per bit. Since

devices in an underwater sensor network are generally battery operated, energy efficient



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communication is crucial. The essential challenge that we address in this paper is how to provide

reliable and energy efficient acoustic communication for underwater networks by designing an

adaptive physical layer.

To this end, this seminar proposes an underwater acoustic modem that adapts its data rate

and modulation scheme to the channel conditions. This idea is also motivated by previous

observations that adaptive modulation is the key to maximizing both channel capacity and

channel efficiency at the physical and MAC layers. As a result of such adaptations nodes that

experience a more favorable channel can communicate at a faster rate, thereby saving energy.

Alternatively, if the channel multipath increases, a node automatically chooses a lower rate to

avoid intersymbol interference. These adaptations are performed on a link by link basis. The

major contribution of this seminar is an adaptive modem architecture. We describe its main

signal processing and control components and motivate our design via simulations and actual

ocean experiments.

Prior work on acoustic modem design also recognizes the fact that communication

performance underwater has a strong dependence on the deployment environment.

Consequently, modems with adaptable features have been previously proposed. For example, in

order to deal with different channels, the Woods Hole Micro-Modem, which is widely used in

the research community, has two operation modes: a low data rate FSK mode and a high data

rate PSK mode. Other examples include a dual-mode acoustic modem and a software modem.

The dual mode modem can switch between two modulation schemes - FH and DSSS according

to channel signal to noise ratio (SNR). The software modem allows the user to select a desired

modulation technique, data-rate and frame length. These modems require the user to set certain

communication parameters prior to deployment and they are not designed to adapt to a variable

channels in real-time, after the network is deployed. The seminar aim to propose the design of

modem architecture that can dynamically change either before deployment or during its

operation.



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Chapter 2

LITERATURE SURVEY

Underwater sound has probably been used by marine animals for millions of years. The

science of underwater acoustics began in 1490 by Leonardo da Vinci. In 1687 Isaac Newton

wrote his Mathematical Principles of Natural Philosophy which included the first

mathematical treatment of sound. The next major step in the development of underwater

acoustics was made by Daniel Colladon, a Swiss physicist, and Charles Sturm,

a French mathematician. In 1826, on Lake Geneva, they measured the elapsed time between a

flash of light and the sound of a submerged ship's bell heard using an underwater listening horn.[2] They measured a sound speed of 1435 metres per second over a 17 kilometre distance,

providing the first quantitative measurement of sound speed in water. The result they obtained

was within about 2% of currently accepted values. In 1877 Lord Rayleigh wrote the Theory of

Sound and established modern acoustic theory.

The sinking of Titanic in 1912 and the start of World War I provided the impetus for the

next wave of progress in underwater acoustics. Systems for detecting icebergs and U-boats were

developed. Between 1912 and 1914, a number of echolocation patents were granted in Europe

and the U.S., culminating in Reginald A. Fessenden's echo-ranger in 1914. Pioneering work was

carried out during this time in France by Paul Langevin and in Britain by A B Wood and

associates. The development of both active ASDIC and passive sonar (SOund Navigation And

Ranging) proceeded apace during the war, driven by the first large scale deployments

of submarines. Other advances in underwater acoustics included the development of

acoustic mines.

In 1919, the first scientific paper on underwater acoustics was published, theoretically

describing the refraction of sound waves produced by temperature and salinity gradients in the

ocean. The range predictions of the paper were experimentally validated bytransmission

loss measurements.



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The next two decades saw the development of several applications of underwater

acoustics. The fathometer, or depth sounder, was developed commercially during the 1920s.

Originally natural materials were used for the transducers, but by the 1930s sonar systems

incorporating piezoelectric transducers made from synthetic materials were being used for

passive listening systems and for active echo-ranging systems. These systems were used to good

effect during World War II by both submarines and anti-submarine vessels. Many advances in

underwater acoustics were made which were summarised later in the series Physics of Sound in

the Sea, published in 1946.

After World War II, the development of sonar systems was driven largely by the Cold

War, resulting in advances in the theoretical and practical understanding of underwater acoustics,

aided by computer-based techniques.



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Chapter 3

UNDERWATER SENSOR NETWORKS

Underwater Acoustic Sensor Networks (UW-ASN) consist of a variable number of

sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given

area. To achieve this objective, sensors and vehicles self-organize in an autonomous network

which can adapt to the characteristics of the ocean environment.

Ocean bottom sensor nodes are deemed to enable applications for oceanographic data

collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation

and tactical surveillance applications. Multiple Unmanned or Autonomous Underwater Vehicles

(UUVs, AUVs), equipped with underwater sensors, will also find application in exploration of

natural undersea resources and gathering of scientific data in collaborative monitoring missions.

To make these applications viable, there is a need to enable underwater communications among

underwater devices. Underwater sensor nodes and vehicles must possess self-configuration

capabilities, i.e., they must be able to coordinate their operation by exchanging configuration,

location and movement information, and to relay monitored data to an onshore station.

3.1 UNDERWATER COMMUNICATION

Underwater communication applications mostly involve long-term monitoring of selected ocean

areas. The traditional approach for ocean-bottom or ocean-column monitoring is to deploy

oceanographic sensors, record the data, and recover the instruments. This approach creates long

lags in receiving the recorded information. In addition, if a failure occurs before recovery, all the

data is lost. The ideal solution for these applications (such as data collection from hot vent

communities, offshore mining or drilling, etc.) is to establish real-time communication between

the underwater instruments and a control center within a network configuration. Basic

underwater acoustic (UWA) networks are formed by establishing two-way acoustic links

between various instruments such as autonomous underwater vehicles (AUV’s) and sensors. The



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network is then connected to a surface station which can further be connected to a backbone,

such as the Internet, through an RF link. This configuration creates an interactive environment

where scientists can extract real-time data from multiple distant underwater instruments. After

evaluating the obtained data, control messages can be sent to individual instruments and the

network can be adapted to changing situations. Since data is transferred to the control station

when it is available, data loss is prevented until a failure occurs. Underwater networks can also

be used to increase the operation range of AUV’s. The feasible wireless communication range of

an AUV is limited by the acoustic range of a single modem which varies from 10 to 90 km [1].

By hopping the control and data messages through a network that covers large areas, the range of

AUV’s can be considerably increased. However, the effects of propagation time delays should be

considered. The shallow-water acoustic channel differs from radio channels in many respects.

The available bandwidth of the UWA channel is limited and depends on both range and

frequency. Within this limited bandwidth, the acoustic signals are subject to time-varying

multipath, which may result in severe intersymbol interference (ISI) and large Doppler shifts and

spreads, relative to radio channels. These characteristics restrict the range and bandwidth for

reliable communications. The propagation speed in the UWA channel is five orders of magnitude

lower than that of the radio channel. Large delays between transactions can reduce the

throughput of the system considerably if it is not taken into account. Also, since the ocean

bottom instruments are battery-powered, power efficiency is a desirable characteristic for

underwater networks. Special attention should be given to these facts when designing a network

protocol.



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3.2 ARCHITECTURE OF UWSN

Fig 3.1: Architecture for underwater sensor network

Reference architecture for two-dimensional underwater sensor networks is shown in Fig.

3.1, where deployed sensor nodes are anchored to the bottom of the ocean. Underwater sensors

may be organized in a cluster-based architecture, and be interconnected to one or more

underwater gateways (uw-gateways) by means of wireless acoustic links. Uw-gateways are

network devices in charge of relaying data from the ocean bottom network to a surface station.

They are equipped with a long-range vertical transceiver, which is used to relay data to a surface



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station, and with a horizontal transceiver, which is used to communicate with the sensor nodes to

send commands and configuration data, and to collect monitored data. The surface station is

equipped with an acoustic transceiver, which may be able to handle multiple parallel

communications with the uw-gateways, and with a long-range radio transmitter and/or satellite

transmitter, which is needed to communicate with an onshore sink and/or to a surface sink.

3.3 CHALLENGES OF UNDERWATER ACOUSTIC NETWORKS

Battery power is limited and usually batteries cannot be recharged, also because solar

energy cannot be exploited.

The available bandwidth is severely limited.

Channel characteristics, including long and variable propagation delays, multi-path and

fading problems.

High bit error rates.

Underwater sensors are prone to failures because of fouling, corrosion, etc.

3.4 FACTORS INFLUENCING ACOUSTIC COMMUNICATIONS

Underwater acoustic communications are mainly influenced by transmission loss, noise,

multipath, 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 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 of bandwidth. In both cases, these factors lead to low bit

rate, in the order of tens of kbps for existing devices. The main factors influencing underwater

acoustic communications include:

1. PATH LOSS

Attenuation. It 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 and

reverberation (on rough ocean surface and bottom), refraction, and dispersion (due to the



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displacement of the reflection point caused by wind on the surface). Water depth plays a

key role in determining the attenuation.

Geometric Spreading. This refers to the spreading of sound energy as a result of the

expansion of the wave fronts.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

2. NOISE

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).

Ambient Noise. Is related to hydrodynamics (movement of water including tides,

currents, storms, wind, rain,etc.), seismic and biological phenomena.

3. MULTI-PATH

Multi-path propagation may be responsible for severe degradation of the acoustic

communication signal, since it generates Inter-Symbol Interference (ISI). 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, whose

value depend on the water depth.

4. HIGH DELAY AND DELAY VARIANCE

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. The very high delay variance is even more harmful for efficient protocol design,

as it prevents from accurately estimating the round trip time (RTT), key measure for many

common communication protocols.

5. DOPPLER SPREAD

The Doppler frequency spread can be significant in UWA channels, causing degradation in the

performance of digital communications: transmissions at a high data rate cause many adjacent

symbols to interfere at the receiver, requiring sophisticated signal processing to deal with the

generated ISI.



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3.5 APPLICATIONS

Underwater sensor networks are envisioned to enable applications for oceanographic data

collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation

and tactical surveillance applications. Multiple unmanned or autonomous underwater vehicles

(UUVs, AUVs), equipped with underwater sensors, will also find application in exploration of

natural undersea resources and gathering of scientific data in collaborative monitoring missions.

Under Water Acoustic Sensor Networks (UW-ASNs) consist of a variable number of sensors and

vehicles that are deployed to perform collaborative monitoring tasks over a given area. To

achieve this objective, sensors and vehicles self-organize in an autonomous network which can

adapt to the characteristics of the ocean environment. 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 Bayfield

experiment demonstrated the advantages of bringing together sophisticated new

robotic vehicles with advanced ocean models to improve the ability to observe

and predict the characteristics of the oceanic environment.

Environmental monitoring. UW-ASNs can perform pollution monitoring

(chemical, biological and nuclear). For example, it may be possible to detail the

chemical slurry of antibiotics, estrogen-type hormones and insecticides to

monitor streams, rivers, lakes and ocean bays (water quality in situ analysis).

Monitoring of ocean currents and winds, improved weather forecast, detecting

climate change, under-standing and predicting the effect of human activities on

marine ecosystems, biological monitoring such as tracking of fishes or micro-



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organisms, are other possible applications. For example, in the design and

construction of a simple underwater sensor network is described to detect

extreme temperature gradients (thermo clines), which are considered to be a

breeding ground for certain marine micro-organisms.

Undersea explorations. Underwater sensor networks can help detecting

underwater oilfields or reservoirs, determine routes for laying undersea cables,

and assist in exploration for valuable minerals.

Disaster prevention. Sensor networks that measure seismic activity from remote

locations can provide tsunami warnings to coastal areas, or study the effects of

submarine earthquakes (seaquakes).

Assisted navigation. Sensors can be used to identify hazards on the seabed,

locate dangerous rocks or shoals in shallow waters, mooring positions,

submerged wrecks, and to perform bathymetry profiling.

Distributed tactical surveillance. AUVs and fixed underwater sensors can

collaboratively monitor areas for surveillance, reconnaissance, targeting and

intrusion detection systems. For example, in a 3D underwater sensor network is

designed for a tactical surveillance system that is able to detect and classify

submarines, small delivery vehicles (SDVs) and divers based on the sensed data

from mechanical, radiation, magnetic and acoustic micro sensors. With respect to

traditional radar/sonar systems, underwater sensor networks can reach a higher

accuracy, and enable detection and classification of low signature targets by also

combining measures from different types of sensors.

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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3.6 PICTURE OF SENSOR NODES AND VEHICLES

Fig 3.2: Real time UWSN and Vehicles



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Chapter 4

ADAPTIVE ACOUSTIC MODEM

4.1 FEATURES OF ACOUSTIC MODEM

Acoustic modem is a modem which sends and receives digital data as sound energy. The main features of such types of modem are:

• It can pick the most appropriate transmission criteria suitable to the environment

• Segments of the same network may use different communication parameters

• Higher energy savings by transmission management

• Can be implemented in software, reducing overall hardware costs

• Geo-location and communication can be combined in the same hardware

4.2 ADAPTIVE ACOUSTIC MODEM DESIGN

Underwater acoustic modems consist of three fundamental components as shown in Fig.

a transducer, an analog transceiver and a digital hardware platform for signal processing and

control. This seminar focuses on the design of the physical layer on the digital platform.



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Fig 4.1: Block Diagram of Adaptive acoustic modem

In the following details about the major parts of the digital hardware platform are

provided. In each case, a general description of its function is given, discussed the parameters

that can be adapted, and described options that is studied in the experiments.

Controller: The controller orchestrates the digital platform. This includes moving data to

and from the analog transceiver, setting the parameters for the various parts of the digital

hardware, and ultimately interfacing with higher level network stack.

Modulation: There are many different types of signals used for underwater

communication. These include FSK,PSK , OFDM , DSSS . While an adaptive modem can

ideally switch between any modulation scheme, we studied FSK and DSSS in our experiments.

FSK is a fairly simple and widely used modulation scheme in underwater communication due to

its intrinsic robustness to time and frequency spreading. Our receiver uses a non coherent energy

detection demodulation method. In DSSS, symbols are spread in frequency domain by

multiplying with a spreading code. We used a DSSS waveform based on Walsh and m sequence.

Channel Estimation: A major component of an adaptive modem is the ability to change

aspects of the modem including selecting a modulation scheme, the data rate, the transmit power

and other configurable portions of the design. Many of these depend upon current and future

characteristics of the acoustic channel. Therefore channel estimation is an important part of any

adaptive modem. The Doppler shift, channel path gains and SNR are some of the important



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channel state information that must be measured and predicted. Prediction is particularly

important since sound travels slowly (1500 m/s) and some channel characteristics vary on the

order of seconds or faster. Ideally, the receiver will measure the channel characteristics and feed

it back to the transmitter. This requires estimation at least one transmission in advance. Some

work has been done such as a channel prediction scheme is proposed for adaptive modulation in.

Our experiments use a chirp signal for estimating the channel due to its good

autocorrelation properties. Using a chirp signal, the Doppler shift, multipath delay spread and

SNR are computed as follows. Doppler shift is induced by the relative motion between the

transmitter and receiver as well as by the motion of the medium. The Doppler scaling factor is

calculated as ∆ = T rp /T tp − 1, where T tp and T rp are the duration of transmitted and received

data packets respectively. The packet structure designed to calculate ∆ is shown in Fig. 4.2. The

received signal correlates with the original chirp and the time duration between two correlation

peaks, T rp is computed. After that, we calculate Doppler shift:

ḟ = ∆ × f where f is the carrier frequency.

Fig. 4.2: Packet length measurement using Chirp signal

Multipath is a cause of intersymbol interference (ISI), which is a limiting factor for

robust high speed underwater communication. The multipath intensity profile can be calculated

using a form of the Matching Pursuits algorithm. In our experiments, we calculate the multipath

delay spread using the previously discussed chirp signal. The received chirp is correlated with

the original chirp to generate the amplitude delay profile. The RMS of the amplitude delay



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profile is used as a threshold and the delay spread is computed as the time difference between the

path with maximum amplitude and the last path whose amplitude is greater than the RMS value.

Our experiments determine the SNR as the ratio of signal’s variance to that of the ambient noise.

As shown in Fig. 4.2, the time guard after the chirp is used to calculate the noise power.

Symbol Synchronization: It is critical that the receiver correctly determine the

beginning of the incoming data. This is an important part of any digital communication and there

are many techniques for symbol synchronization. We perform synchronization by correlating the

received signal with a known preamble. We studied two different signals for candidate

preambles, namely a Gold code and a chirp signal. We used a Gold Code of length fifteen as the

preamble, and an orthogonal Gold code to estimate the noise variance. Based on the noise

variance a dynamic threshold was generated. The start of the packet was determined to be when

the received signal has a maximum correlation with the known preamble and exceeds the noise

threshold.

A chirp signal is tested as the preamble for synchronization due to its autocorrelation

properties. We can also use the same chirp for synchronization and channel estimation to

minimize the amount of data transmitted. As shown in Fig. 2, a correlation peak is detected at

point A, and after a time guard of length T, the receiver starts to demodulate at point B which is

the expected start of the data sequence.

Equalization: Long multipath delay spreads in the underwater channel make channel

equalization significantly more difficult than radio channels and thus play an important role in

the modem design. A significant number of equalization techniques have been proposed. These

are not a direct focus of this seminar, but must be mentioned in the design of a modem due to

their importance in achieving low BER.



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4.3 PICTURE OF ACOUSTIC MODEMS:



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Fig 4.3: Modern Acoustic Modems

Chapter 5

SIMULATION AND SEA TEST RESULTS

To evaluate the proposed adaptive modem, both simulations and sea tests were studied. A

set of simulations were analyzed to find the best data rates for different links in a network and to

understand the potential benefits of modifying the data rates on a per link basis. Also sea tests

were performed to evaluate the performance of the major components of the proposed adaptive

modem in a real environment. The parameters for the chirp, FSK and DSSS modulation

schemes used in the simulations and sea tests are given in Table 5.1.



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Table 5.1: Chirp, FSK and DSSS SIGNAL PARAMETERS

5.1 SIMULATION RESULTS

The simulations were performed using the Actup underwater acoustic propagation

modeling software. It generates the amplitude and delay profile of the received signal. The Actup

simulations require that we set environmental parameters such as the communication range,

water depth and the sound speed profile, and specify the location of the transmitters and

receivers. The underwater acoustic channel for five different transmitter and receiver which were

placed at different locations in the water column pairs was generated as shown in Fig. 3. The

amplitude and delay profiles for links 1 and 5 are shown in Fig. 4. We observe that the two links

have fairly different profiles which support the fact that nodes in the underwater sensor network

are likely to experience different channels.



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Fig 5.1: Underwater environment and the sensor nodes in Actup simulation

Fig 5.2: Multipath amplitude and delay profile of link 1 and link 5

For this network scenario, best data rate for each link in simulations using the channels

generated in Actup were found. The simulations were performed for the FSK and DSSS

modulation schemes. For each modulation scheme, the BER was computed over a range of data

rates by demodulating 10 packets each containing 102 bits. The maximum data rate whose BER



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was smaller than 10 −2 was considered as the best rate for any specific link. The simulation

parameters for the described network scenario for each link and the corresponding best data rate

are given in Table 5.2.

Table 5.2: Best data rates for different Channel Links

The results show that the best data rate varies for different links in a network; it changes

from 40 bps to 400 bps for FSK and from 600 bps to 1900 bps for DSSS. Our results also show

that rate adaptation can save considerable energy. For example in the case of FSK modulated

transmissions, if we consider the best fixed data rate that allows reliable communication under

the worst channel condition, all the links must communicate at 40 bps. The total energy

consumed for each link transmitting one symbol is 0.125 ∗ P t, where P t is the transmitting

power. Alternatively if the nodes perform rate adaptation and communicate at their best data rate,

the total energy consumed is 0.0458 ∗ P t. Therefore, rate adaptation gives an energy saving of

63.4%. In the same way, an energy saving of 45.8% is possible for DSSS. Moreover, faster data

rates decrease the probability of collisions between different links, which in turn causes fewer

retransmissions and therefore costs less power. These results motivate the idea of an adaptive

modem for underwater sensor networks.

5.2 SEA TESTS

To evaluate the performance of the major components of our adaptive modem

experiments were performed in the Pacific Ocean in May 2011. The deployment setup is shown



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in Fig. 5.3. The transmitter was located at UC San Diego Scripps Pier, 20 feet below the water

surface and the receiver was attached to the bottom hull of a boat residing at different locations.

Data was collected at two sites at distances of 265m and 638m from the transmitter. For each

site, FSK and DSSS data was transmitted at six different data rates. At each data rate, 20 packets

containing 2040 symbols were transmitted.

Fig 5.3: Sea test setup (the red line is the route of the boat)

The results showed that the chirp signal had a better synchronization performance

compared to the Gold code. While the chirp was able to successfully synchronize to the start of

the data sequence for all data packets, the Gold code could only successfully synchronize 68% of

the packets at Site 1 and 70% of the packets at Site 2. This indicates that the chirp is a good

candidate for channel estimation as well as symbol synchronization. The BER for FSK and

DSSS modulated data when the chirp signal was used for packet synchronization were

computed. The results are summarized in Table 5.3. We observe that the BER increases with

data rate likely due to intersymbol interference. Further, the results show that DSSS modulation

outperforms FSK. Finally, we observe that the average BER at Site 2 is higher than that at Site 1.



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To explain this, we estimated the channel for both sites as shown in Fig. 6. The figure shows that

the Doppler shift and the multipath delay spread are both higher for Site 2 compared to Site 1.

These sea test results correspond with our Actup simulation results in the sense that BER varies

significantly with the data rates and channel between the sender and receiver.

Table 5.3: Sea Test BER for Site1/site2

Fig 5.4: Doppler shift and multipath delay estimate for sea test.

Chapter 6

CONCLUSION



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This seminar makes a case for an adaptive acoustic modem in underwater sensor

networks. The seminar describes potential benefits of the adaptive modem and general digital

hardware platform architecture. A set of experiments and sea tests that quantify the benefit of

different modulations, types of channel estimation and symbol synchronization are been

discussed. The results show that rate adaptation can lead to substantial energy savings while

ensuring reliable communication. Furthermore, they show that a chirp signal is a good candidate

for symbol synchronization and channel estimation. The simulations and sea tests indicate that

DSSS modulation consistently outperforms FSK. Currently the design of the major components

and the system hardware and software co-design implementation can be done on an FPGA

platform. Eventually the proposed adaptive modem will be able to change its modulation scheme

and data rate automatically according to channel conditions in real-time.

REFERENCES



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[1] “Designing an Adaptive Acoustic Modem for Underwater Sensor Networks”

Lingjuan Wu, Jennifer Trezzo, Diba Mirza, Paul Roberts, Jules Jaffe, Yangyuan Wang,

Fellow IEEE,and Ryan Kastner, Member IEEE.

[2] E. M. Sozer, M. Stojanovic and J. G. Proakis, “Underwater acousticnetworks,” IEEE

J. Oceanic Eng., vol.25, no.1, Jan. 2000, pp.72-83.

[3] I. F. Akyildiz, D. Pompili and T. Melodia, “Challenges for efficient communication in

underwater acoustic sensor networks,” ACM Sigbed Review, Vol.1, No.2, Jul. 2004,

pp.3-8.

[4] J. Jaffe and C. Schurgers, “Sensor networks of freely drifting autonomous underwater

explorers,” in Proc.Wuwnet’06, LA, CA, Sep.2006, pp.93-96.

[5] J. Rice, B. Creber, C. Fletcher, P. Baxley et al., “Evolution of seaweb underwater

acoustic networking,” in Proc. Oceans 2000, pp.2007-2017.

[6] Y. Tao, P. Zhu and X. Xu,“Dual-mode modulation based research of underwater

acoustic modem,” IEEE Int. Conf, WiCOM 2010, pp.1-3.


seminar report akhil 25 10 2013.docx

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