network computing for distributed underwater acoustic …following this approach, we have developed...

Network Computing for Distributed Underwater Acoustic Sensors Fiscal year 2014-2015

Prepared by: M. Barbeau

E. Kranakis, Project Manager Carleton University Ottawa, ON, Canada

Contract Report # AMBUSH.2.3 PWGSC Contract Number: W7707-145688

Contract Scientific Authority: Stephane Blouin, Defence Scientist

The scientif ic or technical validity of this Contract Report is entirely the responsibility of the Contractor and the contents do not necessarily have the approval or endorsement of Department of National Defence of Canada.

Contract Report

DRDC-RDDC-2015-C221

March 2015

c⃝ Carleton University 2015.c⃝ Université Carleton 2015.

Table of contents

Table of contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

List of figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Research Themes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Research Team . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.3 Report Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2 Physical Layer Simulation and Analysis . . . . . . . . . . . . . . . . . . . . . . 1

2.1 Attenuation, Colored Noise and Mobility Simulation . . . . . . . . . . . . 2

2.2 Multipath Propagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2.2.1 Brute Force Approach . . . . . . . . . . . . . . . . . . . . . . . 3

2.2.2 Quest for Optimization . . . . . . . . . . . . . . . . . . . . . . . 3

2.3 Sea Trial 2014 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . 5

3 Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3.1 Location-Free Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.2 Refracted Acoustic Communications . . . . . . . . . . . . . . . . . . . . 8

4 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4.1 Physical layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4.2 Underwater routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

DRDC Atlantic CR XXX i

List of figures

Figure 1: Left side, theoretical performance of PSK. Right side, BPSK WhiteNoise (WN) versus Colored Noise (CN), in the baseline approachcase. The ratio Eb/N0 stands for energy per bit to noise power spectraldensity ratio, also known as Signal-to-Noise ratio per bit. . . . . . . . . 11

Figure 2: Sea trial representative point-to-point link performance. . . . . . . . . . 12

Figure 3: Eigenrays as a function of vibrator and hydrophone depths. . . . . . . . 13

Figure 4: BER versus SNR and message size. Significant correlation isstatistically confirmed. . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Figure 5: Distribution of bit errors as a function of the bit position in a packet. . . 15

Figure 6: Graphical user interface of a Java simulation of a refractedcommunications network. . . . . . . . . . . . . . . . . . . . . . . . . . 16

ii DRDC Atlantic CR XXX

1 Introduction1.1 Research ThemesUnderwater Acoustic Sensor Networks (UASNs) are promising technology for numerousapplications including monitoring of the undersea environment for pollution reduction andcontrol [1].

This report gives an outline of the research conducted on UASNs at Carleton Universityduring the time interval April 2014 to March 2015. The two main research themes arephysical layer simulation and data analysis and routing.

1.2 Research TeamGraduate students involved in the research are Bita Hasannezhad (Masters of ComputerScience), Jie Huang (Masters of Computer Science) and Inam Haq (Ph.D. in ComputerScience). Our collaborators are Stéphane Blouin (DRDC Atlantic Research Centre), GimerCervera (Universidad Tecnológica Metropolitana), Joaquin Garcia Alfaro (Telecom Sud-Paris), Craig Hamm (Maritime Way Scientific Ltd.) and Martin Taillefer (Maritime WayScientific Ltd.).

1.3 Report OutlineSection 2 reviews our work on physical layer simulation and data analysis. Section 3 de-scribes our research on routing. Section 4 discusses future work.

2 Physical Layer Simulation and Analysis

Our simulation work addresses to following aspects of underwater communications:

1. attenuation,

2. colored noise,

3. mobility and

4. multipath propagation.

We have also undertaken the analysis of data resulting from a sea trial, provided by theDRDC Atlantic Research Centre.

DRDC Atlantic CR XXX 1

2.1 Attenuation, Colored Noise and MobilitySimulation

Attenuation, noise and mobility are important facets of UASN communications. UASNsuse acoustic signals to communicate. On the one hand, attenuation and noise impairs theability to communicate with acoustic signals. Attenuation is frequency and propagationdistance dependent. In the underwater environment, noise is colored, which means thatits density depends on the frequency of the acoustic signals. In contrast, the density ofwhite noise, present in electromagnetic-based communications, is not frequency depen-dent. On the other hand, mobility is relevant because there are underwater vehicles andenvironmental conditions causing displacements of network nodes. Because of mobility,transmitter-receiver separation distances are variable.

We have developed a software emulation solution leveraging the work of Borrowski [2].Two tools are used in co-simulation: MATLAB [3] and OMNeT++ [4]. MATLAB dealsparticularly well with signal processing issues, while OMNeT++ can better address net-work protocol problems. The physical layer is modeled using MATLAB functions. Theyare compiled into a dynamic library. They become available in the OMNeT++ environment.Previous works in the related literature heads in the same direction, e.g., Borowski [2],Zhang et al. [5]. Our physical layer model takes into account attenuation and colored noise.We model their effect on a Phase-Shift Keying (PSK) signal as a function of frequency anddistance. Mobility is simulated using the meandering current model of Caruso et al. [6].This work has been accepted for publication [7].

2.2 Multipath PropagationWe studied the simulation of underwater communications using acoustic data signals im-paired by multipath propagation. Multipath propagation of underwater acoustic waves –the phenomenon in underwater acoustic communications whereby signals are received bymore than one path – is one of the greatest sources of communication errors. Multipathpropagation is influenced by reflection, at sea surface and seabed, and refraction due toconditions such as water temperature, salinity, current, depth, surface waves, partial icecover and seabed material.

The specific problem addressed in our research is the evaluation, through simulation, ofcommunications using underwater acoustic data signals impaired by multipath propaga-tion, together with attenuation and colored noise. The signals are called wide band becausetheir bandwidth is relatively large compared to their center frequency [8].

2 DRDC Atlantic CR XXX

2.2.1 Brute Force Approach

Initially, simulation of multipath propagation, together with attenuation and colored noise,was approached by i) translating the acoustic signals from the time domain to the frequencydomain, using a Fast Fourier Transform (FFT), ii) applying attenuation on frequency com-ponents of the acoustic signal and iii) translating the frequency components back into thetime domain. This procedure is repeated for each copy of a signal generated due to mul-tipath propagation. The approach is brute force, because of the FFT computational costwhich is in O(n logn), where n is the number of FFT points.

Following this approach, we have developed a simulator using the MATLAB software andBELLHOP ray tracing program [9, 10]. MATLAB and BELLHOP complement each other.MATLAB facilitates the implementation of the signal processing related features, such asmodulation and demodulation [3]. BELLHOP is a software tool that addresses underwatermultipath propagation of acoustic signals, taking into account environmental parameterssuch as bathymetry, sound speed profile, seabed type and sea surface type [9]. The commu-nication model is based on work described in Section 2.1. We simulate attenuation takinginto account the source-receiver separation distance, sound speed profile and signal fre-quency. We simulate multipath propagation affected by reflection and refraction due to theenvironment boundaries, sea surface and seabed, and sound speed profile. Colored noiseis added to the signal. For this part of the work, we use a BELLHOP-MATLAB wrapperdeveloped by Maritime Way Scientific. This work has been accepted for publication [11].

2.2.2 Quest for Optimization

In a second phase of this research, several alternatives were compared to simulate an un-derwater acoustic communication channel, taking into account attenuation, colored noise,mobility and multipath propagation. Attenuation can be simulated in a coherent or an in-coherent manner, while noise can be white or colored. The phenomena can be appliedto a signal in the time domain or frequency domain. How sensitive is the Bit Error Rate(BER) of a data acoustic signal with respect to each of these simulation choices? How doessimulation performance compare to analytic performance? These are the questions we aretrying to answer. As an analytic reference, we use a binary PSK (BPSK) signal and its errorfunction, which takes into account additive white Gaussian noise.

Incoherent and Coherent Attenuation

Incoherent attenuation models attenuation on all signal propagation paths, but phase effectsare abstracted. Coherent attenuation models attenuation and phase effects on all signalpropagation paths. We studied the simulation of incoherent and coherent attenuation, alongwith white noise and colored noise. For the purposes of the comparison of the alternatives,the BER is analyzed in each case.


Our computation of attenuation relies on information about signal arrival time, transmis-sion loss and arrival acoustic pressure. They are all calculated by BELLHOP. BELLHOPcalculates three types of transmission loss: incoherent, coherent and semi-coherent. Theincoherent calculation accumulates the losses of all paths, without taking into account thephase differences resulting from the fact that signal components are following differentpaths. Coherent means that the transmission loss calculation considers the multipath in-terference and computes the phase of each propagation path. The semi-coherent methoddoes not calculate the phase effects, but takes the surface interference into account (usingLloyd’s mirror pattern). BELLHOP expresses transmission loss as an acoustic intensitylevel (µPa ·m/s), which we convert into an attenuation ratio.

Time Domain Versus Frequency Domain Calculation

Time domain and frequency domain approaches were investigated with respect to the cal-culation of attenuation. In terms of time domain, two methods were investigated, includinga classical method that we call baseline and a method applying attenuation in the timedomain for each frequency component of a signal. In the baseline method, the transmit-ted signal and a multipath fading model are convoluted together [12]. In this calculation,attenuation and path delays are obtained using BELLHOP, only for the signal center fre-quency.This runs very quickly, but variations of attenuation as a function of frequency areabstracted. It results into a certain inaccuracy. Therefore, we investigated the calculation ofattenuation and path delays, using BELLHOP, for each frequency component in a signal,obtained with a FFT.

In the frequency domain approach, the transmission loss is calculated for the frequency ofeach component of a signal. The calculated transmission loss is then applied to the com-ponent. To transform a signal from the time domain to the frequency domain, a FFT isused. After the FFT, a band-pass filter is used to delete all the side lobes (correspondingto processing noise), such noise does not exist in a real modulator. The frequency domainprocedure consists of the following steps: compute the attenuation at each frequency com-ponent, get the amplitude of each frequency component, apply the attenuation correspond-ing to the frequency component to the amplitude to get the new amplitude after attenuationand transform back to the time domain.

Main Conclusions

Simulations results are consistent with the degree of sophistication of the models. In prin-ciple, simulation results should never be better than the analytic model. The analytic modelsolely takes into account white noise. With all conditions being equal, simpler simula-tion models result into better BER, closer to the analytic model. The baseline approachyields the lowest BER of all. Application of attenuation in the time domain for each fre-quency component of a signal produces results close to the frequency domain incoherent


approaches. The worst BERs are obtained with the frequency domain coherent approaches.Among all the options we have explored, we believe that it is the one that most closelyreflects the real situation. Figure 1 illustrates the kind of conclusions obtained with this re-search. In Part (a), we have the theoretical performance for four versions of PSK, includingBPSK. In Part (b), we have the performance obtained with our simulation. It shows theperformance of BPSK with White Noise (WN) and Colored Noise (CN), in the baselineapproach. The simulated white noise performance is close to the theoretical performance.On the other hand, the simulated colored noise performance is significantly worst than thesimulated white noise performance. It means that colored noise is a significant impairmentand is worth simulating. The entire work is described in detail in a research report [13].

2.3 Sea Trial 2014 Data AnalysisA sea trial was performed by the DRDC Atlantic Research Centre [14] using TeledyneBenthos Acoustic Modems [15, 16]. Four nodes were installed in the Bedford Basin (N.S.,Canada). We have examined the data resulting from the sea trial with respect to the follow-ing aspects:

1. BELLHOP simulation of the sea trial environment (taking into account modem depths,modem separation distances, sound speed profile and bathymetry),

2. BER versus Signal to Noise Ratio (SNR) and packet size,

3. BER versus energy per bit to noise power spectral density ratio (Eb/N0)and packetsize,

4. BER versus Automatic Gain Control (AGC) and packet size,

5. packet size and BER correlation,

6. link reciprocity and

7. BER versus bit position in a packet.

Figure 2 is representative of the environmental conditions and results of the sea trial. Part(a) shows a sound speed profile present during the sea trial. It corresponds to actual datacollected by the DRDC Atlantic Research Centre during the sea trial. The sound profile ishighly downward refracting for the first 30 meters of water depth. Form 30 meters untilthe seabed, it is quasi iso-speed. Part (b) shows the eigenrays obtained with a BELLHOPsimulation. Seabed is shown in brown. The vibrator is on the left side, at a depth of fivemeters. The hydrophone is placed right, also at a depth of five meters. They are separatedby a distance of 1.6 kilometer. The blue curves represent the sound propagation paths, i.e.,the eigenrays. The are subject to refraction and reflection, bouncing once or twice on theseabed, no reflection at sea surface. The length of the paths and seabed reflection actually


result in considerable attenuation. Bottom loss is know to be a major issue in shallowwater, with may be a five dB loss and more per seabed hit [17]. The resulting energy perbit to noise power spectral density ratio and signal to noise ratio are actually relatively low,resulting in high BERs. Part (c) shows the corresponding impulse response. Each verticalstem (in blue) represents the acoustic pressure loss through a path represented by one of thefour eigenrays.1 The impulse response diagram shows that the received signal spreads overan interval of more than one and a half millisecond, which is major delay spreading. Part(d) shows BERs: the theoretical (for comparison purposes), and the sea trial ones obtainedat 140, 800 and 2400 bps. The BERs obtained during the sea trial were on the high side.

We believe that a communication environment, dominated by refracted paths, partly ex-plains the poor performance. Figure 3 shows a key issue uncovered by a BELLHOP sim-ulation of the environmental conditions of the sea trial. Sub-figure (a), corresponds to theconditions of the sea trial, as in Figure 2. We have simulated increasing progressively thedepths of the vibrator and hydrophone. In sub-figures (b), (c), (d), (e) and (f), the depthprogressively goes from 10, 15, 20, 30 to 40 meters. Interestingly, from 30 meters quasi di-rect paths start to exist, shown in red. Our hypothesis is that signals traveling through thesepaths would be much stronger with better energy per bit to noise power spectral densityratio and signal to noise ratio, i.e., the performance results would be better.

The sea trial included two packet sizes: 100 bytes and 1595 bytes. Figure 4 shows the BERversus SNR and message size. Significant correlation between the BER and message sizehas been statistically confirmed, with p < 0.05.2

Figure 5 shows the distribution of bit errors as a function of the bit position in a packet. Thebit error remarkably surges towards the end of packets. Most of the times, errors happenin the last 280 bits of a packet. We have not been able to explain the exact reason for highconcentration of errors in packet tails. Hence, the content of shorter packets (100 bytes)are proportionally more affected by errors than longer packets (1595 bytes). Hence, shorterpackets have higher BERs.

3 Routing

Our work on routing includes location-free routing and refracted acoustic communicationnetworks.

1The pressure loss is on a dB scale. It is equal to 20log10 A, where A is the actual transmission loss.2With null true correlation, the p-value is the probability of randomly obtaining a correlation as large as

the observed value. With a p less than 0.05, the correlation is significant.


3.1 Location-Free RoutingUASNs consist of devices enabled with acoustic communication capabilities that are de-ployed underwater at different depths to perform collaborative monitoring tasks [18]. Un-derwater nodes gather and send information to a sink that is also equipped with a radio tocommunicate with other network components located on the surface. We present a location-free routing protocol designed for UASNs. Research in UASNs focuses on both physicaland link layers [19, 20], whereas research on the network layer is still in an early stage. Thedesign of an efficient routing mechanism should consider the limitations of the medium.The underwater acoustic channel is characterized by a high BER, low data rate and largepropagation delay. Underwater routing protocols must be energy-aware since the deploy-ment and maintenance of underwater devices are particularly difficult. UASNs are formedby nodes in constant motion that leads to continuous changes in the network topology.

Electromagnetic and optical waves do not work well underwater due to the nature of themedium, especially in the case of seawater. Acoustic waves are used for underwater com-munication due to the relatively low attenuation (i.e., signal reduction) of sound in water,specially in thermally stable, deep water settings. In shallow water, acoustic waves areseverely affected by temperature, site specific noise and multipath propagation due to re-flection and refraction. The speed of sound in water varies according to the depth and isaffected by temperature, salinity and pressure. The speed of acoustic waves is about 1500m/s [8] close to the ocean surface, which is more than four times faster the speed of soundin air, but five orders of magnitude smaller than the speed of light [21]. Compared withelectromagnetic and optical waves in terrestrial networks, the speed of acoustic waves issignificantly lower. As a consequence, underwater channel communication is also affectedby a severe Doppler effect.

The characteristics of the underwater acoustic communication channel require new effi-cient and reliable data communication protocols. Underwater communication is character-ized by a path loss (i.e., path attenuation) that depends not only on the distance between thetransmitter and receiver, but also on the signal frequency [19]. Low frequency waves (e.g.,below 10 kHz) are effective for long-range communication [21]. Bandwidth for underwatercommunication is typically in the order of kHz. Routing protocols for ad hoc networks arenot suitable for underwater communication. Proactive routing protocols require a constantexchange of control information to keep the routing information up to date. In reactive ap-proaches, the route discovery process is affected by an increased delay.

Several routing protocols in UASNs are based on a greedy hop-by-hop method for packetdelivery [22]. Unlike the end-to-end routing, greedy hop-by-hop routing approaches selectas next hop those one-hop neighbors that have positive progress toward the sink. Greedy


routing protocols do not guarantee to find a path toward the sink, e.g., data packets reacha node with no positive progress. This problem is known as communication void. Routingprotocols for UASsN can be classified as location-based or location-free. Location-basedapproaches assume that nodes know both their geographical position and the sink position.As a main drawback, finding the location information of nodes is a main challenge dueto the inapplicability of GPS under the water. Location-free approaches can be divided inthe pressure-based and beacon-based categories. In pressure-based routing protocols, thedepth information (i.e., pressure) is used to identify the positive forwarding area. Beacon-based approaches implement beacon messages with information to reach the sink, e.g.,distance in hops. Greedy routing protocols in UASNs do not consider the quality of thelinks. In our location-free link state routing approach, every node ranks the quality of thepath that it offers toward a sink. Packet forwarding is performed hop-by-hop. Every nodeselects a one-hop neighbor with the lowest hop count toward a sink. If there is a tie, thenthe one-hop neighbor with the best quality path toward a sink is selected. If the tie persists,then the neighbor with lowest pressure is selected. Our strategy is loop-free. It includes arecovery mode handling network topology changes. Our location-free routing protocol wasimplemented in ns-3. The UAN model developed by Tracy was adapted to interact with ourrouting protocol. The work has been accepted for publication [23].

3.2 Refracted Acoustic CommunicationsRouting is a fundamental network function. For battery-powered UASNs using acousticwaves, routing is challenging due to an ever changing and communication-opaque medium.We proposed a shallow-water routing algorithm that is adapted to a unique physical phe-nomena of the medium. The proposed algorithm exploits downward refraction for UASNscomprising mobile nodes. A solution for point-to-point links has been developed. Then,the concept has been extended to network routing. The necessary and sufficient conditionsfor the existence of a point-to-point link through acoustic refraction have been derived. Theideas have been simulated in MATLAB. The work has been published [24].

We have developed also a Java simulation of a UASN with refracted communications, seeFigure 6. The simulation calculates the existing point-to-point links in a multihop network.The positions of the nodes can be moved to simulate mobility. Point-to-point links arere-calculated when nodes move. The simulation comprises a routing protocol. Between asource and a destination, the total transmission loss of every possible path is calculated. Thebest route is the lowest transmission loss path. The routes are updated when the networktopology changes.


4 Future Work

Our plan is to carry on with our research on physical layer simulation and data analysis androuting.

4.1 Physical layerWith respect to the physical layer, the following ideas may be considered in the future:

1. Separation of path and coherent transmission loss calculation: BELLHOP calcu-lates together multiple propagation paths and coherent transmission loss. However,the multiple propagation paths are frequency independent, which means that if the vi-brator and hydrophone are fixed, paths are invariant. If solely the frequency changes,paths do not have to be re-calculated. On the other hand, coherent transmission lossis frequency dependent, in addition to be path dependent. Logically, these two calcu-lations should be separate. Propagation paths could be traced only once and passedas parameters to a coherent loss calculation function. In many instances, simulationtime will be saved because of the elimination of redundant calculations.

2. Database of transmission loss values: The attenuation for known distances, frequen-cies and environmental conditions can be saved in a database and reused across thetransmission of several packets. Again, to avoid redundant calculations and speed upsimulation time.

3. Use incoherent transmission loss to calculate coherent transmission loss:Probabilistic method: Duncan et al. proposed an algorithm exploiting incoher-ent transmission loss to calculate coherent transmission loss [25]. It is based onthe probability distribution of the acoustic multipath interference. The principleis that if sound arrives at a hydrophone through a sufficient number of differentpaths with random phase relationships, then the combined signal componentsare Gaussian distributed, their amplitudes fluctuate according to a Rayleighdistribution and signal levels (in decibels) are log-Rayleigh distributed. Exper-iments were done using BELLHOP. The amplitude of the coherent acousticpressure is normalized, dividing by the incoherent pressure. Then converting todecibels to give normalized coherent received levels. Histograms of normalizedcoherent received level for a 100 meter range are compared to the expected log-Rayleigh distribution. The fit is good. We can use the Probability Density Func-tion (PDF) provided in this paper. Incoherent transmission loss can be quicklycalculated using linear fitting methods. Coherent transmission loss is inferredusing the PDF and incoherent transmission loss. The parameters in the proba-bilistic model may need to be determined according to the environment.


(a) S-transform: The S-transform time–frequency distribution was developed in1994. Brown and Frayne presented a fast S-transform algorithm [26]. Theyreduced the computational time and resources by at least four orders of magni-tude, with respect to the original work. An implementation is available to the re-search community under an open source license. Schimmel and Gallart use theS-transform to perform a coherence measure of seismic signals enhancementthrough incoherent noise attenuation [27]. It might be used also in underwateracoustic coherent simulation.

4. Doppler effect: If a vibrator or any reflecting boundary has a velocity relative to ahydrophone, then a Doppler effect is present in signals. A multipath channel has timevariations, so a time-variant model and Doppler effect should be taken into account.

5. OFDM modulation/demodulation: Orthogonal Frequency Division Multiplexing (OFDM),based on multicarrier modulation, is proven to be robust and suited to the underwaterenvironment. It offers low complexity design of receivers that can deal with highlydispersive channels.

6. Parallel computing: Using parallel computing, complex calculations can be dividedinto small parts and carried out simultaneously. Simulation running time can begreatly reduced.

7. Data analysis: We are interested to analyze data obtained from future sea trials. Wewish to investigate the fit of data obtained through our simulation models and dataobtained through sea trials. The goal is to determine how they match and identifyareas were the models could be perfected.

4.2 Underwater routingWe wish to extend the work on refracted acoustic communication networks. With the actualmodel, handling only first-order downward refraction, the existence of several point-to-point links may not be detected. With a general refraction-reflection model, à la BELLHOP,we would be able to discover more point-to-point links and more paths between sourcesand destinations. We wish to extend the approach to a general refraction-reflection model.In our past research, fixed vibrator beamwidth and directionality are assumed. We wish toextend the routing algorithms with the assumption that beamwidth and directionality candynamically adapt to the conditions of the medium. Performance metrics such as receivedsignal strength and link quality could be used to guide this adaptation.


(a) (b)

(c) (d)

(e) (f)

Figure 3: Eigenrays as a function of vibrator and hydrophone depths.


Figure 6: Graphical user interface of a Java simulation of a refracted communicationsnetwork.


References

[1] Sevaldsen, E. (1994), Acoustics in underwater environmental monitoring, InProceedings of Oceans Engineering for Today’s Technology and Tomorrow’sPreservation (OCEANS), Vol. 1, pp. I/464–I/468.

[2] Borowski, B. (2010), Application of Channel Estimation to Underwater AcousticCommunicaton, Ph.D. thesis, Stevens Institute of Technology, Castle Point onHudson, Hoboken NJ.

[3] MathWorks (2015), MATLAB - The Language of Technical Computing.

[4] Heijmans, J., Paalvast, A., and Van-der Leij, R. (Last Access: 2014), OMNeT++Discrete Event Simulation System — User Manual. Available atwww.ewh.ieee.org/soc/es/Nov1999/18/manual/usman.htm.

[5] Zhang, Z., Lu, Z., Chen, Q., Yan, X., and Zheng, L.-R. (2010), COSMO:CO-simulation with MATLAB and OMNeT++ for indoor wireless networks, InIEEE Global Telecommunications Conference (GLOBECOM), pp. 1–6.

[6] Caruso, A., Paparella, F., Vieira, L., Erol, M., and Gerla, M. (2008), The MeanderingCurrent Mobility Model and its Impact on Underwater Mobile Sensor Networks, InThe 27th IEEE Conference on Computer Communications (INFOCOM),pp. 771–779.

[7] Barbeau, M., Blouin, S., Cervera, G., Garcia-Alfaro, J., Hasannezhad, B., andKranakis, E. (2015), Simulation of Underwater Communications with a ColoredNoise Approximation and Mobility, In 28th annual IEEE Canadian Conference onElectrical and Computer Engineering (CCECE), Halifax, Nova Scotia, Canada.

[8] Stojanovic, M. and Preisig, J. (2009), Underwater acoustic communication channels:Propagation models and statistical characterization, IEEE CommunicationsMagazine,, 47(1), 84–89.

[9] Rodriguez, O. (2008), General description of the BELLHOP ray tracing program.

[10] Porter, M. (Access: April 2015), The KRAKEN Normal Mode Program (DRAFT).Available at oalib.hlsresearch.com/Modes/AcousticsToolbox/manual

[11] Huang, J., Barbeau, M., Blouin, S., Hamm, C., and Taillefer, M. (2015), Simulationof Communications Based on Underwater Acoustic Signals Impaired by Wide BandAttenuation, In 28th annual IEEE Canadian Conference on Electrical and ComputerEngineering (CCECE), Halifax, Nova Scotia, Canada.

[12] Proakis, J. and Salehi, M. (2008), Digital Communications, Fifth ed, New York, NY,U.S.A.: McGraw-Hill.


[13] Huang, J. (2015), Modeling and Analysis of Underwater Acoustic Sensor networks.Draft Research Report.

[14] Blouin, S. (2014), SR Acoustic Modem Parameter - Data Analysis. Draft reportDRDC-RDDC-2014-RXXX.

[15] Teledyne Benthos (2014), Benthos Acoustic Modems. teledynebenthos.com.

[16] Teledyne Benthos (2014), ATM-900 Series Acoustic Telemetry Modems User’sManual P/N M-270-26, Rev. E. Teledyne Benthos, 49 Edgerton Drive, NorthFalmouth, MA 02556 U.S.A.

[17] Modeling, U. A. and Simulation (2003), Etter, P.C., Third ed, London and NewYork: Spon Press, Taylor and Francis Group.

[18] Pompili, D., Melodia, T., and Akyildiz, I. (2010), Distributed Routing Algorithmsfor Underwater Acoustic Sensor Networks., IEEE Transactions on WirelessCommunications, 9(9), 2934–2944.

[19] Stojanovic, M. (2007), On the Relationship Between Capacity and Distance in anUnderwater Acoustic Communication Channel, SIGMOBILE Mob. Comput.Commun. Rev., 11(4), 34–43.

[20] Pompili, D. and Akyildiz, I. (2010), A multimedia cross-layer protocol forunderwater acoustic sensor networks, Wireless Communications, IEEE Transactionson, 9(9), 2924–2933.

[21] Lanbo, L., Shengli, Z., and Jun-Hong, C. (2008), Prospects and Problems ofWireless Communication for Underwater Sensor Networks, Wirel. Commun. Mob.Comput., 8(8), 977–994.

[22] Kheirabadi, M. and Mohamad, M. (2013), Greedy routing in underwater acousticsensor networks: A survey, International Journal of Distributed Sensor Networks,vol. 2013, 2013(2013), 21.

[23] Barbeau, M., Blouin, S., Cervera, G., Garcia-Alfaro, J., and Kranakis, E. (2015),Location-free Link State Routing for Underwater Acoustic Sensor Networks, In 28thannual IEEE Canadian Conference on Electrical and Computer Engineering(CCECE), Halifax, Nova Scotia, Canada.

[24] Blouin, S., Barbeau, M., and Kranakis, E. (2015), Refracted AcousticCommunications in Wireless Underwater Sensor Networks with Mobility, InProceedings of SENSORNETS, Angiers, France.

[25] Duncan, A., Parnum, I., and Henley, P. (2013), Efficient modelling of mid to highfrequency underwater acoustic propagation, In Proceedings of Acoustics 2013.


[26] Brown, R. and Frayne, R. (2008), A fast discrete S-transform for biomedical signalprocessing, In Engineering in Medicine and Biology Society, 2008. EMBS 2008.30th Annual International Conference of the IEEE, pp. 2586–2589.

[27] Schimmel, M. and Gallart, J. (2007), Frequency-dependent phase coherence fornoise suppression in seismic array data, Journal of Geophysical Research: SolidEarth, 112(B4), n/a–n/a. B04303.


network computing for distributed underwater acoustic …following this approach, we have developed...

Documents