Future Generation Computer Systems Adaptive RTO

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Adaptive RTO for handshaking-based MAC protocols in underwater acoustic networks✩ YanKun Chen a , Fei Ji a , Quansheng Guan a, *, Yide Wang b , Fangjiong Chen a , Hua Yu a School of Electronics and Information Engineering, South China University of Technology, Guangzhou, China University of Nantes, France

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Article history: Received 28 February 2017 Received in revised form 19 July 2017 Accepted 14 August 2017 Available online xxxx Keywords: Underwater acoustic networks Medium access control RTT prediction Bayesian forecasting Dynamic linear model

a b s t r a c t Underwater acoustic networks (UANs) are attracting interest in recent decades. The unique characteristics of the underwater acoustic channel, such as long propagation delay, delay variance, and high bit error rate, present challenges for the medium access control (MAC) protocol design in UANs. Most existing medium access control protocols ignore the delay variance which prevents the accurate estimation of round trip time (RTT). The expected RTT value can be used to compute the Retransmission Time-Out (RTO) or the waiting time in MAC. The estimation of RTT is also meaningful for Automatic Repeat re-Quest (ARQ) scheme because the system should ensure reliable data transmissions in the presence of high bit error rate in the underwater acoustic channel. By analyzing the impact of RTO on throughput under the effect of delay variance, we conclude that the fixed RTO is inefficient and RTO should be adaptively set to improve the throughput. We present a novel approach of predicting the RTT using a Bayesian dynamic linear model, and then adjust RTO adaptively according to the predicted values. Simulation results show that the predicted values can adapt quickly to the sample RTT values. Under the effect of RTT fluctuations, the Bayesian algorithm offers performance gains in terms of throughput and prediction performance, comparing with Karn’s algorithm. Our study highlights the value of predicting the RTT using Bayesian approach in underwater acoustic networks. © 2017 Elsevier B.V. All rights reserved.

1. Introduction In the last decades, the underwater acoustic networks (UANs) have been receiving growing interests [1–4], because there have been a wide range of applications supported by UANs. Although suffering from relatively low absorption makes acoustic waves common in underwater, the underwater acoustic communication still faces challenges, such as high bit error rate, high propagation delay and delay variance. An efficient medium access control (MAC) protocol is of vital importance in designing the underwater networks because the objectives of MAC protocols are to coordinate multiple nodes to access the shared channel to ensure high throughput and data reliability. The MAC protocols are typically classified into two main categories [5]: contention-based and contention-free protocols. Time Division Multiple Access, Frequency Division Multiple Access and Code Division Multiple Access which share time, ✩ This work was supported in part by the National Natural Science Foundation of China under Grants 61431005, 61671208 and 61671211, the Guangdong provincial research project under Grants 2016A030308006 and 2016B090918049, and the Pearl River S&T Nova Program of Guangzhou. Corresponding author. E-mail address: [email protected] (Q. Guan).

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frequency and code respectively are contention-free protocols. Most of research works of medium access control protocols have focused on contention-based technique, such as, random access and handshaking-based protocols for UANs. Although they are designed to take advantages of utilizing full bandwidth of communication channel, they are subjected to high and variable propagation delay, energy consumption and high bit error rate. The propagation delay of an acoustic signal in the water is five orders of magnitude higher than electromagnetic signal in the air. The long propagation delay can considerably reduce the throughput of the system, which has been considered in the existing MAC protocols. The underwater acoustic propagation speed is expressed empirically as [2]: c(z , S , t) = 1449.05 + 45.7t − 5.21t 2 + 0.23t 3 + (1.333 − 0.126t + 0.0009t 2 ) × (S − 25) + 16.3z + 0.18z 2 , where t is 0.1T , S is the salinity in ppt, and z is the depth in km. T represents the temperature in ◦ C. So the propagation delay varies with the depth, salinity and temperature. Due to the delay fluctuation, the RTTs in the underwater acoustic channel show high fluctuations. In our lab test, there has existed about 0.8 ms between the maximum and minimum RTT values

http://dx.doi.org/10.1016/j.future.2017.08.022 0167-739X/© 2017 Elsevier B.V. All rights reserved.

Please cite this article in press as: Y. Chen, et al., Adaptive RTO for handshaking-based MAC protocols in underwater acoustic networks, Future Generation Computer Systems (2017), http://dx.doi.org/10.1016/j.future.2017.08.022.

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we first demonstrate the relationship between the RTO setting and the throughput under the effect of variable RTT values. We conclude that the fixed RTO value can degrade the throughput due to the RTT variance. Then we propose our new approach of setting RTO as the predicated RTT values accordingly. At last we describe how to predict the RTT values based on Bayesian dynamic linear model and make a comparison with Karn’s algorithm followed by the throughput analysis. We validate the above idea in AquaSim platform in Section 4. Finally, Section 5 concludes the paper and highlights the open issue for future work. 2. Related work

Fig. 1. RTT samples in the lab.

for two underwater modems (AquaSeNT OFDM modem [6]) at the distance of only 4 m away from each other, as shown in Fig. 1. In actual undersea environment, the above difference may be larger due to the dynamic undersea environment and the wide range of the sea. The RTT can be used to compute the Retransmission Time-Out (RTO). In this sense, the fixed RTO could degrade the throughput due to the RTT fluctuation (refer to Section 3.1 for details). The RTT is also meaningful for Automatic Repeat re-Quest (ARQ) schemes because the system should ensure the reliable data transmissions in the presence of high bit error rate of the underwater channel. The inaccurate RTT estimation could lead to additional retransmission of handshaking signals or long waiting time to waste system resources, since RTO is set according to the RTT value. The accurate RTT estimation is demanded for designing efficient MAC and ARQ schemes in UANs. The delay fluctuation should be taken into the consideration in MAC protocols design since it may prevent the accurate estimation of the RTT [2]. However, most of the medium access control design for underwater acoustic networks ignores the RTT fluctuations. In the terrestrial networks, a number of research works have focused on RTT estimation. In [7], the authors estimate the end-to-end RTT using a machine-learning technique known as the Experts Framework. In [8] Liang and Xu design a smooth function of RTT with exponential weighted by ARMA models. Zhang mentions in [9] that the system may make a wrong judgment using Jacobson-Karn’s algorithm [10] when applied to a highly unreliable channel. He modifies the algorithm with a dynamic factor based on the past history of the data lost ratio. In order to overcome RTT fluctuations, we estimate RTT using Bayesian dynamic linear models. Bayesian dynamic linear models have been developed extensively during the recent decades and have some applications in a variety of areas in commercial, industrial, scientific and socio-economic fields. The Bayesian dynamic linear model has good adaptability in tracking the dynamic changes. It is insensitive to uncertain model parameters, effective to unexpected events. Due to the low speed of acoustic waves in the water, it usually takes several seconds to obtain an RTT sample. The Bayesian learning does not need a large amount of sampling data, which makes it also practical in UANs. Therefore, we adopt the Bayesian dynamic linear model to predict the RTTs in UANs. Then, in this paper, we present a novel approach of adapting the RTO dynamically based on the predicted RTT values to improve the throughput in handshaking-based MAC protocols. The remainder of the paper is organized into the following sections. In Section 2, we present the related work of RTT estimation algorithms and Bayesian dynamic linear models. In Section 3,

The existing algorithms of RTT estimation are mainly classified into three categories: classical Jacobson-Karn’s algorithm, ARMA and machine learning approach in terrestrial networks. The highly unreliable and non-stationary characteristics of underwater channels make Karn’s algorithm inefficient [10]. The Jacobson-Karn’s equations are: EstimatedRTT = (1 − α ) × EstimatedRTT + α × SampleRTT , RTTVAR = (1 − β ) × RTTVAR + β|SampleRTT − EstimatedRTT |, RTO = max(EstimatedRTT + K × RTTVAR, 2ticks), where RTTVAR is defined as an estimate of how much EstimatedRTT typically deviated from SampleRTT. EstimatedRTT is updated according to an exponential weighted moving average. α , β and K are usually set as 1/8, 1/4 and 4 respectively. In [9], Ludwig addressed some problems with Jacobson-Karn’s algorithm, including the result that a sudden decrease in RTT causes RTTVAR and RTO consequently to increase unexpectedly. Another problem is that although the RTO value needs to be greater than the current RTT to avoid unnecessary re-transmissions, it should not be so great that it results in network under-utilization. In underwater acoustic networks, the propagation delay varies which leads to the variation of the round trip time (RTT). From our collected 12 000 samples of RTT data (see Fig. 4), we find that the RTT series is non-stationary, which means, the statistics characteristics of RTT series may change with time. This uncertainty of RTT series results in that the predictions using the Jacobson-Karn’s algorithm cannot follow quite closely any abrupt changes in the RTT. ARMA is appropriate for the stationary sequence with zero mean. When it is applied to the non-stationary sequence, the system should first preprocess the non-stationary data sequence into a stationary random sequence with zero mean. Therefore, its accuracy in predicting the non-stationary sequence is reduced [8]. There are also a few research works using a passive way in estimating RTT [11,12]. Bayesian forecasting with the dynamic linear model was first proposed by Harrison and Steven in 1976 [13]. And it has been developed quickly in a variety of areas in commercial, industrial, scientific fields [14–17]. Bayesian dynamic linear models are insensitive to uncertain model parameters, effective to unexpected event and have good adaptability. Thus, we adopt the dynamic linear model to predict the RTT series in underwater acoustic networks. To the best of our knowledge, Bayesian forecasting with a dynamic linear model is rarely used to estimate RTT for MAC protocol design in underwater acoustic networks, and we are the first to pay attention to RTT fluctuations and to forecast the RTT using the Bayesian algorithm. The goal of our paper is to predict the RTT value using the Bayesian dynamic linear model to optimize the handshaking-based MAC protocols.

Please cite this article in press as: Y. Chen, et al., Adaptive RTO for handshaking-based MAC protocols in underwater acoustic networks, Future Generation Computer Systems (2017), http://dx.doi.org/10.1016/j.future.2017.08.022.

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In this section, we first illustrate the relationship between the throughput and the RTO under the influence of RTT fluctuation in handshaking-based MAC protocol, which will motivate the prediction of the RTT values. Then, we propose our new approach of adapting the RTO using RTT predicted values. Finally we detail the Bayesian algorithm and compare the prediction performance with Karn’s algorithm via experiment test.

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T =

∞ ∑

n(Tdata + RTO) × (1 − (1 − a)2 )(n−1) × (1 − a)2

n=1

=

∞ ∑

n(Tdata + RTO) × (1 − Ps )(n−1) × Ps

n=1

= (Tdata + RTO) × Ps =

Tdata + RTO (1 − a)2

∞ ∑

(3) n × (1 − Ps )

(n−1)

n=1

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Now we analyze the total time from sending the request to receiving the ACK successfully. T1 is the time for receiving successfully the ACK through the first RTS attempt, that is:

3.1. Analysis of RTO and MAC throughput We briefly show in the following the impact of the RTO on the throughput from the mathematical view. The transmission time of a data packet is denoted as Tdata , and the total time of transmitting one packet successfully is Ttotal . During the whole transmitting process, the packet decoding error probability is assumed as a. (We assumed the same decoding error probability for all types of packets.) Throughput is defined as the numbers of packets successfully received during a unit time. It is equal to the reciprocal of the total time of successfully transmitting one packet. In handshaking-based MAC protocols, the source node sends the RTS before sending data, and receives the response CTS from the destination node. Then data transmissions are commerced, followed by the acknowledgment from the destination node within an RTO. When the RTT is greater than the RTO, the packet is considered as lost. Thus, the probability of losing a packet is: (1)

where F (·) is the cumulative distribution function. The probability that one node cannot receive the CTS response from the destination node is:

w = PRTT >RTO + PRTT RTO = 1 − PRTT RTO and w in Eq. (2). However, from Eqs. (6) and (7), we also know that a large RTO will lead to a low throughput. In this sense, if we can have a best knowledge of RTT and set the RTO dynamically slightly larger than the RTT, the throughput should reach its maximum. To this end, we propose a self-adaptive RTO setting based on RTT predicted values instead of setting RTO to a fixed value in most handshaking-based MAC protocols, RTO = RTTpredicted + guardtime,

(8)

where guardtime refers to the data processing time. The difficulty in designing our MAC exists in the RTT prediction. In order to reveal the characteristics of the RTT series, we collect 12 000 samples of RTT fluctuation data using AquaSeNT OFDM modem in Fig. 3. Two modems are deployed about 13 m away from each other in the lake. The transmitting power of the AquaSeNT modems is −40 dB. The frequency is from 21 to 27 K and the receiver front gain is 20 dB. There are no surroundings around the modems and it is windy on testing days. We get the propagation round trip time indirectly derived from the distance measurement command. In Fig. 4, we get the 120 mean values every 100 data. We can see that the mean values are not constant, thus, the F (RTT ) is uncertain and non-stationary, making it difficult in numerically calculating the real probability of RTT > RTO. Therefore, based on the characteristics of the RTT data, we propose a novel approach to predict RTT values using a Bayesian dynamic linear model.

ˆ is the mean level of the RTT and ϵ is the uncertain error. where RTT ˆ with The model may comprise a set of models for each possible RTT ˆ ). Additionally, at some future its probability distribution p(RTT ˆ , which time, the model may change due to the uncertainty of RTT have been shown in Fig. 4. These embody model uncertainty. The ˆ ) to describe forecaster has a prior probability information p(RTT ˆ before receiving any observation of the uncertainty truth of RTT ˆ provides a method of forecasting RTT using a conRTT . Each RTT ˆ ). According to the laws ditional probability distribution p(RTT |RTT ˆ is of probability, the joint probability distribution of RTT and RTT as follows:

3.3. Bayesian based RTT prediction

ˆ ) = p(RTT |RTT ˆ )p(RTT ˆ ). p(RTT , RTT

We describe the RTT estimation algorithm in details in this section.

When receiving a value RTT ∗ of the real RTT, the updated probabilˆ given RTT ∗ (RTT = RTT ∗ ) is defined by the ity distribution for RTT condition density:

3.3.1. Bayesian learning We consider a dynamic model, where an output variable RTT is related to an input variable X in a parameterized form as follow2 :

ˆ + ϵ, RTT = X RTT 2 RTT stands for the uncertain value before the observation, which becomes certain when observed.

Fig. 4. The non-stationary feature of the RTT data.

ˆ |RTT ∗ ) = p(RTT ∗ |RTT ˆ )p(RTT ˆ )/p(RTT ∗ ). p(RTT Then for any observed value of RTT , the Bayesian theorem is represented by

ˆ |RTT ) = p(RTT |RTT ˆ )p(RTT ˆ )/p(RTT ). p(RTT ˆ ) for RTT ˆ is then used in The revised probability distribution p(RTT the new learning routine to predict the next output RTT . For the

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dynamic model, all the routine learning information contained in ˆ ). the observation RTT is expressed via p(RTT |RTT This concludes that learning routine is expressed as Posterior ∝ Observedlikelihood × Prior. We want to forecast the RTT values to set RTO in MAC protocol. ˆ involves information related to RTT presents the RTT series and RTT predicting the RTT value. The observation model evolves with time. ˆ At the initial time, the forecaster has a prior distribution for RTT ˆ provides a conditional forecast to produce a forecast and the RTT for an output RTT in terms of a probability distribution. Once receiving an observation of RTT , the prior view of the forecaster’s ˆ is then updated and will be used to forecast the next about the RTT output of RTT . 3.3.2. Construction of dynamic linear model Based on the above idea, we introduce the definition of dynamic linear model. Suppose, the time t = 0 represents the current time, and the available existing information is denoted by the initial information set : D0 . The available starting information which is composed of history and all defining model quantities is used to form the initial views about RTT values in the future. Similarly, at any time t, we get the information set at timet : Dt . Dt includes both the previous information set Dt −1 and the observation RTTt∗ . We get the information updating : Dt = {It , Dt −1 }, where It denotes all additional relevant information at time t. Then the objective is to describe the future development of the RTT series through the probability distributions RTTt , RTTt +1 , . . . , conditional on past information Dt −1 , and here we focus on onestep ahead. Usually, such distribution is dependent upon defining parameters to determine the distributional forms and functional relationships, and so on. The one-step forecast distribution is structured in terms of a parametric model,

ˆ , Dt −1 ). p(RTTt |RTT In the following, we introduce some definitions and give concrete settings for the above general idea to exemplify the class of dynamic models: Definition 1. For each t, the univariate, uni-parameter normal dynamic linear model, represented by the quadruple {Ft , λ, Vt , Wt }, is defined by [15]:

ˆ t + νt , νt ∼ N [0, Vt ], Observation equation: RTTt = Ft RTT ˆ t = λRTT ˆ t −1 + ωt , ωt ∼ N [0, Wt ], System equation: RTT ˆ 0 |D0 ) ∼ N [m0 , C0 ], Initial information: (RTT where νt is the observation error and ωt is the evolution error. These two error sequences are internally and mutually indepenˆ 0 |D0 ). The values dent. Additionally, they are independent of (RTT of the variance sequences Vt and Wt may be unknown, but the constant λ and relevant values of the sequence Ft are known. RTTt ˆ t is the mean level of RTT. is the output of RTT and RTT In our predicting model, ωt represents purely random, unpredictable changes in mean levels between time t − 1 and t, thus, we use the constant model characterized by the quadruple {1,1,V,W} to construct our model:

ˆ t + νt , ν (t) ∼ N [0, V ], Observation equation: RTTt = RTT ˆ t = RTT ˆ t −1 + ωt , ωt ∼ N [0, W ], System equation: RTT

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ˆ 0 |D0 ) ∼ N [m0 , C0 ], Initial information: (RTT where V is the ephemeral observation variation and W is the sustained system variance. Initial information obeys normal distribution with mean value m0 and the variance C0 . m0 is initial estimate for the RTT value with C0 as uncertainty measurement. 3.3.3. Updating equations Now we introduce the updating equations for the constant models based on the overall idea described in 3.3.1, and RTT values are iterated through these learning routines. The prediction algorithm is summarized as follows.

ˆ t −1 : (a) Posterior for RTT ˆ t −1 |Dt −1 ) ∼ N [mt −1 , Ct −1 ]. (RTT ˆ t : (RTT ˆ t |Dt −1 ) ∼ N [mt −1 , Rt ], (b) Prior for RTT where Rt = Ct −1 + W . (c) 1-step forecast: (RTTt |Dt −1 ) ∼ N [ft , Qt ], where ft = mt −1 and Qt = Rt + V . ft is the prediction value at time t. ˆ t : (RTT ˆ t |Dt ) ∼ N [mt , Ct ], (d) Posterior for RTT with mt = mt −1 + At et and Ct = At V , where At = Rt /Qt , and et = RTTt∗ − ft , RTTt∗ is the observation value at time t. D0 is the initial available information which comprises the initial estimate of RTT value (m0 ), the associated uncertainty of the initial estimate (C0 ), the ephemeral observation variation (V ) and sustained system evolution variance (W ). At the current time t, ˆ t −1 |Dt −1 ) Dt −1 is the information set at the previous time, and (RTT ˆ t −1 . mt −1 and Ct −1 are the is the posterior distribution for RTT estimate of RTT value and the variance at time t, respectively. Then, ˆ t |Dt −1 ) and the 1-step forecast density p(RTTt |Dt −1 ) is dep(RTT rived. It can be seen that the ft is the estimate of RTT value at time t which is equaled as mt −1 . Finally, in turn, the posterior distribution, ˆ t |Dt ), is updated through the information deriving from the p(RTT RTT observation at time t, because Dt is composed of the historical information Dt −1 and the observation RTTt∗ . ˆ t offers the methods to summarize The model parameter RTT and to form forecast distribution used to forecast the future, and the learning process sequentially updates the state of the knowlˆ t . In other words, the prior density p(RTT ˆ t |Dt −1 ) and edge of RTT ˆ t |Dt ) provide a sequential, coherent transfer of the posterior p(RTT information on the time series process through the passage of time. The prior information of mt −1 is the key difference between the ˆ t Bayesian algorithm and the other traditional algorithms. As RTT evolves with time, the contribution of the initial prior information m−1 in predicting the one-step values decays to zero. In the meanwhile, the current observation becomes more important. 3.3.4. Testing the estimated RTTs The forecasting of RTT is of importance to the design of the MAC protocol in underwater acoustic networks. Now we use the above Bayesian algorithm to estimate the RTT and compare with the results using the Karn’s algorithm. In the experiments, two OFDM modems are deployed in the lake with the distance of about 13 m from each other. RTT data are collected through the modem command. Based on the distance and the acoustic speed, we can calculate that the round trip time from one modem to the other is about 0.02 s. Therefore, the initial expectation value m−1 3 is set as 0.02. However, it is not sure about the precision of this initial value. Thus, the variance C−1 3 The time serials begin from t = 0. So, its prior distribution begins from t = −1.

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Fig. 5. The samples and the estimation values of RTT fluctuations. Fig. 7. The RMSE curves for the Bayesian and Karn’s algorithms.

4. Simulation 4.1. Model construction The throughput is related to the RTO as described in last section, and the RTO cannot be set as a fixed value due to the existence of RTT fluctuation. In our new approach, the key point is to offset RTO based on the RTT predicted values to improve the throughput. The initial prior distribution information m0 and C0 are set as the first observed value and 1 respectively, and the value of this particular subjective prior diminishes rapidly as data is received. The V and W are fixed as 4 and 3 separately. Consequently, the operational model for RTTt in times t is: Fig. 6. The relative error curves for the Bayesian and Karn’s algorithms.

should be a large value set as 1. Noted that, the contribution of the initial values are getting weaker soon after the beginning. The observation variation V and the system variance W are also large, which are set reasonably as 4 and 3, respectively. The operational dynamic model for RTT in time t is :

ˆ t + νt , νt ∼ N [0, 4], RTTt = RTT ˆ t = RTT ˆ t −1 + ωt , ωt ∼ N [0, 3], RTT ˆ 0 |D0 ) ∼ N [0.02, 1]. (RTT With the above initial values, the prior distributions and the updating equations in Section 3.3.3, we can get the one-step predicted values of RTT. We depict a plot of the one-step RTT predicted values and the samples with respect to time in Fig. 5. We draw two lines for Karn’s algorithms for comparison, i.e., K = 2 and K = 4 [18], respectively. We can see that the one-step predicted values using the Bayesian algorithm respond to the samples faster even for the sudden events and follow the real measurements closer than that |Pr edictedValue−Samples| using the Karn’s algorithm. The relative error, Samples , measures the degree of deviations between the samples and the predicted values. We can see that the relative errors using Bayesian are smaller than that of using Karn’s (for both K = 2 and K = 4), as shown in Fig. 6. We also use the RMSE (Root Mean Squared Error) to compare the prediction performance of the two algorithms in Fig. 7. The RMSEs of the Bayesian algorithm are smaller compared with the RMSEs of the Karn’s algorithms. Based on the above elaboration, the Bayesian dynamic linear model is more efficient in such a formidable underwater environment.

ˆ t + νt , νt ∼ N [0, 4], RTTt = RTT ˆ t = RTT ˆ t −1 + ωt , ωt ∼ N [0, 3], RTT ˆ (RTT 0 |D0 ) ∼ N [Obser v edFirstValue, 1]. 4.2. Throughput analysis We implement both the Bayesian and Karn’s algorithms and their corresponding MAC protocols in AquaSim [19] platform to study the throughput performance. AquaSim is developed on the basis of NS-2 and can effectively simulate acoustic signal attenuation and packet collisions in underwater sensor networks. In AquaSim platform, we validate two algorithms via Slotted-FAMA. We set the scenario of two nodes which are deployed at the location (0,0,0) and (500,0,0). The data rate is 1000 bits per second and packet size is 3000 bits. In Fig. 8, the X -axis and Y -axis are the offered load (offered packets/unit time) and throughput (transmission time/simulation time). The red circles are the throughput of the Slotted-FAMA protocol in underwater acoustic networks. The blue diamond dots are the throughput of the Slotted-FAMA protocol using our Bayesian algorithm to predict the RTT, The throughput is improved by 80% averagely by our algorithm compared to the original Slotted-FAMA protocol. This is attributed to the fact that Bayesian forecasting with dynamic linear model is applicable in such a formidable underwater environment, and the algorithm can forecast the next RTT value comparatively accurately. Thus, RTO can be adjusted in real-time according to the predicted values. The adjusted RTO reduces the waste of system resources due to the decrease of the additional retries of sending handshaking signals in reserving the channel and the idle waiting time caused by the delay variances. The black crosses are throughput of the Slotted-FAMA protocol using the Karn’s algorithm to predict the RTT. Because the predicted RTT is a little larger than that using

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Fig. 10. The samples and the predicted RTT values. Fig. 8. The throughput in the 2-node scenario. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 11. The relative error curves for the Bayesian and Karn’s algorithms in the 5node scenario.

Fig. 9. The throughput in the 5-node scenario.

Bayesian, the throughput of it is decreased due to the inaccuracy of the estimations. We also deploy a 5-node scenario to study the two algorithms. The 5 nodes are located at (0,0,0), (500,0,0), (−500,0,0), (0,500,0), (0,−500,0). We draw the throughput in Fig. 9. The throughput of the Slotted-FAMA using the Bayesian algorithm is improved as expected. The RTT samples and the RTT predicted values using the Bayesian and Karn’s algorithms are drawn in Fig. 10. We see the same results as obtained in the 2-node scenario. The relative errors in Fig. 11 and RMSEs in Fig. 12 of the Bayesian algorithm are both smaller than those of the Karn’s algorithm. 4.3. Summary As described above, due to the impact of delay variance, the fixed RTO can degrade the throughput. We then propose our new approach which sets the RTO as the predicted RTT values, i.e., RTO = RTTpredicted + guardtime. Then we predict the RTT values using a Bayesian dynamic linear model. RTO can be adjusted in real-time to reduce the waste of system resources due to the decrease of the additional retries of sending handshaking signals for reserving the channel and the idle waiting time caused by the delay variances. We conclude that the Bayesian algorithm is appropriate for RTT prediction in underwater acoustic networks. 5. Conclusion This paper has analyzed the throughput of medium access control (MAC) and the round-trip time (RTT) in underwater acoustic

Fig. 12. The RMSE curves for the Bayesian and Karn’s algorithms in the 5-node scenario.

networks. The RTT samples collected from two AquaSeNT modems show highly fluctuations, which makes the fixed Retransmission Time-Out (RTO) setting in the handshake-based MAC protocols inefficient. A RTT prediction-based RTO setting has been proposed to improve the throughput of underwater acoustic MAC. We also identify the non-stationary feature in the RTT samples, which makes the traditional Karn’s algorithm inaccurate in predicting the dynamic underwater RTTs. We has then proposed our RTT prediction approach using a Bayesian dynamic linear model. The prediction accuracy has been validated by the experimental test. The simulation results in AquaSim also show significant improvement for the underwater acoustic MAC protocol in terms of throughput.

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Yankun Chen is currently pursuing the Ph.D. degree in School of Electronic and Information Engineering at the South China University of Technology, Guangdong, China. She received the B.E. degree in School of Information Engineering from Liaoning Technical University, Liaoning, China, in 2005 and the M.S. degree from South China University of Technology in 2009. Her main interests are underwater acoustic networks, MAC protocol performance analysis and the simulation of underwater acoustic networks.

Fei Ji received the B.S. degree in applied electronic technologies from Northwestern Polytechnical University, Xi’an, China, and the M.S. in bioelectronics and Ph.D. degrees in circuits and systems both from the South China University of Technology, Guangzhou, China, in 1992, 1995, and 1998, respectively. She was a Visiting Scholar with the University of Waterloo, Canada, from June 2009 to June 2010. She worked in the City University of Hong Kong as a Research Assistant from March 2001 to July 2002 and a Senior Research Associate from January 2005 to March 2005. She is currently a Professor with the School of Electronic and Information Engineering, South China University of Technology. She was the Registration Chair and the Technical Program Committee (TPC) member of IEEE 2008 International Conference on Communication System. Her research focuses on wireless communication systems and networking.

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Quansheng Guan received the B.E. degree in electronic engineering from the Nanjing University of Post and Telecommunications, China, in 2006, and the Ph.D. degree from South China University of Technology (SCUT) in 2011. From 2009 to 2010, he was a Visiting Ph.D. Student with the University of British Columbia, Vancouver, BC, Canada. From 2012 to 2013, he was a Post-Doctoral Researcher with the Chinese University of Hong Kong. He was a Visiting Scholar with the Singapore University of Technology and Design in 2013, and a Visiting Professor with Polytech Nantes, France. He is currently an Associate Professor with the School of Electronic and Information Engineering, SCUT. His research interests include wireless communications and networking, and networked interactions and economics. Dr. Guan is a frequent TPC Member of conferences, a frequent Reviewer of journals and conferences. He was the corecipient of Best Paper Awards from the IEEE ICCC 2014 and the IEEE ICNC 2016. He is a Guest Editor of Mobile Information System.

Yide Wang received the B.S. degree in electrical engineering from Beijing University of Posts and Telecommunications, Beijing, China, in 1984 and the M.S. and Ph.D. degrees in signal processing and telecommunications from the University of Rennes 1, Rennes, France, in 1986 and 1989, respectively. He is currently a Full-Time Professor with the École Polytechnique de l’Université de Nantes (Polytech Nantes), Nantes, France, where he is also the Director of Research. In 2008–2011, he was the Director of the Regional Doctorate School of Information Science, Electronic Engineering, and Mathematics. He is the author or coauthor of seven book chapters in four scientific books, 50 journal papers, and more than 100 national or international conferences. His research interests include array signal processing, spectral analysis, and mobile wireless communication systems.

Fangjiong Chen received the B.S. degree in electronics and information technology from Zhejiang University, Hangzhou, China, in 1997, and the Ph.D. degree in communication and information engineering from the South China University of Technology (SCUT), China, in 2002. He joined the School of Electronics and Information Engineering, SCUT, where he was a Lecturer from 2002 to 2005 and an Associate Professor from 2005 to 2011. He is currently a Full Professor with SCUT. He is also the Director of the Mobile Ultrasonic Detection National Research Center of Engineering Technology, Department of Underwater Detection and Imaging. His research focuses on signal detection and estimation, array signal processing, and wireless communications. Dr. Chen received the National Science Fund for Outstanding Young Scientists in 2013 and was elected into the Program for New Century Excellent Talents in University of the Ministry of Education of China in 2012.

Hua Yu received the B.S. degree in mathematics from the Southwest University, Chongqing, China, in 1995, and the Ph.D. degree in communication and information system from South China University of Technology, GuangZhou, China, in 2004. He was a visiting scholar at the School of Marine Science and Policy, University of Delaware, USA, from August 2012 to August 2013. Currently, he is a Professor at the School of Electronic and Information Engineering, South China University of Technology. He is also the Director of Department of Underwater Communications, National Engineering Technology Research Center for Mobile Ultrasonic Detection. He was the Publication Chair and the Technical Program Committee (TPC) member of the 11th IEEE International Conference on Communication Systems in 2008. His research interests are in the physical layer technologies of wireless communications and underwater acoustic communications.

Please cite this article in press as: Y. Chen, et al., Adaptive RTO for handshaking-based MAC protocols in underwater acoustic networks, Future Generation Computer Systems (2017), http://dx.doi.org/10.1016/j.future.2017.08.022.