Congestion avoidance algorithm using ARIMA(2,1,1) model-based RTT estimation and RSS in heterogeneous wired-wireless networks

Author’s Accepted Manuscript CONGESTION AVOIDANCE ALGORITHM USING ARIMA(2,1,1) MODEL-BASED RTT ESTIMATION AND RSS IN HETEROGENEOUS WIRED-WIRELESS NETWORKS A. Jeyasekar, S.V. Kasmir raja, R. Annie Uthra www.elsevier.com/locate/jnca

PII: DOI: Reference:

S1084-8045(17)30206-0 http://dx.doi.org/10.1016/j.jnca.2017.05.008 YJNCA1921

To appear in: Journal of Network and Computer Applications Received date: 6 November 2016 Revised date: 6 April 2017 Accepted date: 25 May 2017 Cite this article as: A. Jeyasekar, S.V. Kasmir raja and R. Annie Uthra, CONGESTION AVOIDANCE ALGORITHM USING ARIMA(2,1,1) MODEL-BASED RTT ESTIMATION AND RSS IN HETEROGENEOUS WIRED-WIRELESS NETWORKS, Journal of Network and Computer Applications, http://dx.doi.org/10.1016/j.jnca.2017.05.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

CONGESTION AVOIDANCE ALGORITHM USING ARIMA(2,1,1) MODEL-BASED RTT ESTIMATION AND RSS IN HETEROGENEOUS WIRED-WIRELESS NETWORKS A. Jeyasekar 1, S.V. Kasmir raja 2, R. Annie Uthra 3 1

Assistant Professor, SRM University, Chennai, Tamilnadu, India 2

3

Dean (Research), SRM University, Chennai, Tamilnadu, India

Assistant Professor, SRM University, Chennai, Tamilnadu, India [email protected] [email protected] [email protected]

Abstract In the past two decades, there has been a fast growth in the heterogeneous wired-wireless network which connects variety of networks such as wired network, wireless network, high speed network and sensor network with the Internet. Since the wired network and wireless network use the different technologies, there is a necessity of developing new protocols or redesigning of existing protocols. Transmission Control Protocol is redesigned to achieve the reliable and effective communication in the heterogeneous wired-wireless network. The basic assumption made in Transmission Control Protocol is that the packet losses are caused by the network congestion but in case of wireless network, the packet losses may be due to temporary link failure, high Bit-Error-Rate etc. Hence the congestion avoidance algorithm of Transmission Control Protocol needs to be redesigned to cope with heterogeneous wired-wireless network. Therefore in this paper, we propose a congestion avoidance algorithm which differentiates the cause of packet losses. The essential components of proposed algorithm are round-trip-time estimation, backlogged packet estimation, channel status notification, loss differentiation algorithm and congestion window control. A round-trip-time estimation using ARIMA(2,1,1) model is proposed in this paper which estimates the sudden changes in the round trip time. It helps us to estimate the backlogged packet and thereby differentiating the congestive packet loss from non-congestive packet loss. In addition, we use the received signal strength of wireless receiver to differentiate the packet losses. Based on the packet loss differentiation, the congestion window size is controlled to avoid the congestion in the network. The proposed algorithm is simulated using network simulator under different network environments. From the simulation results, it is realized that the proposed congestion avoidance algorithm performs well in the heterogeneous wired-wireless network.

Keyword: Round trip time estimation, Auto regressive integrated moving average model, Loss differentiation algorithm, Received Signal Strength, Congestion Avoidance Algorithm, Heterogeneous Network 1. Introduction Now-a-days, the wireless networks are common and wireless links are everywhere in today’s networks. Although wireless networks are popular, the wired networks are still existence because the bandwidth and reliability of wireless network is very low. It inspires the researchers to envision the Heterogeneous Wired-Wireless Networks (HWWN) which has distinct communication paradigms and challenges imposed by their unique characteristics. For example the asymmetric networks such as Asymmetric Digital Subscription Line (ADSL), Digital Video Broadcast (DVB), and Packet Radio Network (PRN) have difference in transmit and receive capacity of the link. The satellite network takes high turn-around-time and the wireless network experiences high Bit-Error-Rate than the traditional wired networks. Consequently, it is necessary to take into account the communication media, its characteristics and connecting hardware because they differ from network to network. In case of the protocol supporting the reliable data service in HWWN, congestion avoidance algorithm of TCP faces serious performance issues [1-13]. This is because of high speed links (Optical Fibers networks), long and variable delay links (Satellite networks), lossy links (Wireless networks) and asymmetric links (Hybrid satellite networks) embedded with the HWWN. Many TCP variants were proposed for improving the performance in HWWN to cope with different network environments [4-10]. Among them, TCP-Vegas provides better performance than other TCP variants [11-15]. But the congestion avoidance algorithm of TCP-Vegas has attracted significant attention in improving the performance in the different types of network [16][17][18-21]. The difference between these TCP variants relies on the mechanism used to avoid/control the congestion in the network. The performance of congestion avoidance/control algorithm is affected significantly in HWWN. The IETF RFC 6077 describes the several challenges of congestion avoidance algorithm of TCP [3]. Among them, some of the issues are well known for many years and may require more study. The RTT estimation and packet loss differentiation (Discriminating the congestive packet loss from non-congestive packet loss) are two important issues among them. The variation in RTTs of packets is used to detect the incipient of congestion in the network in the early stage. The RTT of a packet is generally affected by the buffering time (queuing delay) of packet in the intermediate nodes (routers), changes in the route and delay due to temporary link failure etc [57]. These delays are random and difficult to estimate. Next, the packets are usually lost due to congestion in the wired network but in case of wireless network, the packet loss may be due to poor link quality, high Bit-Error-Rate (BER) etc. Hence it is desirable to have a good packet loss differentiation algorithm to distinguish the cause of packet loss. Therefore in this paper, we propose a congestion avoidance algorithm which consists of RTT estimation, loss differentiation using RTT and Received Signal Strength (RSS) of wireless node in HWWN. The research contribution of this paper is as follows:

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The sudden changes in RTT caused during the built-up of buffer, abrupt increase of data traffic in the bottleneck link and temporary failure in the wireless link, affect the data rate at the sender because delay-based congestion avoidance takes the variation in RTT to control the sending data rate at sender [57][58]. Therefore we analyze the RTTs measured through passive measurement and propose ARIMA(2,1,1) model-based RTT estimation (ARTT) which estimates the sharp and sudden changes in the RTT accurately. The proposed RTT model takes recently measured three RTTs so that the sudden changes in RTT are estimated better than other models. In HWWN, the packet losses may be due to the reason other than congestion like temporary link failure which causes the sudden changes in the RTT. Therefore in this paper, we make use of the proposed RTT estimation using ARIMA(2,1,1) model to estimate the sudden changes in RTT, thereby differentiating the non-congestive packet loss from congestive packet loss. The proposed packet loss differentiation algorithm performs well in HWWN. We propose a congestion avoidance algorithm which uses the proposed packet loss differentiation algorithm to prevent the congestion in the network. It improves the performance with respect to throughput by distinguishing the non-congestive packet loss from congestive packet. In order to further improve the performance of the proposed congestion avoidance algorithm, the received signal strength of wireless receiver is also taken into account to differentiate the cause for the packet losses. The proposed congestion avoidance algorithm is evaluated under various network environments like, high BER network, high latency network, asymmetric bandwidth network, different packet size and cross traffic. It provides better performance in the different network environments

The rest of the paper is organized as follows. In Section 2, we survey existing works in RTT estimation as well as the packet loss differentiation algorithms. In Section 3, we present the overview of proposed congestion avoidance algorithm. Section 4 describes the steps followed to model the RTT i.e. Model identification, Model parameter estimation and Model diagnosis. The proposed RTT estimation model (ARTT) is also presented in this section. In Section 5, the proposed LDA using ARTT is presented. Section 6 describes the congestion avoidance algorithm using proposed LDA. The experimental and simulation setup used for evaluating the proposed RTT estimation, LDA and Congestion avoidance algorithm are provided in Section 7. In Section 8, the performance metrics are discussed. Section 9 discusses the performance analysis of the proposed RTT estimation, Loss differentiation algorithm and congestion avoidance algorithm under the various network environments. The discussion about the proposed RTT estimation, LDA and congestion avoidance algorithm are presented in Section 10. Finally in Section 11, we conclude the paper with future research works. 2. Related Works 2.1 Mathematical background for RTT Estimation Because of the varying data traffic in the network and different characteristics of communication media like wired, wireless etc, the RTT is always uncertain in HWWN. Therefore, it is difficult to estimate the RTT of a packet from the sender side at every instant. Since the RTT data sets exhibit the time varying characteristics, the time series analysis can be

used to estimate the RTT. An Autoregressive Moving Average model is a time series model, abbreviated ARMA(p,q) and is of the form

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(2) where Y(t), t Є Z, is stationary time series that has constant mean and finite variation. The parameters ‘p’ and ‘q’ are the order of AR (AutoRegressive) and MA (Moving Average) model which indicates the number of prior inputs and number of predictor error terms required to estimate the series respectively. {ε(t); t=1,2,…} is Gaussian white noise sequence. Φ1, Φ2, …. Φp Є R are constant with Φp ≠ 0 and θ1, θ2, …. θp Є R are constant with θp ≠ 0. Φp and θq are called coefficients of AR and MA model respectively and determined by the Least Square Estimation method (LSE). While modeling the time series, the common assumption made is that the time series is stationary. Suppose Y(t) is non-stationary, then the non-stationary components in Y(t) must be removed before modeling time series. Box-Jenkins proposed a systematic approach called ARIMA (p,d,q) model in which the non-stationary components of Y(t) is removed by the difference of successive data points of Y(t) [22][23][24]. For example ( )

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which is called the first order difference of Y(t). The differencing of successive data points of Y(t) can be continued until the resultant time series has constant mean and finite variation. The resultant time series can be expressed as ( ) ( ( )) where ‘d’ denotes the order of difference or number of successive difference of data points of Y(t) required to make Y(t) stationary. A time series Y(t) is said to be ARIMA(p,d,q) if ( ) ( ( )) is ARMA(p,q) where p and q are order of AR and MA respectively and d is order of differencing. () (

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2.2 RTT Estimation Many RTT estimation methods were proposed in the literature which takes time series model such as AutoRegressive model (AR), Moving Average model (MA), AutoRegressive Moving Average model (ARMA) and AutoRegressive Integrated Moving Average model (ARIMA). The AR model is denoted as AR(p) where p indicates the number of prior RTTs needed to estimate the RTT of a next packet. AR (5) and AR(2) model were proposed in [26] and [28] respectively to estimate the RTT. These models assume that the measured RTT is stationary but

in heterogeneous environment, the RTTs are changed over the time. Therefore it is essential remove the non-stationary components from the measure RTTs before determining the model because the time series model assumes that the data set used for modeling are stationary. AutoRegressive eXogeneous model (ARX) was proposed in[27][38]. It is denoted as ARX(p,b) where p indicates the number of prior RTTs needed to estimate the RTT and b indicates the number of exogenous terms which means that the observable variable that are determined from outside of system. For example, packet transmission rate is the outside variable that is used to estimate the RTT. In this model, the RTT is made into linear by averaging the measure RTT. It may remove the sudden changes in the RTT caused by temporary link failure, heavy traffic etc which are very important characteristics to distinguish the cause of packet loss. ARX(5,1) was proposed in [38] in which five prior RTTs and packet transmission rates are used to estimate the RTT of a next packet. In [27], ARX(1,1) model is used to estimate the RTT. The number of prior RTT needed to estimate the RTT is one. A single RTT is not sufficient to differentiate the cause of packet loss. The MA model is denoted as MA(q) where q indicates the number of prediction error terms needed along with current RTT to estimate the RTT of a next packet. Exponentially Weighted Moving Average model (EWMA) was proposed in [20] which is coming under Moving Average model. It uses the weighted average between the previous estimate and newly measured RTT. Recent investigations have indicated that the usage of the algorithm results in an unsatisfactory performance because it is biased towards the long history of measured RTT [3][23][29][30][31]. But the quick and high variation in the RTT due to temporary link failure and high BER are high frequency components which affects the recent RTTs frequently [7][24]. Therefore the RTT of recently acknowledged packets are very much significant to predict the RTT of next packet. Next the drawback EWMA is that the averaged value reacts more slowly to sudden changes in the measured RTT because it biased towards long history of previous RTTs. ARMA model is denoted as ARMA(p,q) where p indicates the number of prior RTTs and q indicates the number of prediction error terms needed. ARMA(1,1) model was proposed to predict the traffic in the network [32] which means that one prior output and one prediction error terms have significant effect over the current value of RTT to predict the RTT of next packet. But it is not adequate when it is used to decide the cause of packet loss. Further the ARMA model is generally not suitable to model the internet traffic because the internet traffic is a nonstationary component. The basic assumption made in the ARMA model is that the original time series must be stationary [27][28].

ARIMA model is denoted as ARIMA(p,d,q) where p indicates the number of prior RTTs, q indicates the number of prediction error terms and d indicates the number time the differencing operation performed over the measured RTTs to remove the non-stationary components from the measured RTTs. Mohamed Faten Zhani et al [33] presented a neuro-fuzzy model and ARIMA(1,1,1) model to predict traffic in the network. They showed that the training-based model captures the strong correlation of the traffic and one lag is practically sufficient to perform accurate prediction of RTT. In case of ARIMA(1,1,1) model, the first order difference operation is performed to make the non-stationary RTT samples into stationary

2.3 Packet Loss Differentiation Algorithm

Differentiating the congestive loss from non-congestive loss is very difficult. NonCongestive losses are mostly caused by transmission errors and packet corruption by poor channel condition which varies dynamically due to the mobility of stations or obstacle crossing the wireless path, multipath interference and interference from other devices etc [34][35]. Suppose The TCP assumes the non-congestive packet loss as congestive packet loss and deploys the congestion avoidance algorithm, the growth of congestion window size is slowed down which results in poor performance in HWWN [17][36][38][40][57][58][59]. Wang et al [37] proposed a congestion avoidance algorithm (NTG) based on the normalized throughput gradient in which the difference between the current throughput and previous throughput is taken into account to differentiate the packet losses. The ratio of current window size and RTT of current packet is called throughput. Therefore NTG increases as the propagation time increases which results decreasing in frequency of congestion prediction. TCP Vegas [38] and TCP Veno [17] takes backlogged packets to differentiate the cause of packet loss. The backlogged packets mean the packets that are yet to be acknowledged at sender. Lim and Jang [40] suggested a robust end-to-end loss differentiation scheme (RELD) which takes baseRTT as one of the parameters to classify the cause of packet loss. The baseRTT, MinRTT and SRTT are used to estimate the backlogged packets (N) in the network. The baseRTT is the smallest RTT measured throughout the life time of TCP connection. When there is a change in route or environmental parameters, the baseRTT may become unbefitting. In order to solve it, the baseRTT is reset whenever a packet loss is detected either due to time-out or duplicate acknowledgement [17]. The SRTT is smooth RTT which takes EWMA model to predict the RTT of next packet. Since the SRTT smoothes the RTTs by filtering the high frequency components and biased to long history of RTT, the sharp and sudden changes in RTT caused by random delay are not able to predict.

TCP New-Vegas [39] takes the difference between the RTT of two packets acknowledged recently because the RTT of two packets are separated with long duration if there is congestion in the network. Based on it, it adjusts the estimated number of packets in flight dynamically. But it is not suitable for asymmetric networks in which the bandwidth of uplink is less than the bandwidth of downlink. In asymmetric networks, the bottleneck link is mostly in the acknowledgement path, not in the data path. Even though there is no congestion in the data path, it reduces the data flow rate. Uttam Ghosh et al [36] proposed P-TCP which takes the feedback from network layer to differentiate the wireless packet loss from congestive packet loss. When a node on the active route detects the congestion, it sets the Explicit Congestion Notification (ECN) bit in the ACK packet and sends it to the source node. On receiving this message, the sender reduces the congestion window size otherwise it assumes that there is no congestion and packet loss is only due to lossy link. Since the loss differentiation algorithm takes the RTT variability as a signal for detecting the network congestion and presently used RTT

estimation is not being perfect [40] for detecting the sudden changes in the RTT, we propose a loss differentiation algorithm using a new RTT estimation technique that avoids the congestion in the network.

3. Overview of Proposed Congestion Avoidance Algorithm Fig. 1 shows an overview of the proposed enhancement of congestion avoidance algorithm and its components: RTT estimation using ARIMA(2,1,1) model, backlogged packet estimation, loss differentiation algorithm and congestion window control using implicit information (RTT and no. of backlogged packets) and explicit information (channel status notification). The sender side algorithm takes the measured RTT for the RTT estimation. It also takes sequence number of a packet just sent (Sseq) and sequence number of a packet just acknowledged (Aseq) for backlogged packet estimation

PROPOSED ENHANCEMENT OF CONGESTION AVOIDANCE ALGORITHM

Sseq and RTT estimation

Backlogged Packet Estimation

Loss Differentiation Algorithm

Measuring the Received Signal Strength

Channel Status Notification

Congestion Window Control Regulated Packet

Fig. 1. Overview of Proposed Congestion Avoidance Algorithm 4. Proposed RTT Estimation A RTT is composed of propagation delay, transmission delay, queuing delay, router processing overhead and random delay due to medium access contention. The transmission delay, router processing overhead and propagation delay are deterministic components whereas the queuing delay is random delay which is difficult to estimate [69]. There are several models in the literature as discussed in Section 2.2 to model the RTT. In this section, we propose a model to estimate the RTT in HWWN. 4.1. RTT Data Collection The idea behind using of time series model is that some aspects of the past pattern will continue to remain in the future [22] [41]. In order to analyze the past pattern of RTT, RTT data

sets are collected from Cooperative Association for Internet Data Analysis (CAIDA). Presently CAIDA is called as Center for Applied Internet Data Analysis [42]. We collected the RTTs from 01/01/2009 to 05/01/2009. The collected RTTs are divided into 30 sets, each containing 120 RTTs. We use Box-Jenkins approach for modeling the RTT. It has three basic steps to build a model for a time series [22][41][43] that are Model identification, Model parameter estimation and Model diagnosis. First we take the RTT data set 1 which contains 120 RTTs measured at equal time interval. The above said three primary steps are followed to determine the adequate model for RTT estimation using RTT data set 1. Similarly the empirical analysis on the rest of RTT data sets is performed to determine the adequate model. 4.2. Model Identification The aim of the identification stage is to identify the order of AR denoted as p, order of MA denoted as q and order of differencing denoted as d. The run sequence plot, autocorrelation plot and partial autocorrelation plot are used to determine the order of p, d and q. The aim of the run sequence plot is to determine whether or not time series is stationary. A time series is said to be stationary if the mean, variance and autocorrelation structure of time series does not change over time. The stationarity of a time series is visualized with help of run sequence plot. If the run sequence plot is flat looking, constant variation over time and no periodic fluctuation, then the time series is said to be stationary. The run sequence plot of RTT data set 1 is shown in Fig 2(a). The RTT data set 1 does not have fixed variation and fixed location. Further, the run sequence is not flat and has significant shift in many locations. It indicates that the RTT data set 1 has nonstationary components. The mean of RTT data set 1 is plotted in Fig 2(b). It has significant shift in location (from index number 0 to 80) and after the index number 80, there exists an almost constant mean over time. It shows that the mean of RTT data set 1 is not constant over the time. It indicates that the RTT data set 1 has some non-stationary components. Since the RTT data set 1 has non-stationary components, the first order differencing is applied over the RTT data set 1 to remove the non-stationary components [22][23][33][44]. The resultant dataset is denoted as RTT’. It is observed that the vertical spread of RTT’ shown in Fig. 2(c) appears to be same and better than the vertical spread of RTT shown in Fig 2(a). The mean of first order differenced RTT (μ’) is plotted in Fig 2(d). It is observed that the μ’ varies from 9.7 to 4.5 after the index number 15 and almost uniform and constant. It indicates that the mean of RTT’ is almost constant over time. Since run sequence plot of RTT’ is almost flat and nondrifting, constant mean and fixed variation, the RTT’ is stationary. Therefore the order of differencing (d) is 1. Once the stationarity is addressed, the next step is to identify the order to AR and MA terms i.e. p and q. The autocorrelation plot and partial correlation plot are used to identify the order p and q.

(a)

(b)

(c)plot of RTT data set 1, (b) Mean of RTT data set 1,(d) Fig. 2. (a) Run sequence (c) Run sequence plot of first order differenced RTT (RTT’), (d) Mean of first order differenced RTT (RTT’) Autocorrelation is commonly used to check the randomness in the measured values and identify the order of AR term and MA term. The randomness is used to determine whether or not the time series is in control. If the time series is random, then the autocorrelation coefficients are near to zero for any or all lag [23]. If non-random, then one or more of the autocorrelation coefficients are significantly non-zero. The solid red line in Fig 3(a) indicates the 95% confidence limits. It is to check whether the autocorrelation coefficient is significantly different from zero or not. The first order differenced RTT (RTT’) is random because the spikes in the autocorrelation plot are near to zero for many lags and most of the autocorrelation coefficients are below the confidence intervals [23]. It indicates that the RTT’ is stationary. The autocorrelation coefficient at lag 3 is significant because the spike in the autocorrelation plot is high and the correlation coefficient lies outside the confidence limit. Therefore the order p and q are less than 3 and adequate to model the RTT. The partial autocorrelation plot for RTT’ is shown in Fig 3(b). The partial autocorrelation function at lag 3 is significant because the correlation coefficient at lag 3 is very high as compared with other coefficient and lies outside of confidence limit. Therefore partial autocorrelation plot suggests that the order of AR term is less than 3. Hence the tentative model for RTT may be any one of forms as p and q varying from 0 to 2: ARIMA(0,1,1), ARIMA(0,1,2), ARIMA(1,1,0), ARIMA(1,1,1) ARIMA(1,1,2), ARIMA(2,1,0), ARIMA(2,1,1) and ARIMA(2,1,2). The model parameter estimation and model diagnosis are helpful to determine the adequate and suitable model from the tentative models [46].

(a)

(b) Fig 3. (a) Autocorrelation plot of first order differenced RTT, (b) Partial autocorrelation plot of first difference RTT. 4. 3. Model Parameter Estimation The model parameters such as ε(t), Φ1, Φ2, θ1 and θ2 which is given in Eq. (2) is estimated using least square estimation method (LSE). The values of estimated parameter are those values which minimize the sum of square of prediction error (residual) [23]. The residual is the difference between the actual value of RTT and predicated value of RTT. The estimated parameters (Φi and θi) for all the tentative models are tabulated in Table 1. Selecting the adequate model for estimation of RTT from the tentative models is difficult because the underestimation of orders ‘p’ and ‘q’ leads to inaccurate estimation of RTT. In the recent years, information based criteria such as Akaike Information Criteria (AIC) are preferred and used to select the suitable model from a group of tentative models [45][46]. The model with minimum value of AIC is adequate for estimation of RTT. AIC is computed as given below ̂

[

]

(5)

where ̂ is the residual variance. It is equivalent to the residual sum of squares divided by the number of observation or index (T). ̂ is called loss function. ‘p’ and ‘q’ are the order of AR and MA term of ARIMA(p,d,q) model. The values of AIC for all the tentative models are given in Table 1. The AIC of ARIMA(2,1,1) is less than all other models. Therefore, the ARIMA(2,1,1) is adequate for estimation of RTT based on the RTT data set 1. In addition to AIC, Final Prediction Error (FPE) is also used to reduce the potential dangers of underestimation of orders ‘p’ and ‘q’ and select the suitable model from a group of tentative models. The model with minimum value of FPE is adequate for estimation of RTT [45][46]. FPE is computed as given below [

]

(6)

T is the number of observations or index. ‘p’ and ‘q’ are order of AR and MA term. V is the loss function and is equivalent to ̂ where ̂ is the residual variance. The values of FPE for all the tentative models are given in Table 1. The FPE of ARIMA(2,1,1) is less than all other models. Therefore ARIMA(2,1,1) is adequate for estimation of RTT based on RTT data set 1. Table 1. The estimated value model parameters for the tentative models Parameters for AR Term

Tentative ARIMA(p,d,q) models for RTT data set 1

Parameters for MA Term AIC

FPE

Φ1

Φ2

Θ1

Θ2

ARIMA(0,1,1)

---

---

0.985

----

8.8367

6.8828

ARIMA(0,1,2)

---

---

0.6613

0.3106

8.7475

6.2950

ARIMA(1,1,0)

-0.2385

---

---

---

9.0381

8.417

ARIMA(1,1,1)

0.3168

---

0.9882

---

8.7425

6.2636

ARIMA(1,1,2)

0.1277

---

0.7765

0.1973

8.7531

6.3306

ARIMA(2,1,0)

-0.2554

-0.0719

---

---

9.05122

8.5290

ARIMA(2,1,1)

-0.9474

-0.0342

-0.7766

---

8.73964

6.2457

ARIMA(2,1,2)

0.5120

-0.3475

-0.1369

-0.8028

8.7455

6.2829

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Applying RTT’(t) = RTT(t) – RTT(t-1) in Eq. (7) ()

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4. 4. Model Diagnosis The validity of ARIMA(2,1,1) model depends on the validity of assumption made while modeling. The assumptions are that the residual has fixed location, fixed variation and fixed distribution [46]. If this assumptions hold good for residual, then ARIMA(2,1,1) model is suitable for estimation of RTT. The histogram and normal probability are the tools used to check the validity of assumptions made on residual. In the histogram of the residual shown in Fig. 4(a), most of the frequency counts are in the middle and frequency counts are moderately dying off out in the tails. Since the histogram indicates a symmetric, moderate tailed distribution, it is essential to further check the normal distribution using normal probability plot. The normal probability plot of the residual is shown in Fig. 4(b). A straight line is fitted with this plot as a reference line. The normal probability plot shows a linear pattern. There are only minor deviations from the reference line. Thus the histogram and normal probability plot indicate that the normal distribution and fixed distribution provide an adequate fit for ARIMA(2,1,1) model.

(a)

(b) Fig. 4. (a) Histogram of residuals, (b) Normal probability of residuals

4. 5. Selection of Adequate Model for RTT Estimation The above said three basic steps are repeated for rest of RTT datasets. Table 2 shows the identified model for 30 RTT data sets, the parameters for model (Φ1, Φ2, θ1 and θ2) and corresponding value of AIC and FPE. There are three different models identified from 30 RTT data sets i.e. ARIMA(1,1,1), ARIMA(2,1,1) and ARIMA(2,1,2). It is essential to select a model from these three models. As said earlier AIC and FPE are good choice to select a model from a group of models [23]. Therefore the model with least AIC and FPE from these group of model is selected. ARIMA(2,1,1) model is repeated many times with less value of AIC and FPE which indicates that the some traffic pattern of RTT is repeated and continued to remain in future. Therefore the ARIMA(2,1,1) model with Φ1=-0.1530, Φ2=-0.1890 and θ1 = 0.08687 is suitable for RTT estimation.

Table 2. Adequate Model for 30 RTT data sets

RTT Data set 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

ARIMA(p,d,q) ARIMA(2,1,1) ARIMA(2,1,1) ARIMA(2,1,1) ARIMA(2,1,2) ARIMA(1,1,1) ARIMA(2,1,1) ARIMA(2,1,2) ARIMA(2,1,1) ARIMA(2,1,1) ARIMA(1,1,1) ARIMA(1,1,1) ARIMA(2,1,2) ARIMA(1,1,1) ARIMA(1,1,1) ARIMA(2,1,1) ARIMA(2,1,1) ARIMA(2,1,2) ARIMA(2,1,1) ARIMA(2,1,1) ARIMA(2,1,2) ARIMA(2,1,1) ARIMA(2,1,2) ARIMA(2,1,2) ARIMA(2,1,1) ARIMA(2,1,1) ARIMA(2,1,2) ARIMA(1,1,1) ARIMA(1,1,1) ARIMA(1,1,1) ARIMA(2,1,1)

Parameters for AR Term Φ1 Φ2 -0.9474 -0.0342 0.3233 -0.2625 0.5001 0.0806 0.3732 -0.2374 0.2049 ---0.9563 -0.1653 -0.4734 0.2674 0.2346 -0.1474 0.3438 -0.3495 0.0848 --0.2099 --0.5426 -0.3203 0.1467 --0.1979 ---0.9073 -0.1338 -1.5203 -0.6649 -0.6287 -0.3725 -0.3414 -0.2424 -0.2043 -0.2844 -0.4653 -0.2564 -0.2428 -0.1602 -0.5541 -0.4283 -0.9046 -0.2080 -0.1530 -0.1890 -0.2220 -0.0251 -0.6742 -0.0982 0.0384 --0.1580 ---0.369 ---1.4453 -0.4888

Parameters for MA Term Θ1 Θ2 -0.7766 --0.9903 --0.9004 --0.9921 -0.0082 0.9889 ---0.6633 --0.2760 0.6994 0.9749 --0.9819 --0.9913 --0.9768 --1.2820 -0.2755 0.9821 --0.9844 ---0.7711 ---0.9168 --0.5716 0.4099 0.9810 --0.9791 --0.6060 0.3700 0.9873 --0.5907 0.3859 0.1817 0.8238 0.0868 --0.9808 --0.3575 0.6108 0.9864 --0.9776 --0.9763 ---0.9754 ---

AIC

FPE

8.7396 8.6089 8.5589 8.5242 8.5907 8.6938 8.8250 8.5791 8.5089 8.5379 8.7505 8.3634 8.8033 8.6844 8.6232 8.5117 8.3527 7.8835 7.9982 8.0817 8.0950 7.7587 8.0396 7.7373 7.9599 8.1611 8.0834 8.0121 8.0031 8.4360

6.2457 5.4800 5.2131 5.0354 5.3813 5.9658 6.8021 5.3194 4.9586 5.1046 6.3139 4.2875 6.6561 5.9100 5.5595 4.9731 4.2421 2.6532 2.9756 3.2348 3.2781 2.3421 3.1016 2.2923 2.8638 3.5023 3.2401 3.0173 2.9904 4.6102

4. 6. Proposed RTT Estimation ) be real valued RTT with non-stationary components. Let ( ( ) Let ( ( ) ) be a white noise. Let and . Therefore the RTT(t) is represented as given below

( )

∑

(

)

∑

(

)

(11)

Let first order difference of RTT(t) be (

( ))

( )

( )

The

( )

(

)

(12)

is said to be ARIMA(2,1,1) if it satisfies that

( )

(

)

(

)

(

)

()

(13)

which has a stationary solution i.e. the mean, variance and autocorrelation structure do not ( )) ( )) change over time so that ( , ( , ( ) ( ) ( ) ( ) and all p roots of Eq. (11) with p = 2, and q roots of Eq. (11) with q=1, are outside of the unit circle. The coefficients , and are estimated using Least Square Estimation Method. By substituting Eq. (12) in Eq. (13), then ()

( (14)

(

)

)

(

)

(

)

(

)

(

)

()

Therefore, the one-step-ahead estimation of RTT(t) is calculated as follow (

)

(

)

()

(

)

(

)

(

)

(

)

()

(15)

where Φ1 = -0.1530, Φ2 = -0.1890 and θ1 = 0.08687 and residue standard deviation of 0.075. It is realized from this model that one-step-ahead forecast of RTT depends on recently observed three RTTs and one prior prediction error or random error. 5. Proposed Loss Differentiation Algorithm The sudden changes in RTT are estimated using the proposed RTT estimation better than other estimation models because it takes the recently measured three RTTs for estimation as discussed in the Section 4.6. The packet losses due to the reason other than congestion like temporary wireless link failure, high BER etc leads to sudden changes in the RTT [57]. By estimating the sudden changes in the RTTs, we can differentiate the non-congestive loss from congestive loss. Therefore in this section, we make use of proposed RTT estimation using ARIMA(2,1,1) model for differentiating the cause of packet losses because it estimates the sudden changes in RTT well. The TCP at source side keeps on increasing the congestion window size (cwnd) until the packet loss occurs. If the source node detects the packet loss in the network, the cwnd is decreased. The increasing/decreasing of the cwnd adapts the offered load to the network. As the cwnd increases, the throughput also increases. Therefore the actual throughput is close to the estimated throughput because there is no built-up of buffer space. At some point, the throughput is not increased even the cwnd is increased. It is because the packet starts queuing at the buffer. Beyond a point, any increase in the cwnd only results in the built-up of buffer space. Therefore the actual throughput is smaller than the estimated throughput. Hence the throughput of backlogged packets (Tb) is the difference between the estimated throughput (Te) and actual

throughput (Ta) ie Tb = Te – Ta [17][31][38][47][51]. The estimated throughput Te = cwnd/ARTT where ARTT be the one-step-ahead estimation of RTT using the proposed RTT estimation ie ARIMA(2,1,1) model. The actual throughput Ta = cwnd/RTT where RTT be the average RTT of the packets acknowledged during the last RTT. The RTT of backlogged packets is greatly affected by the queuing time and is greater than the baseRTT. Therefore the estimated delay caused by the backlogged packets Db = ARTT – baseRTT. Therefore Tb = N/(ARTT-baseRTT) = cwnd/ARTT – cwnd/RTT, where N be the number of backlogged packets which is estimated as N = [(cwnd/ARTT) – (cwnd/RTT)] * (ARTT – baseRTT) There are three states of the network ie Congestion-free state, Congestion-loss state and Non-congestion-loss state. These states of a network are determined by checking the number of backlogged packets (N) with two threshold (α and β). If N < α, the network is assumed to be free from congestion. If N > β, then it is the indication for incipient of congestion in the network. If α < N < β, the packet loss is due to non-congestive reasons. The suggested value of α and β are 1 and 3 [17][38][47]. 6. Proposed Congestion Avoidance Algorithm This section describes the end-to-end congestion control that changes the congestion window size (cwnd) dynamically based three states discussed in the Section 5. These states are identified by monitoring the number of backlogged packets in the network which is estimated with help of delay experienced by the previous packets. Sometimes the implicit information such as packet delay and number of backlogged packets is not sufficient. Therefore this section describes the congestion avoidance algorithm that uses the explicit information such as received signaling strength in addition to implicit information.

For every acknowledgement received at sender, it measures the round trip time (RTT) of the packets. The measured RTTs are then used to estimate the RTT of a packet using ARIMA(2,1,1) model. The variation between the measured RTT and the estimated RTT are taken into account to calculate the number of packets in transit in the network from the sender side. Based on the number of packet in flight and the predefined two threshold values denoted as α and β, the sending data rate of sender is controlled to avoid the congestion in the network proactively so that the throughput of proposed congestion avoidance algorithm using ARIMA(2,1,1) model-based RTT estimation (CA-ARTT) is improved.

Based on these states, we propose the congestion avoidance using ARTT (CA-ARTT) which adjusts the cwnd as given below ⟦

(16)

Since the implicit information like RTT and number of backlogged packets are not sufficient, the explicit information such as Received Signal Strength (RSS) is used to effectively differentiate the cause of packet loss. Since the RSS is a physical layer parameter, ISO-OSI reference model doesn’t permit to share the RSS with TCP of transport layer. Hence cross layer approach is used to share the RSS with TCP. The measured received signal strength is compared with the threshold value called RXThresh. If the received power is less than RXThresh, then it is assumed that the wireless link is bad condition and a possibility for loss of packets. Hence the channel status bit is set to 0. Otherwise channel status bit is set to 1. The channel status bit is put in the reserved field of ACK packet header and sent to the source node. The TCP of source node uses the CSN bit with number of backlogged packet to effectively differentiate the cause of packet loss. The congestion avoidance algorithm using cross layer information and ARTT (CACLARTT) adjusts the congestion window size as given below [(

)

]

⟦

(17) (

)

6.1 Sender Side Algorithm

On receipt of non-duplicate acknowledgement // one-step-ahead estimation of RTT using ARIMA(2,1,1) model Measure the RTT based on time stamp of packet sent and ACK received. Assign the measured RTT to rtt if (first packet’s acknowledgement) rtt1 = rtt2 = rtt3 = rtt

// initialization

else rtt3 = rtt2; rtt2=rtt1; rtt1=rtt; endif Estimate the ARTT using Eq. (15) // resetting the baseRTT if current RTT is less than existing baseRTT if current RTT is less than the baseRTT Update the baseRTT with current RTT endif

// resetting baseRTT if packet loss is determined by Triple Duplicate Acknowledgement if number of duplicate acknowledgement is greater than 3 Update the baseRTT with current RTT endif // Estimation of average RTT for the packets ACKed during the last RTT if the current sequence no. is greater than sequence no. of previously ACKed packet Sum of RTT is updated using the measured RTT (rtt) Number of RTT during last RTT is updated i.e. cntRTT+=1 if (cntRTT > 0) RTT = sumRTT/cntRTT else RTT = rtt endif endif // Estimation of backlogged packets Calculate the estimated delay due to backlog packets (ARTT – baseRTT) Calculate the Te using cwnd and ARTT. Calculate the Ta using cwnd and RTT Calculate the Tb using Te and Ta Estimate the backlog packets (N) using Tb and (ARTT - baseRTT) // Prediction of three states of network and control of cwnd If N > β cwnd = cwnd-1 else if N < α and csn = 1 cwnd=cwnd+(1/cwnd) else if csn = 1

cwnd = cwnd + (1/cwnd) else if csn = 0 no change in current cwnd size // current data rate is unchanged

6.2. Receiver Side Algorithm On receipt of packets at wireless receiver node //Create the acknowledgement packet and update the ACK fields Copy the header of received packet to new ACK packet Update the time-stamp in the new ACK packet Update the sequence number in the new ACK packet // Cross layer information If the received signal strength of mobile node is less than RXThresh, then set the reserved field of ACK packet to 0 i.e. csn = 0 (bad channel) Otherwise Set the reserved field of ACK packet to 1 i.e. csn = 1 (good channel) 7. Experimental/Simulation Setup This section describes the experimental/simulation setup used to evaluate the proposed algorithms: RTT estimation (ARTT), Loss Differentiation Algorithm (LDA) and Congestion avoidance algorithms (CA-ARTT and CA-CLARTT). We use two different networks as shown in Fig. 5 to collect RTTs and analysis the performance of proposed RTT estimation (ARTT). The performance of the proposed congestion avoidance algorithm is evaluated in two different networks as shown in Fig. 6 under different network environments. Two different approaches are generally used to measure the RTT of a packet flowing in a network that is active measurement and passive measurement [48]. In the active measurement approach, the source computer generates a request intentionally to measure the RTTs and gets the reply from the destination. The difference between the time stamps of request/reply is taken as RTT. The PING command is used to measure the RTT in the network shown in Fig. 5(a). It uses an ICMP echo request from a source computer as probing packet to destination computer. On receiving probe packet, the destination computer sends back an ICMP echo reply to the source computer. From the time clock on the source computer, the RTT is readily measured [28][38]. In the passive measurement, a software tool is used to monitor the network traffic and tracks the request/reply from a source/destination computer. Based on the difference between the

time stamps of request/reply, the RTT is calculated. The CAIDA uses the passive measurement approach to collect the RTTs from the network shown Fig. 5(b)

(a) (b) Fig. 5. (a) Network environment used for active measurement of RTT, (b) Network environment for passive measurement. We evaluate the proposed LDA, CA-ARTT and CA-CLARTT using ns-2 simulator [49]. The evaluations of proposed algorithms are performed using the network topology shown in Fig. 6. The LDA, CA-ARTT and CLARTT are evaluated using the network topology shown in Fig. 6(a). In this topology, two nodes denoted as R1 and R2 are considered as routers and a node is set as base station (BS) which is connected with last hop wireless node (Dk). A source/destination pair is assumed to be observable node pair (Sk,Dk) that goes through a wired path and terminated with last hop wireless node. The source/destination pair (Sk,Dk) is used to generate the single TCP flow in the network. Only one TCP flow is set between the observable node pair (Sk, Dk). The network topology with single TCP flow does not fully evaluate the performance of algorithms. Therefore it is essential to do the performance analysis of proposed algorithms in presence of cross traffic TCP flow because in the Internet, the bottleneck link is used by many users who generate the forward and reverse data traffic in the bottleneck link [50]. To do so, source/destination pair (Si,Di) and (Sj,Dj) are used to generate the forward traffic and reverse traffic respectively. Fig. 6(b) shows the bandwidth asymmetric network in which the forward link and the reverse link have the different bandwidth. The networks like Digital Video Broadcast and Asymmetric Digital Subscriber Line networks tend to increase capacity in the forward direction using satellite links or cables, whereas a low-speed path such as a dial-up modem line is used to carry ACKs back to the source. Even if ACKs are smaller in size than data packets, the reverse path is unable to carry the high rate of ACKs. Sometimes the data may be piggybacked with ACK packets which increase the size of ACK packets. It results in congestion on the reverse path. The congestion in the reverse path increases the RTT and causes loss of ACKs. The increase in RTT slows down the growth of congestion window size, which reduces throughput performance.

(a) (b) Fig. 6. (a) Heterogeneous wired-wireless network, (b) Bandwidth asymmetric network

Hence the proposed CA-ARTT is evaluated in the bandwidth asymmetric network as shown in Fig. 6(b). The bandwidth of bottleneck link in the forward direction (Cf) is set ranging from 2Mbps to 16Mbps. The size of data packet (Sd) and the size of acknowledgement packet (Sa) are set to 1000 bytes and 40 bytes respectively. The bandwidth of bottleneck link in the reverse direction (Cr) is calculated based on normalized asymmetric factor (K) is defined as the ratio of the raw bandwidth of the forward direction to the reverse direction, divided by the ratio of the data packet size to ACK packet size [20][51][52]. (18)

where Cf and Cr are bandwidth of forward and reverse channel respectively and Sd and Sa are the size of a data packet and ACK packet respectively. 8. Performance Metrics In this section, various performance metrics that are used to measure the performance of the RTT estimation model, packet loss differentiation algorithm and congestion avoidance algorithm. 8.1. RTT Estimation The RTT measured using active and passive measurement are used to evaluate the proposed RTT estimation using ARIMA(2,1,1). . In order to show the performance of proposed RTT estimation, we compare it with RTT estimation based on the models ARIMA(1,1,1) and ARIMA(2,1,2). One-step-ahead RTT is estimated with respect to the RTTs measured through active and passive measurement. The estimated RTT is compared with the measured RTT. The difference between the measured and estimated RTT is calculated which is called residual or prediction error. Using the residual, the Mean Square Error (MSE), Root Mean Square Error (RMSE) and Normalized Mean Square Error (NMSE) are calculated [53]. The RMSE and NMSE are used to measure how far the average prediction error is from zero. The lower the value of RMSE and NMSE is good for a model.

8.2. Loss Differentiation Algorithm The performance metrics defined in [40][54] are used to evaluate the proposed algorithm. They are 1) Accuracy of congestion loss prediction, 2) Accuracy of wireless loss prediction, 3) Accuracy of overall loss prediction of LDA and 4) Percentage of misclassification. The percentage of congestion loss prediction Ac is calculated using [Dc/Nc] * 100, where Dc is the number of packet losses exactly identified as congestion loss by a LDA and is measured from the simulation as the packet loss rate varies from 0% to 10%. Nc is the total number of packet loss caused by congestion. The percentage of wireless loss prediction Aw is [Dw/Nw] * 100, where Dw is number of packet loss exactly identified as wireless loss by a LDA and is measured from the simulation as the packet loss rate varies from 0% to 10%. Nw is total number of packet loss caused by high BER. The accuracy of LDA means the accuracy of predicting both congestion loss and wireless loss and is [[Dc+Dw]/[Nc+Nw]] * 100. It gives the total accuracy of loss differentiation algorithm. Sometimes the loss differentiation algorithm may wrongly differentiate the congestion loss as wireless loss or vice versa which is called as misclassification of packet loss. This is because of the deviation in estimation of backlogged packets and threshold value used to differentiate packet losses. In general, the percentage of misclassification of any LDA must be as low as possible. The percentage of misclassification is calculated as given: [(Mc + Mw)/(Nc+Nw] * 100 where Mc is number of packet loss misclassified as congestion loss, Mw be number of packet loss misclassified as wireless loss. 8.3. Congestion Avoidance Algorithm The important performance metrics for evaluation of proposed congestion avoidance algorithm is throughput which is defined as the ratio between the number of bytes sent from a node of interest and transferring time in seconds. The transferring time is defined as the time that a node of interest takes to transfer a block of data to the destination. The block of data is measured in bytes. The throughput is calculated as given below [50][55].

(26) 9. Performance Analysis This section describes the performance analysis of proposed RTT estimation, LDA and congestion avoidance algorithm. In the Section 9.1, the RTTs collected using active/passive measurement approaches are taken to evaluate the proposed RTT estimation. Section 9.2 presents the performance analysis of proposed LDA and Section 9.3 presents the performance analysis of CA-ARTT under different network environments. Finally the Section 9.4 presents the performance analysis of CA-CLARTT. 9.1. RTT Estimation using ARIMA(2,1,1) model The prediction error or residual obtained from a good model is always low. Less residual is a good evident for an estimator. In order to quantitatively assess the performance of proposed RTT estimation, Mean Square Error, Root Mean Square Error (RMSE) and Normalized Mean

Square Error (NMSE) of residual are used. The calculated MSE, RMSE and NMSE are shown in Table 3 from which it is observed that ARIMA(2,1,1) model based RTT estimation has lower RMSE and NMSE. It indicates that ARIMA(2,1,1) model is suitable for RTT estimation. Table 3. Residual Characteristic of RTT RTT Measurement Approach

Passive Measurement

Active Measurement

Model

MSE

RMSE

NMSE

ARIMA(1,1,1)

8784.045965

93.72324133

0.388991623

ARIMA(2,1,1)

8243.297817

90.79260882

0.331774392

ARIMA(2,1,2)

9109.434

95.44335

0.34935

ARIMA(1,1,1)

3000.690952

54.77856289

0.05508519

ARIMA(2,1,1)

2411.868911

49.11078203

0.050336315

ARIMA(2,1,2)

2852.196

53.40596

0.051878

The RTT of a packet is greatly affected by the queuing or buffering time, wireless link failure, change in route. The delay caused by congestion have a linear growth mostly rather than sudden growth. But the delay caused by the reroute, temporary link failure, multipath routing causes sudden increase/decrease in RTT [57]. These changes are estimated by the RTT estimation using ARIMA(2,1,1) model better than other two models. It is highlighted with circle in the Fig. 7(a) It indicates the sudden increase/decrease in RTT which is estimated by the proposed ARIMA(2,1,1) model better than other models. It helps us to differentiate the congestive packet loss from non-congestive packet loss because the non-congestive packet loss induces sudden changes in the RTT. Therefore the ARIMA(2,1,1) model-based RTT estimation is suitable for differentiating the cause of packet losses. Second the RTTs measured using active measurement is taken to evaluate the proposed RTT estimation by comparing other two models. From Fig. 7(b), the RTT estimation using ARIMA(2,1,1) is very close to actual RTT whereas the estimations of other two models are always higher than the actual RTT. Further the RTT estimation using ARIMA(2,1,1) model is matched with actual RTT better than the other models. The sudden changes in RTT is also detected and estimated better than the other models. Presently, baseRTT, average RTT, minimum RTT and Smooth RTT are used in the packet loss differentiation algorithm. baseRTT is the minimum value of RTT measured

throughout the life time of measurement. Minimum RTT is the minimum value of RTT measured but it is reset to the current value of RTT whenever a packet loss is detected. Smooth RTT is a RTT estimation using Exponentially Weighted Moving Average (EWMA) model. Fig. 7(c) shows the comparison of proposed RTT estimation with SRTT, baseRTT, MinRTT and average RTT. The sharp and sudden changes in actual RTT are not perfectly matched with the SRTT, average RTT. Since the SRTT smoothens the actual RTT, it is robust but far from being perfect. In case of packet loss differentiation, it is essential to detect the sharp and sudden changes in the RTT. Further, SRTT is biased toward the long history of measured RTT. But the sudden variation in RTT due to temporary link failure or high BER affects the recent RTTs frequently [7][24]. Therefore recently measured RTT are significant to estimate the RTT and differentiate the cause for the packet losses. The proposed RTT estimation takes recently measured three RTTs so that the sudden changes in RTT are estimated better than other models. Therefore we make use of the proposed RTT estimation using ARIMA(2,1,1) model for differentiating the congestive packet loss from non-congestive packet loss.

(a)

(b)

(c) Fig. 7. (a) Comparison of Proposed RTT Estimation with RTTs obtained from Passive Measurement, (b) Comparison of Proposed RTT Estimation with RTTs obtained from Active Measurement, (c) Comparison of RTT Estimation with SRTT, baseRTT, MinRTT and Average RTT 9.2. Loss Differentiation Algorithm (LDA) The performance of proposed LDA is assessed under the single TCP flow and cross traffic TCP flow. In the first scenario, single TCP flow is set between the observable node pair (Sk, Dk) of the network shown in Fig. 6(a) and the cross traffic generating node pair (Si, Di and

Sj, Dj) are not connected. In the second scenario, apart from observable node pair, two traffic generating node pair (Si, Di and Sj, Dj) are connected with bottleneck link to generate the cross traffic TCP flow. First TCP without LDA is simulated in the network topology shown in Fig. 6(a) for 100sec with the packet loss rate of 0%. Only a single TCP flow is generated in this network. From the simulation, the total number of packet loss caused by congestion (Nc) and total number of packet loss caused by wireless loss (Nw) are measured. Next the packet loss rate is varied from 1% to 10% and simulation is carried out. Each time changing the packet loss rate, Nc and Nw are measured and tabulated in Table 4. Similarly the simulation is performed for cross traffic TCP flow with the packet loss rate varying from 0% to 10%. The measured value of Nc and Nw are tabulated in Table 4. With the known causes for packet losses (Nc, Nw, Nt) given in Table 4, Ac, Aw, At and Mt of various loss differentiation algorithm like TCP-Vegas, Normalized throughput gradient algorithm(NTG), TCP-Veno, Robust end-to-end loss differentiation algorithm (RELD) are calculated and shown in figures 8, 9, 10 and 11. Table 4 Performance of TCP without LDA in presence of Single TCP Flow Nc and Nw (in packet) measured by varying Packet loss rate from 0% to 10% TCP Flow

PLR

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10 %

Nc

153

150

130

144

96

70

46

13

4

1

2

Nw

0

0

3

2

10

26

37

37

38

27

10

Nc

61

77

68

67

47

43

19

9

9

1

2

Nw

19

22

16

17

30

34

62

37

34

8

2

Single

Cross Traffic

The accuracy of congestion loss prediction of the proposed LDA is shown in Fig. 8. It is realized that the proposed LDA provides consistently better performance as the PLR varies from 0% to 10%.. The proposed LDA under single TCP flow interprets the congestion with more than 87% of accuracy in HWWN as compared with other LDAs as shown in Fig. 8(a). The proposed LDA under cross traffic TCP flow differentiate the congestion loss with 33% to 93% accuracy as the PLR varies from 0% to 10%. The performance of proposed LDA is consistently in between 79% to 90% as the packet loss rate varies from 0% to 5%. After that, the accuracy of congestion loss prediction is degraded up to 33%. The accuracy of congestion loss prediction of other LDAs is less than 10% whereas the proposed LDA has 93.44% of accuracy in congestion loss prediction. Since the proposed LDA uses the ARTT and delay caused by backlogged packets for

Ac

accurately estimating the number of backlogged packets in the network, it provides better performance than other LDAs.

(a) Fig. 8. Accuracy of Congestion Loss Prediction (a) Presence of Single TCP(b) Flow, (b) Presence of Cross Traffic TCP Flow

Aw

The accuracy of wireless loss prediction is shown in Fig. 9. It is observed that the percentage of accuracy of wireless loss prediction of NTG and Veno are higher than the RELD and proposed LDA. This is because NTG and Veno misinterpret the congestion loss as wireless loss i.e the misclassification of congestion loss. But RELD and proposed LDA differentiate packet losses better than the NTG and Veno. Since the ARTT used in the proposed loss differentiation algorithm takes RTTs of recently acknowledged three packets for estimation of RTT, it accurately predicts the fluctuation in the RTT thereby the congestion loss events and wireless loss events are predicted better than others. The proposed LDA provides 40% to 90% of wireless loss prediction exactly in HWWN.

(a) (b) (b) Presence of Fig. 9. Accuracy of wireless loss prediction (a) Presence of Single TCP Flow, Cross Traffic TCP Flow The accuracy of LDA means the accuracy of both congestion loss and wireless loss prediction in HWWN. The performance of LDA is shown in Fig. 10. The proposed LDA provides 67% to 89.6% of accuracy under the single TCP flow and 55% to 89% of accuracy under cross traffic TCP flow. The performance of proposed LDA is improved because the percentage of misclassification is considerable less as compared with other LDAs. This is because the sharp and sudden changes in RTT due to cross-traffic TCP flow in the bottleneck

At

link and wireless transmission error are able to capture by ARTT.

(a) (b)Cross Traffic TCP Fig. 10. Accuracy of LDAs (a) Presence of Single TCP Flow, (b) Presence of Flow

Mt

The important issue of a loss differentiation algorithm is that differentiating the congestion loss as wireless loss and vice versa. It causes the congestion avoidance algorithm to unnecessarily increase/decrease the congestion window size which degrades the performance of TCP in terms of throughput. For a good LDA, the percentage of misclassification must be as low as possible. The proposed LDA provides less misclassification as compared to other LDAs as shown in Fig. 11. The estimated RTT and estimated number of backlogged packets are essential in differencing the cause for the packet losses. Since the proposed LDA uses RTT estimation using ARIMA(2,1,1) model and delay caused by backlogged packets to differentiate the packet losses, the percentage of misclassification is less compared to other LDAs.

(a) (b) TCP Flow, (b) Fig. 11. Percentage of misclassification of packet losses (a) Presence of Single Presence of Cross Traffic TCP Flow 9.3. Congestion Avoidance using ARTT (CA-ARTT) In this section, we evaluate the CA-ARTT in the topology shown in Fig. 6(a) with single TCP flow, cross traffic TCP flow and different network environments like network with high BER (wireless network), network with high latency (satellite network), network with small packet size (wireless sensor network) and network with different uplink and downlink bandwidth (asymmetric network).

9.3.1. Analysis of CA-ARTT in presence of Single TCP flow The number of bytes sent from the sender node is decided by the dynamic variation of congestion window size based on the available bandwidth of bottleneck link. The variation in congestion window size is decided by the number of backlogged packets in the network. The proposed algorithm (CA-ARTT) estimates the backlogged packets better than TCP-Vegas because it takes the RTT estimation using ARIMA(2,1,1) model for estimation of backlogged packets. In case of CA-ARTT, the number of bytes sent from Sk is higher than TCP-Vegas at every time instant as shown in Fig. 12(a). The throughput increases as the simulation time increases. After about 100sec, throughput stabilizes which means that the available bandwidth of bottleneck link is effectively and fully utilized. The improvement in throughput of CA-ARTT is due to better estimation of RTT and backlogged packets using ARIMA(2,1,1) model 9.3.2. Analysis of CA-ARTT in presence of Cross Traffic TCP Flow The performance evaluation of congestion avoidance algorithm with no cross traffic does not fully provide the investigation of its behaviors. Hence the behavior of proposed algorithm is likely to be observed when the congestion avoidance algorithm is deployed in the heterogeneous network. Therefore it is essential to evaluate the performance of proposed congestion avoidance algorithm in presence of cross traffic because the aggregate behavior of cross traffic in the bottleneck link may induce a queue fluctuations, patterns of packet losses and fluctuations of the total link utilization at the bottleneck link [57][58]. Therefore the forward data traffic and reverse data traffic are introduced in the network topology shown in Fig. 6(a). The proposed CA-ARTT triggers the increase/decrease/unchanged of congestion window size dynamically based on estimation of backlogged packets using ARTT. The CA-ARTT provides better performance in terms of throughput than TCP-Vegas as shown in Fig. 12(b). The CA-ARTT provides 12.8% to 24.4% of throughput improvement than TCP-Vegas. Next, as compared with throughput performance in presence of single TCP flow, the throughput of TCP-Vegas is reduced by 13% whereas the throughput of CA-ARTT is reduced by 5%.

(a) (b)(b) Throughput of Fig. 12. (a) Throughput of TCP-Vegas and CA-ARTT with Single TCP Flow, TCP-Vegas and CA-ARTT with Cross Traffic TCP Flow 9.3.3. Analysis of CA-ARTT with varying BER

Since the network topology shown in Fig. 6(a) terminates with wireless link, it is essential to analysis the performance of CA-ARTT with varying Bit Error Rate (BER). Generally the wireless link has higher BER than the wired link. i.e. BER of 10-3 bits/s for wireless link and BER of 10-6 for wired link. The BER in the wireless or satellite links is as high as 10-4 i.e. one bad bit out of 10000 bits. Therefore the BER is introduced in the wireless link that varies from 10-5 to 10-2. Generally higher the BER, then lower the throughput. This is because the higher BER leads to more number of packet losses in the wireless link. The BER of 10-2 is first set in the wireless link of network topology shown in Fig. 6(a). The proposed CA-ARTT provides 11.1% to 22.8% performance improvement in terms of throughput than TCP-Vegas as shown in Fig. 13(a). Next the BER is varied from 10-3 to 10-5 and the simulation is performed for 200sec

(a) (b) (b) Throughput Fig. 13. (a) Throughput of TCP-Vegas and CA-ARTT with BER of 10-2 bits/sec, vs BER varying from 10-5 to 10-2 bits/sec Since the different BER ranging from 10-2 to 10-5 is set in the wireless link, the packet losses are induced in the wireless link. These packet losses are non-congestive packet losses. Therefore it is essential to differentiate these packet losses from congestive packet losses. Since the proposed LDA differentiate the non-congestive packet losses from congestive packet losses, the throughput of CA-ARTT is better than TCP-Vegas as shown in Fig. 13(b). It provides 10% to 22% throughput improvement than Vegas. 9.3.4. Analysis of CA-ARTT with varying packet size The packet size used in X.25 and Ethernet interface is 576bytes and 1460bytes respectively. Similarly packet size used in wireless sensor network is less 512bytes depending on configuration of sensor nodes. Therefore it is essential to evaluate the performance of proposed congestion avoidance algorithm by varying the size of data packets. The data packet size varies from 128bytes to 2048bytes. From Fig. 14, it is found that the throughput is approximately 65Kbps for TCP-Vegas and 72Kbps for CA-ARTT. The throughput of CA-ARTT improves by 7.9% to 14.4% than TCP-Vegas. Since the throughput of CA-ARTT is greater than TCP-Vegas with packet size ranging from 128bytes to 2048bytes, the proposed CA-ARTT is suitable for HWWN. The throughput of TCP-Vegas and CA-ARTT are consistent and stable for varying packet size.

TCP-Vegas

Throughput in Kbps

60

CA-ARTT

50 40 30 20 10 0

128256

512

1024 Packet size in Bytes

2048

Fig. 14. Throughput of TCP-Vegas and CA-ARTT as the packet size varies from 128bytes to 2Kbytes with PLR of 1% 9.3.5. Analysis of CA-ARTT with varying Latency Considering the satellite of geostationary orbit with one way delay of 200msec, we choose 400msecs as the maximum round-trip time. Hence, the performance of proposed CAARTT is done by varying the round-trip propagation delay from 100msec to 400msec. From Fig. 15(a), it is found that the performance of CA-ARTT is improved by 30% for the round-trip delay of 100msec and 31% for round-trip delay of 150ms and 12% for round-trip delay of 200msec. But the performance of CA-ARTT is similar to TCP-Vegas for the network taking RTT more than 200msec like satellite communication. Next the above said simulation is performed by setting the packet loss rate to 1% and the throughput are measured and shown in Fig. 15(b). It is found that the performance of CA-ARTT is improved for the network taking round-trip propagation delay up to 250msec by 10% to 20% under the packet loss rate of 1%. After that, the performance of CA-ARTT is similar to TCP-Vegas. This is because of the RTT estimation method used in the LDA of CA-ARTT. The RTT estimation method (ARTT) takes RTT of recently acknowledged three packets for estimation which is suitable for detecting sudden changes in the measured RTT. In case of satellite communication with RTT more than 200msec, the estimate of RTT using ARTT is very similar to the estimate of RTT using SRTT and average RTT because it smoothen the measured RTT. Therefore the proposed LDA triggers the congestion avoidance phase similar to TCP-Vegas so that the throughput of CA-ARTT and TCPVegas are same for network with RTT above 200msec.

(a) Fig. 15. (a) Throughput of TCP-Vegas and CA-ARTT as the RTT varies(b) from 100msec to

400msec with PLR of 0%, (b) Throughput of TCP-Vegas and CA-ARTT as the RTT varies from 100msec to 400msec with PLR of 1% 9.3.6. Analysis of CA-ARTT in bandwidth asymmetric network Fig. 16(a) shows the performance of TCP-Vegas in terms of throughput in which the maximum throughput is 10.1Kbps for the simulation time of 200sec. Fig. 16(b) shows the performance of CA-ARTT in terms of average throughput in which the maximum throughput is 31.4Kbps for the simulation time of 200sec. The reason for introducing the backward traffic is to make the reverse link more congested. Because of the more traffic at reverse link, the ACK packets may get delayed or ACK packets may be dropped. Since the proposed RTT estimation and LDA estimates the delay and the backlogged packets respectively better than the TCP-vegas, the proposed CA-ARTT provides 4 times improvement in throughput for the normalized asymmetric factor (K) varying from 2 to 16.

(a) (b) in presence of Fig. 16. Comparison of Average Throughput in Bandwidth Asymmetric Network Backward Traffic.(a) TCP-Vegas (b) CA-ARTT It is essential to evaluate the performance of proposed CA-ARTT under different Bit Error Rate (BER). Therefore we set the BER ranging from 10-5 to 10-2 in the forward link. Since the bandwidth of reverse link is low as compared with forward link, there is a possibility of packet losses in the reverse link. But in case of forward link, the packet losses are introduced by setting the BER at forward link i.e. the link between the node R1 and R2 as shown in Fig. 6(b). Fig. 17 shows the performance of proposed TCP-Vegas and CA-ARTT under different BER. Under low BER, the performance of CA-ARTT is improved by 0.6 to 1.1 times than TCPVegas for different value of K. Since the proposed loss differentiation algorithm predicts the cause for packet loss better than the TCP-Vegas, the CA-ARTT provides better performance in presence of higher BER.

Fig. 17. Average Throughput in Bandwidth Asymmetric Network as BER varies from 10-5 to 10-2 bits/sec (a) TCP-Vegas (b) CA-ARTT. (b) (a) Further we evaluate the proposed CA-ARTT with the round-trip time ranging from 100msec to 400msec [56]. In Fig. 6(b), the one way propagation delay between Si to R1 and R1 to Di is set to 1msec. The one way delay between R1 and R2 is 48msec. Therefore a packet in this network takes 50msec as one way propagation delay to destination and 100msec as round trip time. In order to set 150msec as the round trip time, the propagation delay between the R1 and R2 is set to 73msec. Similarly, the propagation delay between the R1 and R2 is set accordingly in order to get 200msec, 250msec, 350msec and 400msec as round trip time.

(a) Fig. 18. Comparison of Throughput in Bandwidth Asymmetric Network (b) as RTT varies from 100msec to 400msec with PLR of 0% (a) TCP-Vegas (b) CA-ARTT Fig. 18 shows the performance of TCP-Vegas and CA-ARTT in terms of throughput for different propagation delay with 1% packet loss rate. The throughput of CA-ARTT improves by 4% to 65% than TCP-Vegas for K varying from 2 to 16. The TCP-Vegas provides 10% to 52% improvement in throughput than CA-ARTT for K=32.

Fig. 19. Comparison of Throughput in Bandwidth Asymmetric Network as RTT varies from 100msec to(a) 400msec with PLR of 1%. (a) TCP-Vegas (b) CA-ARTT. (b)

9.4. Congestion Avoidance using ARTT and RSS (CA-CLARTT) The proposed CA-CLARTT is evaluated in heterogeneous wired-wireless network in presence of single TCP flow and cross traffic TCP flow. The network topology is shown in Fig. 6(a). It is observed from Fig. 20(a) that under BER of 0%, the throughput performance of CACLARTT is improved by 10% than TCP-Vegas and better than the CA-ARTT. It is realized from Fig. 20(b) that under BER of 10-2, the throughput performance of CA-CLARTT is improved by 15 to 25% than TCP-Vegas.

Fig. 20 Throughput Comparisons under Single TCP Flow (a) BER of 0% (b) BER of 10-2 bits/sec Next the proposed CA-CLARTT is evaluated in presence of cross traffic TCP flow. Under BER of 0%, the throughput performance of CA-CLARTT is improved by 23% than TCPVegas as shown in Fig. 21(a). Next, the BER is set to 10-2 in the wireless link of network topology shown in Fig. 6(a). It is observed Fig. 21(b) that under BER of 10-2, the throughput performance of CA-CLARTT is improved by 32% than TCP-Vegas.

Fig. 21 Throughput Comparisons under Cross Traffic TCP Flow (a) BER of 0% (b) BER of 10-2 bits/sec 10. Discussion Our contribution in this paper is explained in three folds: 1) RTT Estimation, 2) Packet loss differentiation and 3) Congestion avoidance algorithm. First we develop a model called ARIMA(2,1,1) to estimate the RTT after analyzing RTT data sets collected from CAIDA. It is observed from the results that RTT estimation using ARIMA(2,1,1) model has low MSE, RMSE and NMSE as compared to other RTT estimations such as SRTT, Average RTT, and different type of ARIMA model as shown in Section 9.1. It is observed from the results that the proposed ARTT estimates the sudden changes in the RTT better than other estimations. This is because the proposed ARTT takes the RTTs of recently acknowledged three packets for estimation. But SRTT takes the weighted average of all previous RTTs and current RTT so that it is biased toward the history of RTTs which is not suitable for detecting the sudden changes in RTT. The RTTs of recently acknowledged packets are very much suitable for differentiating the cause for the packet loss. Hence the proposed estimation model provides better performance in the congestion avoidance algorithm. In the second fold, we propose a LDA which takes ARTT for estimating the number of backlogged packets in the network which is then used to distinguish the cause of packet loss. The proposed loss differentiation algorithm (LDA) provides improved performance over other LDAs in two network environments i.e. Single TCP flow and Cross traffic TCP flow as shown in Section 9.2. The performance of proposed algorithm is measured through the four metrics: 1) percentage of accuracy of congestion loss prediction (Ac), 2) percentage of accuracy of wireless loss prediction (Aw), 3) percentage of accuracy of LDA (At) and 4) percentage of misclassification (Mt). In presence of single TCP flow and packet loss rate varies from 0% to 10%, the Ac of proposed LDA is consistently from 87% to 100%, the Aw of proposed LDA is from 40% to 90%. At of proposed LDA is 67% to 87.6% and Mt is in between 10.3% and 32.5%. Similarly in presence of cross traffic TCP flow, the overall performance of the proposed LDA is maintained from 55% to 89% in differentiating the congestion packet loss and wireless packet loss. Since the proposed LDA takes the ARTT and delay due to backlogged packets for differentiating the congestive packet loss from non-congestive packet loss, its performance of LDA is improved. For a good LDA, the percentage of misclassification must be as low as possible. In case of proposed LDA, the percentage of misclassification is low compared with other LDAs. The main advantages of the proposed LDA are 1) It is a simple algorithm and

requires small changes at sender side TCP implementation, 2) It does not require any coordination with the receiver side TCP other than acknowledgement, 3) Only the RTTs of recently acknowledged three packets are required for estimation of RTT. In the third fold, we propose a congestion avoidance algorithm (CA-ARTT) which control the data rate at sender by adjusting the congestion window size based the packet loss differentiation. The proposed CA-ARTT is evaluated in the two networks (Heterogeneous wiredwireless network and Bandwidth asymmetric network) under different network environments such as varying RTT from 100msec to 400msec, varying BER from 10-2 to 10-5. In case of bandwidth asymmetric network, the performance of CA-ARTT is improved for the normalized asymmetric factor (K) ranging from 2 to 16. In case of K = 32, the bandwidth of reverse link varies from 2.5Kbps to 20Kbps. Since it is low bandwidth in the reverse link, the average throughput of CA-ARTT is low as compared to TCP-Vegas for K=32. The performance of CAARTT is improved with respect to throughput under various network environments as shown in Table 5. Table 5 Comparison of CA-ARTT and TCP-Vegas in terms of Throughput

Heterogeneous Wired-Wireless Network (HWWN)

Heterogeneous Wired-Wireless Network (HWWN)

Type of Network

Network Environmental settings

% of throughput improvement compared with TCP-Vegas

In presence of single TCP flow

4.3% to 10.1%

In presence of cross traffic TCP flow

12.8% to 24.4%

In presence of cross traffic TCP flow and 11.1% to 22.8% BER set to 10-2 bit/sec In presence of cross traffic TCP flow and 5.9% to 13.2% BER ranging from 10-5 to 10-2 bits/sec In presence of cross traffic TCP flow, 7.9% to 14.4% packet size ranging from 128bytes to 2048bytes and packet loss rate set to 0% In presence of cross traffic TCP flow, 6% to 12.6% packet size ranging from 128bytes to 2048bytes and packet loss rate set to 1% In presence of cross traffic TCP flow, 0% to 31.8% RTT ranging from 100msec to 400msec

and packet loss rate set to 0% In presence of cross traffic TCP flow, 0% to 18.5% RTT ranging from 100msec to 400msec and packet loss rate set to 1%

Bandwidth Asymmetric Network

In presence of single TCP flow and Performance is same as TCP Vegas packet loss rate set to 0% In presence of backward traffic TCP flow 1.5 to 4 times improvement for and packet loss rate set to 0% normalized asymmetric factor varying from 2 to 16. The throughput of CAARTT is degraded by 0.39 times In presence of backward traffic TCP 0.5 to 1.5 times improvement for flow, BER ranging from 10-5 to 10-2 normalized asymmetric factor varying bits/sec from 2 to 16. The throughput of CAARTT is degraded by 0.6 to 1.1 times In presence of backward traffic TCP flow, packet size ranging from 128bytes to 2048bytes and packet loss rate set to 1%

0.5 to 2.65 times improvement for normalized asymmetric factor (K) varying from 2 to 16. K=32, The throughput of CA-ARTT is degraded by 0.01 to 0.6 times for K=32

In presence of backward traffic TCP 4.3% to 65.3% improvement for flow, RTT ranging from 100msec to normalized asymmetric factor (K) 400msec and packet loss rate set to 1% varying from 2 to 16. The throughput of CA-ARTT is degraded by 10.7% to 52.11% for K=32

In order to improve the performance CA-ARTT in differentiating the congestive packet loss from non-congestive packet loss, the explicit information (RSS) is used through a cross layer signaling method. The Received Signal Strength (RSS) of receiver is taken into account for differentiating the packet losses in HWWN. The performance of enhanced CA-ARTT called CACLARTT is measured in terms of throughput and found better than CA-ARTT and TCP-Vegas. The cross layer information i.e. RSS is piggybacked with ACK packet so that the communication overhead is negligibly small. Since the wireless link quality in terms of received signal strength is informed to the sender, the non-congestive packet losses are determined at the sender better

than TCP-Vegas and the congestion window size is adjusted accordingly. Therefore the performance of CA-CLARTT is by 15% to 25%. 11. Conclusion and Future work In this paper, we propose a congestion avoidance algorithm using ARIMA(2,1,1) modelbased RTT estimation and RSS in heterogeneous wired-wireless network. The proposed algorithm consists of following components: RTT estimation, backlogged packet estimation, packet loss differentiation and congestion window control. We propose a RTT estimation using ARIMA(2,1,1) model after analyzing the RTTs measured from internet. This model takes recently acknowledged three RTTs to estimate the RTT and provides better performance in estimating the sudden changes in RTT. The sudden changes in RTT are caused by temporary link failure and high BER etc. Therefore we make use of this model to differentiate the noncongestive packet loss from congestion packet loss by estimating the backlogged packets. Based on the cause of the packet loss, the proposed CA-ARTT adjusts the congestion window size to mitigate the congestion in the network. In order to improve the accuracy of packet loss differentiation, we use Received Signal Strength of wireless receiver in HWWN in the proposed CA-CLARTT. The proposed Loss Differentiation Algorithm using ARTT, CA-ARTT and CACLARTT are evaluated under the different network environments. The performance of CAARTT is improved with respect to throughput because of the proposed ARTT and LDA. In this paper, two thresholds α and β are used to determine the state of the network (congestion-free state, congestion loss state and non-congestion loss state). Since the behavior of network is dynamically changed time to time, the value of α and β can be determined dynamically in future. Next the Retransmission Time-Out (RTO) is presently calculated with the help of SRTT. The RTO has a critical effect on the congestion control of TCP. Low value of RTO results in frequent retransmission of packets and high value of RTO results in overflow of buffer and unstable network. Therefore it is necessary to have good RTT estimation method to calculate the RTO of a packet. The proposed ARTT may be used to estimate the RTO in future. Hence in future, we plan to include the dynamic variation of thresholds (α and β) to estimate the state of the network and calculation of RTO using ARTT in the congestion avoidance algorithm (CA-CLARTT) and test it in the real network.

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Mr. A. Jeyasekar was born at Vickramasingapuram, Tirunelveli, Tamilnadu, India on June 11, 1969. He received his B.E. (Electronics and Communication Engineering) from The Indian Engineering College affiliated to Madurai Kamaraj University, Tamilnadu, India. He received his M.E. (Computer Science and Engineering) from Karunya Institute of Technology affiliated to Anna University, Tamilnadu, India in the year 2004. He has published/presented 14 papers in international/national journals and conferences. He is currently pursuing Ph.D in the area of congestion avoidance algorithm in heterogeneous wired-wireless network. His area of interest includes Network Security, Software Quality Management. He is presently working as Assistant Professor (Selection Grade) in the department of Computer Science and Engineering, SRM University, Tamilnadu, India.

Dr. S.V. Kasmir Raja was born at Kallikulam, Tirunelveli district, Tamilnadu, India, in the year 1947. He graduated in the year 1967 at St. Joseph’s College, Thiruchirapalli, Tamilnadu, India. He did his research in Indian Institute of Science, Bangalore, India. He was awarded National

Associateship by UGC to do Post Doctoral work from 1983-86. Six candidates have been awarded Ph.D. degree under his guidance in computer Science. He has more than 60 papers in International/National Journals and Conferences. His area of interest includes computer networks and software engineering. He served as Professor and Head, Department of Computer Science, St. Joseph’s College, Tiruchirapalli, Tamilnadu, India, for more than 17 years. He is currently working as Dean (Research) at SRM University, Chennai, Tamilnadu, India. American Bibliographical Institute, USA, awarded “The Man of Year – 1999” and International Bibliographical Centre, Cambridge, England awarded “2000 Outstanding Scholar of 20th Centaury”

Mrs. R. Annie Uthra is an Asst. Professor in the Department of Computer Science and Engineering at SRM University. Additionally, she is a certified Adjunct Faculty Carnegie Mellon University, Pittsburgh, USA for the two courses Software Architecture and Analysis of Software Artifacts from 2013 to till date. A graduate of SRM University's Masters of Engineering in Computer Science & Engineering program, she has completed her PhD from SRM University and the dissertation topic is entitled "Predictive Congestion Control Algorithms for Wireless Sensor Networks". She has published more than 20 International/National Journals and conference papers. Her scholarly and teaching interests include Wireless Sensor Networks, Cloud Computing, Positioning and Navigation, Congestion Control in WSN, Energy Aware Routing Techniques and IoT. Prior to coming to SRM, she worked as a Systems Analyst for Computer Point, Tirunelveli.