Available Bandwidth Prediction using Wavelet Neural ... - IEEE Xplore

Proceedings of International Conference on Circuits, Communication, Control and Computing (I4C 2014)

Available Bandwidth Prediction using Wavelet Neural Network in Mobile Ad-hoc Networks Shilpa Shashikant Chaudhari1 , Rajashekhar C. Biradar2 1 Department of Electronics and Communication Engineering 2 Department of Information Science and Engineering Wireless Information Systems Research Laboratory Reva Institute of Technology and Management, Bangalore-560 064, India E-mail:{shilpachaudhari, raj.biradar}@revainstitution.org

Abstract—Many multimedia applications over Mobile Ad hoc NETworks (MANETs) require Quality of Service (QoS) to meet real-time services. Accurate Available Bandwidth (AB) prediction and allocation of AB are important component for QoS provisioning which is affected by many factors such as latency, bandwidth, reliability, packet-loss, memory size, buffer cache, available capacity, and CPU speed. Media Access Control (MAC) protocol is responsible for efficient usage of AB in MANET to provide QoS. In this paper, we propose a novel AB prediction mechanism in MANET that is necessary for efficient AB allocation to support real-time and multimedia communication. AB prediction mechanism is being designed with wavelet neural networks. Simulation result shows that the predicted resource closely match with the actual values. Maximum variation between predicted AB and real AB is approximately 20%.

Keywords: MANETs, Wavelet neural network, Available bandwidth prediction I. I NTRODUCTION MANET provides flexible means of communication with no infrastructure, where the nodes operate as end hosts as well as routers and may dynamically enter/leave the network which affects routing path rapidly and unexpectedly. Ever increasing demand for streaming applications to reach remote wireless devices constrained the growth of wireless networks due to various factors such as limited bandwidth, limited size of mobile devices, delay involved in processing and communicating with other devices, etc. The main challenge of providing such applications in MANETs is QoS. Unique characteristics of MANET (such as shared wireless medium, mobility, distributed multi-hop communication) make QoS provisioning more challenging. QoS solutions for these applications rely on the exact availability of precise AB utilization for a link. During packet transmission, the entire capacity of a channel can not be used because some amount of bandwidth is needed for communication related overheads (such as initiating communication, neighbor node interferences, etc.) which reduces the node’s AB[1]. Prediction of accurate AB challenges in MANET includes identification of the nodes in the carrier sense range, intra-flow contention, synchronization of idle period at sender and receiver, and estimation of the collision probability. Researchers have proposed various techniques to estimate

MSRIT, BANGALORE, India, 21-22 NOVEMBER 2014

accurate AB in wireless networks. These AB estimation techniques are classified into passive, active and model-based categories. In active techniques, probe packets are sent from source to the receiver to recover individual link level information which may disturb and delay the normal network traffic. The accuracy of these techniques is still not satisfactory. In passive techniques, aggregate data is collected at router level to infer network performance information from passive observation which does not disturb the network traffic but waste limited computation and storage resources causing delay. In model-based techniques, mathematical model will be developed which requires complex mathematical structure. The tradeoff between accuracy and overhead with respect to AB estimation is explained in [2]. The performance of highly dynamic nature of MANET could be enhanced by adopting some intelligent techniques which have significant potential to solve the challenges of predicting AB. Determining future status of the wireless network is not straightforward since many external factors play an important role in determination of variations in the traffic generated by the users as well as interferences. Some prediction strategies must be adopted which does not require much overhead. The existing prediction technologies can be classified into linear prediction and non-linear prediction [4]. The representatives of linear prediction model are Auto Regressive Integrated Moving Average (ARIMA) prediction model and Kalman filter prediction model. ARIMA prediction model requires linear stationary resource. MANET is multi-conformation, self-similarity, multi-scale and long-range dependence which makes ARIMA prediction model unsuitable for MANET resource prediction. Kalman filter is unable to accurately describe all characteristics of MANETs even though it effectively deals with the system noise and measurement noise. The non-linear prediction model such as Wavelet model and Neural Network (NN) model is suitable for MANET resource prediction. Wavelet model is more accurate compare to NN model. Wavelet model does not predict real-time resource availability and recursive characteristics. NN model evaluates and predicts the behavior of non-linear and non-stationary systems which relies on the observed data

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rather than on an analytical model. NN can estimate any function in an efficient and stable manner, when the underlying data relationships are unknown. The characteristics of NNs include nonlinear mapping and generalization ability, robustness, fault tolerance, adaptability, parallel processing ability, etc. Inherent defects of NN are low learning speed, getting only local sub-optimal solution and difficulty in choosing network size to suit a given problem. Combining wavelet and NN model forms Wavelet Neural Network (WNN) which shows better performance in training and adaptation efficiency[6] in general. WNN model includes number of processing units, hidden unit activation function which is chosen from a family of wavelet, criteria for modeling a concept, training algorithm for finding its parameters. The major difference between NN and WNN lie with activation function at hidden layer which is the wavelet function instead of sigmoid function. From the literature on estimation of AB, we concluded that passive techniques are more suitable in MANET and WNN model has prediction properties suitable in MANET environment compared to other forecasting techniques[4][5]. As far as we know, existing passive techniques does not estimates the future AB. In this paper, we proposed a passive technique for estimation of future AB using WNN. A. Related Work The passive estimation technique CACP[7] computes AB by monitoring the channel idle time ratio. It does not consider wastage of AB due to random period such as RTC/ CTS. Extensions to CACP considers such random period includes AAC[8], ABE[9], IAB[10]. Bandwidth wastage due to random backoff, collision due to hidden terminal and idle period synchronization were ignored in AAC. ABE considers all those but Lagrange interpolation based collision probability computation of specific simulation does not reflect in general scenario. Extension of ABE is IAB which considers synchronization between sender and receiver by differentiating the channel busy caused by transmitting or receiving from that caused by carrier sensing. It improves the accuracy of estimating the overlap probability of two adjacent nodes idle time. Our previous work, we have proposed a passive scheme of estimating AB in MANETs using synchronization of idle period, collision probability and random waiting time called as Distributed Lagrange Interpolation based AB Estimation (DLI-ABE)[3]. It computes Lagrange Interpolation polynomial before the actual transmission of data at each node in distributed manner unlike ABE and IAB. It also computes idle period synchronization based on states of the nodes for actual channel utilization and collision rate unlike IAB. This results in a more accurate AB estimation but it does not give predicted AB very close to real AB. Moreover all these do not estimate future AB. In this paper, we propose a novel future AB prediction mechanism using WNN model. The proposed mechanism is necessary for AB allocation to support real-time and multimedia communication in multi-hop MANET for QoS provisioning. Usage of this mechanism for routing protocol


flows to provide an efficient network management will be our next work. II. AVAILABLE BANDWIDTH P REDICTION M ECHANISM USING WNN MANET has unpredictable behavior of network traffic due to various factors such as mobility of nodes, arrival pattern of traffic data, and diversified network requirement of applications. The number of nodes and amount of traffic generated by them has no correlation. This results in to non-linear and time varying traffic flow without ensuring QoS guarantee over MANET. To provide QoS, accurate and timely forecasting of non-linear and non-stationary time series AB is predicted using WNN. Following assumptions are made in MANET environment for developing the model. (1) Network topology in simulation environment is dynamic in nature. (2) All nodes have same transmission power. (3) Memory requirements and computational overhead are ignored as WNN does not add much overhead compare to existing active/passive/model-base techniques. (4) Communication ranges of the nodes should be small to avoid interference. (5) Nodes can transmit without causing collision when there is no sender in its carrier sense range. In this work, first time series of bandwidth usage is extracted from realistic wireless trace with forty nodes in the scenario considering random mobility and initial location. From the wireless scenario, Bandwidth Usage (BU ) at a node is collected for pre-decided time interval T i = 1 second from ns2.35 P trace file using AWK script. It is computed T iEnd−time as BUi = T istart−time P S, where P S is the size of a packet exchanged over air during that T i time interval. The packets may be actual traffic packets or control packets such as RTS/CTS, ACK, and HELLO. We collect the bandwidth usage every second from the scenario for 800 seconds. Then, time series AB for each time interval is computed as ABi using the Equation 1. ABi = C − BUi

(1)

where C is the capacity of the channel for that node. Using these sample data sets of AB are available for a node, the WNN model is developed. The training phase of WNN uses a back-propagation algorithm to adjust the weights on the links between neurons of WNN. Once the WNN gets trained, the testing phase does not uses back-propagation algorithm to predict AB as weights are frozen during training phase. The WNN model is described and evaluated in subsequent sections. A. WNN model for AB Prediction at Node WNN learn correlated patterns between input sets and the corresponding target values in training phase. The training set feed forward at the input layer and weights on every connection are dynamically adjusted using the back-propagation of the error to achieve the desired output value. The weights are changed continuously until the output error falls below a preset value in the back-propagation algorithm. Testing of WNN is

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done in the prediction phase by predicting the outcome of new input data. A new input (not included in the training set) is presented to the WNN and the output is calculated. The proposed AB prediction mechanism uses current and previous information about actual AB to predict AB in WNN model. The amount of predicted AB is stored for efficient allocation of the AB among the nodes in its transmission range. Initial WNN based AB prediction model with feed-forward threelayer architecture is shown in Figure 1. xk

Σ Ψ

t−b1 a1

Σ Ψ

t−b2 a2

xk+l

Σ

^ xk+l Compare Adjust

x k−p+1

Σ Ψ t−bn

Weights

an

Input Layer output is b1 jk = xk−j+1

Hidden Layer p

2 2 Input is a jk = Σ wij x k−j+1 i=1 Output is 2 b jk = Ψ

Fig. 1.

2 a jk − bj

Output Layer out[ut is 3 bjk = ^xk+l n 2 3 = Σ bjkw j j=1 n a2 jk − bj = Σ Ψ j=1 aj

w3 j

aj

Wavelet neural network for Available Bandwidth prediction

There are p input for input layer containing value of the time series AB, n neurons on the hidden layer, and one neuron on the output layer. To simplify the cost of AB prediction, input parameters of WNN model is chosen carefully based on underlying AB characteristics of environment under study. Each location has its own unique AB pattern, which makes it hard to reuse AB predictor designed for an environment in a different environment. The 800 samples of AB is calculated as shown in equation 1 for WNN analysis and evaluation. Initially, the input of the network is a p time series AB values namely xk , xk−1 , ..., xk−p+1 and the output is the k + lth step predictive AB value. During the training phase, we executed WNN model developed in C++ with the different combinations of input value p, number of neurons in hidden layer, and number of epoch to obtain predicted AB. Based on many trial, we concluded that the input layer should have eight neurons, the hidden layer should have ten neurons and number of epoch should be 15000 to obtain more accurate AB. The final adjusted weights value between neuron i on m − 1 layer and neuron j on m layer is m represented as wij . The k th input of the neuron j on m layer m is ajk and calculated as Equation 2. The transfer function on layer m is Ψm and the corresponding output on layer m is bm jk which is calculated as Equation 3. X m m−1 am wij bik (2) jk =

layer basis function. In this WNN, we also chosen Morlet 2 wavelet non-linear transfer function Ψ(t) = cos(1.75t)e−t /2 . Each hidden and output node processes its input by multiplying each of its input values by a weight, summing the product and then passing the sum through a Morlet wavelet non-linear transfer function to produce the result. For training process, where weights are selected, gradient descent method with back-propagation algorithm used to modify randomly selected weights of the nodes in response to the errors between the actual output values and the target value. The 800 samples of AB is collected using ns2.35 on forty node scenario. The WNN model is simulated on Linux platform using C++. First 300 samples used to train the WNNs to fix the weights at links between two layers with backpropagation learning for accurate predicted output. Next 200 samples used to test the prediction efficiency and remaining are used to evaluate WNN. We consider Bias input in our WNN. Finally, initialization parameters are set. The number of steps is equal to 8, training learning rate equals 0.01, momentum coefficient equals 0.95, the biggest training step equals 15000 epochs, and training goal is 0.025. The collected network bandwidth samples injected as input to the WNN model during training phase. Few trial to achieve the training goal yields 8-10-1 WNN model. It gives stable weight on each line connecting input layer unit and hidden layer unit as well as line connecting hidden layer and output layer units. These weights vary with sample data. The stable weights are given in Table I and Table II. From the training phase of our WNN model, we get the stable weights between neurons of input layer and hidden layers as shown in Table I. Similarly, stable weights between neurons of hidden layer and output layers in our AB prediction model is shown in Table II. These weights are used to predict AB as shown in Figure 1. The accuracy of the proposed methods is checked by finding performance metrics given in simulation section. B. AB Prediction on Multi-hop Path The output of WNN model on a node gives node AB. Every node predicts its AB and store. The link AB between two nodes is the minimum AB of the nodes connecting link and is computed as ABlink = min(ABnode1 , ABnode2 ). The path may consist of more than one link if it is multi-hop path. The AB of such multi-hop path is minimum AB on one link connecting that path and is computed as ABH−hopP ath = min(ABnode1 , ..., ABnodeH ). III. S IMULATION The proposed solution is tested using ns2.35 and Linux. This section explains the simulation model used to test our proposed solution with performance of it. A. Simulation Model

m bm jk = Ψm (ajk )

(3)

Hidden layer neuron activation function is chosen from wavelet family in our WNN. Morlet wavelet has simple explicit expression which motivates researcher to use it as hidden


Simulation model for the MANET scenario consists of network model, propagation model, mobility model, channel model, traffic model, and WNN model. In MANET model, MANET consists of a collection of Nmax = 40 mobile nodes

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TABLE I W EIGHTS ON LINK BETWEEN I NPUT L AYER AND H IDDEN L AYER N EURONS 2 w1j 2 = w11 0.288896 2 w12 = 0.012901 2 = w13 0.210048 2 = w14 0.484189 2 w15 = 0.177873 2 = w16 0.277728 2 w17 = 0.167627 2 = w18 0.258205 2 = w19 0.419618

2 w2j 2 w21 = 0.376764 2 = w22 0.469122 2 w23 = 0.203560 2 = w24 0.443657 2 = w25 0.137964 2 w26 = 0.005557 2 w27 = 0.375002 2 = w28 0.436209 2 = w29 0.003799

2 w3j 2 w31 = 0.304558 2 = w32 0.553067 2 w33 = 0.220325 2 = w34 0.562402 2 w35 = 0.034216 2 = w36 0.080914 2 = w37 0.323461 2 w38 = 0.236328 2 w39 = 0.237983

2 w4j 2 = w41 0.212074 2 = w42 0.110056 2 w43 = 0.103718 2 w44 = 0.425733 2 w45 = 0.087383 2 = w46 0.446705 2 = w47 0.306944 2 w48 = 0.128855 2 w49 = 0.167832

2 w5j 2 = w51 0.196024 2 = w52 0.244901 2 = w53 0.502661 2 w54 = 0.099607 2 = w55 0.306205 2 = w56 0.408678 2 = w57 0.229363 2 = w58 0.268188 2 = w59 0.037225

2 w6j 2 w61 = 0.366251 2 = w62 0.365303 2 w63 = 0.044442 2 = w64 0.245704 2 = w65 0.411448 2 w66 = 0.238533 2 = w67 0.087843 2 = w68 0.100457 2 = w69 0.002977

2 w7j 2 w71 = 0.121868 2 = w72 0.078189 2 = w73 0.347021 2 w74 = 0.083047 2 w75 = 0.330016 2 w76 = 0.090044 2 = w77 0.269955 2 w78 = 0.362027 2 = w79 0.333603

2 w8j 2 = w81 0.450885 2 = w82 1.119467 2 w83 = 0.069958 2 = w84 0.578870 2 w85 = 0.188733 2 = w86 0.301603 2 = w87 0.275051 2 = w88 0.000328 2 = w89 0.092436

2 w9j 2 w91 = 0.062517 2 w92 = 0.693652 2 w93 = 0.826156 2 w94 = 0.818639 2 w95 = 0.236851 2 = w96 0.285038 2 w97 = 0.174059 2 w98 = 0.454363 2 = w99 0.217817

w120 j w120 1 = 0.312529 w120 2 = 0.098624 w120 3 0.108126 w120 4 0.357784 w120 5 = 0.250783 w120 6 = 0.517378 w120 7 = 0.659206 w120 8 0.132793 w120 9 = 0.403395

= = = -

TABLE II W EIGHTS ON LINK BETWEEN H IDDEN L AYER AND O UTPUT L AYER N EURONS 3 w2j 3 = w21 0.008274

3 w3j 3 = w31 0.109698

3 w4j 3 = w41 0.240145

3 w5j 3 = w51 0.061448

3 w6j 3 = w61 0.016242

placed randomly in an area l × bm2 = 500 × 500m2 . The nodes move within the area. The coverage area around each node has a bandwidth BWtotal = 2M bps that is shared among its neighbors. All the nodes between the links are bidirectional. Free space propagation model is used with transmission range for each node as T R = 250m for a single-hop distance. Mobility model uses random waypoint. The mobility of the nodes varies uniformly. Each host is initially placed at a random position. As the simulation progresses, each host pauses at its current location for a period 1s and then randomly chooses a new location to move. Each host continues this behavior, alternately pausing and moving to a new location, for the duration of the simulation time St = 1000s. To access the channel, ad hoc nodes use CSMA/CA of 802.11b MAC layer standard protocol to avoid possible collisions and subsequent packet drops. We set queue length in MAC layer to be fifty packets to avoid packet dropping due to buffer overflow. Constant bit rate traffic was generated by using fixed size packet. Using this ns2.35 simulation model, wireless scenario with command line argument for number of nodes using AODV is developed. Finally, the scenario with 40 nodes used to generate training set of 800 samples. B. Simulation Results through Performance Parameters Before using the predicted AB for its efficient allocation, the prediction methods are analyzed to quantitatively asses the overall performance. The analysis is shown through the performance parameters/metrics such as Mean Square Error (MSE), prediction efficiency, and memory overhead. This section presents few simulation results obtained during analysis of prediction accuracy. The AB curves in Figure 2 are


3 w7j 3 = w71 0.139562

3 w8j 3 w81 = 0.029491

3 w9j 3 = w91 0.249499

w130 j w130 1 = 0.043636

w131 j w131 1 = 0.079111

obtained during evaluation of AB prediction WNN model. It is seen that AB prediction curves generally coincide with the real AB curve. Occasionally, there are some variations between predicted AB and real AB. These various may be due to unexpected behavior of MANET such as such as shared wireless medium, dynamic topology due to mobility of nodes, and distributed multi-hop communication. Maximum variation between predicted AB and real AB is approximately 20%. Available Bandwidth Prediction at Node 0 1

Observed Available Bandwidth Predicted Available Bandwidth

0.8

Available Bandwidth

3 w1j 3 = w11 0.162774

0.6

0.4

0.2

0

0

50

100

Fig. 2.

150 time

200

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AB Prediction Curve

The prediction accuracy is analyzed using MSE and prediction efficiency. (1) MSE is defined as the difference between observed value and predicted value by computing the average sum of the squared error with ideal performance yielding zero MSE as in equation 4. M SE =

N 1 X (yi − yî )2 N i=1

(4)

where yi is observed value, yî is predicted value and N repre-

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sents total number of predictions. The MSE for the predicted AB is shown in Figure 3. MSE curve shows that the error in most of the prediction is very small and close to zero which is indication of ideal/acceptable performance. Occasionally, MSE is more due to the same reason of variations between predicted AB and real AB. The maximum MSE obtained is 0.12 for normalized values which is again acceptable for MANET environment. Mean Square Error {MSE) 0.16

MSE for Predicted Available Bandwidth

0.14 0.12

MSE

0.1 0.08

for MANET does not have much overhead and have significant potential to solve the challenges of predicting AB. Prediction of AB is necessary for efficient AB allocation to support realtime and multimedia communication. AB prediction mechanism is being designed with three-layer 8-10-1 WNN model with back-propagation trough time for training. WNN of the AB prediction model is examined using time series bandwidth usage information collected at a node. Simulation result shows that the predicted AB closely matches with the real values. Maximum variation between predicted AB and real AB is approximately 20%. MSE, covariance and memory overhead are very small. Usage of this prediction mechanism before transmission at MAC and network layer is our future work.

0.06

ACKNOWLEDGMENT

0.04 0.02 0

0

50

Fig. 3.

100

150 time

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The authors wish to thank Visvesvaraya Technological University (VTU), Karnataka, INDIA, for funding the part of the project under VTU Research Scheme (Grant No. VTU/Aca./2011-12/A-9/753, Dated: 5 May 2012.

250

MSE curve for predicted AB

(2) Prediction efficiency (γ) is defined as a degree of association between two variables being a measure of linear dependence as equation 5. COV (Y, Yˆ ) γ= σY σYˆ

(5)

where σY and σYˆ are standard deviation of the observed value and the predicted value respectively. COV (Y, Yˆ ) is the covariance between Y and Yˆ . Covariance is the relaˆ tionship PN between two data sets computed as COV (Y, Y ) = 1 ˆ i=1 (yi − y¯i )(yî − y¯i ). N The value of γ = 0 indicates lack of correlation, γ = 1 indicates positive correlation and γ = −1 indicates negative correlation among the data sets. During the performance analysis of our result, it is observed that the value of COV (Y, Yˆ ) is very very low like 0.000013 which indicates observed value and the predicted value are not related. The corresponding prediction efficiency shows negative correlation. (3) Memory overhead is defined as number of bytes required predicting AB as a function of time. Each node take 8 bytes extra to store the previous AB for prediction of next irrespective of the number of neighboring nodes in its transmission range. This is very low memory overhead which can be considered as negligible overhead. IV. C ONCLUSION AB in MANET is nonlinear and time varying which results in lack of QoS provisioning. Guaranteeing network QoS requires AB prediction. The existing techniques have few unavoidable overhead. The proposed WNN based AB prediction


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