Resource Prediction based Routing using Wavelet ... - IEEE Xplore

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

Resource Prediction based Routing 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. Routing, an important component for QoS provisioning, affected by many factors such as limited resources, shared channel, unpredictable mobility, improper load balancing, and variation in signal strength. QoS based routing protocol must consider efficient usage of resources. In this paper, we propose a resource prediction based routing in MANET that routes the packets based on future availability of buffer-space, energy and bandwidth resources. Future availability of these resources is predicted using wavelet neural networks based traffic and mobility prediction model. The proposed routing scheme is simulated to evaluate the performance in terms of packet delivery ratio, computation overhead, memory overhead and packet delay.

Keywords: MANETs, Wavelet neural network, Resource prediction, Routing 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. Unique characteristics of MANET (such as shared wireless medium, mobility, distributed multi-hop communication) make QoS provisioning for such applications more challenging. QoS solutions for these applications mainly rely on the exact availability of precise resource for a link before routing[1]. QoS routing aims at providing route which has required quality[2]. Routing protocol must provide the QoS routes that delivers the message within time, reduces packet loss, provide stable connectivity at less routing overheads. The QoS route establishment must consider future availability of the resources on the path before allocation of load in addition to number of hops on route, delay, data rate, and mobility. Multimedia transmission requires QoS path for continuous real-time services. 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 gener-

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ated by the users as well as interferences. Some prediction strategies must be adopted. The existing prediction technologies can be classified into linear prediction and non-linear prediction[3]. 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 longrange 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 realtime 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 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. Combining wavelet and NN model for resource prediction shows better performance in training and adaptation efficiency[4][5]. This combined model called as Wavelet Neural Network(WNN) model that has good prediction properties in MANET environment. The existing routing protocols in MANET do not consider future availability of all resources on the path. Most of the protocols are mainly based on only one factor such as bandwidth, energy, mobility etc. They do not consider future availability of those resources using prediction method. Many resource allocation methods also does not consider future resources availability [6][7]. In this paper, we propose a resource prediction based routing in MANET that provides route based on future availability of buffer-space, energy and bandwidth resources. Future availability of these resources are predicted using WNNs based traffic prediction[3][8][9] and mobility prediction model[10]. The logical organization

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

of buffer space resource uses hashing for storing the packets data according to priority. The priority based buffer space utilization helps the proposed routing to support real-time and multimedia communication in multi-hop MANET with QoS provisioning. The rest of the paper is organized as follows. In section II, resource prediction based routing scheme is explained through various models. In section III, we discuss the simulation model, simulation procedure, performance parameters, and simulation results. Section IV concludes the paper. II. R ESOURCE P REDICTION BASED ROUTING USING WNN Existing routing schemes has some inherent limitations like low reliability due to scare resources and failure of node on the path, low scalability, limited robustness in dynamic topology, limited QoS support due to variations in the traffic generated by the users as well as interferences on the shared channel. In this paper, QoS routing scheme using WNNs based resource prediction is proposed. Initially, the routes are established according to AODV routing protocol. Periodically, the routes are updated according to future availability of the resources that are predicted using WNNs based future traffic and future mobility. Routing table of AODV modified to store the factor regarding future resource availability. The proposed QoS routing scheme has five main phases as shown in Figure 1. It operates in the following phases. (1) Prediction of traffic and mobility using corresponding WNNs as given in our previous work[11]. (2) Prediction of buffer space, energy and bandwidth using the predicted traffic and mobility as given in our previous work[11]. (3) Computation of resource prediction factor using the predicted buffer space, energy and bandwidth for reliable route establishment. (4) Discovery of routes using normal AODV operation and computation of resource factor. (5) Transmission of data from source to destination using the discovered routes. (6) Route maintenance against link and node failure. Resource Prediction Mechanism

Resource Prediction Factor

Maintaining Routing Table

Route Selection

Data Transfer

Fig. 1.

Phases of the Proposed Routing Scheme

Resource predication models explained in brief in Section II-A is our previous work[11]. A. Resource Prediction Model 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

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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 traffic is predicted using WNN in our resource prediction mechanism given in our previous work[11]. Mobility models are based on different parameters setting related to node movements such as starting location of a node, movement direction, velocity range and speed changes over time. Knowing node’s previous locations (Xtk−i , Ytk−i , Ztk−i ) i = 0..p where p is the number of locations recorded before the current time tk , a mobile node can estimate their future ones (Xtk+i , Ytk+i , Ztk+i ) i = 0..N where N is the number of predicted locations. Mobility prediction used to estimate link expiration time in order to improve routing performance. Our resource prediction mechanism given in our previous work[11] consists of resource repository, resource prediction modules for traffic, mobility, buffer space, energy, and bandwidth. Initially, traffic and mobility of the node is predicted using WNN. The predicted traffic at each node can be used for bandwidth allocation, flow control, routing, network security, and network management. For the predicted traffic and mobility, remaining resources are estimated that are used to compute the resource factor for QoS routing. The resources considered in our mechanism are traffic, bandwidth, buffer, and energy. The amount of predicted resources is stored in the resource metric repository. The mechanism has different phases which run asynchronously and concurrently. These predicted resources are used for establishment of the routes using AODV as explained in subsequent section. B. Resource Prediction Factor Predicted buffer space, predicted energy, predicted mobility and predicted bandwidth based resource prediction factor is used for reliable route establishment. It defines the future connectivity with neighbor nodes and denoted as Frp . It is a function of factors computed for buffer space, energy, bandwidth and mobility. These factors indicate the predicted resource availability for the predicted resource requirements for future communication and mobility of nodes. The buffer space factor denoted as FBS is computed from predicted buffer space requirement for the predicted traffic, its availability in future, and total buffer space of a node which is given in Equation 1. The energy factor denoted as Fe is computed from predicted energy requirement for the predicted traffic considering mobility, its availability in future, and maximum energy of a node before any transmission which is given in Equation 2. The bandwidth factor denoted as FBW is computed from predicted bandwidth requirement for the predicted traffic, its availability in future, and maximum bandwidth of anode before any transmission which is given in Equation 3. The mobility factor denoted as Fm is computed from predicted mobility requirement, its availability on the established route in future using transmission range which is given in Equation 4. The resource prediction factor is computed as given in Equation 5. The 0 or −ve value of Frp indicates that one

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

of the resource is not sufficient during future communication. The +ve value of Frp indicates sufficient resources for future communication are available. This Frp is computed and stored in the routing table for each route as well as at each node during the route establishment.

III. S IMULATION The proposed routing scheme is simulated using ns2.35 to assess the effective performance of it. This section explains the simulation model used to test our proposed solution. A. Simulation Model

FBS

5 F BSi=0 (i) − Qpred (P rioT rpred ) = T otalBuf f erSize

(1)

EnergyAvailable − Energypred Energymax

(2)

Fe =

FBW =

ABWpred − BWpred BWmax

Fm =

2 ∗ T R − dpred 2 ∗ TR

Frp = FBS × Fe × FBW × Fm

(3)

(4)

(5)

C. Resource Prediction based Routing Routes are constructed with neighbors nodes using Frp . The route maintenance is initiated on node failure and change in topology due to mobility. The maintenance operations involves following sequence of actions. (1) Computation of Frp for each node in the network. (2) Selection of neighbor for data transmission which has higher value of Frp . (3) Route establishment using AODV control packets and repository at node. (4) Compute path factor for the established route that consists of h-hops for future availability of it as P Frp = minhi=0 Frpi . Data transfer on the established reliable route is processed. The algorithm for resource prediction based routing is given in Algorithm 1. The reliable route is established as explained above using predicted resources, predicted traffic and predicted mobility. Algorithm 1 Algorithm for Resource Prediction based Routing 1: Input: T R, T otalBuf f erSize, Energymax , BWmax , TR 2: Begin 3: Schedule event for predicting traffic load 4: Schedule event for predicting mobility 5: Predict buffer space, energy and bandwidth requirement for predicted traffic and mobility 6: Compute Frp 7: Select neighbor with higher Frp for route establishment. 8: Compute P Frp for the established routes. 9: if P Frp > 0 then 10: Transfer packet — due to resource availability in future. 11: else 12: Drop packet — due to lack of resource in future. 13: end if 14: End

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Simulation model for the MANET scenario consists of network model, propagation model, mobility model, channel model, traffic model, and energy model. In network model, MANET is generated in an area l × bm2 . It consists of maximum of Nmax mobile nodes at a time placed randomly. Free space propagation model is used with transmission range for each node is T R 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. The nodes movement is restricted within the specified area. As the simulation progresses, each host pauses at its current location for a period Tm 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 . To access the channel model, 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 as fifty packets to avoid packet dropping due to buffer overflow. Traffic model uses constant bit rate traffic that was generated at the starting and exist till end of the simulation by using fixed size packet T rpkts . The coverage area around each node has a bandwidth BWtotal that is shared among its neighbors. All the nodes between the links are bidirectional. Every node is assumed to have energy model parameter such as energy available at start InitialEnergy, energy consumed during idle state IdleP ower, energy consumed during receiving Rx , energy consumed during transmission Tx , energy consumed for transition from one location to other T ransitP , energy consumed during sleep mode SleepP ower. The time taken to transit from one location to the chosen location is T ransitionT ime. B. Simulation Procedure The proposed routing scheme is simulated using ns2.35 simulation model with the following simulation inputs. l × bm2 = 500m2 , Nmax = 50, T rpkts = multiple of 500, BWtotal = 2M bps, T R = 250m, Tm = 1s, St = 1000s, InitialEnergy = 150Joule, IdleP ower = 1.0w, Rx = 1.1w, Tx = 1.4w, T ransitP = 0.6w, SleepP ower = 0.001w, T ransitionT ime = 0.005s. Simulation procedure executes the following sequence of action. (1) Generation of MANET environment: The wireless scenario with specified number of nodes using AODV routing scheme is developed. The nodes are randomly placed in a fixed area with specified initial energy. A topology is changed by mobility model during the transmission of traffic according to given traffic model. (2) Execution of the generated MANET environment for unmodified ns2.35 environment. (3) Computation of performance parameters such as PDR, delay, overhead using AWK on generated trace file before modification of

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

ns2.35 environment. (4) Modifying ns2.35 code for inclusion of the proposed routing scheme at medium access and network layer. (5) Computation of performance parameters such as PDR, delay, overhead using AWK on generated trace file after modification of ns2.35 environment. (6) Assessing performance parameters and plotting the corresponding graphs. The following performance parameters are assessed during the simulation of the proposed routing scheme. (1) PDR: It is defined as the number of packets received at destinations to the number of packets sent from a source. (2) Computation overhead: It is defined as the average number of computations needed by all nodes to estimate resources and calculate routing information at any given time. (3) Memory overhead: It is defined as the total number of bytes to be stored in node database to predict the resource, establish the route and maintain the routes. C. Simulation Results The proposed routing scheme is analyzed to quantitatively asses the overall performance. The analysis is shown through the explained performance parameters. This section presents few simulation results obtained during analysis of the proposed routing scheme. The same experiment runs for ten times and average of it is calculated as the simulation result here. For static scenario (no mobility), PDR is fairly high compared to dynamic scenario (with mobility) as shown in Figure 2. PDR falls due to change of the route because of mobility of the node that causes packet loss triggering retransmission. PDR of the proposed scheme is better compared to existing AODV routing since these paths do not consider future availability of resource, traffic and mobility resulting less packet drops. Existing with mobility Existing no mobility

Proposed with mobility Proposed no mobility

Average PDR Vs. Number of nodes 100 90

Average PDR

80 70 60 50 40 30 20 10

10

15

20

25 Number of nodes

30

35

Fig. 2.

Packet Delivery Ratio Vs. Number of Nodes

40

Computation overheads are little more for our proposed method than existing AODV due to prediction of the various resources and computation of the resource factors on the route. Simulation results shows that the time taken for resource prediction is between 0.000001 to 0.084787 seconds which is equal to 800 to 67,829,600 CPU cycle respectively on 800MHZ CPU.

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For one destination node, the resource repository takes 16 bytes plus 20 bytes for the hash table pointers. As number of nodes in its transmission range increases memory overhead increases in multiple of 10 bytes in the resource repository. For each established route, routing table record size is increased by six bytes. IV. C ONCLUSION MANET traffic is nonlinear and time varying which results in lack of QoS provisioning. Guaranteeing network QoS requires resource prediction based routing. This paper proposed resource prediction based routing in MANET by computing path factor for each established path from the predicted resources. Simulation result for packet delivery ratio and overheads illustrate the effectiveness of the proposed routing scheme over well established AODV routing. Reducing overhead involved in the proposed methods is the main focus of attraction in future. We plan for further rigorous analysis of the proposed methods in future by comparing with other well established QoS routing schemes to understand it’s behavior. ACKNOWLEDGMENT 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. R EFERENCES [1] I. Hanzo and R. Tafazolli, “Quality of Service Routing and Admission Control for Mobile Ad-hoc Networks with a Contention-based MAC Layer”, Proceeding of IEEE MASS, pp. 501-504, 2006. [2] Rajashekhar C. Biradar, and Sunilkumar S. Manvi, “Neighbor supported reliable multipath multicast routing in MANETs”, Journal of Network and Computer Applications, vo. 35, pp. 1074-1085, 2012. [3] H. Feng and Y. Shu, “Study on network traffic prediction techniques”, Proceedings of the International Conference on Wireless Communications, Networking and Mobile Computing; Wuham, China, vol. 2, pp. 1041-1044, 2005. [4] J. C. Lu, Z. H. Gu, and H. Q. Wang, “Research on the application of the wavelet neural network model in peak load forecasting considering of the climate factors”, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, China, 2005. [5] N. M. Pindoriya, S. N. Singh, and S. K. Singh, “An adaptive wavelet neural network-based energy price forecasting in electricity markets”, IEEE Transactions on Power Systems, vol. 23, pp. 1423-1432, 2008. [6] Yuan Xue, et al., “Optimal resource allocation in wireless ad hoc networks: a price-based approach”, IEEE Transactions on Mobile Computing, vol. 5, no. 4, pp.347-364, 2006. [7] Chen Yi, et al., “A Resource Allocation Control for Wireless Ad Hoc Networks”, Proceeding of International Multi-Symposiums on Computer and Computational Sciences, pp.508-511, 2007. [8] Xu Lan, “Analysis and research of several network traffic prediction models”, Chinese Automation Congress (CAC), pp.894-899, 2013. [9] Run Zhang, Yinping Chai, and Xing-an Fu, “A network traffic prediction model based on recurrent wavelet neural network”, Proceedings of the International Conference on Computer Science and Network Technology, pp.1630-1633, 2012. [10] Heni Kaaniche and Farouk Kamoun, “Mobility Prediction in Wireless Ad Hoc Networks using Neural Networks,” Journal of Telecommunications, vol. 2, no. 1, pp 95-101, 2010. [11] Shilpa Shashikant Chaudhari and Rajashekhar C. Biradar, “ Resource Prediction using Wavelet Neural Network in Mobile Ad-hoc Networks”, International Conference on Advances in Electronics, Computers and Communications, Bangalore, India, Accepted, 2014.

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