II A SURVEY OF MOBILITY MODELS

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Research Paper ANALYZING QOS BASED STABLE ENERGY AWARE ADHOC ROUTING PROTOCOL WITH DIFFERENT MOBILITY MODELS Dr.K. Sumathi, Dr. V.Seethalakshmi, Dr.M.A.Raja Address for Correspondence Assistant professor (SG) /ECE, Dr.

P Institute of Technology, Coimbatore ABSTRACT A mobile adhoc network (MANET) is a self-configuring infra- structure less network.

, and the widespread use of wireless and mobile devices has generated the need to provide quality-of-service (QoS) support in MANET.

1 Research Paper ANALYZING QOS BASED STABLE ENERGY AWARE ADHOC ROUTING PROTOCOL WITH DIFFERENT MOBILITY MODELS Dr.K. Sumathi, Dr. V.Seethalakshmi, Dr.M.A.Raja Address for Correspondence Assistant professor (SG) /ECE, Dr.Mahalingam College of Engineering and Technology, Pollachi Assistant professor(sg) /ECE, Dr.Mahalingam College of Engineering and Technology, Pollachi Associate Professor/ECE, Dr N.G.P Institute of Technology, Coimbatore ABSTRACT A mobile adhoc network (MANET) is a self-configuring infra- structure less network.the emergence of real-time applications such as multimedia services, disaster recovery etc., and the widespread use of wireless and mobile devices has generated the need to provide quality-of-service (QoS) support in MANET. The mobility model represents the realistic behavior of each mobile node in the MANET.In this paper, the performance of QOS based stable energy aware adhoc routing protocol (QSEAAR) is analyzed with various mobility models for Mobile Ad Hoc Network. For high acceptability of routing protocol, analysis of routing protocol in ad hoc network only with random way point mobility model is not sufficient. Here Random Way Point (RWP) Model, Manhattan Grid (MG), Reference Point Group Mobility (RPGM), Gauss-Markov (GM) mobility Model are considered for proper analysis of QSEAAR routing protocol. The simulation of proposed protocol is carried out using network simulator ns-2.35 under Linux platform. KEY TERMS QSEAAR, QoS, RPGM, RWP, GM, IEEE, MAC. I. INTRODUCTION MANET is a wireless infra-structureless network having mobile nodes. Communication between these nodes can be achieved using multi hop wireless links. Each node will act as a router and forward data packets to other nodes. Since the nodes are independent to move in any direction, there may be frequent link breakage. Adhoc networking is becoming very popular nowadays and will emerge as an effective complement to wired or wireless LANs, and even to wide-area mobile networking services, such as Personal Communication Systems (PCS). The most important design criterion for any type of network is guaranteeing Quality of Service. QoS measures include bandwidth, delay and delivery guarantee. Different classes of traffic (e.g. voice, data, image, video, etc.) have different bandwidth and delay requirements. QoS-aware routing takes into consideration multiple QoS requirements, link dynamics, as well as the implication of the selected routes on network utilization, rendering QoS routing a particularly challenging problem [1]. Mobility models represent the movement of mobile users, and how their location, velocity and acceleration change over time. Such models are frequently used for simulation purposes when new communication or navigation techniques are investigated [2][3]. In random-based mobility simulation models, the mobile nodes move randomly and freely without restrictions. To be more specific, the destination, speed and direction are all chosen randomly and independently of other nodes. This kind of model has been used in many simulation studies [4]. The Manhattan mobility model uses a grid road topology. This mobility model was mainly proposed for the movement in urban area, where the streets are in an organized manner. In this mobility model, the mobile nodes move in horizontal or vertical direction on an urban map. The Manhattan model employs a probabilistic approach in the selection of nodes movements, since, at each intersection, a node chooses to keep moving in the same direction [5]. Simulation studies of MANET routing protocols have mostly assumed Random Waypoint (RW) as a reference mobility model. In order to examine many different MANET applications there is a need to provide additional mobility models.in this paper, QSEAAR protocol is analyzing by using different mobility models such as Random Way Point model, Manhattan model and Reference point group mobility, Gauss Markov mobility model to measure the performance metrics such as packet delivery fraction, delay and control overhead. II A SURVEY OF MOBILITY MODELS Figure 1. The categories of mobility models in Mobile Ad hoc Network One frequently used mobility model in MANET simulations is the Random Waypoint model, in which nodes move independently to a randomly chosen destination with a randomly selected velocity. The simplicity of Random Waypoint model may have been one reason for its widespread use in simulations. However, MANETs may be used in different applications where complex mobility patterns exist. Hence, recent research has started to focus on the alternative mobility models with different mobility characteristics. In these models, the movement of a node is more or less restricted by its

history, or other nodes in the neighborhood or the In Fig.1 a categorization for various mobility models into several classes based on their specific mobility In Fig.

For some mobility models, the movement of a mobile node is likely to be affected by its movement history. This type of mobility model is referred as mobility model with temporal dependency.

2 history, or other nodes in the neighborhood or the In Fig.1 a categorization for various mobility models into several classes based on their specific mobility In Fig.1 a categorization for various mobility models into several classes based on their specific mobility characteristics is provided. For some mobility models, the movement of a mobile node is likely to be affected by its movement history. This type of mobility model is referred as mobility model with temporal dependency. In some mobility scenarios, the mobile nodes tend to travel in a correlated manner and they are referred as spatial dependency. Another class is the mobility model with geographic restriction, where the movement of nodes is bounded by streets, freeways or obstacles [6]. environment. speed. Upon arrival, the mobile node pauses for a specific time period before starting the process again. Fig 2 shows an example traveling pattern of a mobile node using Random Waypoint Mobility Model starting at a randomly chosen position, the speed of the mobile node in the figure is uniformly chosen between 0 and 10 m/s. In most of the performance study that use the Random Waypoint Mobility Model, the mobile nodes are initially distributed randomly around the simulation area. When the simulation starts, each mobile node randomly selects one location in the simulation field as the destination. III. MOBILITY MODELS USED IN ADHOC NETWORK Dynamic topology changes in wireless multi-hop network will cause lower network connectivity and/or lower network performance. To capture the nature of mobility of nodes in a mobile ad-hoc network (MANET), different mobility models have been proposed. The mobility models used in simulations can be roughly divided into two categories: independent entity models and groupbased models. In the independent entity models, the movement of each node is modeled independently of any other nodes in the simulation. In the group mobility models, there is some relationship among the nodes and their movements throughout the cells or field. In order to thoroughly simulate a new protocol for an ad hoc network, it is imperative to use a mobility model that accurately represents the mobile nodes that will eventually utilize the given protocol. Only in this scenario it is possible to determine whether or not the proposed protocol will be useful when being implemented. Mobility model should attempt to mimic the movements of real mobile nodes. Changes in speed and direction must occur in a reasonable manner. Models from different classes of motion, including random, path-based, and group based movements are chosen. Mobility models can be differentiated according to their spatial and temporal dependencies. Spatial dependency is a measure of how two nodes are dependent in their motion. If two nodes are moving in the same direction then they have high spatial dependency. Temporal dependency is a measure of how the present velocity (magnitude and direction) is related to previous velocity. Nodes having the same velocity have high temporal dependency. Movements of mobile nodes considered in this class are completely uncorrelated. Each mobile node follows an individual independent mobility scenario. Random Waypoint, Manhattan and Random Drunken model belong to this class[7]. 3.1 Random Way Point (RWP) Model The Random Waypoint Mobility model includes pause times between changes in direction and/or speed. A mobile node begins by staying in one location for a certain period of time [8].Once the time expires, each node chooses a random destination in the simulation area and moves towards it with a random velocity. The mobile node then travel towards the newly chosen destination at the selected Figure 2. Traveling pattern of mobile node using Random Waypoint Mobility Model The mobile nodes then travel towards this destination with constant velocity chosen uniformly and randomly from [0,Vmax], where the parameter Vmax is the maximum allowable velocity for every mobile node. The velocity and direction of a node are chosen independently of other nodes. Upon reaching the destination, the node stops for a duration defined by the 'pause time' parameter Te. If Te=0, this leads to continuous mobility. After this duration, it again chooses another random destination in the simulation field and moves towards it. The whole process is repeated again and again until the simulation ends Manhattan Model Manhattan Grid (MG) model has originally been developed to emulate the Manhattan street network, i.e. a city section which is only crossed by vertical and horizontal streets as defined by the map in Fig 3. The map is composed of a number of horizontal and vertical streets. Each street has two lanes for each direction. The mobile node is allowed to move along the grid of horizontal and vertical streets on the map [9]. At an intersection of a horizontal and a vertical street, the mobile node can turn left, right or go straight. This choice is probabilistic. Figure 3. Manhattan model 3.3. Random Drunken Mobility Model In the Random Drunken mobility model, each node is assigned a random position within a field. When the node is next considered for movement, the mobility model checks all the possible directions in which the node can move to ensure that it stays within the field boundaries. The node then moves in the direction randomly chosen from the set of possible directions. Each node moves by one unit distance in that

direction during the mobility interval. Here the pause time is set to zero seconds. The random drunken model periodically moves to a position chosen randomly from its immediate neighboring positions.

This movement pattern is also known as random walk mobility model. 3.

3 direction during the mobility interval. Here the pause time is set to zero seconds. The random drunken model periodically moves to a position chosen randomly from its immediate neighboring positions. The frequency of the change in node position is based on a parameter specified in the configuration file. Here each node moves by one unit distance in that direction during the mobility interval. This movement pattern is also known as random walk mobility model. 3.4 Reference Point Group Mobility Reference Point Group Mobility (RPGM)model represents the random motion of a group of mobile nodes and their random individual motion within the group. All group members follow a logical group center that determines the group motion behavior. The entity mobility models should be specified to handle the movement of the individual mobile nodes within the group. Purpose of logical group center is to guide group of nodes continuously calculating group motion vector GM and this way defining behavior, speeds and directions for mobile nodes. Once the updated reference point RP(t+1) has been updated they are combined with random motion vector RM values to represent the random motion of each mobile node around its reference point[10]. 3.5 Gauss-Markov Gauss-Markov (GM) model enables different levels of randomness by setting only one parameter. Initially, each mobile node has preset speed and direction parameter values. This model captures the velocity correlation of a mobile node in time and represents random movement without sudden stops and sharp turns. At fixed intervals of time movement occurs by updating the speed and direction of eachnode. At each iteration, the new parameter values are calculated depending respectively on the current speed and direction and on a random variable. Value of speed and direction at the nth instance of time is calculated based on the value of speed and direction at the n-1st instance and a random variable [11]. IV. ON-DEMAND MULTICAST ROUTING PROTOCOL (QSEAAR) In QSEAAR protocol, IEEE MAC layer is taken and the same MAC layer bandwidth is considered for the transmission. With this, available bandwidth estimations are done. The parameters considered for QOS are explained below. Error Count (EC) -The EC is the maximum value between set of node error counts (linkage break and node failure) for the feasible path. The smaller EC represents the more reliable routing path. Hop Count (HC)-The HC is the number of hops for the feasible path. The smaller HC represents the more reliable and less cost of routing path. BandWidth(BW)-Bandwidth estimation is a basic function that is required to provide QOS in MANETs. It is a way to determine the data rate available on a network route. It is of interest to users wishing to optimize end-to-end transport performance, overlay network routing, and peer-topeer file distribution. Techniques for accurate bandwidth estimation are also necessary for traffic engineering and capacity planning support. QOS is calculated using equation given below, QOS = C ( ) + C ( ) + C ( ) Where C1 + C2 + C3 =1, EC=error count, HC= hop count, BW=bandwidth, C1, C2,C3 are the values which can be chosen according to the system needs. For example, bandwidth is very important in MANETs, thus the weight of C3 factor can be made larger. C1, C2 factor related to path error and hop count reduce the weight of path so C1 and C2 factor can be made smaller [12]. In QSEAAR protocol, the value of C1, C2 & C3 are chosen as C1=0.10, C2=0.10&C3=0.80. QOS values are calculated for the selected path and the source node tends to select the path with the high QoS value from multiple paths and data is forwarded in that path. V. RESULTS AND DISCUSSION Mobile ad hoc networks (MANETs) have been widely studied in the literature. Due to the nature of self-organization, the dynamic topology caused by mobility and transmission power control, and the multiple-hop routing in MANETs, it is difficult to build a complete analytical model to study the network performance. On the other hand, a real test bed is expensive. Therefore, the simulation study of MANETs is important. Different simulation tools such as ns-2 with CMU monarch extension, GloMoSim and its commercial successor QualNet, OPNET, and SWANS have been developed for MANET evaluation. The simulation study presented in this paper is based on ns-2 (NS2.34) under LINUX platform because it is open source and is widely used in both academia and industry[13]. Using a simulator written in C++, topologies are randomly generated, and perform the computations on these fixed graphs, which represent snapshots of the Ad-Hoc network state. 5.1 Network Scenario The table 1 shows the important parameters chosen for the NS2 simulation. Table 1: Simulation Environment Parameters of the investigated mobility models are presented in Table 2. Table 2: Parameters of Mobility models (1)

$Some of these quantitative metrics [14] are defined as follows: 1. Packet delivery fraction Packet delivery fraction, pdf, is defined as a ratio of delivered and sent packets.$ The node speed has been varied in the range 5 10 m/s. Simulation results are presented in Fig. 4. In 20 node network, the group model RPGM is superior compared to entity models.

The node speed has been varied in the range 5 10 m/s. Simulation results are presented in Fig. 4. In 20 node network, the group model RPGM is superior compared to entity models.

4 5.2 Simulation Parameters RFC 2501 describes a number of quantitative metrics that can be used for evaluating the performance of a routing protocol for mobile wireless ad-hoc networks. Some of these quantitative metrics [14] are defined as follows: 1. Packet delivery fraction Packet delivery fraction, pdf, is defined as a ratio of delivered and sent packets. The node speed has been varied in the range 5 10 m/s. Simulation results are presented in Fig. 4. In 20 node network, the group model RPGM is superior compared to entity models. This happens because the entire communication takes place between a few groups (four groups, each with five nodes). With RPGM, in 100 node network as in fig 4, the sparse network effect disappears (20 groups, each with fivenodes). There is higher probability that sources and destinations are located in different groups and their distances might become greater. More nodes and groups on the path between source and destination contribute to increased packet loss. In contrast, RW model performs better for higher density networks, due to higher probability of generating correct routes and maintaining them since there are no space constraints as in RPGM model. In all cases, the worst results are obtained for the MG model. This happens due to severe restriction of the node movement, irrespective of their density. Additionally, when two nodes diverge, the probability of traffic signal breaking up increases. the entire communication takes place between a few groups. The delay performance suffers from transient partitions that exist in a sparse network. When increasing the number of mobile nodes the sparse network effect disappears and RPGM becomes the most recommendable mobility model. Among entity models, RW demonstrates the most stable results, irrespectively of network size and routing protocols. The GM model assumes only slight changes of speed and direction. When the speed is high, it is very likely that the node will continue moving at high speeds, thus generating frequent links breaks. In the case of low initial node speed, the frequency of link breakage is also lower Higher network density involves more nodes on the paths, which results in higher frequency of finding new routes. However, the MG model experiences considerably higher average delays with the increase of network size. This happens because MG model has high spatial and temporal dependence. It presumes that nodes can move only in four possible directions with predefined probabilities to change direction to any other when being at the intersection points. There is also a problem of street blocks which can disable the possibility of communication between nodes when they are not close enough. Figure 5 Average end-to-end delay vs. mobile node speed, 20 nodes Figure 3 Packet delivery fraction of QSEAAR for 20 nodes Figure 4 Packet delivery fraction of QSEAAR for 100 nodes 5.3 Average end-to-end delay The average end-to-end (e2e) delay of data packets are investigated. Node speeds are in the range m/s. Simulation results, considering network size of 20 mobile nodes, are presented in Fig. 5. Simulation results, considering network size of 100 mobile nodes, are presented in Fig. 6. With the group model, RPGM, delay performance improves with the increase of network size. The network with 20 mobile nodes is much sparser and Figure 6 Average end-to-end delay vs. mobile node speed, 100 nodes 5.4 Routing protocol overhead In the third experiment, the routing protocol overhead (RPO) in the network with 100 mobile nodes is investigated (Fig. 7). RPO is defined as the ratio of generated routing messages and received data packets. Node speeds are in the range m/s. When nodes are moving fast there is higher rate of disconnections, which produces more route errors and frequent needs for re-initialization of route discovery process. Due to restriction of the node movement, this problem is most obvious in the case of MG model. RPGM and RW have similar RPO performance while in the case of GM, when speeds reach 5m/s, QSEAAR protocol suffers from highest RPO as the topology changes are very frequent.

5 Figure 7 Routing Protocol Overhead vs. mobile node speed, 100 nodes VI. CONCLUSION This paper studied performance of the three QSEEAR routing protocol with respect to group (RPGM) and entity (RW, GM and MG) mobility models are analyzed. A set of simulation scripts for the NS2 simulation environment merged with the BonnMotion scenario generation tools is developed [15]. Simulation results have indicated that the relative ranking of routing protocols may vary depending on mobility model. The relative ranking also depends on the node speed as the presence of the mobility implies frequent link failures and each routing protocol reacts differently during link failures. QSEAAR performs best with the group model RPGM. With entity models, QSEAAR experiences the highest routing overhead with the increase of node speed, but has acceptable average delays. Future work should be focused to extending set of the experiments by taking intoconsideration energy-consumption reduction, different propagation models and MAC protocols. Models For Network Mobility Environments,ARPN Journal of Engineering and Applied Sciences Asian Research Publishing Network (ARPN). VOL. 10, NO. 21, NOVEMBER 2015 ISSN Megha Jain, V. K. Patle and Sanjay Kumar, Performance of Mobility Models with different Routing Protocols by using Simulation Tools for WSN: A Review,International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 1, January Fatma Louati Heni, Farouk Kamoun, Kaaniche & Mounir Frikha2011, A QoS Routing Protocol Based on available Band width Estimation for Wireless Ad hoc Networks, International Journal of Computer Networks and Communications (IJCNC), vol. 3, no. 1, pp Hanzo, L & Tafazolli, R 2007, A Survey of QoS Routing Solutions for Mobile Ad hoc Networks, IEEE Communications Surveys and Tutorials, vol. 9, no. 2, pp Gomathy, C & Shanmugavel, S 2005, Supporting QoS in MANET by a Fuzzy Priority Scheduler and Performance Analysis with Multicast Routing Protocols, EURASIP Journal on Wireless Communications and Networking, pp Renu Bahuguna and Hardwari Lal Mandoria, Simulation Based Performance Comparison of MANET Routing Protocols Using Different Mobility Models and Node Density, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 7, July 2015 REFERENCES 1. Seethalakshmi, V& Mohan Kumar, G 2014, Fuzzy analysis and Performance Evaluation of QoS based Routing in MANET, Journal of Electrical Engineering, vol. 14, no.3, Article , pp Geetha jayakumar, Gopinath Ganapathi, Reference point group mobility and random way point models in performance evaluation of MANET routing protocols, Hindwi publication corporation, Journal of Computer systems, Networks, Communication Vol.2008 (2008), Article ID Aschenbruck. N, E. Gerhands-Padilla, P. Martini, A Survey on mobility models for Performance analysis in Tactical Mobile networks, Journal of Telecommunication and Information Technology, Vol.2 pp.54-61, Kumar. J and R. Rajesh, Performance analysis of manet routing protocols in different mobility models, IJCSNS International Journal of Computer Science and Network Security, vol. 9, no. 2, pp , Feb Malarkodi. B, P. Gopal, B. Venkataramani, Performance Evaluation of Ad hoc Networks with Different Multicast Routing Protocols and Mobility Models, International Conference on Advances in Recent Technologies in communication and Computing, pp.81-84, Fan Bai and Ahmed Helmy, A Survey of Mobility Models in Wireless Adhoc Networks 7. K.Kavitha and K. Selvakumar, Performance Evaluation of ODMRP and ADMR using Different Mobility Models, International Journal of Computer Applications, Volume 4 No.10, September A. Madani, N. Moussa, Self-Organized Behavior Based Mobility Models for Ad Hoc Networks, Journal of Theoretical and applied Information Technology Niclolas Cooper and Natarajan Meghanathan, Impact of Mobility Models on Multi-path Routing in Mobile Adhoc Networks, IJCNC Vol2, No1, Shayla Islam, Aisha-Hassan A. Hashim, Mohamed HadiHabaebi, Suhaimi A. Latif, and Mohammad Kamrul Hasan, A Simulation Analysis Of Mobility

3. Evaluation of Selected Tree and Mesh based Routing Protocols

33 3. Evaluation of Selected Tree and Mesh based Routing Protocols 3.1 Introduction Construction of best possible multicast trees and maintaining the group connections in sequence is challenging even in