Chapter 2 Mobility Model Characteristics

Chapter 2 Mobility Model Characteristics Abstract The salient characteristics of mobility models of mobile nodes in mobile ad hoc networks are described. We have described how the different mobility models are grouped, although different methods can be used to classify them. The overall criteria for formulations of a mobility model are explained. The mobility parameters and impact of the mobility models on the communications protocols over the MANET are briefly summarized. 2.1 Introduction The mobility patterns are the key criteria that influence the performance characteristics of the mobile ad hoc networks. Each mobile node of a MANET is treated as an autonomous peer, and the random mobility patterns of mobile nodes need to be analyzed to investigate the dependency of performances of the variable topology network. Moreover, a MANET is a resource-constrained communications network with limited energy, computing resources, and memory. Over the years, many mobility models have been used to analyze the mobile ad hoc network performances. Many mobility models are designed in order to recreate the real world scenarios better for application to MANET. The statistical properties of these mobility models are analyzed designing different mobility metrics and studying the influences of mobility models on performances of networking protocols including routing, service discovery, and mobile peer-to-peer applications. 2.2 Mobility Model Classifications A mobility model attempts to mimic the movement of real mobile nodes that change the speed and direction with time. The mobility model that accurately represents the characteristics of the mobile nodes in an ad hoc network is the key to examine whether a given protocol is useful in a particular type of mobile scenario. The possible approaches for modeling of the mobility patterns are of two types: traces and R.R. Roy, Handbook of Mobile Ad Hoc Networks for Mobility Models, DOI 10.1007/978-1-4419-6050-4_2, C Springer Science+Business Media, LLC 2011 23

24 2 Mobility Model Characteristics syntactic. The traces provide those mobility patterns that are observed in real-life systems. In trace-based models, everything is deterministic. However, mobile ad hoc networks are yet to be deployed widely to know the traces involving a large number of participants and an appropriately long observation period. In absence of traces, the syntactic models that have been proposed to represent the movements of mobile nodes realistically in ad hoc networks are presented. The syntactic mobility models can also be classified based on the description of the mobility patterns in ad hoc networks [1]: individual mobile movements and group mobile movements. In the former case, mobility models attempt to the anticipate mobile s traversing patterns from one place to another at a given point of time under various network scenarios. In the latter case, mobility models try to characterize the group s traversing patterns with individualism averaged. Unlike trace-based mobility models, syntactic mobility models considered here have randomness, and further classifications can be made based on randomness [2]: constrained topology-based models and statistical models. In constrained topology-based mobility models, mobile nodes have only partial randomness where the movement of nodes is restricted by obstacles, pathways, speed limits, and others. If the nodes are allowed to move anywhere in the area and the speed and direction are allowed to choose, it is termed as total randomness. The model that is based on total randomness is defined as statistical mobility model. Based on specific mobility characteristics, the classification of mobility models is also made primarily into four categories [3]: random models, models with temporal dependency, models with spatial dependency, and models with geographical restrictions. In random models, like statistical models, nodes move randomly and can be classified further based on the statistical properties of randomness, and random waypoint, random direction, and random walk mobility model fall into this category. The movement patterns of the mobility models with temporal dependency are likely to be influenced by their movement histories, and Gauss Markov and smooth random mobility model are the examples of this mobility model category. In some mobility scenarios, the mobile nodes tend to travel in a correlated manner. These mobility models are termed as mobility models with spatial dependency, and mobility models like reference point group mobility model and other spatially correlated mobility models belong to this category. Another class is the mobility model with geographic restriction, same as the constrained topology-based model, where the movements of the mobile nodes are constrained by streets, freeways, and/or obstacles, and pathway and obstacle mobility model are two examples of this mobility model. Mobility models can also be categorized by using other criteria such as mobility patterns and histories: random mobility, directional mobility, and habitual mobility. The mobility models described here are primarily applicable for mobile ad hoc networks deployed in ground, airborne, space, and/or undersea where all nodes are mobile and mobility criteria are derived from the relative movements among the nodes. Recently, many more new mobility models have been proposed based on many more criteria that can be relevant to the present and future mobile ad hoc networks. We have classified the mobility models into the following areas:

2.3 Formulation of Mobility Models 25 Individual mobility models Group mobility models Autoregressive mobility models Flocking and swarm mobility models Virtual game-driven mobility models Non-recurrent mobility models Time-variant community mobility models Knowledge-driven mobility models In individual mobility model, the mobility pattern of the individual mobile node is considered, and random walk is an example of this mobility model. In group mobility model, the cooperative group movement of the mobile nodes acts in synchrony as a group, and reference group mobility model is an example of this category. The autoregressive mobility model considers mobility patterns of individual nodes (or a group of nodes together) correlating the mobility states that may consist of position, velocity, and acceleration at consecutive time instants. In flocking and swarm mobility model, a coordinated movement task is performed by dynamic mobile nodes over (visually invisible) self-organized networks in nature. The virtual game-driven mobility model deals with an individual node or of a group of mobile nodes based on user/player strategies that are mapped from the real world to virtual agents interacting with each other or with groups of mobile users. In non-recurrent mobility model, the moving objects move in a totally unknown way without repeating the previous patterns, and these moving objects can be mobile nodes of the ad hoc network that constantly changes its topology or the continuously moving data arises in a broad variety of applications, including weather forecast, geographic information systems, air-traffic control, and telecommunications applications. The time-variant community mobility model captures the non-homogeneous behaviors in both space and time with inter-node dependency of the community of interest (COI)-based mobile network structure. The knowledgebased mobility model exploits the knowledge of movement patterns of mobile nodes and can accommodate any arbitrary mobility patterns of mobile nodes including random walk, random waypoint, community-based mobility, and other well-known mobility models. We have described the mobile models in each area of classification and their impact on performances of physical to application layer protocols of the mobile ad hoc networks as mentioned in the subsequent sections. 2.3 Formulation of Mobility Models The random characteristics of mobile nodes in a MANET may consist of a stochastic process, and each node s movement may consist of a sequence of random length intervals called mobility epoch during which a node moves in a constant direction at a constant speed. The speed and direction of a mobile node may vary in accordance to mobility criteria depending on the kinds of mobility models from epoch to

26 2 Mobility Model Characteristics Fig. 2.1 Movement of a node in a MANET: (a) epoch mobility vectors and (b) resultant mobility vector [4]. IEEE Reproduced with permission epoch. In group mobility, the similar may be the case for a group of mobile nodes. Figure 2.1a depicts the movement of a node over certain epochs by an arbitrary node n from its position n to another position n over an interval of length t. If we assume that node n moves with a velocity Vn i and direction θ n i at epoch i and the duration of epoch i of node n is Tn i, node n moves a distance of Vi n Ti n at an angle θ n i. Let us define the distance traversed during time interval Tn i by a mobile node at epoch i as an epoch mobility vector R i n = Vi n Ti n [4].

2.3 Formulation of Mobility Models 27 In fact, Fig. 2.1b shows the resulting epoch mobility vector R(t) and it can be seen that this R(t) is the vector sum of the individual epoch vectors. However, we need to examine the following parameters in order to articulate a mobility model: The epoch lengths may be identical or may not be identical, and their distribution may be independent and identically distributed (i.i.d. ) or may not be i.i.d. random variables, and their statistical characteristics (mean, variance) need to be known. A mobile node may or may not pause at the end of an epoch before starting a new epoch. The direction of the mobile node during each epoch may be i.i.d. uniformly distributed over (0, 2π) and may remain constant only for the duration of the epoch or they may not be uniformly distributed between 0 and 2π. The speed during each epoch may be an i.i.d. distributed random variable (e.g. i.i.d. normal, i.i.d. uniform) with a certain mean and variance and may remain only for the duration of the epoch, or they may be characterized using some other different statistical criteria. The speed, direction, and epoch length may be uncorrelated or may be correlated. The mobility pattern may be uncorrelated or correlated among the nodes of a MANET, and link failures can be independent or dependent. These mobility statistics will depend on the mobility models of mobile nodes. The probability density function (pdf) of the individual or joint node mobility pattern needs to be determined knowing the statistical parameters of the mobility model. All mobile nodes in a MANET will have a limited transmission range and are expected to experience frequent changes in speed and direction with respect to the length of time a link remains active between any two nodes. The distribution of each node s mobility characteristics may change with time, and such is the case for the rate of link breakage or failure. The distribution of the number of mobility epochs also needs to be considered with respect to an active link s lifetime. The statistics of the mobility vector R(t) needs to be known. The distributions of mobile node distance and direction are used to derive the expressions for link/path availability, link/path persistence, link/path duration, and link/path residual time based upon different initial conditions [4, 5]. Let us consider Fig. 2.2 that demonstrates the mobility of two nodes m and n initially separated by a distance C. Figure 2.2a shows the joint movement of two nodes while Fig. 2.2b illustrates the joint mobility transformation. The dotted circle around the center of each node shows the transmission range of each node. We have to consider the resultant mobility vector of these two nodes by transforming from single node random mobility vectors to the equivalent random mobility vector by noting the progression of the distance between the nodes proceeds in an identical manner in both cases. The movement of m relative to n is shown in each epoch, along with the resulting equivalent random mobility vector R m,n (t). Ifm lies within the circular region of

28 2 Mobility Model Characteristics Fig. 2.2 Joint mobility transformation: (a) joint node case and (b) joint mobility transformation [4]. IEEE Reproduced with permission radius centered at n, the link between the two nodes is considered to be active. One of the two possible cases of activation for communications between two nodes can happen as follows: a. Two mobile nodes moving in their respective autonomous modes come within the range of each other and start communication. b. A mobile node becomes active at any given time at a random place and it happens to be in the range of communications of another mobile node and both of them start communicating.

2.4 Mobility Metrics 29 These initial conditions of active communications will have impact on calculation of the link/path metrics of the mobile ad hoc network. The key is that the mobility model that is intrinsic for each mobile node of the mobile ad hoc network plays the key role in influencing the performance metrics including link/path metrics. So, we have to know the characteristics of the mobility models and metrics of the mobile nodes in order to investigate the MANET performances. 2.4 Mobility Metrics The mobility metrics [3, 5 9] aim to capture the characteristics of different mobility patterns and can be used to analyze the impact of mobility models on the performance of communications protocols used over mobile ad hoc networks. The metrics should also, if possible, be independent of the particular network technology used. Therefore mobility metrics proposed here are geometric in the sense that the speed of a node in relation to other nodes is measured, while t is independent of any links formed between nodes in the network [9]. Many mobility models can be used in mobile ad hoc networks, and each mobility model has its own mobility patterns that will impact the protocol performances. For example, there are many individual, group, flocking, and other mobility models. Again, each mobility model will also behave differently when there are obstructions. But models themselves do not give clear images of how mobility patterns are different from each other. The mobility metrics that describe these mobility patterns will be termed as the direct mobility metrics [8]. The parameters for the direct mobility metrics are considered as follows: relative velocity, temporal dependence, spatial dependence, and pause time. The direct mobility metrics will then be used to find the derived mobility metrics using some mathematical models primarily representing the generic network performances: mobility measure, link-based metrics, path-based metrics, node connectivity, network connectivity, and quality of service. The combined metrics are then termed as the mobility performance metrics for the mobile ad hoc network. The direct mobility metrics measure the speed and direction-related parameters of the mobile node directly. The direct mobility metrics are used to measure different mobility models successfully, although some metrics cannot accurately capture different characteristics of mobility models. For example, the metric indicates the degree of mobility, but it fails to reflect relative motions between hosts. More important, direct mobility metrics often do not directly reflect topology changes, while the latter is believed to be more influential to network performance. The derived mobility metrics take care of this aspect of mobility. The derived mobility metrics that are formulated using some mathematical models based on the direct mobility metrics are well suited to this purpose. These mobility metrics constitute precise mathematical relationships between network connectivity and node mobility. These expressions can, therefore, be employed in a number of ways to compare the different mobility models. In turn, it will improve

30 2 Mobility Model Characteristics the performance of mobile ad hoc networks such as in the development of efficient algorithms for communications protocols. Evaluations can then be performed to investigate how the derived mobility metrics are related to direct mobility metrics, how well the derived metrics can differentiate different mobility models, and how well the metrics can quantify protocol-specific performances. 2.5 Impact of Mobility Models on MANET The every aspect of the mobile ad hoc network is influenced by the mobility patterns of mobile nodes, and we will gain more insights into the impact of mobility for different mobility models described more in detail in subsequent sections later. Many cluster-based algorithms for the MANET topology generation have been proposed over time, but it is clearly shown that the mean cluster size (that is, number of mobile nodes) under any transmission ranges, probability of a node remaining in a cluster, or node residence time in a cluster decreases with the increase in speed of the mobile nodes [4]. It is observed that the networking protocols from the physical to the application layer that are designed to be adaptive to the general characteristics of the variable topologies caused by different mobility models perform better in the MANET. For example, the traditional centralized client server protocols are not suitable for MANET networking environments. In Section 1.10, we have provided hints of how mobile P2P distributive application protocols are being designed to be applicable for the MANET. Similarly, a new distributive network management application for the MANET known as ad hoc network management protocol (ANMP) [10], a modified version of simple network management protocol (SNMP), has been proposed. For example, a large MANET may be managed forming the clusters dynamically in mobile environments, and the distributive cluster algorithms may be used for the variable cluster-based topology. The simulation results show that the number of ping-, non-ping overhead-, and MAC layer messages as well as the percentage of unmanaged nodes goes up dramatically with increase in speed of mobile nodes, although it is not clearly shown how different mobility models will affect these results. Since the ANMP has been adaptive to the dynamic environments of the MANET, this management protocol has been effectively used in managing the variable cluster-based MANET topology including applications like secure multicasting and level-based access control. Similarly, all the MANET routing protocols that are described in Section 1.5 are being made adaptable to mobility of mobile nodes. Many QOS protocols including modifications of existing protocols described in Section 1.7 are being made suitable in mobile environments as it is really hard to provide guarantee of QOS over the MANET. Even new distributive QOS routing protocols [11] have been proposed for integration of both routing and QOS schemes to be appropriately adaptable to mobility of mobile nodes. In addition to routing

2.7 Problems 31 and QOS metric, the energy and link/path metric should be used to integrate with the mobility metric for some routing and application protocol developments to be adaptable with mobility of mobile nodes. The improvements are needed for the existing MAC protocols considering both mobility and multihop environments of the MANET, since the traditional MAC protocols that are used in the dynamic mobile multihop have serious problems as discussed in Section 1.4. It would be appropriate to look into for development of new MAC protocols for the MANET. Finally, the physical layer protocol like bit error rate (BER) control is another area that needs to be seriously investigated as the mobility of the MANET nodes increases the BER particularly due to shadowing, and if speed of the mobile nodes increases too much the shadowing may cause unstable links whose excessive BER cannot be controlled at all by using the protocols like physical layer forward error correction (FEC), link layer automatic repeat request (ARQ), and/or other higher layer schemes. 2.6 Summary We have described how the mobility model that is intrinsic to mobile nodes of mobile ad hoc networks is influencing the networking performances. There can be many ways to classify all mobility models that are used in a MANET. However, we have grouped them into eight areas examining all existing and emerging mobility models. A general approach is briefly discussed for formulation of a mobility model for the mobile ad hoc network. The parameters of the mobility metrics that are used for the MANET performance analysis are also discussed in addition to the MANET performance metrics. Finally, we have described the impact of mobility in all mobile ad hoc networking protocols from the physical to the application layer. 2.7 Problems 1. What are the typical characteristics of the mobile ad hoc network? 2. Why does a mobile ad hoc network need to have more control traffic than that of the wire-line or cellular wireless network? 3. What are major differences between the traces and the syntactic mobility model? 4. Describe different classifications of mobility models that are being discussed in the context of a MANET. 5. Describe a general analytical approach of how you can develop a mobility model for a MANET. 6. What are the consequences of mobility in the mobile ad hoc network? Why are the mobility patterns in mobile ad hoc networks important?

32 2 Mobility Model Characteristics References 1. Camp, T et al (2002) A Survey of Mobility Models for Ad Hoc Network research. WCMC 2. Zheng, Q et al (2004) Recent Advances in Mobility Modeling for Mobile Ad Hoc Network Research. ACMSE 3. Bai, F et al (2003) IMPORTANT: A Framework to Systematically Analyze the Impact of Mobility on Performance of Routing Protocols for Ad Hoc Networks. INFOCOM 4. McDonald, AB and Znati, TF (1999) A mobility-based framework for adaptive clustering in wireless ad hoc networks. IEEE Journal on SAC 17(8) 5. Xu, S et al (2007) An analysis framework for mobility metrics in mobile ad hoc networks. EURASIP Journal on WCN, Article ID 19249 6. Kwak, B et al (2003) A standard measure of mobility for evaluating mobile ad hoc network performance. IEICE Transaction on Communications, E86-B, no. 11 7. Tsumochi, J et al (2003) Impact of Mobility Metric on Routing Protocols for Mobile Ad Hoc Networks. Communications, Computers and signal Processing, PACRIM 8. Zheng, Q et al (2004) Recent Advances in Mobility Modeling for Mobile Ad Hoc Network Research. ACMSE 9. Johansson, P et al (1999) Scenario-based Performance Analysis of Routing Protocols for Mobile Ad-Hoc Networks. MobiCom 10. Chen, W et al (1999) ANMP: ad hoc network management protocol. IEEE Transaction on SAC 17(8) 11. Chen, S et al (1999) Distributed quality-of-service routing in ad hoc networks. IEEE Transaction on SAC 17(8)

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