Mobility model

Although network simulators have been an essential element of research in MANETs for about ten years, mobility models are still surprisingly limited, with the most commonly used model being a random waypoint model. As a result, many state- ments about the behaviour of MANETs that are based on such simulations may be questionable. We develop a suitable mobility model for tactical networks incorpo- rating both environmental constraints and tactical doctrine. While sometimes the argument is made that random mobility provides the worst case scenario for proto- cols, we claim that one of the worst case scenarios is provided by groups in urban environments. In these scenarios groups are likely to be separated, obstacles may abruptly cut communication links, and high node densities may cause an overload of the wireless communication channel. In this section we introduce our Coalition

Mobility Model (CMM), and define simulation scenarios showing the capabilities of

the CMM in Section 3.2.

3.1.1.1 Background

Research in mobility models has resulted in a number of models ranging from proba- bilistic to completely deterministic. Random mobility models represent (almost) probabilistic models since the movements of the nodes is only bound to a few para- meters such as the variance of a Gaussian distribution or some constraints which keep the nodes in a bounded area; see [28] for a survey and simulation-based comparison of random mobility models, [16] for a concise categorisation of mobility models in

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general, and [6] for a good recent survey on mobility models in tactical networks.

One of the most utilised probabilistic models is the Random Waypoint Model [95, 17], in which nodes trace positions which are determined by a uniform distribution. Since the nodes in this model use the shortest path to reach their destination, node density in the centre of the simulation area tends to be higher than in marginal regions. The Random Direction Model [122] attempts to avoid this behaviour by sending the nodes on a “detour” via the border of the simulation area.

All of these random models are configurable by few parameters such as the variance of the Gaussian distribution and provide basic mobility patterns for net- work simulators. A more deterministic movement strategy is provided by the Graph

Model [135], which restricts the nodes to move randomly on predefined trails. Ex-

tensions of this model are commonly used in mobile Vehicular Ad hoc NETworks (VANETs), where the nodes (cars) are stopping at cross-ways to simulate traffic lights [106] or move smoothly through curves to simulate bends in the road [16]. Re- cently, two easy-to-use VANET mobility generators have been implemented [104], [13], which facilitate the automatic generation of VANET mobility files.

A topography-aware mobility model was proposed by Jardosh et al. [69, 70]. In Jardosh’s Obstacle Mobility Model, buildings are modelled as polygons, and the transmission between two nodes is interrupted or highly attenuated if their line-of- sight is intersected by a polygon. The nodes are either allowed to walk on predefined trails or reach their randomly defined aim by the shortest pathway through the obsta- cle area. An elaborated mobility model for desaster area scenarios was proposed by Aschenbruck et al. [5]. Their model supports heterogeneous area-based movement on optimal paths avoiding obstacles with joining and leaving nodes. Aschenbruck et al. show how packet loss and data throughput is influenced by heterogeneous node mobility.

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Certainly, the most realistic mobility model is one that directly reflects real movements from mobility traces as proposed by Tuduce and Gross [137] and by Lu et al. [84]. Their models use traces that are taken from real movements, trans- ferring them in a mobility file which can be processed by the according network simulator. However, generating these traces is very expensive and restricts the sim- ulations to some available trace files.

All previously described mobility models treat nodes independently and thus do not provide any group movement. A generalisation of these models are group models, in which every node moves relative to the logical centre of a group, while the movement of this logical centre can be provided by any of the models above. Consequently, group mobility models need to handle both the movement of the group centre and inter-group movements. They are therefore harder to implement and less well-studied so far.

The first group mobility model for MANETs, the Reference Point Group Mobility

Model (RPGMM), was proposed by Hong et al. [65] in 1999. In the RPGMM, each

group has a logical centre and the nodes are randomly but uniquely distributed, moving around the group centre. Wang and Li [140] extended the RPGMM to their

Reference Velocity Group Mobility Model, in which the movements of the nodes in

the group are dependent on each others’ velocities. Blakely and Lowekamp [20] fix the relative positions of the nodes to the group centre in their Structured Group

Mobility Model.

A first model that allows nodes to change groups was proposed by Biao et al. [18], but the relative position of the nodes to their group centre is not discussed. Recently, Orchisuren et al. [96] proposed an actor-based group mobility model based on RPGMM. In their model, movements of single nodes in the group are influenced by the velocity of the group centre and a random factor that reflects unpredictable

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influences on the movement of single nodes. In 2006, Williams and Huang [142] proposed the first group mobility model that combines group mobility and obstacles. They refer to the RPGMM, but use repulsion forces to avoid collusions with other nodes and obstacles.

In all existing group mobility models, the nodes are either in a fix relative position to the group centre or perform random movements within their group. While these approaches are suitable to model groups in which each node moves autonomously within the boundaries of the group, they cannot reflect structured group movements (as in military and emergency response networks and processions). We therefore propose a mobility model that is based on Hong’s RPGMM but replace the random mobility within the group with flexible formations.

Several basic implementations, especially of the random mobility models, can be found in network simulators. In this thesis we use the simulator NS-2[51], which offers the possibility to either create totally deterministic movements by writing every single movement directly in the simulation script, or to generate a random waypoint scenario with the script setdest. More modular and reusable software for this purpose is provided by the tools BonnMotion [138] and CanuMobiSim [29]. Both tools are Java-based mobility generators, which provide several random models as well as the possibility to generate mobility files for several common network simulators including NS-2. Moreover, CanuMobiSim provides a graph model, where the graph can either be read from a separate file or directly from an XML file. Due to this functionality, CanuMobiSim was chosed as the basis for our implementantion.

3.1.1.2 Model and implementation

In Tactical MANETs, envisaged in military and emergency response networks, the participants (nodes) are likely to move in groups, which split up, coalesce, and lose

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or add single members. As noted in Section 3.1.1.1, a number of random mobility models for pairwise independent node movements have been developed, while the investigation of group mobility models is limited to models that only provide random or no mobility within the group.

In this section we extend the basic idea of the RPGMM and report on a new

Coalition Mobility Model (CMM), which is designed to be used in conjunction with

our topography aware propagation model [114] (both for use on the mobile nodes and to provide more realistic simulations). We illustrate our mobility model using a hierarchicly organised platoon. We note, however, that the mobility model can be used to model any MANET that is organised in one or several groups.

The doctrine for the tactical movements of military formations as described in [61] is to hierarchically organise nodes into one or more formations. Formations are arrangements of soldiers and organised subgroups. Leaders choose formations based on their analysis of the terrain, the likelihood of enemy contact and the need for speed. The smallest group in an infantry operation is the fire team. Fire teams typically consist of four soldiers that follow the orders of the team leader. Squads form the next group in the hierarchy and consist of fire teams and a squad leader. Squad formations describe the relationships between the fire teams in the squad. Finally, platoons present the highest group in this hierarchy and consist of squads in special formations, the platoon leader and other additional soldiers such as the platoon sergeant or a machine gun crew.

squad leader

team leader team leader

| {z } | {z }

wedge-left wedge-right

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Figure 3.1 shows one possible formation for a squad that is organised as a line. In order to enable the modelling of arbitrary tactical units with changing formations, our implementation of CMM contains a flexible and reusable definition of a group. The entire mobility model, including the groups with their different formations, are defined in an XML file. A group, as considered in CMM is defined in the Extended

Backus-Naur Form (EBNF) as follows:

distance = “real number”

angle = “real number”

name = “string”

node = distance angle

formation = name_{{{node} {group distance angle}}} group = name formation_{formation}

According to this definition, every node has a fixed desired position in its formation, which is described by the distance to the group centre and the angle relative to the direction of the group motion. A formation itself can also contain complete (sub)groups that are also positioned relatively to the group centre via distance and angle. Finally, a group contains at least one formation. In the case of fire teams, squads and platoons, the group “fire team” could contain several formations with four nodes. The higher-levelled group “squad” could then consist of two “fire team” groups and an additional node as “squad leader”, while the highest level

group “platoon” could consist of nodes, “fire team” groups and “squad” groups.

Finally, for completion of the CMM, the movement of the group centres needs to be defined. We use an extension of the Graph Model, which has already been implemented in the mobility framework CanuMobiSim by Stepanov et al. [29]. The nodes in this model are restricted to walk on edges of a connected graph, i.e., there exists a path between each two vertices on the graph. In Stepanov’s graph model

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[131], every node chooses the next destination vertex uniformly distributed under all vertices, and traces its aiming point on the shortest path. Given that pathways in tactical networks are typically not chosen randomly, and for simulating well-specified scenarios, the routes in the CMM are predefined. Moreover, the CMM supports the consideration of several groups with independent configurations, so that, e.g., several taskforces could walk on different predefined routes. The CMM deliberately does not consider the influence of the topography such as buildings or vegetation. Feasibly complex realisations, such as nodes bouncing on walls or finding the shortest path quoin by quoin are not realistic, while more suitable models tend to be very complex and are subject of ongoing research. Instead we propose the consideration of the topography separately during the simulation calculation. According to a predefined topographical area, the edges of the graph and the group-configurations can be determined manually.

Implementation We have implemented the CMM as an extension of the frame- work CanuMobiSim [29], which already contains random mobility models and a graph mobility model. An essential feature of CanuMobiSim is the configuration of the respective mobility model in a XML file. We have extended the scope of this XML file to include the description of groups and additional parameters for the CMM. The configuration strategy of a group as defined in EBNF previously allows the re-use of groups of arbitrary depth and thus enables an almost deterministic, but still manageable setup of the CMM. Further extensions of the CMM, such as the changing of nodes between groups or the collection of nodes, can be implemented as required.

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3.1.1.3 Summary

We have defined our Coalition Mobility Model CMM that facilitates the generation of formation-based group mobility files for NS-2and other network simulators by the configuration of an XML file. Several hierarchically organised groups can be defined in combination with other mobility patterns as provided by the framework CanuMobiSim. Implementing our model in CanuMobiSim allows an easy extension for further mobility patterns, which are discussed in Section 8.2.1.