Chapter 3

Mobility Models for Next Generation Wireless Networks

In the previous chapter, we motivated the importance of simulation in next generation wireless network analysis and introduced models for the radio channel, network topology, and energy consumption in a wireless node. In this chapter, we will focus on models of wireless node mobility, starting with motivating their importance within the network performance evaluation process. We will then extensively discuss the reasons why the existing know-how on mobility models for cellular networks cannot be applied (at least, directly) to model mobility in next generation wireless networks. Next, a taxonomy of existing mobility models for next generation wireless networks based on different criteria will be introduced. Finally, we will present the CRAWDAD web community resource for archiving mobility traces derived from real-world experiments and testbeds.

3.1 Motivation

We saw in the previous chapter that, given the difficulties related with realization, operation, and maintenance of testbeds, analysis and simulation play a fundamental role in next generation wireless network performance evaluation, especially when the network designer is interested in understanding network behavior when the number of nodes becomes large. Hence, suitable models must be developed to deal with the various aspects that might influence network performance; these models can then be combined and used to estimate expected network performance through either analysis or simulation.

Node mobility is a prominent feature of most of the next generation wireless network scenarios and applications briefly described in Chapter 1:

1. In WLAN and mesh networks, wireless stations are mostly mobile devices, such as laptops, PDAs, smart phones, etc., which are typically carried by people. Note that, although WLAN and mesh networks are typically composed of a combination of fixed (WLAN APs and mesh stations) and mobile (wireless stations) devices, the number of mobile devices in a WLAN can be very high (think of a campus network serving several thousand university students). Thus, human mobility plays a major role in WLAN and mesh network performance evaluation.
2. In ad hoc networks, network nodes are typically mobile. For instance, nodes can be soldiers of a platoon moving on the battlefield, or members of a rescue squad moving in a disaster area. Thus, human mobility plays a major role also in ad hoc networks.
3. In vehicular networks, vehicle mobility is of paramount importance: not only are the vast majority of network nodes moving vehicles, but the degree of mobility of vehicles is typically very high, with absolute speeds in the order of 100 km/h or above, and relative speeds higher than 200 km/h—think of two vehicles traveling in opposite directions on a highway.
4. Mobility plays a role also in wireless sensor networks, at least in some scenarios. For instance, if wireless sensors are mounted on animals for tracking purposes, animal mobility is an important aspect to model. Another scenario involving mobility in wireless sensor networks is when floating sensors are deployed to monitor ocean currents. In this scenario, mobility of ocean flows plays a role. Finally, even in a scenario where fixed wireless sensor nodes are used to gather environmental data, the data collection might be performed by a mobile data sink—a person, or a vehicle, or a robot. Thus, mobility plays a role also in this case.
5. Mobility is the prominent communication means in opportunistic networks: if node mobility were part of the picture, no network-wide communication could take place, given the very sparse node density resulting in a mostly disconnected network architecture. Depending on the application scenario, different types of mobility—or a combination of them—play a role, the most typical being human and vehicle mobility.

Summarizing, in the vast majority of next generation wireless network application scenarios, mobility not only is present, but plays a major role in governing network behavior and performance. This explains the extensive research efforts devoted to next generation wireless network mobility modeling in recent years, which is the focus of this book.

3.2 Cellular vs. Next Generation Wireless Network Mobility Models

Since node mobility is an important aspect also of more traditional types of wireless networks, such as cellular networks, the reader might be wondering why the extensive literature on cellular network mobility modeling cannot be applied—at least, directly—to next generation wireless networks. In this section, we will describe the main features of cellular network mobility models and explain why they are not suitable for modeling mobility in next generation wireless networks.

In a cellular network, space is divided into a well-organized cell structure, typically according to an hexagonal pattern as represented in Figure 3.1. A base station is located in each cell and is assumed to fully cover all nodes residing within its cell, that is, all nodes located in a cell can establish a direct wireless link with the corresponding base station.

Figure 3.1 A cellular network architecture.

3.1

The performance of a cellular network, expressed for instance in terms of number of served clients and QoS parameters, depends greatly on the number of “active” nodes (i.e., customers making a call) residing in each cell, and how these numbers evolve over time. The number of active nodes in the various cells and their evolution over time depend on many factors, including node mobility. In particular, a call initiated in a certain cell Ci can be transferred to an adjacent cell Cj as the active node moves from Ci to Cj (see Figure 3.1). The process of transferring an active call between adjacent cells is called cell handoff, and it is a very critical operation in cellular networks, since handoff should be completed without interrupting the call and with minimal impact on QoS parameters. Another parameter influencing cellular network performance is the cell residence time, that is, the time that an active node is expected to spend in a certain cell, which is important for optimizing the allocation of radio resources in the cell. Cell handoff and residence time are not the only cellular network parameters influenced by node mobility, but they are sufficient to illustrate why cellular network mobility models cannot be directly used to analyze the performance of next generation wireless networks.

The effect of node mobility on cell residence time and handoff probability can be modeled and evaluated using a macro mobility model, operating at the spatial granularity of a network cell: what is relevant from the point of view of a network designer is not the actual trajectory that a specific active node follows, but the instants of time at which a node trajectory crosses the border of a cell. This is illustrated in Figure 3.2, which is adapted from Kim and Choi (2010): the trajectory of an active node is divided into a number of segments S1, …, Sk, corresponding to intervals of times in which the node resides in the same cell. The first segment is determined by the time interval elapsing between time t1 when the call is initiated and time t2 at which the node exits from the initial cell. Segments S2, …, Sk−1 are determined by cell crossing times t2, …, tk−1, while the last segment Sk is determined by the time interval elapsing between time tk−1 when the node enters the last cell and time tk when the node terminates the call.

Figure 3.2 Node mobility in a cellular network.

3.2

It is important to observe that, from the network designer's point of view, the relevant events related to a node mobility are the cell crossing times ti, as they determine cell residence time and handoff probability. On the other hand, the details of the trajectory that a node follows within a specific cell—that is, the position of node u within a time interval (t′, t′′), with ti < t′ < t′′ < ti+1 for some 1 ≤ i ≤ k − 1—has very little relevance from the network designer's point of view. In this case, the extreme case of a node moving within a single cell is seen as no mobility.

In the previous paragraph, we emphasized the word network when referring to a designer interested in characterizing macro-level node mobility, and stated that, from this designer's point of view, mobility of a node within a cell is not very relevant. Intra-cell mobility is instead of interest to the wireless communication engineer in the process of optimizing the quality of the radio transmission between the base station and the client node. However, as explained in Section 2.1, the effects of small-scale, intra-cell node mobility on link quality are typically considered in the definition of the radio channel model—more specifically in the definition of small-scale fading models.

Macro-level node mobility in cellular networks is typically modeled using discrete-time Markov chains—see Appendix A.1 for a formal definition of Markov chains and their main properties—with states corresponding to cells, and transition probabilities corresponding to the probability of a node moving to another cell at the next time slot. In Figure 3.3, a portion of a cellular network is shown on the left hand side, with a node u located in cell C1 at time t. Under the quite common assumption that nodes can move only to adjacent cells, the right hand side of Figure 3.3 shows the portion of Markov chain representing possible transitions from state C1, and the respective probabilities. Thus, node u has probability p11 of remaining in cell C1 at time t + 1, and probability (1 − p11) of moving to another cell. In particular, node u moves to cell C2 with probability p12, to cell C3 with probability p13, and so on.

Figure 3.3 Portion of a cellular network (left) and portion of the corresponding Markov chain modeling node mobility between cells (right).

3.3

In the vast literature on cellular network mobility modeling, several Markov chain-based mobility models have been introduced, and their properties extensively studied. The interested reader is referred to Kim and Choi (2010) and references therein.

Cellular network mobility models as described above are not suitable for modeling mobility in next generation wireless networks for the following reasons:

1. Different spatial granularity: as commented above, cellular network mobility models are concerned with modeling inter-cell mobility. Even if the size of a cell has been reducing over the years, the spatial granularity of movements considered in cellular network mobility models is still in the order of hundreds of meters, or a few kilometers. In other words, movement of the nodes within the range of a few tens of meters is typically not relevant in cellular network mobility models. On the other hand, given short communication ranges typical of next generation wireless networks, movement of nodes even over short ranges in the order of a few tens of meters is relevant in that context. Thus, while cellular network mobility models are concerned with macro-level mobility, models for next generation wireless networks must deal with micro-level mobility.
2. Different network architecture: in cellular networks, base station deployment is carefully planned and optimized, resulting in a quite regular tiling of the plane in non-overlapping cells. Given optimized base station deployment, the assumption underlying cellular network mobility models—a base station entirely covers its cell—turns out to be quite accurate. Furthermore, communication in cellular networks occurs only between a base station and a mobile station, in a single-hop fashion. Assumptions of full radio coverage in a cell and single-hop communication justify the interest in characterizing inter-cell mobility, since mobility within a single cell is not interesting from a network-level perspective: as long as a node moves within a cell C, the node establishes a wireless link with the base station located in C, independently of its specific position within the cell. On the other hand, next generation wireless networks are typically characterized by the presence of at most a light, relatively unplanned network infrastructure, and heavily rely on wireless multi-hop communications. Thus, the interest of a next generation wireless network designer lies in answering questions such as: “Are two specific network nodes u and v within each other's transmission range?,” or “Assuming there exists a link between nodes u and v, what is the expected duration of that link?,” and so on. These questions can be answered only by looking at micro-level mobility, where trajectories of single nodes are taken into account.

3.3 A Taxonomy of Existing Mobility Models

In the previous section, we explained why cellular network mobility models cannot be directly applied to model mobility in next generation wireless networks. This fact, coupled with the paramount importance of mobility when studying next generation wireless network performance, explains the many efforts made in recent years by the research community to define and study suitable next generation wireless network mobility models. Surveys of this body of research work can be found in Bai and Helmy (2006), Camp et al. (2002), and Musolesi and Mascolo (2009). In this book, we will provide a more thorough and systematic treatment of this body of knowledge, which we will attempt to characterize and present as a scientific discipline rather than a collection of loosely related mobility models and results.

As a first effort in this direction, in this section we propose a taxonomy of existing mobility models—from now on, by the term “mobility model” we mean “mobility model for next generation wireless networks.” Instead of proposing a well-structured, hierarchical taxonomy with categories and subcategories, we present and describe extensively a set of independent criteria according to which mobility models can be classified. The criteria that will be used to classify existing mobility models are listed in Table 3.1, along with a classification of some of the mobility models which we will describe in this book according to these criteria.

Table 3.1 Classification of representative mobility models according to different criteria

images/c03tnt001.jpg

3.3.1 Spatial Scope

The first criterion used to classify mobility models is spatial scope, as already described in the previous section. As commented above, given the relatively short transmission range typical of nodes in a next generation wireless network, all mobility models proposed in the literature aim at modeling mobility in a relatively small spatial scope, that is, with a spatial granularity in the order of a few tens of meters. Accordingly, mobility models for next generation wireless networks can be considered as members of the class of micro-mobility models (see Table 3.1).

3.3.2 Application Scenario

A second criterion useful for classifying mobility models is whether a model is tailored to a specific application scenario or not. In the former case, we use the term application-specific mobility model, while in the latter we use the term general-purpose mobility model.

General-purpose mobility models are typically synthetic (for a definition of synthetic mobility model see Section 3.3.3) models defined through simple mathematical rules. Simplicity is the primary reason why general-purpose mobility models have been used so extensively in the next generation wireless networking literature. In fact, the simple mathematical rules used to define node mobility can be easily embedded into a wireless network simulator, thus significantly easing the complex task of simulating wireless network behavior. A further boost to usage of general-purpose mobility models is that an implementation of the most representative models in this class is included in recent releases of widespread wireless network simulators, such as Ns2 (Team 2011d), GloMoSim (Team 2011b), and GTNetS (Team 2011c). Another factor contributing to the success of general-purpose mobility models is that they are among the few mobility models that, due to their simplicity, allow derivation of theoretical, foundational results about mobile networks.

Representative examples of the class of general-purpose mobility models are the family of random walk models, which will be described in Chapter 4, and the random waypoint mobility model (Johnson and Maltz 1996), which is by far the most commonly used mobility model in ad hoc network simulation.

The major shortcoming of general-purpose mobility models comes from the primary reason for their widespread use, namely, their simplicity. Since these models are defined through simple mathematical rules, it is very difficult to account for the many factors influencing mobility in a real-world application scenario. For instance, general-purpose mobility models typically assume that nodes move in a spatial domain without any constraint regarding the trajectories they can follow—that is, they are unconstrained mobility models, see Section 3.3.5. Except for a few situations (e.g., people or animals moving in a vast, unobstructed space), movement is instead highly constrained in the real world: individuals move along sidewalks and pathways, cars move along roads, etc.

Given these shortcoming, several mobility models have been proposed with the explicit purpose of modeling a specific application scenario with a related network architecture. Mobility models specifically designed for WLAN environments, vehicular networks, and so on, have been introduced in the literature in recent years. Unfortunately, tailoring a mobility model to a specific application scenario often comes at the expense of significantly complicating the definition of the model, making the simulation and (even more) the analysis task much more challenging than in the case of general-purpose mobility models.

3.3.3 Nature

Mobility models can be classified also according to whether they are synthetically defined through simple mathematical rules, or rather they are stochastically defined to resemble mobility patterns observed in real-world data traces. In the former case, we use the term synthetic mobility model, while in the latter case we use the term trace-based mobility model.

In a synthetic mobility model, rules are defined to determine the initial position of a node, as well as its trips, where a trip is typically characterized by a starting point, an endpoint, a trajectory connecting the starting and endpoints, and a velocity. On the other hand, in trace-based mobility models, what is relevant are not the actual trajectories followed by the nodes, but rather their contact patterns, which should resemble as closely as possible those observed in representative traces collected in real-world experiments. In this respect, trace-based mobility models display some resemblance to cellular network mobility models, since in these models also the specific trajectory followed by a node is not of interest to the network designer. However, the spatial scope of trace-based mobility models is typically small, so differently from cellular network mobility models they belong to the class of micro-level mobility models.

3.3.4 Correlation

Another criterion according to which mobility models can be classified is whether the movement of a node is independent of or correlated with the movement of other nodes. In the former case, we use the term independent mobility model, while in the latter case we use the term correlated mobility model.

In an independent mobility model, the movement of a node is stochastically independent of the movement of any other node in the network. Typically, movement patterns of different nodes are not only independent, but also stochastically equivalent. In other words, the probabilistic rules governing node mobility are the same for all the nodes, resulting in the same stochastic behavior of all network nodes.

Anyone who has some experience in the simulation and analysis of relatively complex systems will understand the importance of the independence assumption when simulating or analyzing the performance of a wireless network: both simulation and analysis are significantly simplified if nodes move in a stochastically independent fashion. Unfortunately, the mobility of nodes is indeed highly correlated in my real-world situations. Think for instance of a typical vehicular network scenario: well-defined traffic rules strongly correlate the movement of a vehicle with those of surrounding vehicles. Another example is instances of group mobility, such as a scenario where a group of tourists follows a tourist guide. Correlated mobility models have been introduced to model situations in which the movement of a node is highly correlated with, if not completely dependent on, the movement of other nodes in the network.

3.3.5 Geography

A further criterion distinguishing mobility models is whether node mobility is geographically constrained or not. In the former case, we use the term geographically constrained mobility model, while in the latter case we use the term unconstrained mobility model.

In unconstrained mobility models, nodes are free to move everywhere within a predefined geographical domain representing the range of possible node movements. Typical representatives of the class of unconstrained mobility models are general-purpose, synthetic—hence, simple—mobility models. While simplicity is definitely a positive feature of a mobility model, unconstrained mobility models typically share with general-purpose, synthetic models also the major shortcoming of being scarcely representative of many real-world scenarios, in which node mobility is indeed highly geographically constrained. Examples of geographically constrained mobility abound: vehicles moving along roads, individuals moving along sidewalks and pathways, etc.

It is interesting to observe the relation between geographical constraints and correlation of node movements: while correlation of node movements might indeed be imposed by geographical constraints, a geographically constrained mobility model is not necessarily correlated. For instance, when considering mobility models for vehicular networks, vehicles can be constrained to move along roads, but traffic rules can be ignored—like considering vehicles as “transparent” entities to avoid accidents.

3.3.6 Trajectory

Finally, another criterion that can be used to classify mobility models is whether they generate explicit trajectories for each node, or rather generate traces of mutual contacts between nodes. In the former case, we use the term trajectory-based mobility model, while in the latter we use the term contact-based mobility model.

While node trajectories, coupled with a notion of transmission range, can be used to generate node contact patterns if needed, the main difference between the two classes of models is that trajectory-based mobility models explicitly model and give access to a notion of node trajectory, while in contact-based mobility models no notion of node trajectory is explicitly or implicitly used to generate the contact traces which are returned as an output of the model. Contact traces are typically obtained through stochastically modeling the occurrence/disappearance of links between pairs of nodes, using Markov chain-based models.

As the reader might have guessed, trace-based mobility models typically do not explicitly represent node trajectories, but rather their contact patterns; that is, they typically belong to the class of contact-based mobility models.

3.4 Mobility Models and Real-World Traces: The CRAWDAD Resource

Mobility models have the main goal of mimicking certain types of mobility (human, vehicular, animal, etc.), or at least of resembling their most distinguishing features. Thus, the ultimate way of assessing the “performance” of a mobility model is to compare the mobility traces it generates with those collected in real-world mobility experiments for the relevant mobility scenarios.

Mobility traces collected in such experiments are a fundamental resource for next generation wireless network designers. On the one hand, they can be used to validate and/or fine-tune artificial mobility models as described above. On the other hand, they can also be directly used in the network performance evaluation process, for example, by feeding wireless network simulators with the collected mobility traces. However, real-world mobility traces should be seen as complementing, not replacing, the role of artificial mobility models in the network performance evaluation process, for the following reasons:

1. Mobility parameters flexibility: when optimizing the design of a networking protocol or a mobile application, it is important to understand how the performance of the protocol/application varies under different mobility conditions. This type of evaluation can be done only using artificial mobility models, where mobility parameters such as node speed, average length of a trip, etc., can be changed. Real-world data traces can instead be used as fixed benchmarks upon which a protocol/application performance can be tested. If these data traces are made publicly available, other researchers can use the same traces to test their protocols, making it possible to really “benchmark” their design.
2. Cost issues: the process of generating a mobility trace is costly, in terms of both monetary and time resources. Thus, collection of data traces is usually possible with considerable funding and in established research projects of relatively long duration. To better understand why producing mobility traces is a time-consuming process, the reader has to consider that generating a data trace typically requires a considerable amount of preparatory work, such as developing software for data collection, installing and testing the software on different types of mobile devices, and so on. Once these tasks have been accomplished, the data collection campaign itself can also be of relatively long duration: for instance, the trace collected during the reality mining experiment at MIT (Team 2006) covered a period of nine months.
3. Privacy issues: even if the above cost issues are resolved, collecting mobility traces requires tracing the (at least approximate) position of a mobile device for a relatively long period of time. If the mobile device is carried by a person, privacy issues arise. If, besides tracing positions, also interactions between people are traced, privacy concerns become even more important. A first action to lessen privacy issues is tracing the mobility of volunteers, who have to be carefully informed about the purposes of the experiment, the use of the data collected, etc. Furthermore, data anonymization techniques are typically used to conceal the identity of individuals participating in the mobility experiment. However, the fact remains that severe privacy issues are raised in any data collection campaign involving people, and that dealing with (or, at least, softening) these issues adds further complexity to the mobility data collection task.
4. Data availability: since collecting data traces is a costly and complex task, it is often not possible to generate data traces for the protocol/application under study. Thus, data traces generated as outcomes of other research projects should be used to benchmark the designed protocol/application. However, many mobility data traces are not publicly available and thus cannot be used for benchmarking purposes.

While many of the issues listed above are in a sense intrinsic to the mobility data collection task, significant efforts have been recently undertaken in the research community to make a large number of mobility traces publicly available. The most important initiative in this direction is the Community Resource for Archiving Data At Dartmouth (CRAWDAD) (Team 2011a), a project led by David Kotz of Dartmouth College, USA. The project started in 2004 with a data collection campaign of syslog, SNMP, and tcpdump data from the WLAN installed at Dartmouth College. Since then, the project has evolved to become a community resource for archiving mobility traces related to wireless networks.

The homepage of the CRAWDAD website is displayed in Figure 3.4. CRAWDAD resources amount not only to a large set of publicly available mobility traces—as of the beginning of 2012, 78 traces are available—but also to a set of tools for visualizing and processing these traces—23 tools are available—and to browsing tools allowing navigation of the traces by keywords, etc.

Figure 3.4 The CRAWDAD website.

3.4

In recent years, CRAWDAD has become a very valuable resource for next generation wireless network designers, as witnessed by the fact that—as of the beginning of 2011—more than 500 scientific papers using CRAWDAD mobility traces have been published.

3.5 Basic Definitions

In this section, we provide the basic definitions and terminology that will be used to describe the various mobility models presented in this book. Most of these definitions apply to the class of synthetic mobility models which, as can be seen from Table 3.1, covers the vast majority of mobility models commonly used in the literature.

A synthetic mobility model can be formally defined through a set of rules specifying the following:

1. A spatial domain within which nodes move. The spatial domain is typically a region R of one-, two-, or three-dimensional space. A simple example of a spatial domain is the unit cube [0, 1]d, with d = 1, 2, or 3. In the case of geographically constrained mobility, the formal definition of a spatial domain is more difficult, and informal definitions (e.g., a set of roads is defined, and nodes are constrained to move only along roads) of the spatial domain are used instead.
2. The initial position of nodes. The position of nodes at the beginning of the time period of interest should be determined. Typically, this is done through randomly selecting node positions according to some spatial probability distribution, with uniform node distribution being the most common choice.
3. How to perform a trip. A trip can be defined in several ways: for example, specifying a destination point and a geographical path—the trajectory—connecting the current node position with the destination point. Note that both the destination point and the trajectory must be entirely contained in the spatial domain R. Alternatively, a direction of movement and the duration of the trip can be defined.
4. How to select speed. The speed of a trip is typically selected according to some random process. A common choice is to select speed uniformly at random in an interval [vmin, vmax], where vmin and vmax represent the minimum and maximum possible speed, respectively.
5. The transition from one trip to the next. A common transition rule is based on the notion of pause time, that is, a node is assumed to remain stationary at the destination point for a certain time before starting the next trip.
6. The border rule. In some mobility models, it is possible that a node will hit the border of the spatial domain R during a trip. In such a case, since nodes are not allowed to exit R, a border rule must be defined to describe how to deal with this situation. Typical border rules are billiard-like reflection and toroidal wrapping.

A very important notion in mobility modeling is that of stationary node spatial distribution. Consider a certain synthetic mobility model images/c03_I0001.gif, and let X0 be a variable denoting the position of a node u at time 0. Note that X0 is determined according to the rules for initial node positioning as defined in images/c03_I0002.gif. In particular, if X0 is randomly selected, X0 is a random variable with values in R, with f0 being the corresponding pdf. As node u starts moving, its position varies within R. Let X(t) denote the position of u at time t > 0. If trips are randomly selected, X(t) also can be considered as a random variable defined within the spatial domain R. Let ft denote the pdf of random variable X(t). Assuming for simplicity that R is a two-dimensional domain, function ft(x, y) corresponds to the probability density of finding node u exactly at position (x, y) at time t. Note that, given the definition of mobility model images/c03_I0003.gif, the support of both f0(x, y) and ft(x, y) is contained in R.

Definition 3.1
A mobility model images/c03_I0004.gif is said to be stationary if there exists a unique pdf images/c03_I0005.gif such that

images/c03_I0006.gif

that is, if, images/c03_I0007.gif. In such a case, function images/c03_I0008.gif is said to be the stationary node spatial distribution of images/c03_I0009.gif.

It is important to observe that the stationary node spatial distribution of a mobility model might be different from the initial node spatial distribution, that is, images/c03_I0010.gif in general.

References

Bai F and Helmy A 2006 A survey of mobility modeling and analysis in wireless ad hoc networks. Wireless Ad Hoc and Sensor Networks. Springer, Berlin.

Camp T, Boleng J and Davies V 2002 A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing 2, 483–502.

Johnson D and Maltz D 1996 Dynamic source routing in ad hoc wireless networks. Mobile Computing, pp. 153–181. Kluwer Academic, Dordrecht.

Kim K and Choi H 2010 A mobility model and performance analysis in wireless cellular network with general distribution and multi-cell model. Wireless Personal Communications 53, 179–198.

Musolesi M and Mascolo C 2009 Mobility models for system evaluation. State of the Art on Middleware for Network Eccentric and Mobile Applications. Springer, Berlin.

Team C 2011a http://crawdad.cs.dartmouth.edu/index.php.

Team G 2011b http://pcl.cs.ucla.edu/projects/glomosim/.

Team G 2011c http://www.ece.gatech.edu/research/labs/MANIACS/GTNetS/.

Team N 2011d http://www.isi.edu/nsnam/ns/.

Team R 2006 http://reality.media.mit.edu/.

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