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  • Research Article
  • Open Access

Emulating Opportunistic Networks with KauNet Triggers

  • 1, 2Email author,
  • 3,
  • 3,
  • 3 and
  • 3
EURASIP Journal on Wireless Communications and Networking20102011:347107

  • Received: 15 May 2010
  • Accepted: 11 October 2010
  • Published:


In opportunistic networks, the availability of an end-to-end path is no longer required. Instead opportunistic networks may take advantage of temporary connectivity opportunities. Opportunistic networks present a demanding environment for network emulation as the traditional emulation setup, where application/transport endpoints only send and receive packets from the network following a black box approach, is no longer applicable. Opportunistic networking protocols and applications additionally need to react to the dynamics of the underlying network beyond what is conveyed through the exchange of packets. In order to support IP-level emulation evaluations of applications and protocols that react to lower layer events, we have proposed the use of emulation triggers. Emulation triggers can emulate arbitrary cross-layer feedback and can be synchronized with other emulation effects. After introducing the design and implementation of triggers in the KauNet emulator, we describe the integration of triggers with the DTN2 reference implementation and illustrate how the functionality can be used to emulate a classical DTN data-mule scenario.


  • Packet Loss
  • Adaptation Layer
  • Opportunistic Network
  • Emulation Effect
  • Contact Opportunity

1. Introduction

Opportunistic networks have received a great deal of attention within the research community in recent years. These networks are characterized by the opportunistic use of networks or other resources as they become available. In contrast to traditional networks, opportunistic networks do not require an end-to-end path to be available between the communicating application end points, but may instead rely on intermittent connectivity. The availability/unavailability of communication opportunities is typically caused by some form of mobility. One of the most well-known examples of opportunistic networks is Delay/Disruption Tolerant Networks (DTN) [1, 2]. The DTN architecture [2] defines a message-based overlay, the bundle layer, that can operate over a collection of networks of different types and which can each use its own protocol stack internally. DTNs may be characterized by occasional connectivity, high and variable delays, and asymmetric data rates. Oppnets [3] are another example of opportunistic networks, in which a small seed network is deployed and then opportunistically expands itself to include additional nodes and resources as needed. Oppnets thus do not only use communication opportunities, but the network is also opportunistically enlarged in order to acquire the resources necessary to carry out a specific application task.

In general, evaluating the performance of any communication system is challenging due to the complexities and number of variables involved. Performance can be evaluated by several metrics and at several levels of abstraction ranging from analytical evaluation, via simulation, experiments in an emulated environment, up to full-scale live experiments. As opposed to analytical modeling and simulation, the emulation approach uses a mixture of real entities and abstractions.

Emulation of communication systems can take place at different levels of abstraction: researchers have developed solutions ranging from physical-level to transport-level emulation, including link-level and network-level emulation. For many performance evaluation tasks, emulating communication systems is an attractive approach, since it allows parts of the evaluated system to be real entities capturing all their inherent complexity. Other parts of the system may be abstracted to a degree, and the behaviors of these abstracted parts are emulated. In comparison to real live tests, emulation is typically less expensive to perform and produces more easily reproducible results.

Few examples of physical-level emulation exist, mainly focusing on wireless networks. The most well-known physical-level emulation system is ORBIT [4], a radio grid testbed developed for scalable and reproducible evaluation of next-generation wireless network protocols. In particular, Orbit uses radio signal attenuators that allow to mimic distance and radio signal propagation conditions over a wireless network. JEmu [5] is another type of physical-level emulator: a virtual radio layer is inserted below the MAC layer and intercepts outgoing MAC frames generated by the communication stack. These frames are re-encapsulated and sent via TCP/IP and an Ethernet LAN to a central emulation node which decides whether to relay them to the destination or not, according to the emulated mobility and propagation conditions. In both examples, it is possible to use a specific routing protocol such as DSR.

Link- or MAC-level emulation is widely used to evaluate real implementations of routing protocols. It abstracts away the physical and data link layers. Most existing solutions are based on a distributed architecture where a virtual MAC layer is embedded on each terminal. For instance, EMWin [6] is fully distributed across the end nodes and emulates the medium access CSMA/CA on an Ethernet experimentation testbed. Each terminal has a neighbor table evolving over time, and the virtual MAC layer uses it to determine whether frames to the next hop are lost or not.

Network- or IP-level emulation is targeted at the evaluation of transport protocols or distributed applications. It is based on only a few parameters, mainly: bandwidth, delays, and packet losses, which are the effects perceived at the transport level. Traffic shapers such as Dummynet [7], NetEm [8], ModelNet [9], or NISTNet [10] can enforce such parameters on real IP packets that pass through the shaper. In addition, Netbed/Emulab [11] or ModelNet [9] are large testbeds federating and coordinating several such traffic shapers, using various approaches to dynamically configure a traffic shaper during emulation. In [12] a trace-based emulation system using traffic shapers is proposed, where the parameters used are derived from previously captured traces. The traces are processed during a so-called "distillation" phase to produce an emulation model made of bandwidth, delays, and losses, that will be interpreted by the traffic shaping part of the emulator. This allows the reproduction of the conditions that were captured with the original traces. Finally, some IP-level emulation systems are based on real-time discrete event simulation, like NCTUns [13].

Transport-level emulation is quite rare, although recently Xylomenos and Cici developed a transport-level emulator to test Publish/Subscribe mechanisms in the context of Content-Centric Networking [14]. This transport-level emulator has the same interface as the Socket API and allows packets addressed to some IP address and port number to be diverted to a specific library.

The variety of existing emulation systems reflects a variety of conflicting requirements. Researchers needing very detailed models or testing low-level protocols will resort to the lowest abstraction level possible, in order to have a maximum number of concrete components activated in the communication stack. However, such emulation systems are difficult and expensive to deploy, and their management and configuration are complex. When choosing higher-level emulation systems, researchers work at a higher abstraction level which improves ease of use. As long as the important characteristics for the study at hand are still captured by the emulation system, the obtained results will still be valid. In this paper, we will focus on IP-level emulation for opportunistic networks. At that abstraction level, we will abstract away the physical environment as well as the lower layers of the communication stacks at the participating nodes. The aim is to support efficient evaluation of higher layer protocols and applications, such as applications using the DTN bundle protocol.

Examining the literature on opportunistic networking reveals that very few studies in this field are based on IP-level emulation. In [15], the preliminary DTN reference implementation has been tested in the Emulab [11] environment. NASA Glenn Research Center has developed the Channel Emulator [16] on top of NetEm [8] as part of the end-to-end Network Emulation Laboratory [17]. In these works, it is not explicitly described how disconnections are made. In [18], the experimental setup uses link-level emulation with a Spirent SX equipment emulating an Earth-LEO satellite link. Opportunistic networks pose a difficult challenge for IP-level emulation, as the traditional end-to-end communication path is no longer present and the nodes in the network need to react to dynamically appearing communication opportunities. Hence, the typical network black box approach used in IP-level emulation, where application/transport endpoints interact with the network only by sending and receiving packets, may no longer be sufficient. Instead many opportunistic networking protocols and applications need to react to the dynamics of the underlying network beyond what is conveyed through the exchange of packets.

The concept of emulation triggers as a mean for supporting emulation of opportunistic networking configurations that are dependent on lower layer dynamics was introduced in [19]. Emulation triggers provide a generic mechanism for passing control information to applications or protocols during emulation run-time. Additionally, an adaptation layer interprets the trigger values and converts them into a format that can be used by the final recipient of the control information. In this paper, we describe the implementation and use of triggers in the KauNet emulator and show how triggers can be used for emulation in a DTN scenario. The DTN adaptation layer is implemented as a custom Discovery mechanism that reacts to received triggers by bringing the opportunistic link associated with the trigger up or down. The considered emulation scenario is a simplified version of the well-known village example in which a bus acts as a data mule in order to bring Internet services to remote villages [20, 21].

The remainder of the paper is organized as follows. In the next section, we introduce the KauNet emulator and describe the design, implementation, and use of triggers in KauNet. In Section 3, the implementation and basic use of the DTN adaptation layer is detailed. Section 4 exemplifies the use of triggers and the DTN adaptation layer by examining the emulation of a bus data mule, first with a small 3-node setup then with a more extensive 14-node setup. Finally, Section 5 concludes the paper.

2. Emulation Triggers

This section describes the KauNet network emulator, and the new trigger functionality that enables KauNet to emulate, for example, cross-layer information for evaluation of opportunistic network scenarios.

2.1. KauNet Overview

KauNet is an extension to the well-known Dummynet emulator [7]. By using high-resolution patterns, KauNet enables deterministic and fully repeatable emulation of effects like packet loss, bit-errors insertion, bandwidth changes, and delay changes. The KauNet patterns that are used to control the emulation are created ahead of time. The KauNet system is very flexible with regards to the origin of emulation patterns, which can be created from sources such as analytical expressions, collected traces, or simulations. The KauNet patterns can be used to emulate a certain effect either in a time-driven or a data-driven mode. In the time-driven mode, emulation effects can be applied with millisecond granularity. Alternatively, in the data-driven mode effects can be applied with packet granularity.

Like Dummynet, KauNet is a FreeBSD kernel module which is configured via the FreeBSD firewall using the ipfw command. In the FreeBSD firewall, so-called pipes can be configured to carry specific flows. KauNet patterns are then assigned to a specific pipe in order to apply the desired emulation effects on the pipe's traffic. KauNet achieves this by stepping through the patterns as experimental traffic enters the corresponding pipe (data-driven mode) or as time goes by (time-driven mode). In the data-driven mode, the patterns are thus advanced for each packet that enters the pipe, and in time-driven mode, the patterns are advanced each millisecond. In data-driven mode the emulation effect encountered by a packet is dependent on the position of the packet in the data stream. Correspondingly, in time-driven mode it depends on at which instance in time the packet enters the pipe. If multiple effects are to be emulated at the same time it is necessary to provide one pattern for each type of emulation effect, that is, packet loss, bit-errors insertion, bandwidth change, and delay change. To simplify emulation of multiple interrelated effects, several patterns can be combined into one emulation scenario.

The patt_gen command line tool is used to create and manage patterns. The tool can generate patterns according to several parameterized distributions and can also import pattern descriptions from simple text files. These text files can be generated by arbitrarily complex models, off-line simulators or trace collection equipment. To complement the patt_gen tool, a GUI called pg_gui has also been developed that allows graphical manipulation of the patterns.

A simple experimental setup, involving KauNet, is shown in Figure 1. Consider an emulation scenario over a network with a fixed delay, but where the bandwidth suddenly drops at a specific instance in time. Assume we want to evaluate the performance of several IP-based applications in this scenario. The basic steps to set up the emulation for the setup shown in Figure 1 would then be as follows. First, the patt_gen utility is used to generate a bandwidth pattern. To generate the bandwidth pattern, the -bw switch is used together with the -pos switch to specify that the positions where the bandwidth changes occur will be explicitly provided. The name of the generated bandwidth pattern file is, and it is a time-driven pattern covering 1 minute. Assume that the initial bandwidth is 10 Mbit/s and that there is a sudden drop in bandwidth to 500 kbit/s after 20 seconds and that the normal bandwidth is restored after 30 seconds. The resulting patt_gen command thus becomes

patt_gen -bw -pos time 60000 1,10000,20000,500,30000,10000

Figure 1
Figure 1

Simple KauNet Setup.

The length of the pattern is given in milliseconds. The position in time of the bandwidth changes and the bandwidth values themselves are specified as a sequence of <position>   <value> pairs. In the example above the bandwidth pattern is explicitly provided on the command line. Normally, the pattern would be provided to patt_gen as a simple text file. This text file can for instance be generated by arbitrarily complex models, off-line simulators, or trace collection equipment. Assuming the pattern "1,10000,20000,500,30000,10000" from the example above is put in the file file1.txt, an equivalent command would be

patt_gen -bw -pos time 60000 -f file1.txt

Next it is time to set up the firewall with ipfw on the KauNet machine. The firewall is first configured to flush out any old configurations that may be left and to add a default rule that allows general traffic:

ipfw -f flush

ipfw -f pipe flush

ipfw add allow all from any to any

The next step is to create a firewall rule that routes the traffic of interest, in this case IP traffic from Host A to Host B, to a pipe where emulation effects are applied.

ipfw add 1 pipe 100 ip from to in

The pipe must now be configured with the emulated conditions. In this example, the command uses the delay keyword to set a static delay of 10 ms. The bandwidth changes are configured by using the pattern keyword to load the bandwidth pattern stored in the file (which was generated above).

ipfw pipe 100 config delay 10ms pattern

The emulation scenario is now set up and the impact of a sudden bandwidth change on various applications can be evaluated. Although the specified bandwidth change pattern is only one minute long, the default KauNet behavior is to wrap around and start using the pattern over again when the end of the pattern is reached. The configuration above thus results in a scenario where a sudden bandwidth drop is experienced for 10 seconds each minute. Note that this is not intended as a particularly useful or interesting scenario, but merely serves to illustrate the setup of a simple KauNet emulation. Further details on KauNet are available in [22].

2.2. Creation of Patterns

The pattern files described in the previous section are a key element in any emulation setup. Their content largely decides whether the emulation is realistic or not, although realism is not the only reason to create patterns.

Patterns can be created from a wide spectrum of tools: they can be written from scratch, generated from an arbitrarily complex set of models, or distillated from existing traffic captures, even distillated from the output of a general-purpose simulator such as ns-2. In a previous work, we have investigated the coupling of KauNet with an off-line simulator called SWINE (Simulator for Wireless Network Emulation) [23]. The input of SWINE is a high-level experiment description including the involved nodes, their radio equipment, their mobility model, as well as the general propagation conditions, the obstacles, and walls. The output includes a set of KauNet bandwidth and packet loss patterns, one per pair of nodes. The SWINE simulator engine takes into account the mobility model of each node to compute its successive positions, and then the propagation conditions to compute the successive received signal strength (RSS) samples using a combination of different radio propagation models on different scales (e.g., path-loss exponent with log-normal shadowing, Rice or Rayleigh fading). With the RSS, a decision is made on which transmission rate is used by the communicating nodes, and then the effective bandwidth and packet loss rate to apply at the IP level are computed. The successive values of bandwidth and packet loss rate are stored in time-driven patterns that can be applied during the live emulation stage.

In [24] we demonstrated the use of KauNet with satellite-specific packet loss patterns, which were produced using the Markovian Land Mobile Satellite model. A measurement campaign was led by CNES, the French Space Agency, to set up parameters values for this Markovian model, which allowed the generation of carrier-to-noise ratio (C/N) values for various outdoor environments and land mobile speeds. These values were then processed by a specific IT++-based simulator that converted them to a packet loss sequence, emulating a communication stack similar to a DVB-H stack.

When patterns are generated from arbitrarily complex models or based on previously collected traces, some degree of realism can be reached. However, the level of realism reached depends on the quality of the models and the appropriateness of the parameter values or on the relevance of the original trace. Generally speaking, realism is not the only motivation driving the creation of patterns. Patterns can also be set up to create test situations that rarely happen in the real world, but under which the user wants to investigate the reaction of the protocol or application under test. We have used such "artificial" patterns (as opposed to "realistic" patterns) during an evaluation of the TCP stack implementation on FreeBSD 6, which did not behave as expected when packet losses were inserted at some specific positions in the real traffic [25]. Such artificial patterns can serve as unit tests for the validation of protocol implementations.

2.3. Trigger Patterns

Triggers in KauNet can be seen as a general information passing functionality that can be used to deliver precisely positioned control information to applications or protocols during emulation run-time. As for other patterns, the trigger patterns can be either data- or time-driven, according to what is being emulated.

While the trigger mechanism is not tied to any particular type of control information, it is reasonable to assume that some types of information will be more prevalent in emulation scenarios involving opportunistic networking. One such example is upward flowing cross-layer information that conveys information on the connectivity of a link. Triggers allow emulation of cross-layer information in cases where connectivity is intermittent and the link layer has the ability to inform upper layers about the presence or absence of connectivity. This can be combined with other emulation effects that emulate varying link conditions. Consider for example a scenario with intermittent connectivity and where the bandwidth available during periods of connectivity varies heavily. In such a case, bandwidth patterns can be used to model the bandwidth variations that occur during connectivity periods. This is then combined with trigger patterns that generate the upwards flowing connectivity information that for a real link would come from the link layer. The bandwidth and trigger patterns are synchronized with each other to form a consistent emulation scenario.

A general view of a simple emulation setup using triggers is shown in Figure 2. In this setup, two hosts are connected using a KauNet-enabled host. The KauNet host emulates the conditions of the particular link or network that is being emulated by means of bandwidth change, delay change, bit-error insertion, and/or packet loss patterns, as appropriate. These patterns control the behavior of the KauNet host only. The trigger pattern is, just as any pattern, located at the KauNet host. In contrast to other pattern types, however, triggers are often relevant to other hosts than the KauNet host. Triggers might, for instance, signal connectivity information that should be available to a protocol or an application at Host A. Thus, in addition to trigger patterns, a trigger communication module and an adaptation layer are needed to convey and use trigger information accordingly.
Figure 2
Figure 2

KauNet Trigger Overview.

KauNet already provides a pattern handling framework which is reused for encoding the semantics of triggers, thus simplifying the implementation. The framework provides the means to create and load compressed pattern files composed of position and value pairs. The values are represented as short values (i.e., 0–65535) that contain pattern specific information. For triggers the implication is that no more than 65536 mutually exclusive trigger values can exist. As the trigger values that are inserted in a pattern are under the control of the user, it is possible to generate triggers leading to arbitrary complex behavior.

As mentioned, the KauNet pattern framework allows patterns to be either time-driven or data-driven. In time-driven mode, the emulation effect of a pattern is applied on a per millisecond basis. Similarly, with data-driven patterns emulation effects can be applied on a per packet basis. The maximum resolution of a trigger pattern is therefore one millisecond or one packet. The memory requirements of patterns are dependent on the emulation length and pattern entropy, that is, how often value changes occur in the patterns. For example, consider a time-driven emulation setup with 50 individually emulated links, with 3 different patterns (BW/delay/trigger) per link, and where the values for each pattern change on average 10 times per second. For a 30-minute emulation run, the 150 patterns would in total encompass 10.3 Megabyte.

Trigger patterns are created in the same way as other patterns. Using the patt_gen tool to create a time-driven trigger pattern that covers 1 minute and inserts trigger values 1, 2, and 3 after 10, 20, and 30 seconds, respectively, would then result in the following command:

patt_gen -trig -pos test1.trg time 60000 10000,1,20000,2,30000,3

As for other patterns, the trigger pattern may also be provided to patt_gen using a text file.

2.4. Trigger Communication and Interpretation

Since KauNet is implemented in the FreeBSD kernel, triggers must be conveyed to external processes to be useful. Otherwise, only kernel space processes running locally on the KauNet host would be able to benefit from the trigger functionality. Thus, a mechanism to transfer the trigger value of a fired trigger to an arbitrary receiver is needed. The recipient of a trigger should be able to reside in either user space or kernel space, locally or on another host.

This functionality is implemented by the trigger communication module shown in Figure 2. This module has a number of responsibilities. First, to enable both local and nonlocal communications the module provides a UDP interface. Using this interface, adaptation layers can register themselves to receive triggers from a certain pipe. The trigger communication module keeps a list of all registered adaptation layers to enable multiple adaptation layers to subscribe to the same trigger. Second, whenever a trigger is fired within KauNet, the module transmits the trigger information to the registered adaptation layers. The communication of triggers is also done using UDP. Finally, the trigger communication module also provides the means for adaptation layers to unregister themselves.

To ensure that there is no possibility for trigger control traffic and experimental traffic to interfere with each other, trigger traffic should be separated by using a separate control network with separate network interfaces. The low bandwidth consumed by trigger control traffic ensures that the UDP control packets are very unlikely to be lost due to buffering or congestion in the control network. The bandwidth requirements for trigger traffic can be exemplified by the requirements of the 50-link scenario described in the previous subsection, and where triggers are used to signal bandwidth changes that in a real environment would be propagated by an intelligent link layer. Counting all header overhead, the bandwidth requirements for that 50-link scenario is only 312 kbps, that is, less than 0.03% of the capacity of a gigabit Ethernet control network. From a scalability viewpoint, control traffic bandwidth is thus unlikely to become a concern. Even if the trigger control traffic is scaled up to the point where UDP losses might occur, it can be noted that the effect of a trigger packet loss is localized both in space and time. Thus, it will cause a missed update for one single link and only for the time period until the subsequent trigger is received. It is also possible to employ multiple KauNet nodes to perform emulation and send out triggers. Depending on the use-case, this may, however, also require the added complexity of synchronization between multiple KauNet hosts, and such synchronization has not been tested yet.

In addition to the UDP-based trigger communication discussed here, it is also possible to employ other methods for trigger communication. In [26], alternatives for trigger communication were examined with a tilt towards communication methods that allow triggers to be communicated locally from the kernel to local user-space processes. In addition to UDP traffic, which uses AF_INET sockets, examined approaches were signals with different shared memory setups, and AF_UNIX IPC sockets. While signal-based approaches were found to have higher throughput than external AF_INET sockets, internal AF_UNIX sockets in some cases had worse performance than external AF_INET UDP traffic. Although slightly less efficient than signal-based approaches, the performance of external AF_INET UDP traffic well surpassed the expected requirements for trigger communication. As UDP-based trigger communication was considered to be the most flexible of the evaluated approaches, it was chosen for the implementation.

The interpretation of triggers is handled by different adaptation layers. In general, an adaptation layer is a process, or part of a process, that is able to interface with the trigger communication module and thereby receive triggers. In the context of opportunistic networking, trigger values are likely to represent some sort of cross-layer information. The role of the adaptation layer is then to work as an interface between the trigger communication module and the upper layer consumer of cross-layer information. Applications and protocol implementations may use cross-layer information in different ways and the information may take different formats. It is the role of the adaptation layer to reshape the semantics-free trigger values contained in the triggers into the specific type of cross-layer information that is used by the application or protocol that is being evaluated. In the next section, we describe an adaptation layer for the DTN2 reference implementation [27], which interprets the trigger values (1 or 2) as connectivity information (link up or down) and interfaces with the link discovery mechanism within the DTN2 implementation.

3. A DTN Adaptation Layer

This section describes how to implement and use an adaptation layer for DTNs where the experimentation nodes run the Bundle Protocol [28], in this case the DTN2 reference implementation [15, 27]. One of the most distinctive features of DTNs is the intermittent connectivity between nodes. Over time, contact opportunities arise and allow the forwarding of data bundles towards their final destination. Those opportunities are mainly characterized by a time window and the contactable peer ID (EID). Opportunities may be predictable or not, according to the considered application. In some cases, several links allow for the contact between two peers, in which case there are several contact opportunities, one per available link for the contact. Each node running the DTN2 implementation has a component that manages contact opportunities, as well as optional components providing contact discovery.

The adaptation layer implementation described here mainly consists in using triggers to emulate contact opportunities. It implies the development of a new Discovery mechanism in the DTN2 reference implementation. On each DTN2 node, the new discovery mechanism is in charge of detecting contact opportunities by connecting to the KauNet trigger communication module and extracting contact information from the trigger values, thus constituting the trigger adaptation layer. Bundle forwarding is unchanged and carried out over Ethernet on the experimental network according to the announced opportunities and the local DTN2 node configuration. Regardless if the contact opportunities are predictable or not at higher layers, hooking into the DTN2 implementation via a custom Discovery adaptation layer allows the emulation of many different flavors of DTNs; the only difference lies in the creation of the contact triggers. The implementation of the custom Discovery mechanism is further described below.

3.1. Implementation

In the DTN2 reference implementation, an IP Discovery/Announce mechanism is provided for detecting and creating opportunistic links in IP-based networks. A convergence layer may announce its presence by sending out beacons with regular intervals, either as a broadcast message across the entire network or as a unicast for specific nodes. The IP Discovery mechanism listens for these beacons. When an announce packet is detected, the contained information is extracted and a new opportunistic link is created using the IP address, port and EID of the remote node, as well as the type of the convergence layer that is broadcasting.

In order for KauNet to control the opening and closing of opportunistic links in DTN2, a new KauNet Discovery/Announce mechanism is implemented. The discovery mechanism is implemented as a new Discovery subclass. Similarly to the IP Discovery mechanism, it listens for custom KauNet triggers (that act similarly to beacons) on a specific port. It provides functionality for the DTN2 node to register (and unregister) itself at a KauNet host, in order to receive the contact triggers that describe the status of a link. Note that the KauNet triggers are received over the control network and not over the interface whose connectivity they control. The KauNet Discovery mechanism parses the triggers it receives and opens a link (creating it if necessary) or closes a link, depending on the received trigger value: 1 means "open" and 2 means "close".

The announce mechanism is not implemented within the DTN2 adaptation layer. Instead, it is provided by the KauNet trigger communication module. The trigger communication module simply stores the DTN2 clients registered for each pipe and sends out KauNet triggers to subscribing clients whenever a trigger event takes place in a pipe.

KauNet does not handle the link discovery information used by DTN2 (specifically, the type of convergence layer, the IP address used to communicate with it, and the EID of the DTN2 node on which the convergence layer resides). The KauNet triggers contain no link information other than the status (i.e., open or closed). Therefore, the discovery mechanism requires alternative means in order to obtain the required link information. This information is instead provided in the configuration file of each DTN2 node that uses KauNet controlled links (see Section 3.2). The configuration file also contains the information required to send a subscription request to the KauNet host controlling the links. Note that an alternative implementation would have been to include the link discovery information in the KauNet triggers. This alternative was rejected, as it was considered more consistent with the DTN2 configuration to include the link information as part of the configuration for KauNet Discovery instances at each node. The chosen solution also allows the connectivity trigger patterns to be easily reused in different communication scenarios.

3.2. Usage

As in the original DTN2 implementation, opportunistic links are detected using a discovery mechanism. A node must add one entry to its configuration file for each opportunistic link it needs to discover. The entry contains the required link and subscription information and has the following syntax:

discovery add <discovery_name> kaunet

kaunet_ip=<addr> [kaunet_port=<#>] pipe=<#>

cl_ip=<addr> [cl_port=<#>] cl_type=<type> eid=<eid>


The parameters of the discovery entry are detailed below.


The name of this discovery instance, used for identification by DTN2.


The IP address of the KauNet host that emulates the connectivity of this link.


(Optional) The port used by the KauNet host to listen for subscription requests. If no value is specified, the default KauNet port (1066) is used.


The ID number of the pipe used to emulate the link at the KauNet host.


The IP address of the convergence layer on the DTN2 node that this link represents a connection to.


(Optional) The port of the convergence layer on the DTN2 node this link represents a connection to. If no value is specified, the default TCP/UDP convergence layer port (4556) is used.


The type of convergence layer used on the DTN2 node this link represents a connection to.


The EID of the DTN2 node this link represents a connection to.


(Optional) A flag indicating that the link should be initialized as an open and available link. If this flag is not set, the link must first be opened with a KauNet trigger before DTN2 can use it to send data.

As DTN2 is loaded, the kaunet_ip, kaunet_port and pipe custom arguments are used to generate and send a single subscription request to the KauNet host at initialization. It is therefore important to load the trigger communication module on the KauNet host before starting any DTN2 node, or the subscription requests will fail.

An example setup is shown in Figure 3. It features a control network (192.168.1/24) for non-experiment traffic and two experiment networks (10.0.1/24 and 10.0.2/24). It features two DTN2 nodes, A and B, and a node running KauNet that is used to control the availability and properties of the link between DTN2 nodes A and B. As DTN links may provide asymmetric data rates, two pipes are used to model the link between node A and B. In our example, node A runs the KauNet discovery mechanism to keep track of the connectivity of the link between node A and node B. It receives triggers over the control network from the KauNet machine. When a trigger is received indicating that the link is available, node A establishes a connection to a TCP convergence layer on host B. The discovery part of the DTN2 configuration file at node A has the following entry:

discovery add kaunet_disc kaunet

kaunet_ip= pipe=1

cl_ip= cl_type=tcp


Figure 3
Figure 3

Emulation setup.

Node A registers at the KauNet host ( via the control network on the default port. It subscribes to pipe 1, which is the pipe configured with the connectivity pattern for the link between nodes A and B. When the discovery instance is loaded, the subscription request will be automatically sent using the information from the configuration file. The details of the remote convergence layer are also given by the configuration.

As the discovery is made through the KauNet triggers there is no need to run an Announce mechanism on node B. Node B only has to define an interface that creates a standard TCP convergence layer. In our example, the default TCP convergence layer port is used. The corresponding interface command in the DTN2 configuration file at node B is

interface add tcpB tcp local_addr=

When a connectivity opportunity appears and a connection over TCP has been established, the established DTN link will be available for bidirectional communication, in other words implicitly discovered by node B. There is therefore no need to run a discovery mechanism on node B. Note that the same behavior is not true for the UDP convergence layer which requires each node to discover its own uplink.

4. The Village Example

This section describes an emulation scenario typical of a delay-tolerant network. It is a data mule setup where a bus is used as a mechanical backhaul intermittently connecting villages to the Internet. This Village example is inspired by the DakNet [20] and KioskNet [21] projects and is illustrated by Figure 4. In this section, however, we simplify the architecture to keep the example tractable. Three DTN nodes are considered: a kiosk, which is typically in an isolated village, used by the inhabitants for intermittent access to the Internet; a bus that periodically comes to the village; and a gateway in the city that is permanently connected to the Internet. When a villager sends a mail, a bundle is created and waits at the kiosk until the bus arrives. The bundle is then forwarded to the bus node and travels with the bus until the next arrival of the bus to the gateway in the city. The bundle is then forwarded to the gateway, which can retrieve the mail and send it the usual way.
Figure 4
Figure 4

The Village Example (from [ 15 ]).

Under these conditions, the distribution of opportunistic contacts over time is as follows in a periodic way: no contact, kiosk/bus contact, no contact, gateway/bus contact, and so forth. In a real setup, contacts may be several hours apart and would not follow a strict period. In our tests, we have scaled down the duration of a complete bus trip to a few minutes, and made it strictly periodic, to improve the readability of the obtained results. However, this is not a limitation of the KauNet tools, and much more random contacts can be implemented.

4.1. Trigger and Bandwidth Patterns Used

In this setup, two types of time-driven patterns are used to model what happens during opportunistic contacts: trigger patterns model contact opportunities, and bandwidth patterns model the bandwidth available over the link at the IP level.

First, due to the mobility of the bus, the quality of the link with the encountered kiosk or gateway varies during the contact. The connection is made with TCP over WiFi. Several papers [29, 30] show that the TCP goodput as well as the MAC bit rate has a specific shape, which follows a three phase model, for drive through connections. We have simplified this to a trapezoid for modeling the bandwidth of both the kiosk/bus and the gateway/bus contacts. Figure 5 shows both shapes and when they take place in one full period. To implement both shapes, we first generate bandwidth shape values for both links with a granularity of 100 ms to files KB.txt and BGW.txt with a simple python script. We then generate time-driven bandwidth patterns and with the patt_gen command:

patt_gen -bw -pos time 360000 -f KB.txt

patt_gen -bw -pos time 360000 -f BGW.txt

Figure 5
Figure 5

Bandwidth patterns.

Although the period of 6 minutes used is quite unrealistic, the duration of a contact of approximately one minute as well as the maximum goodput of 22 Mb/s matches the observations made in the above cited papers, for a bus traveling at 80 km/h and a gateway next to the road.

Second, trigger patterns emulate contact discovery with the mechanism described in Section 3, involving the trigger adaptation layer added to the DTN2 reference implementation. One pattern is needed for each pair of opportunistically connected nodes: one pattern for kiosk/bus contacts and one for bus/gateway contacts. The trigger pattern is a time-driven pattern with a very simple semantic: a trigger value of 1 means "enable contact" and a trigger value of 2 means "disable contact". These patterns are also defined manually using the patt_gen command. We have considered different sensitivities: high sensitivity means that the contact is discovered as soon as some signal is available, that is, at the beginning of the bandwidth ramp; low sensitivity means that the contact is discovered only when the maximum bandwidth is available, that is, when the top of the bandwidth ramp is reached; medium sensitivity lies in-between. Sensitivities and how they relate to the bandwidth ramp are illustrated in Figure 6.
Figure 6
Figure 6

Trigger patterns: three different sensitivities.

For instance, the high sensitivity trigger patterns for both links are generated as follow:

patt_gen -trig -pos KB.trg time 360000 60000,1,120000,2

patt_gen -trig -pos BGW.trg time 360000 180000,1,240000,2

Note that both trigger and bandwidth patterns can be generated based on a mobility model or on the output of a simulator, thus introducing potentially more randomness and/or c crealism.

The ability to couple the contact opportunity trigger pattern with another type of pattern using KauNet's time-driven mode is a very convenient feature that can be used in various other scenarios. For example, in a DTN space scenario such as the one described in [18], contact opportunities correspond to the low earth orbit satellite UK-DMC passing above a ground-station, for a duration of 5 to 14 minutes. During each pass, delay and throughput are constant while the bit error rate varies a lot (high BER at the start and the end of the pass, when the satellite elevation is low). This can be emulated with a bit-errors insertion pattern coupled with a high-sensitivity contact trigger pattern.

4.2. Infrastructure Deployment on KauNet

We choose to deploy the Village Example on a 4-host setup, with one host per DTN node (kiosk, bus, and gateway) plus the KauNet host. The DTN hosts are Linux hosts (Ubuntu 9.10) with the DTN2 reference implementation as well as the KauNet adaptation layer previously described. As illustrated by Figure 7, the kiosk (K) is mapped to host A (, the bus (B) is mapped to host B (, and the gateway (GW) is mapped to host C ( The fourth host is the KauNet box. Having the kiosk and the GW on one subnet and the bus on another is a convenient deployment for setting up the routing to ensure that all data transmissions go through KauNet.
Figure 7
Figure 7

KauNet Setup for the Village Example.

KauNet is configured with two unidirectional pipes per link. The kiosk to bus pipe has number 1 and the reverse bus to kiosk has number 2. The bus to gateway link has number 101, and the reverse gateway to bus pipe has number 102. The same bandwidth pattern is applied on both directions of the kiosk/bus link, that is, pipes 1 and 2. The contacts trigger pattern KB.trg is loaded only on pipe 1, but once the DTN2 TCP convergence layer discovers a contact on the forward direction, it automatically "discovers" the contact also on the reverse direction. Similarly, a bandwidth pattern is shared on pipes 101 and 102, while the contact pattern BGW.trg for bus and gateway is loaded only on pipe 101:

ipfw add 1 pipe 1 ip from to in

ipfw add 2 pipe 2 ip from to in

ipfw add 101 pipe 101 ip from to in

ipfw add 102 pipe 102 ip from to in

ipfw pipe 1 config delay 1ms pattern pattern KB.trg

ipfw pipe 2 config delay 1ms pattern

ipfw pipe 101 config delay 1ms pattern pattern BGW.trg

ipfw pipe 102 config delay 1ms pattern

4.3. DTN2 Configuration

For each of the mapped entities (K, GW, and B), a TCP convergence layer or a KauNet discovery mechanism is added to the DTN2 configuration of its host. The bus has a simple convergence layer statement, while the kiosk and gateway have discovery statements that refer to B's convergence layer. Below is the DTN2 configuration of the bus:

# Bus config in B.dtn

route local_eid "dtn://B.dtn"

interface add tcpBus tcp local_addr=

The DTN2 configuration of the kiosk, shown below, has a discovery statement referring to the interface declared in the bus configuration above. It also refers to the KauNet host and to pipe 1, on which the kiosk/bus contact trigger pattern KB.trg was loaded:

# Kiosk config in K.dtn

route local_eid "dtn://K.dtn"

discovery add discBus kaunet

kaunet_ip= pipe=1

cl_ip= cl_type=tcp


Below is the configuration of the gateway. Also this discovery statement refers to the interface declared in the bus configuration above, as well as to pipe 101, on which the bus/gateway contact trigger pattern BGW.trg was loaded:

# Gateway config in GW.dtn

route local_eid "dtn://GW.dtn"

discovery add discBus kaunet

kaunet_ip= pipe=101

cl_ip= cl_type=tcp


4.4. Applications Deployment

Now that the infrastructure is deployed and the throughput and contact patterns have been defined, the platform is ready for application-level testing. Among all possible tests, we report on a simple data delivery test, evaluating how the sensitivity of the contact patterns impacts bundle delivery.

The applications used are the dtnsend and dtnrecv commands packaged with the DTN2 reference implementation. The sender is hosted by the kiosk (K) and the receiver is hosted by the gateway (GW). The sender application sends 5000 bundles of 1 MByte, which are then slowly forwarded to the bus, the gateway, and finally the receiver application as contact opportunities arise. We measure how many bundles are delivered to the gateway for every bus round-trip, over 30 rounds. Figure 8 shows the evolution of the amount of delivered bundles during the 10 first bus rounds for high, medium, and low sensitivities. Over 30 bus rounds, we obtain the results in Table 1.
Table 1

Bundles delivered per round.



95% c.i.










Figure 8
Figure 8

Bundle delivery over time.

The steps observed in Figure 8 closely match the no-contact periods between the bus and the gateway, that is, the receiver. One can also see that the first bundles are delivered at the receiver application after 3 minutes, that is, at the beginning of the first bus/gateway contact and one minute after the end of the first kiosk/bus contact. Another observation that can be made is that the proportion of contact time lengths is not matched by the proportion of packet delivery steps: although a high-sensitivity contact lasts twice as long as a low-sensitivity one, there are not twice as many bundles delivered. This is due to the effect of the bandwidth ramp shown in Figure 6: the extra time of the high-sensitivity contact corresponds to the beginning and ending ramps where less data can be forwarded. Finally, in Figure 9 a magnified view of the first step shows that for the medium-sensitivity, case bundle delivery occurs a few seconds after the high-sensitivity curve, and a few seconds later for the low-sensitivity case. These times are related to the 7.5-second offsets in the sensitivity timings of Figure 6. The exponential shape of the beginning of the high-sensitivity packet-delivery curve is due to the increase of the bandwidth over time, which leads to a decrease of delivery delays for the bundles. This shape can also be observed to a smaller extent in the medium-sensitivity curve. It is not present in the low-sensitivity case because in that case the bandwidth is constant during the whole contact time.
Figure 9
Figure 9

Bundle delivery over time (magnified).

4.5. Multiple Kiosk Scenario

The emulated example described above is limited to three DTN nodes to keep the description simple. The emulation capabilities of KauNet in general and of the trigger mechanism in particular, is however easily scalable to scenarios involving a larger number of nodes. As a single KauNet node can emulate multiple links, the emulated scenario can be scaled up with more DTN nodes by adding pipes to the KauNet configuration. The number of links (i.e., pipes) that can be supported is mainly dependent on the amount of simultaneous traffic generated over the emulated links and on the capacity of the experimental network.

To verify the trigger mechanism in a slightly larger setup, we have extended the basic village scenario, discussed previously, to a scenario where the bus passes through 12 different villages (with one kiosk in each village) during a round-trip. The bandwidth patterns used for the multiple kiosk scenario are illustrated in Figure 10, showing one complete bus round-trip. As in our previous example, we use a fully periodic schedule and have compressed the contact opportunities in time. As seen in the figure, the bus makes contact with one of the kiosks or with the gateway every other minute, which allows one round-trip to be completed in 26 minutes. As before, each contact lasts for one minute and follows a three-phase trapezoidal model.
Figure 10
Figure 10

Bandwidth patterns for multiple kiosk scenario.

The extended scenario involves a total of 14 DTN nodes. To limit the number of physical machines required, in this setup we deploy the kiosk nodes and the GW node using virtualization. One of our experimental machines runs VMware ESXi [31] and we deploy 13 virtual machines on the VMware host. One DTN node is run in each virtual machine. Note that the scalability and deployment of the DTN nodes is a separate issue from the scalability of the KauNet emulation itself.

The setup of the multiple kiosk scenario is illustrated in Figure 11. As before, two pipes are used to emulate each DTN link resulting in a total of 26 pipes. The configuration of the scenario is done in the same way as for the 4-node case detailed earlier. A bandwidth pattern and trigger pattern is created for each link. As described in Section 4.2, the bandwidth pattern is applied in both directions of a link whereas the trigger pattern only needs to be loaded for one direction. The trigger patterns are based on the medium sensitivity pattern explained in Section 4.2. The configuration of the DTN nodes follows the configuration described in Section 4.3. Each kiosk node and the GW node have a KauNet discovery mechanism configured. The only difference between the discovery statements of these nodes is that they register to different KauNet pipes to receive the correct connectivity information for their respective link to the bus. The configuration for the bus node is unchanged.
Figure 11
Figure 11

KauNet setup for the multiple kiosk example.

For the multiple kiosk scenario, we choose to evaluate the impact on bundle delivery performance of the number of kiosks that need to communicate via the gateway. As before, dtnsend and dtnrecv are used for the communication, with senders hosted at the kiosks and the receiver hosted at the gateway. When active, the sender application at a kiosk sends 1000 bundles of 1 MByte. We evaluate the bundle delivery performance of Kiosk 1 with varying number of sending kiosks. Four configurations are compared: only Kiosk 1 is sending, 4 kiosks (Kiosks 1, 4, 7, and 10) are sending, 8 kiosks (Kiosks 1, 3, 4, 6, 7, 9, 10, and 12) are sending, and all 12 kiosks are sending. Figure 12 shows the evolution of the amount of delivered bundles from Kiosk 1 during the 20 first bus rounds.
Figure 12
Figure 12

Bundle delivery of Kiosk 1 over time.

The impact of the number of sending kiosks is clearly displayed in Figure 12. When Kiosk 1 is the only kiosk with bundles to send, a regular number of bundles is delivered each round. Less than 20 rounds are needed to deliver all the 1000 bundles. When multiple kiosks have bundles to send, on the other hand, bundles from Kiosk 1 are only delivered to the gateway in some of the rounds, and only a fraction of the 1000 Kiosk 1 bundles are delivered during the first 20 rounds. The link between the bus and the gateway is shared by all kiosks on their paths from sender to receiver and becomes a bottleneck. It can be seen that the DTN2 reference implementation of the bundle layer uses FIFO queuing. Kiosk 1 is the first kiosk passed by the bus during a bus round, and the Kiosk 1 bundles picked up during the first round are the first to be delivered to the gateway. The Kiosk 1 bundles picked up during the second round are queued behind bundles from other kiosks picked up during the first round. They cannot be delivered until all bundles from the first round have been delivered. As can be seen in the figure, the queuing delay encountered by the Kiosk 1 bundles is directly dependent on the number of kiosks competing for the bottleneck link. The exact number of bundles delivered from a kiosk to the bus or from the bus to the gateway varies slightly between contacts. As a consequence, the Kiosk 1 bundles received by the bus in a given round are typically delivered to the gateway during more than one round once they reach the head of the queue.

The multiple kiosk scenario aims to illustrate the use of KauNet triggers in a slightly larger setup where several DTN senders interact. It also illustrates the importance of the placement of the gateway. In our example, bundle delivery delay increases roughly proportionally with the number of sending kiosks. A simple solution to reduce the delay in bundle delivery would of course be to allow the bus and gateway a longer contact time, thus reducing the impact of the bottleneck link. Depending on the scenario, it may also be interesting to consider different queuing disciplines at the bundle layer.

5. Conclusions

By extending the pattern-based KauNet emulation system with pattern-driven triggers, it is possible to emulate opportunistic communication scenarios that depend on cross-layer information. The triggers can be tightly controlled and accurately synchronized with other emulation effects. The triggers are distributed to local and remote processes by the trigger communication module, where the adaptation layer is responsible for translating the triggers into application-specific semantics and actions. An adaptation layer for DTNs was described and its use in emulating a simple DTN scenario was detailed. The DTN examples serve to highlight the functionality and applicability of the trigger mechanism. Adequate emulation support allows effective debugging, accurate and reproducible performance evaluations, and user studies of protocols and applications. It is our hope that the proposed triggers will allow researchers in opportunistic networks to perform all these tasks in a more cost- and time-efficient way, and thereby contribute to the further advancement of the field.

Possible future work includes emulating a realistic end-to-end space scenario where data must be collected in remote field areas through a Low Earth Orbit satellite. Such a scenario would interconnect mixed technologies: outdoors wireless sensor network, an Earth-satellite link and the Internet, also including specific protocols like Saratoga or LTP as convergence layers. Emulation would be used to generate Earth/satellite contacts, including bit-error rate evolution during a contact. The purpose of the setup would be to assess different infrastructure deployments with respect to maximum delivery time.

The emulation setups presented in this paper are well suited for experiments involving a few dozens physically interconnected DTN nodes. However, emulating opportunistic networks involving hundreds of nodes is not possible. For future work, we intend to explore to what extent a majority of such emulated nodes can be abstracted away, leaving a physical emulation setup involving only a few end nodes. We are currently investigating approaches to abstracting down a DTN network to only a few nodes while retaining the characteristic behavior.



The authors wish to thank Andreas Midestad for his work on the implementation of triggers. This work was partly supported by the European Commission in the framework of the FP7 Network of Excellence in Wireless COMmunications NEWCOM++ (contract no. 216715).

Authors’ Affiliations

CNRS; LAAS, 7 avenue du Colonel Roche, F-31077 Toulouse, France
UPS, INSA, INP, ISAE, UT1, UTM; LAAS, Université de Toulouse, F-31077 Toulouse, France
Department of Computer Science, Karlstad University, 65188 Karlstad, Sweden


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