Matthias Transier

A hierarchical approach to position-based multicast for mobile ad-hoc networks

In this paper we present Scalable Position-Based Multicast (SPBM), a multicast routing protocol for ad-hoc networks. SPBM uses the geographic position of nodes to provide a highly scalable group membership scheme and to forward data packets in a way that is very robust to changes in the topology of the network. SPBM bases the forwarding decision on whether or not there are group members located in a given direction, allowing a hierarchical aggregation of membership information. The farther away a region is from an intermediate node, the higher the level of aggregation for this region should be. Because of aggregation, the overhead for group membership management scales logarithmically with the number of nodes and is independent of the number of multicast senders for a given multicast group. Furthermore, we show that group management overhead is bounded by a constant if the frequency of membership updates is scaled down with the aggregation level. This scaling of the update frequency is reasonable since the higher the level of aggregation is, the lower the number of membership changes for the aggregate will be. The performance of SPBM is investigated by means of simulation, including a comparison with ODMRP, and through mathematical analysis. We also describe an open source kernel implementation of SPBM that has been successfully deployed on hand-held computers.

Vehicular ad-hoc networks: from vision to reality and back

VANETs, or vehicular ad-hoc networks, have recently gained scientific and commercial interest. In fact, they have drawn many people from neighboring fields like general mobile ad-hoc networks (MANETs), into their wake. In this invited paper, we discuss the history of vehicular ad-hoc networks from our perspective. In detail, we will show the early vision of creating a huge MANET that would facilitate cheap and ubiquitous communication on the ISM bands, and how this vision was reduced to cars sending emergency information in a geographically limited area. Also, we will describe how new challenges emerge from these new constraints, and then argue that VANETs are still an interesting research area

Backpressure multicast congestion control in mobile ad-hoc networks

In mobile ad-hoc networks, the multicast paradigm is of central importance. It can help to save scarce medium bandwidth if packets are to be delivered to multiple destinations. We consider the problem of congestion control for multicast traffic in wireless multihop networks. We propose to apply a congestion control concept which is tailored to the very special properties of the wireless multihop medium: implicit hop-by-hop congestion control. The idea, so far only having been considered for unicast traffic, is here generalized to multicast. We implement it in the Backpressure Multicast Congestion Control (BMCC) protocol, with a focus on how to realize it in combination with geographic multicast routing in the Scalable Position-Based Multicast (SPBM) protocol. Our evaluation points out a number of highly desirable properties of the proposed scheme. In particular, it achieves and maintains high throughput and high packet delivery ratios at low packet latencies, even in the presence of significant network load.

Huginn: a 3D visualizer for wireless ns-2 traces

Discrete-event network simulation is a major tool for the research and development of mobile ad-hoc networks (MANETs). These simulations are used for debugging, teaching, understanding, and performance-evaluating MANET protocols. For the first three tasks, visualization of the processes occurring in the simulated network is crucial for verification and credibility of the generated results. Working with the popular network simulator ns-2, we have not yet found a visualization toolkit capable of reading native ns-2 trace files and providing means to change the evaluated parameters without changing the visualization software. Thus, we developed Huginn, a software providing an intuitive way to visualize simulation properties and to determine how they should be displayed without the need of programming. In addition, Huginn has a 3D interface allowing an improved exploitation of the (human) users perceptual system. It helps to handle the significant cognitive load associated with the mental reconstruction of simulated network processes. Besides presenting the software interface and architecture, we describe algorithmic solutions that might be of a more general interest for similar problems.

On the application of dead-reckoning to position-based routing for vehicular highway scenarios

Position-based routing suffers from inaccurate neighbor positions, especially at low beaconing rates. In this paper we propose to add dead-reckoning to the forwarder selection process to predict the positions of neighboring nodes. We have carried out simulations to investigate the consequences of this modification and explain the trade-offs. With the modified algorithm, we achieve a complete packet delivery for beaconing intervals of as much as 6 seconds for a medium-density German highway setting. We will show that the dead-reckoning scheme results in (a) less beacon traffic with equal or better neighbor position prediction and (b) less overhead induced by transmit failures when using the same beaconing frequencies.

Dynamic load balancing for position-based routing

Position-based routing algorithms [3] promise an effective delivery of data packets by making use of the geographic positions of the nodes in the network. When developing new algorithms, different factors have to be taken into account. Besides achieving high packet delivery ratios and low delays, the network load is an important characteristic which has to be kept as low as possible. Distributing the load within the network is desirable for different reasons. One of these is the effective usage of the available resources. A good load balancing mechanism helps to raise the packet delivery rate of the transmissions through the network as well as the throughput of the data. Another point is that through an equally distributed load the nodes’ energy consumption is balanced. Typically, mobile nodes only have limited energy available and if these energy resources are exhausted, the nodes will have to leave the network and will not be available for the routing of packets anymore. Load balancing thus prolongs the lifetime of the nodes and consequently, the lifetime of the ad-hoc network.