The idea of position-based routing was originally developed for packet radio networks in the 1980s [1 and 2]. Due to the availability of GPS it received renewed interest during the last few years as a method for routing in mobile ad hoc networks [3, 4, 5 and 6]. The general idea of position-based routing is to select the next hop based on position information such that the packet is forwarded in the geographical direction of the destination.
The most important characteristic of position-based routing is that forwarding decisions are based on local knowledge. It is not necessary to create and maintain a global route from the sender to the destination. Therefore, position-based routing is commonly regarded as highly scalable and very robust against frequent topological changes. It is particular well suited in environments where the nodes have access to their geographical position, such as in inter-vehicle communication [7 and 8].
Position-based routing can be divided into two main functional elements: a location service and a position-based forwarding strategy. The location service maps the unique identifier (such as an IP address) of a node to its current geographical position. It can be seen as analogous to the route discovery process of reactive topological routing algorithms such as DSR [9] or AODV [10]. For the remainder of this work we assume that an appropriate location service is present which supplies the sender of a packet with the geographical position of the packets’ destination. Candidates for location services are outlined in the section on related work.
Position-based forwarding is performed by a node to select one of its neighbors in transmission range as the next hop the packet should be forwarded to. Usually, the forwarding decision is based on the node’s own geographical position, the position of all neighbors within transmission range and the geographical position of the destination. The sender requests the position of the destination from the location service and then includes it in the header of the packet. Given this information, the node forwards the packet to one of its neighbors such that the packet makes progress toward the destination. This process is called greedy forwarding. It is possible that there is no neighbor with positive progress toward the destination while a valid route to the destination exists. The packet is then said to have reached a local optimum. In this case, a recovery strategy is used to escape the local optimum and to find a path toward the destination.
In all existing strategies for greedy unicast forwarding, the position of a node is made available to its direct neighbors (i.e., nodes within single-hop transmission range) in form of periodically transmitted beacons.1 Each node stores the information it receives about its neighbors in a table and thus maintains position information about all direct neighbors.
While the beaconing frequency can be adapted to the degree of mobility the fundamental problem of inaccurate position information is always present: a neighbor selected as a next hop may no longer be in transmission range. As will be outlined later (see Section 4, Fig. 12) this leads to a significant decrease in the packet delivery rate with increasing node mobility and to a high load on the wireless channel due to several MAC layer retransmissions. To reduce the inaccuracy of position information it is possible to increase the beaconing frequency. However, this also increases the load on the network up to a point where the available capacity is almost exclusively used for the transmission of beacons. Alternatively, it has been proposed to hand packets back to the routing layer if the next hop is no longer available [3]. At the routing layer the packets are then rerouted to a different neighbor. While this eliminates the problem of packet drops, the trial-and-error approach can cause even more bandwidth-consuming MAC layer retransmissions. Our experiments (see Fig. 14) indicate that under high mobility, the beacon-based forwarding approach requires on average more than three MAC transmissions for one single-hop packet forwarding, increasing the load on the network caused by data packets by more than a factor of three. Existing work (e.g., [3]) does not take this effect into account since there the load is measured at the routing level instead of the MAC layer.
Thus, for a given packet delivery rate, the load at the MAC layer increases dramatically with beacon-based greedy unicast forwarding (either through an increased beaconing frequency or through trail-and-error) with increasing node-mobility. In addition, a node forwarding a packet can only select a neighbor as next hop if it is contained in its neighbor table. Nodes that just moved into transmission range and that have not yet sent a beacon are therefore not considered as next hop nodes. This may lead to the failure of greedy forwarding even though an appropriate neighbor is present.
In this paper, we propose a novel greedy forwarding strategy for position-based routing algorithms. We call the approach contention-based forwarding (CBF). CBF performs greedy forwarding without the help of beacons and without the maintenance of information about the direct neighbors of a node. Instead, all suitable neighbors of the forwarding node participate in the next hop selection process and the forwarding decision is based on the actual position of the nodes at the time a packet is forwarded. This is in contrast to existing greedy forwarding algorithms that base their decision on the positions of the neighbors as they are perceived by the forwarding node. In order to escape from local optima, existing recovery strategies, as mentioned in the section on related work, can either be used directly or may be adapted to be used with CBF.
CBF shows advantages over existing greedy forwarding strategies in two important aspects:
1. Use of accurate position information: in CBF each neighbor uses the (very accurate) position information it has about itself to determine if it should become the next hop for a given packet. For a given delivery rate the required bandwidth for CBF does therefore not increase with node mobility (i.e., neither an increased beaconing frequency, nor trial-and-error is needed). In addition, CBF always bases the selection of the next hop on all direct neighbors, even those that have just moved into transmission range.2. Elimination of beacon overhead: removing the beacons eliminates a major part of routing overhead which occurs independently of the actual data traffic. This includes the bandwidth used for the transmission of beacons,
2. and the memory required in the nodes to store neighbor information.
CBF consists of two parts: the selection of the next hop is performed by means of contention, while suppression is used to reduce the chance of accidentally selecting more than one node as the next hop. We present three suppression strategies with different suppression characteristics. The results of our study show that suppression of duplicate packets works well, that CBF has similar packet delivery ratios as beacon-based greedy routing, and that it dramatically reduces the load on the wireless medium for a given delivery rate if node mobility is high. CBF, therefore, represents a good alternative to traditional beacon-based greedy forwarding.
The contention process of CBF used for next-hop selection represents a paradigm change in the forwarding of packets. In traditional protocols, the forwarder actively selects the desired next-hop by unicasting the packet to the corresponding MAC address. In contrast, with CBF the responsibility for next-hop selection lies with the set of possible next hops. Furthermore, if no other interaction between forwarder and next hop is required, which is the case for two of the three presented strategies, MAC layer addresses become obsolete.
The remainder of this paper is structured as follows: In Section 2 we summarize related work. Section 3 contains a description of CBF with three alternative suppression schemes. In Section 4, the properties of CBF are analyzed and its performance is further investigated in Section 5 by means of simulation. Finally, Section 6 points out directions of future work and concludes the paper.
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