This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 2008 proceedings.
Connectivity Aware Routing in Vehicular Networks Qing Yang and Alvin Lim
Prathima Agrawal
Computer Science and Software Engineering Auburn University Auburn, Alabama, 36849 Email: {yangqin, limalvi}@auburn.edu
Electrical and Computer Engineering Auburn University Auburn, Alabama, 36849 Email:
[email protected]
Abstract—Multi-hop car to car communications are useful for supporting many vehicular applications that provide drivers with safety and convenience ranging from office on the wheel to real traffic query, vehicle safety, parking space searching and on-road advertisement. Developing multi-hop communication in vehicular ad hoc networks (VANET) is a challenging problem due to the rapidly changing topology and frequent network disconnections, which cause failure or inefficiency in traditional ad hoc routing protocols. The problem of frequent network disconnections can be partially addressed using a carry-andforward mechanism which may incur higher delay. However, our connectivity aware routing (CAR) protocol addresses this problem by selecting an optimal route with the least probability of network disconnection and avoids carry-and-forward delay. This can be achieved using our new probabilistic model of network connectivity which takes into account a more realistic clustering phenomenon of vehicle traffic in city scenarios that is caused by traffic lights. Our simulation results show that the proposed CAR protocol outperforms existing VANET routing protocols in terms of data delivery ratio, data packet delay and network throughput. In addition, CAR improves performance for both sparse and dense networks.
I. I NTRODUCTION Wireless communications among moving vehicles are increasingly the focus of research in both the academic research community and automobile industry, driven by the vision that exchange of information among vehicles can be exploited to improve the safety and comfort of drivers and passengers [1], [2]. Some automobile manufacturers has equipped their new vehicles with GPS, digital map and even wireless interfaces, e.g. Honda ASV-3 [3]. In the near future, large scale vehicular networks will be available to provide people with more conveniences in their driving experience. For example, through such networks, people can query the price and services provided by gas stations in a certain region, or remotely control their smarthouses [4] while driving home. Drivers can even download the real-time traffic image from the traffic camera located at a certain point, or connect to access points of parking lots to inquire the number of available parking slots. These types of applications could tolerate some delay, e.g. a few minute. If the information could be successfully retrieved from the remote server, it will be very helpful and desirable to drivers. To realize this vision, we must first select the most appropriate architecture. Three broad categories of architectures are: infrastructure-based, ad-hoc networks and hybrid. The infrastructure-based architecture takes advantage of the exist-
ing cellular networks. However, it has three drawbacks: high operation cost, limited bandwidth and symmetry channel allocation for uplink and downlink. Ad-hoc networks do not need infrastructure, so the cost of building such network will be very low and it can even operate in the events of disasters. As a result, this paper focuses on the flexible deployment and selforganizing capabilities of vehicular ad-hoc network (VANET) architectures. The hybrid architecture combines these two architectures by considering vehicles as data relays between roadside base-stations [5]. This architecture also requires the function of multi-hop communication between vehicles, which is the essential part of ad hoc network architecture. Unlike other ad-hoc networks, e.g. sensor networks, VANET has its own unique characteristics [6]. The most distinguishing one is the dynamic and rapidly changing topology which leads to frequent communication disconnections among vehicles. Moreover, the mobility of nodes in VANET cannot be simply expressed by traditional models, since the driving behavior of each vehicle can be affected by many factors including speed limit, neighboring vehicles, traffic lights and even pedestrians crossing the road. As revealed in [7], the frequent network disconnection problem is the most important issue in designing VANET. Another problem is the uneven deployment of vehicles on the roads, which makes route selection more complex. Moreover, due to the blocking of wireless signal by objects such as skyscraper in the city, communication between vehicles must have line-of-sight in addition to being in range of one another. Since temporary disconnection in vehicular network is unavoidable and packet must been routed along the roads, choosing a route that will encounter as little disconnection as possible will not only increase the data delivery ratio but also decrease the transmission delay. To implement this, we develop a new routing protocol called connectivity aware routing (CAR). It first models the probability of connectivity of each road segment, and then selects the route with the highest probability of connectivity to forward packets. As a result, the packet delivery ratio can be increased up to at least 90% and the delay is in the acceptable range as well. The remainder of this paper is organized as follow. Section II discusses currently available routing protocols for VANET. Then we describe the assumptions and system model for CAR in Section III. In Section IV and V, the routing strategy and simulation results are presented, respectively. Section VI gives the conclusions.
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This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 2008 proceedings.
II. R ELATED W ORK Traditional wireless ad-hoc networks routing protocols, such as DSR [8] and AODV [9], are not suitable for VANET because frequent disconnections cause high routing maintenance overhead. Although GPSR [10] is stateless and can partially handle mobility of nodes, it still suffers from the problem of selecting the wrong next hop due to out-of-date neighbors information, routing loop and too many (detour) hops as stated in [11]. To address the local disconnection problem, [2] used the information on vehicle headings to predict a possible link breakage event prior to its occurrence and then avoid routing to a disconnected next hop. From the global perspective of connectivity, [12] introduced a new metric – expected disconnection degree (EDD) – to evaluate the probability that a candidate routing path would be broken. However, it requires the network is to be fully connected and can tolerate only a few seconds of network disconnection. These assumptions are often not true in VANET. Network disconnection is addressed in [13] by actively modifying mobile nodes trajectory to cover the disconnected area and then continue the transmission. Unfortunately, they are not applicable to vehicular networks since vehicle routes are decided by drivers, not the network. In [14], if the network is disconnected, packets are buffered and later forwarded when the vehicle moves closer to other vehicles and connection is restored. In [11], packets are forwarded along the Dijkstra shortest path as calculated from road maps, but did not account for disconnections in a unevenly deployed networks. Methods in [15] adapt trajectory routing [16] for VANET, but they did not give any mechanism of avoiding network partitions. VADD [1] selects routes with low delay, but has a drawback that when the average distance between vehicles is a little smaller than the communication range, the transmission delay will be much longer than expected in VADD. This is based on our analysis of this scenario where the probability of network connection is very low. Existing routing protocols for VANET either requires fully connected networks or requires packet buffering but do not avoid network disconnection. In contrast, CAR takes advantage of statistical traffic data to model the connectivity of networks, and then selects the route with the highest probability of connection using a modified Dijkstra algorithm. Since our connectivity model is not constrained by the density of nodes, CAR performs well in both dense and sparse networks. III. S YSTEM M ODEL
B. Connectivity Model This section discusses the connectivity model of each road segment, which is defined as the portion of a street between two adjacent intersections. We first consider the one lane case and later generalize it to multiple lanes. In the one lane case, we divide the road segment equally into m cells so that each cell can contain at most one vehicle. The length of cell d can be set as the average length of vehicles, e.g. 5m. Then the probability of connectivity can be formulated as follows. If there are n vehicles (also called nodes) on a road segment, what is the probability that the distance of any two neighboring nodes is less than the communication range R = n0 ∗d, where there are no more than n0 successive empty cells on the road. If the number of any successive empty cells is greater than n0 , the network is disconnected, otherwise it is connected. Obviously, in the one lane case the number of empty cells is m − n; but in the case of multiple lanes, the number of empty cells will range from m − n to m − n/n where n is the number of lanes. For multiple lanes, each cell of the road can contain different number of nodes 0, 1, · · · , n . Therefore, in the extreme case, if every occupied cell contains only one node, the number of empty cells is m−n. On the other hand, if each occupied cell has n node, then the number will become m−n/n . Intuitively, if the number of empty cells k is equal or less than n0 , then the network must be connected. If k > n0 , the networks may be connected or disconnected depending on how the empty cells were distributed. We denote Pdis and Pcon as the probability of network being disconnected and connected, respectively. Obviously, Pcon = 1 − Pdis . To obtain Pdis , we first need to calculate the probability that there exists exactly k empty cells, denoted by P {µ(n, m) = k}. If there are k empty cells, we have to compute the probability that there exists more than n0 successive empty cells, denoted by P {ϕ(m, k) > n0 }. Then the probability that the network is disconnected is: max(m−n/n ,n0 )
Pdis =
P {µ(n, m) = k}·P {ϕ(m, k) > n0 }
k=max(m−n,n0 )
(1) We now investigate the probability that there exists exactly k empty cells on the road. Let Ai be the event that the ith cell is empty, and let Ai be the event complementary to Ai . Then we have: P {µ(n, m) = k} (2) = P Ai1 · · · Aik Aj1 · · · Ajm−k 1≤i1
