- Open Access
Link residual lifetime-based next hop selection scheme for vehicular ad hoc networks
© The Author(s) 2017
- Received: 24 November 2015
- Accepted: 18 January 2017
- Published: 1 February 2017
In Vehicular Ad Hoc Networks (VANETs), geographic routing protocols rely on a greedy strategy for hop by hop packet forwarding by selecting vehicle closest to the destination as the next hop forwarding node. However, in a high-mobility network such as VANET, the greedy forwarding strategy may lead to packet transmission failure since it does not consider the reliability of the newly formed link when next hop forwarding nodes are chosen. In this paper, we propose a scheme for next hop selection in VANETs that takes into account the residual lifetime of the communication links. In the proposed approach, a source vehicle selects a forwarding vehicle from a given set of candidate vehicles by estimating the residual lifetime of the corresponding links and finding the link with maximum residual lifetime. Initially, we present Kalman filter based approach for estimating the link residual lifetime in VANETs. We then present the details of the proposed next hop selection method. Simulation results show that the proposed scheme exhibits better performance in terms of packet delivery ratio and average end-to-end delay as compared to other conventional method.
- Vehicular Ad Hoc networks
- Residual lifetime
Vehicular Ad Hoc Networks (VANETs), an integral component of intelligent transportation systems (ITS), are aimed to provide support for road safety, traffic management and comfort applications by enabling communication in two distinct modes: vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) . Since the nodes in VANETs (i.e., vehicles with on-board units) move with very high speed, the network topology is highly dynamic and consequently the inter-vehicle communication links will be highly unstable or may even become disconnected frequently. A route that is established between a source-destination pair through a sequence of road segments will cease to be invalid when at least one communication link along the route fails. Hence, it is very important and desirable for the routing algorithm to choose an optimal route consisting of highly reliable links in the network .
Generally, routing within a road segment is performed using a greedy forwarding approach in which the tagged node carrying a data packet will select a vehicle from among its neighboring set that is closer to destination or the next junction, for forwarding the data packet. The greedy forwarding approach is continued until the next junction or the destination is reached. Geographic routing, which is the preferred means of routing in VANETs, also employs greedy forwarding approach . Adoption of greedy forwarding reduces the number of hops for a packet to move from the source to the destination leading to a decrease of end-to-end delay experienced by the packet. However, greedy forwarding does not take into account the quality and reliability of the link that is chosen for forwarding the packet. In VANETs, since the established link may become highly unreliable from time to time, the probability of packet transmission failure may become very high when greedy forwarding is employed. This in turn can result in more retransmissions leading to reduction of the network throughput and significant increase of end-to-end delay.
The mean link lifetime is defined as the mean time period for which two vehicles are within the communication range of each other, while the residual lifetime of an existing link is defined as the time duration from the current time until the time the link breaks. Both these quantities have direct impact on many performance metrics such as route reliability, packet delivery ratio, throughput, and end-to-end delay of the network. Accurate knowledge of mean link lifetime and the residual lifetime of existing links will aid the design of reliability based routing protocols to improve the routing performance, and to achieve the desired network performance.
In this paper, we propose a method for the selection of next hop forwarding node in VANETs that improves the reliability of communication links along the path from source to destination. In the proposed method, a packet-carrying vehicular node (i.e., source vehicle) selects a forwarding vehicle from a given set of candidate vehicles by estimating the residual lifetime of the corresponding individual communication links. We present an algorithm to predict the link residual lifetime in VANETs by making use of Kalman filter based prediction technique. The proposed method relies on predicting the relative location and speed of vehicular nodes using Kalman filter. Once the estimates for the residual lifetimes of all the probable one-hop links are available, a vehicle belonging to the forwarding set that result in maximum value for the link residual lifetime is chosen as the forwarding vehicle. Simulation results reveal that the proposed scheme significantly improves the packet delivery ratio. The rest of the paper is organized as follows: In the Section 2, we briefly describe the related work. The system model employed for the analysis is presented in Section 3.1. In Section 3.2, we describe a procedure for the prediction of link residual lifetime based on Kalman filter. In Section 3.3, we present the residual lifetime based approach for packet forwarding. The simulation results are presented in Section 4, and finally, the paper is concluded in Section 5.
Several papers have recently appeared that deal with the design of reliable routing protocols for VANETs [4–20]. In , Taleb et al. describe a reliable routing protocol in which vehicles are grouped according to their velocity vectors and, the routing algorithm dynamically searches for the most stable route that includes only hops from the same group of vehicles. S. Wan et al.  propose a reliable routing protocol for V2I networks on rural highways based on prediction of link lifetime. However, the proposed protocol requires the exchange of a large number of route request (RREQ) and route reply (RREP) packets. Namboodiri et al.  describe a routing algorithm, specifically tailored to the mobile gateway scenario, that predicts how long a route will last and creates a new route before the failure of the existing route. In , Menouar et al. describe a routing algorithm, that can predict the future coordinates of a vehicle and build new stable routes. In , the same authors propose a movement prediction-based routing (MOPR) in which each vehicle estimates the link stability for each neighboring vehicle before selecting the next hop for data forwarding. In the above mentioned papers, the link lifetime is computed by assuming vehicle speed to be a constant. Sofra et al.  discuss an algorithm capable of finding reliable routes in VANETs. In , Rao et al. present a protocol called GPSR-L, an improved version of greedy perimeter stateless routing (GPSR) protocol that takes into account the link lifetime to ensure reliable routing. However, the author assumes vehicle velocity to be a constant for finding the link lifetime. In , Eiza et al. propose a reliable routing protocol known as AODV-R by incorporating link reliability metric in the original AODV routing protocol. In , Niu et al. describes a QoS routing algorithm based on the AODV protocol and a criterion for link reliability. In , Yu et al. present a routing procedure, AODV-VANET, that use vehicle’s movement information in the route discovery process. Notice that protocols described in [11–13] are based on AODV. Recently several studies have reported that, topology based routing schemes such as AODV performs badly in VANETs, as compared to the geographic routing protocols .
In , Eiza and Ni propose a reliable routing algorithm that exploits the evolving characteristics of VANETs on highway. Naumov et al. in , propose connectivity aware routing (CAR), which adapts to current network conditions to find a route with sufficient connectivity, so as to maximize the chance of successful packet delivery. In , Boukerche et al. describe a routing approach for providing QoS in VANETs in which the link reliability is estimated based on exchange of beacons among vehicles. Shelly et al.  propose an enhancement for the well-known GPSR protocol, which exploits information about link reliability for the selection of forwarding node. In , Yu et al. propose a routing protocol for VANETs based on vehicle density so as to provide fast and reliable message delivery. In , Cai et al. propose a link state aware geographic opportunistic (LSGO) routing protocol, in which the forwarding nodes are selected based on their geographic location and the link quality. Here, the link quality is expressed in terms of a metric known as expected transmission count (ETX), which is the expected number of data transmissions required to send a packet over the source-destination link. However, the computation of ETX involves exchange of Hello packets across each link, leading to significant increase in the overhead. Further, ETX is computed by considering transmission of Hello packets during a window of w seconds (s), leading to higher end-to-end delay. Wang et al.  propose a Stochastic Minimum-hops Forwarding Routing (SMFR) algorithm for VANETs with heterogeneous types of vehicles that minimizes the number of hops to the destination. However, the work reported in  does not consider link reliability for the selection of end-to-end route. Since VANETs are poised to support critical road safety related applications in a highly dynamic environment, communication reliability along the end-to-end route is of prime importance as compared to other design criterion such as the number of hops along the route, as investigated in . Accordingly, it is desirable for the routing protocol to consider link reliability when vehicles are chosen for forwarding the packet.
When routing in VANETs is considered, the main disadvantage of the traditional greedy forwarding method is that next hop selection procedure does not consider the quality and reliability of the resulting link. While the source vehicle forwards the data packet to the vehicle closest to the destination node under traditional greedy forwarding, it is very important to consider the residual lifetime of the link formed by the source vehicle and the selected one-hop neighbor. This is because, if the residual lifetime of the newly formed link is very low, the probability of packet transmission failure is very high that will lead to more retransmissions and deterioration of the network throughput. In this paper, we investigate the problem of improving communication reliability when a source vehicle selects next hop nodes for data forwarding. We propose a method for the selection of reliable one-hop neighbor based on the residual lifetime of the corresponding communication link. To meet this objective, we present an algorithm to predict the residual lifetime of links in VANETs by making use of Kalman filter based prediction technique. In this case, a source vehicle tries to predict the residual lifetime of one-hop links to all the available neighbor vehicles. The neighbor with maximum value for the link residual lifetime is chosen as the next-hop forwarding vehicle. Kalman filter is a recursive filter that can be used to estimate the state of a linear dynamic system from a series of noisy measurements . A major advantage of Kalman filter is that they can quickly and efficiently compute estimates and can be used for both state estimation and prediction. Kalman filter is a convenient tool for online real-time processing of data. The optimal estimate is derived by the Kalman filter based on minimizing the mean square error . Due to the simplicity and robust nature of the Kalman filter, they are extensively used for velocity and location prediction techniques in ad hoc networks [23–25].
In this section, we describe the procedure for the selection of next-hop forwarding vehicle that relies on estimates of link residual lifetime. We begin this section by introducing the system model employed throughout the paper and then describe the residual lifetime estimation procedure. This is followed by a description of the proposed method for the next hop selection.
3.1 System model
3.2 Residual lifetime prediction using Kalman filter
3.3 Next-hop selection based on link residual lifetime
In this subsection, we describe the proposed method for next-hop selection that relies on the prediction algorithm discussed previously. It is assumed that all the vehicles possess GPS facility to know their location and speed. Each vehicle generates a beacon for every Δt time duration which contains the information of its location coordinates and speed. From the beacon message, a vehicle will get the measurement values from each neighbor node. A tagged vehicle, on receiving the beacon message from a node, can perform the one-step ahead prediction of the location and relative speed of the particular node, from which the residual lifetime of the corresponding link can be calculated. The tagged vehicle then forms the neighbor list by including all the one-hop neighbors, their ID’s and the residual lifetime of the corresponding links. Since the tagged vehicle receives the beacon from its one hop neighbors for every Δt time duration, the entries in the neighbor list would get updated periodically for every Δt time duration. The neighbor list also gets updated when a new vehicle enters the effective transmission range of the tagged vehicle or when the tagged vehicle fails to receive beacon from a node in the neighbor list.
In this section, we present the results of our investigation. Initially, we perform a detailed simulation study using Matlab tool to find the R eff of a vehicular node for a given set of parameters such as transmit power, path loss exponent etc. We simulate a realistic channel environment with lognormal shadow fading and Rayleigh distribution for the multipath fading model, and measure the R eff for various channel conditions. It is observed that the R eff is significantly affected by path loss exponent, shadow fading standard deviation and multipath fading. Later, we use these values of R eff for the computation of residual lifetime of the communication links.
Channel bit rate
2 km long
IEEE 802.11e EDCA
Each vehicle transmits its location and speed information to its neighbor vehicles through the beacons, which are transmitted every Δt time duration. On receiving this beacon, a tagged vehicle will calculate the relative position and relative speed with the neighboring nodes, which forms the measurement data for the Kalman filter. In the simulations, every tagged vehicle predicts the residual lifetime of the link formed with every other node that enters the communication range of the tagged vehicle. We consider the data traffic to be constant bit rate (CBR) that is attached to each source vehicle to generate packets of fixed size. We further assume user datagram protocol (UDP) as the transport layer protocol for the simulation studies. A total of 10 source-destination pairs are identified in the simulation which generate packets of size 512 bytes for every 0.25 s (we consider the case of variable packet size as well). Total time duration for the simulation is set as 200 s. The source vehicle will start generating the data packet after the first 10 s of the simulation time and stops generating the data packet at 150 s. For each simulation experiment, the sender/receiver node pairs are randomly selected.
The time correlation factor τ can be used to model the operation of VANETs in three different traffic flow conditions: uncongested (i.e, free flow traffic state with low vehicle density), near capacity (i.e., vehicle density takes intermediate values) and congested state (i.e., high values for the vehicle density). Higher values of τ results in negligible temporal variations in the vehicle speed, which represents an uncongested highway scenario where drivers can drive independent of other vehicles. However, in the uncongested highway scenario, there would be deterioration of link lifetime because of frequent disconnections and non-availability of neighbor nodes for forwarding the packets. When time correlation factor is less, the vehicle speed would exhibit very high temporal variations and this is equivalent to a congested traffic state. We have selected τ to be equal to 0.9 for some of our simulation experiments so that the performance of the protocol can be studied for a free flow traffic state. The beacon interval determines the frequency with which measurement values are taken for the prediction. It has been observed that, beacon interval Δt has strong influence on accuracy of residual link lifetime prediction as shown in Fig. 6. When Δt is reduced, more measurement values would be available, resulting in accurate prediction at the cost of an increase in complexity. For some of our simulation experiments, we have chosen Δt=0.6.
We consider the following performance metrics for the evaluation of the proposed protocol.
Packet delivery ratio (PDR): this quantity is the ratio of average number of successfully received data packets at the destination vehicle to the number of packets generated by the source.
Average end-to-end (E2E) delay: this is the time interval between receiving and sending time for a packet for a source to destination pair averaged over all such pairs. Here, the data packets that are successfully delivered to destinations are only considered for the calculation.
Even though the paper provides results for a unidirectional scenario alone, the results presented in this paper are valid for multilane unidirectional highways as well. This is because, the transmission range of a vehicle is usually much greater than the highway width and a vehicle can always communicate with any other vehicle located within its range . Consider a scenario where two vehicles denoted A and B of equal transmission range R move along two distinct lanes in a multilane highway of width L. If vehicle A intends to transmit a message to vehicle B along the direction of the highway, it must use a larger transmission range R ′. In other words, if destination vehicle is on a different lane (i.e., interlane transmissions), the transmission range of vehicle A must be R ′, which is slightly larger than R. However, the standard highway’s lane width is approximately 3.6m, and the vehicle transmission range can be increased to 500m or so as suggested by the dedicated short-range communication (DSRC) Standard . Therefore, the difference between R and R ′ is negligible. So, the scenario consisting of two vehicles travelling in the same direction on multiple lanes along a multilane highway can be considered as equivalent to both of them moving on the same lane. These vehicles can communicate with each other if they are in the transmission range of each other. This means that highway width does not introduce major changes in the calculations. However, vehicles moving on different lanes will have different mean speeds (i.e., dynamic range of their speeds could be different). This should be considered in the problem formulation. Further, the results can be immediately extended for the bi-directional scenario as well. When the vehicles are assumed to move in the same direction, the relative speed among a pair of vehicles is calculated as the difference of their individual speed and is given by Eq. (6). In the bidirectional scenario, the relative speed among a pair of vehicles would be the sum of their speed. Appropriate changes have to be made in the measurement equations accordingly.
In this paper, we have proposed a new scheme for the selection of next hop link based on knowledge of link residual lifetime. We assumed vehicle speed to follow the Gauss Markov mobility model and the notion of effective transmission range was considered for the analysis and evaluation. We have also described a method for the prediction of residual lifetime using Kalman filter. The proposed next hop selection method ensures that links with maximum residual lifetime is chosen for forwarding the data packet. Through extensive simulation results, we have showed that the proposed selection scheme is superior to conventional method of greedy forwarding. Even though the end-to-end delay was observed to be slightly higher for the proposed scheme, significant empowerment in communication reliability (i.e; expressed as PDR) was obtained, as compared to greedy forwarding.
The authors declare that they have no competing interests.
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