[IEEE 2013 12th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET) - Ajaccio, France (2013.06.24-2013.06.26)] 2013 12th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET) - A delay-sensitive vehicular routing protocol using Ant Colony Optimization

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<ul><li><p>A Delay-Sensitive Vehicular Routing Protocol UsingAnt Colony Optimization</p><p>Guangyu LiLaboratoire de Recherche en Informatique (LRI)CNRS UMR 8623, University of Paris-Sud 11</p><p>Building 650, 91405 Orsay, FranceEmail: guangyu@lri.fr</p><p>Lila BoukhatemLaboratoire de Recherche en Informatique (LRI)CNRS UMR 8623, University of Paris-Sud 11</p><p>Building 650, 91405 Orsay, FranceEmail: Lila.Boukhatem@lri.fr</p><p>AbstractVehicular Ad hoc Networks (VANETs) are confront-ed with numerous dif culties and challenges, such as scalabilityissues, rapid changes of network topology and channel capac-ity restriction, which can induce communication deterioration.In this paper, we propose a delay-sensitive vehicular routingprotocol, which uses the intersections as anchors to establishoptimal delay routing paths consisting of a list of intersections.The main feature of our protocol is the periodic estimation ofthe road segment delay expressed in the combination of averagedelay and delay variance using multi-hop vehicle relaying. Asthis estimation is local to road segments, we make use of ACO(Ant Colony Optimization) concept to discover end-to-end bestdelay paths from source to target intersection which is closestto the destination. Route setup process is achieved by reactiveforward ants and backward ants, which are in charge of networkexploration and pheromone dissemination respectively. Routingselection is implemented at each intersection to opportunisticallychoose best next intersection based on a pheromone routingtable. A proactive route maintenance is initiated by source toupdate, expand and improve the routing information during datatransmission period using periodic proactive ants sampling. Inaddition, we make use of simple carry and/or greedy forwardingtechnique to relay packets between adjacent intersections. Thesimulation results indicate that our protocol shows better commu-nication performance compared with a basic geographical routingprotocol (GPSR) and a min-delay routing protocol (CAR) inregard to delivery ratio, average end-to-end delay and overhead.</p><p>I. INTRODUCTION</p><p>Vehicular Ad hoc Networks (VANETs) are introduced asa subclass of Mobile Ad hoc Networks (MANETs), and con-stitute a promising approach for the intelligent transportationsystems (ITS) [1]. In VANETs, the vehicles are able to act asrouting nodes to exchange information using either vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) commu-nications. V2V communications take place among On-BoardUnits (OBUs) equipped in each vehicle, and V2I communi-cations occur between Road-Side Units (RSUs) and OBUs,as illustrated in Figure 1. VANETs are utilized to support alarge range of distributed applications including traf c control,safety application and driver assistance in ITS [2], [3].</p><p>In VANET environment, there are many challenges includ-ing the large network size, the high mobility, the broadcaststorm problems and the limited channel bandwidth, whichmake data transmission frequently suffer from either networkholes or traf c congestion, and lead to great dif culties in</p><p>Servicer</p><p>Internet</p><p>RSUs</p><p>RSUs</p><p>OBUs</p><p>OBUs</p><p>Ad Hoc Network Domain</p><p>V2V</p><p>V2I</p><p>V2V</p><p>V2I</p><p>V2I</p><p>V2V</p><p>Internet</p><p>OBUs</p><p>OBUs</p><p>V2V</p><p>V2I</p><p>OBUs</p><p>OBUs</p><p>OBUs</p><p>OBUs</p><p>Fig. 1: VANETs fundamental architecture</p><p>estimating links relaying quality. To resolve these problems, wepropose an adaptive routing protocol based on the cumulativerelaying delay of road segments (road between two adjacentintersections). Firstly, our protocol exploits the ACO conceptsand makes use of reactive forward ants to explore possiblepaths which is composed of a list of intersections between thesource and the target intersection (the closest intersection to thedestination vehicle). Then, reactive backward ants generated bythe target intersection are sent back to the source following thecorresponding reverse paths of reactive forward ants, and dis-seminate pheromone at intersections using the relaying delayvalues of the visited road segments. The pheromone routingtable available at each intersection helps vehicles opportunisti-cally select best next intersection for packet forwarding, and asimple carry and/or greedy forwarding strategy is used to relaypackets between adjacent intersections. Finally, we initiate aproactive route maintenance phase to periodically adapt to thevariation of routing information.</p><p>A. Related Works</p><p>Geographic routing is a promising method in VANETs,and it can progressively forward packets to the neighboringnode closest to the destination using the physical location.GPCR [4] assumes that each road segment is an edge of aplanar graph and each vehicle located at an intersection is avertex. Routing decisions are then made at intersections. The</p><p>978-1-4799-1004-5/13/$31.00 2013 IEEE</p><p>2013 12th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET)</p><p>49</p></li><li><p>assumption of GPCR may not exist in realistic environments,once there is not any vehicle on intersections, data packetswill be forwarded over intersections and suffer from routingloops. By detecting potential network partitions in advance ormaking use of channel overhearing capability, [5] and [6] havebeen proposed to decrease the hop counts on recovery paths.However, both proposals do not consider the real-time relayingquality of the current routing paths.The concept of anchor-based routing has been utilized</p><p>in VANETs. GyTar [7] utilizes vehicles density and roadcurvilinear distance between the current intersection and thedestination to dynamically select the next candidate roadsegment. However, GyTar does not consider for route selectionthe traf c load condition, which increases packets delay anddropping ratio in case of channel congestion. SADV [8] makesuse of static nodes at road intersections to assist in datarelaying. Packets can then be buffered for a while in thestatic nodes until a suitable vehicle is available along the bestdelivery path to further forward the packet. Although SADVexploits the real-time delay between two adjacent intersections,single average delay can not re ect the whole performance ofan end-to-end routing path.</p><p>In the combinatorial optimization eld, ACO [9], [10],[11], [12] is a famous swarm intelligence approach and takesthe inspiration from real ants wandering around their nests tosearch for food. Once reaching the food source, the ants returnback to their nests and simultaneously deposit pheromone trailsalong the paths. The pheromone is used for the followingants to select their next hops. Because of its robustnessand adaptive characters, ACO is applied widely for routingprotocols in wireless networks. [13] proposes a multi-agent antbased routing algorithm, which combines both proactive andreactive components to achieve good performance in regard toend-to-end delay and packet delivery ratio. Ant-E [14] makesuse of the blocking expanding ring search (Blocking-ERS) tocontrol the overhead and limit local retransmissions. Based ondynamic zones, AD-ZRP [15] acts together with ACO, andyet improves the ef ciency of route discovery and the routemaintenance.</p><p>B. Main Contributions</p><p>The main contributions of our protocol are as follows:</p><p>First of all, our protocol sets up optimal routing paths fromsource to target intersection using either unicast or broadcasttransmission, rather than a systematic route between end-to-end vehicles by broadcast. Obviously, our protocol is bene cialin the exploration ef ciency of routes setup procedure, theprovision of more backup routing paths and the alleviationof traf c congestion along paths.</p><p>In addition, we design a simple but ef cient model to esti-mate the delay of road segments periodically by combining twolatest parameters (average delay and delay variance) instead ofinaccurate statistical data or instantaneous delay values, whichare subject to rapid variation.</p><p>Moreover, in order to resolve the proposed NP (non-deterministic polynomial) hard routing problems, we makeuse of ACO algorithm to implement routing decision at eachintersection along the path. Compared with other routing infor-mation consisting of the total sequence of hops or intersections</p><p>D</p><p>I3 I5</p><p>I4</p><p>I2</p><p>I1</p><p>I6=Itar</p><p>SUnoptimizable </p><p>Route</p><p>UnoptimizableRoute</p><p>The route of forward ant A</p><p>The route of backward ant BThe route of backward ant BThe route of backward ant A</p><p>Fig. 2: A simple illustration of our protocol concept</p><p>of the paths, our protocol can increase the stability of routingpaths, relieve the effect of link failure and the amount ofoverhead.</p><p>Last but not least, our protocol takes advantage of proactiveroute maintenance to update and extend routing paths. Thismethod can availably cope with the rapid changes of topologyin VANET environment in time compared with other reactivemaintenance processes.</p><p>The rest of this paper is organized as follows. SectionII presents our model to estimate the relaying delay of roadsegment. Our adaptive routing protocol based on ACO conceptis described in Section III. Section IV shows the simulationand results analysis. Finally, Section V concludes the paper.</p><p>II. ESTIMATION MODEL OF ROAD SEGMENTDELAY</p><p>Delay is a key parameter for data relay and can indirectlyre ect the current transmission loads, the vehicles density andthe vehicles distribution on the road segment. However, itis very dif cult to accurately estimate the delay due to itshigh variability, especially in a dynamic environment such asvehicular networks. To resolve this issue, we capture the globalfeature of this parameter rather than its instantaneous value,and propose the following estimating model for delay inspiredby RTT (Round Trip Time) computation in TCP (TransmissionControl Protocol).</p><p>Arriving at the intersection Ii, a data packet adds both theidenti er of intersection Ii and the packet sending time Tij sndinto its header when it is forwarded to the next intersection I j .Upon receiving the data packet, Ij can estimate the relayingdelay of the road segment. The expected average delay D ij avand delay variance Dij var between Ii and Ij are estimatedas follows:</p><p>Dij av (1 ) Dij av + dij (1)Dij var (1 ) Dij var + |dij Dij av| (2)</p><p>where and are weighting factors with values between 0and 1, dij is an instantaneous delay illustrated as follows:</p><p>dij = Tij rev Tij snd (3)</p><p>2013 12th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET)</p><p>50</p></li><li><p>Ii Ij</p><p>Type Delay to target IntersectionPheromone to target </p><p>IntersectionList of </p><p>IntersectionSourceTarget </p><p>IntersectionTy S Itar D_bant _bantIlist</p><p>Fig. 3: Backward ant packet format</p><p>where Tij rev denotes the receiving time of data packet at Ij .</p><p>Then, after normalization, Dij av and Dij var are ex-pressed respectively as:</p><p>Dij av 1 2 arctanDij av (4)</p><p>Dij var 1 2 arctanDij var (5)</p><p>Finally, the delay Dij of the road segment is derived as:</p><p>Dij = Dij av + (1 ) Dij var (0 &lt; &lt; 1) (6)</p><p>In order to keep the freshness of road segment delay, weset a timer Texp at each intersection. Once timer expirationoccurs, the relaying delay parameter stored at each intersectionis reset.</p><p>In contrast to other estimating models, our model is simplebut effective to avert the direct utilization of instantaneousmetric values, which are subject to rapid variations due tothe highly dynamic communication environment. Besides, wemake use of data packets travelling from neighboring inter-sections rather than hello packets to implement the delayestimation, so our model can signi cantly reduce the amountof overhead and alleviate the traf c congestion.</p><p>III. OUR ADAPTIVE ROUTING PROTOCOL</p><p>In this section, we describe the different components of ourprotocol in detail. In this routing protocol, we assume that asimple road communication infrastructure (such as Road SideUnits, RSUs) is installed at each intersection, which can helpthe vehicles make routing decisions. We also assume that everyvehicle is equipped with a digital map, a GPS facility and anavigation system. In addition, an available location serviceproviding the geographical location for vehicles, is required.</p><p>Our protocol combines both reactive and proactive com-ponents to respectively establish and maintain optimal routesbetween the source and the target intersection (closest to thedestination) in terms of relaying delay mentioned in Section II.At the beginning, data source executes a reactive route setupprocess. Derived from the ACO algorithm, reactive forwardants generated by data source are relayed towards the targetintersection to explore routing paths. Upon reactive forwardants arrive at the target intersection, reactive backward antsare generated and then sent back to the source following thereverse paths of the forward ants, to update pheromone valuesand set up optimal routing paths at intersections. Pheromonetables attached at each intersection are utilized to select the bestnext intersection for data packets with certain probability. Sim-ple carry and/or greedy forwarding is used by vehicles to make</p><p>data packets arrive at next candidate intersection. Besides, ourprotocol makes use of proactive ants sampling to carry outroute maintenance mechanism, which can periodically update,expand and improve routing information along routing paths.Figure 2 illustrates a simple concept of our protocol to set</p><p>up the best routes from the source S to the target intersectionItar , which is the nearest intersection to the destination D.Based on relaying delay of road segments, our protocol canavoid the selection of the shortest path consisting of [I2, I1]to D where the vehicle density is very sparse. Besides, ourprotocol prefers to choose the routing path composed of [I 3,I4, I5, I6], rather than the one made up of [I3, I6] which isshorter but suffers from far higher vehicles density causingserious congestion collision. Here, solid lines mean the routesof reactive forward ants to explore routing paths towards I tar,and dashed lines denote the routes of reactive backward antsto disseminate pheromone and set up routing tables.</p><p>A. Reactive Route Setup</p><p>This section elaborates the reactive route setup process,which involves the transmission of reactive forward and back-ward ants between the source and the target intersection.1) Reactive Forward Ants Process: Initially, reactive for-</p><p>ward ant packets are generated by the source S to explorethe network and nd routing paths towards the target in-tersection Itar. When arriving at each intersection, reactiveforward ants add the identi er of the intersection to their ownheaders. Based on carry and/or greedy forwarding scheme,reactive forward ants are then relayed progressively to nextcandidate intersection by either broadcast or unicast trans-mission depending on whether the current intersection I i hasrouting information for the target intersection I tar . If therouting information at Ii is available, reactive forward ants areforwarded to the next candidate intersection by unicast withcertain probability. Concretely, Ii chooses next intersection Ijwith probability P Itarij , which is declared as follows:</p><p>P Itarij =(Itarij )</p><p>kNItari (</p><p>Itarik )</p><p>( 1) (7)</p><p>where N Itari is the set of neighborin...</p></li></ul>