the host where the data resides, as first-class citizen in the network. In ICN, a data object is retrieved based on its content identity instead of the IP address.
Scalable Opportunistic VANET Content Routing With Encounter Information Yu-Ting Yu, Yuanjie Li, Xingyu Ma, Wentao Shang, M. Y. Sanadidi, Mario Gerla University of California Los Angeles, CA 90095, USA {yutingyu, yuanjieli, xingyuma, wentashang, medy, gerla}@cs.ucla.edu
Abstract— Recently, Information Centric Networking (ICN) has attracted much attention also for mobiles. Unlike host-based communication models, ICN promotes data names as the firstclass citizen in the network. However, the current ICN namebased routing requires Interests be routed by name to the nearest replica, implying the Interests are flooded in VANET. This introduces large overhead and consequently degrades wireless network performance. In order to maintain the efficiency of ICN implementation in VANET, we propose an opportunistic geoinspired content based routing method. Our method utilizes the last encounter information of each node to infer the locations of content holders. With this information, the Interests can be georouted instead of being flooded to reduce the congestion level of the entire network. The simulation results show that our proposed method reduces the scope of flooding to less than two hops and improves retrieval rate by 1.42 times over flooding-based methods. Keywords—VANET; ICN; Routing
I.
INTRODUCTION
Information Centric Networking (ICN) [1], also referred as named data networking (NDN), has attracted much attention. ICN treats the name of the data, rather than the IP address of the host where the data resides, as first-class citizen in the network. In ICN, a data object is retrieved based on its content identity instead of the IP address. In order to fetch the data with a certain name, the content requester must issue an Interest packet with that name. The Interest packet is then routed to the content holder using name-based routing. Once the content holder receives the Interest, it returns the corresponding data content along the breadcrumb path left by the Interest. Recently, several applications of ICN to VANETs have been published [2][3]. Intuitively, the multi-source nature and innetwork caching feature of ICNs are helpful to overcome the mobility and intermittent connectivity challenges that were difficult to solve with traditional IP networks. For example, a data retrieval failure due to intermittent connectivity can be recovered more quickly by leveraging distributed caches. However, with so much content around, the major challenge in VANET ICN design is routing scalability. A method that can efficiently locate the content holder without flooding is much desired. In VANETs, it has been difficult to maintain stable routes (either name-based or IP-based) to various destinations without flooding [4]. To improve VANET routing scalability, 978-1-4799-1270-4/13/$31.00 ©2013 IEEE
researchers have proposed to utilize geo-coordinate information (available to vehicles with GPS service) in the r+outing process [5][6][7]. The basic idea is to forward the packets in the direction of the destination in a greedy manner. The geo-routing routes are constructed on-demand; the next hop is dynamically selected from the neighbors according to their distances to the destination. In the simplest greedy georouting method, the neighbor that is closest to the destination is selected. Geo-routing works well if the sender knows the position of the destination. However, in ICN, the requester node only knows the name of the content it is requesting. Therefore, in order to apply geo-routing, we first need a mechanism that performs content location discovery correctly and efficiently. Our target scenario is as follows. The ad-hoc network is represented by vehicles connected with WiFi. Each vehicle carries some contents, which can be either location dependent (e.g. ”traffic jam information on highway 110”) or location independent (e.g. ”widget.mp3”), which means the location of the content is not reflected in its name. In this paper, we focus only on location-independent content since the location dependent contents can be retrieved simply by forwarding the Interest packets to the associated location. We propose Last Encounter Content Routing (LER), which keeps track of content locations using last encounter information and performs opportunistic geographical routing. To reduce content discovery overhead, the content locations are maintained at each node. When two vehicles encounter each other, they exchange their content lists and the content locations known to them. In this way, the local content location information is updated each time when a vehicle encounters others. Later if this vehicle receives an Interest with a name that matches an entry in its list of content locations, it forwards this Interest by geo-routing instead of flooding, which is widely used in current ICN. The Interest is only flooded at first when the location is unknown, and only until a relay found matching location information. The rest of this paper is organized as follows. We discuss related work in Section II. In Section III, we introduce our protocol, Last Encounter Content Routing (LER). The system implementation details are further discussed in Section IV. The simulation results are discussed in Section V. We conclude this paper in Section VI.
II.
RELATED WORK
ICN in mobile environments is a recently emerging research area. In [8], J. Wang et al. propose an ICN-based data collection system for vehicular networks. This system requires car manufacturers to reserve special prefixes and ask network providers to announce name prefixes in advance. J. Lee et al. propose a proxy-based scheme for increasing efficiency of mobile retrievals [9]. In [2], L. Wang et al propose an ICN-based traffic dissemination system with opportunistic-style flooding. Flooding-based Interest forwarding is the standard in ICN. Two flooding-based forwarding paradigms, proactive and reactive, have been analyzed in [10]. The proactive approach disseminates the content names and locations periodically so that all nodes may maintain routing information for known contents. In contrast, the reactive approach does not advertise content locations in advance; instead, data consumers floods Interests to retrieve Data. In [10], the authors compare the performance of reactive flooding, proactive flooding, and Geographic Hash Table (GHT) in MANET ICN. Their results show that the reactive flooding approach outperforms the other two in both latency and data availability. In [11], a broadcast-based routing mechanism that reactively locates data, Listen-First Broadcast-Later (LFBL), is proposed. In LFBL, only the first Interest is flooded. Data and the following Interests are forwarded via the best route the previous Interests traversed. LFBL improves the data availability and avoids the routing message exchange. However, LFBL encounters route maintenance issues and rediscovery costs that escalate in high mobility. Proactive content routing is difficult in ICN due to the routing overhead induced by the significant volume of names. Hierarchical Bloom Filter Routing (HBFR) [3] is an attempt to tackle the challenge by using a geographical hierarchy and Bloomfilters to enable scalable content lookup. However, HBFR is designed specifically to accommodate high priority applications. A content routing scheme for general-purpose ICN applications that does not incur the overhead of reactive flooding or of proactive maintenance is still to be defined. In this paper, we answer this challenge using Geo-routing combined with Last Encounter destination discovery. Our scheme leverages and adapts to content retrieval the previously published LER protocol developed for conventional MANET routing [12]. Last Encounter exploits node mobility and is thus ideally suited to VANETs. III.
PROTOCOL DESIGN
In this section, we introduce the opportunistic content routing protocol, Last Encounter Content Routing (LER) . A. Basic Idea Since we consider only the location-independent contents, every content here will be assigned a globally unique name, Table. 1. The format of Last Encounter List (LEL)
Content Name Widget.mp3
Vehicle ID B
Location
Time
(x,y)
12:00pm
although the unique name may still follow the hierarchical naming format in [1]. LER is based on name-based ICN routing. In other words, a vehicle must issue an Interest to retrieve the content, and the data follows the breadcrumb of Interests. Therefore, we focus on discussing the Interest forwarding hereafter. We enhance the ICN routing by integrating the last encounter information concept [12][13], which helps vehicles gather the location of content providers. LER has two phases. In the first phase, the forwarding nodes do not know the location of the content. Thus, like in traditional ICN Interest routing, the naïve requester floods the Interest to search for the content location, which is defined as a geo-coordinate. The second phase starts once the Interest reaches a relay that has the location information of the particular content name requested. At this point, the routing module stamps the destination location in the Interest and afterwards the Interest routing switches from flooding to geo-routing. During the geo-routing process, the relay nodes keep updating the destination location if they have newer information. B. LER Example We use the following example to further explain the idea. Suppose a vehicle A wants to retrieve a content named widget.mp3. While A does not know who holds the content, vehicle B has the content widget.mp3. Each vehicle maintains two data structures: content list and Last Encounter List (LEL). The content list, which is periodically advertised to one-hop neighbors, summarizes all the contents the node itself has. When vehicles receive their neighbors’ content lists, they merge the information carried within the lists into their LELs. Each entry of the LEL includes the content name, the provider ID, the encounter location, and the encounter time. Note that (as a difference from [12]) one content can be provided by multiple vehicles, so there can be multiple entries for the same content name, each of which represents a different vehicle. Suppose vehicle B has the content that vehicle A desires. B previously broadcasted its content list to its nearby vehicle C at 12:00 pm at location (x, y). The LEL of C is shown in Table 1. That is, vehicle C can provide an approximate destination location for content widget.mp3. Suppose now vehicle A broadcasts its Interest for widget.mp3. The Interest is received by all nearby vehicles, C and D. C and D then search their LELs for widget.mp3. As vehicle D cannot find any entry matching this name, it prepares to broadcast this Interest to its neighbors to continue the search. However, C has found the match. It adds the destination location geo-coordinates, destination nodeID, and encounter time to the Interest packet, and send the Interest packet out by opportunistic geo-routing, as discussed in the next section. Upon receiving C’s Interest, D realizes the Interest is delivered by geo-routing and carries content provider information. It thus aborts the rebroadcast. If there are multiple vehicles for one content, there can be many strategies to choose the vehicle to serve the Interest. In our implementation, we randomly choose one vehicle to serve the Interest for the purpose of diversity and load balance. Note that during the geo-routing process, if the relay has newer location information about the selected vehicle with content (i.e. the encounter time is newer than the one recorded in the Interest), it
preferred. In Phase 2, we are still looking for more recent location advertisemants (as in Greedy LER [12]).Therefore, we select nodes satisfying one of the following conditions as eligible forwarders: 1.
Relays who have more recent location information.
2.
Relays who are nearer to the destination than the last hop is.
Only the eligible forwarders join the contention process in phase 2. The other vehicles are ineligible and automatically drop the Interests. For the eligible forwarders without more recent location information, the expiration timer is calculated as follows: T = Figure 1. LER Geo-routing prioritization
updates the destination location and encounter time in the Interest and redirects the packet to the new location coordinates. C. Collision Avoidance and Opportunistic Geo-Routing Each time a vehicle broadcasts an Interest, potentially all nearby vehicles may rebroadcast the same Interest, inducing unnecessary redundant Interest and consequently redundant data transmissions. Therefore, we apply timer-based rebroadcast mechanism [15] to reduce the overhead. The basic idea is as follows: once the vehicles receive an Interest, they rebroadcast this Interest upon the expiration of a timer. Before the timer expires, if the vehicle hears other vehicles rebroadcast the same Interest, it may decide to cancel its scheduled transmission. Hence among all the vehicles within a small range, only few vehicles will rebroadcast Interest first due to their shorter timers, and others will cancel their Interest rebroadcast accordingly. This significantly reduces the number of Interest rebroadcast. The expiration timer design is as follows. Nodes that have more recent location information than the one carried in the packet set their expiration timer randomly within [0, Tupdate]. Since highest priority is always given to forwarders who have relatively recent location information, the random timer for other nodes is set to be larger than Tupdate. In phase 1 (the flooding-based content search phase), the primary goal is to expand the search range. In other words, the farther neighbors must be given higher priority. Therefore, phase 1 expiration timer is calculated as follows. =
+(
−
)
(
,
)
+(
−
)
where Dref is the distance from the vehicle to the reference point and Dmax is the maximum distance between the eligible forwarders within last hop’s range to the reference point. The reference point is defined as the nearest possible geo-coordinate to the destination within last hop’s transmission range. That is, position R in Figure 1. In Figure 1 A and D represents the last hop and the destination, respectively. The intuition of this setting is to normalize the timer by the maximum distance of the forwarder set to the reference point. The cancelling mechanism is straightforward: when a node receives a duplicate Interest from other nodes, it cancels its scheduled rebroadcast if 1.
It does not have newer location update.
and 2.
(a) It is closer to last hop (in phase 1) than the duplicate packet sender.
(b) It is farther to the reference point (in phase 2) than the duplicate packet sender. IV IMPLEMENTATION A. Overview In this section, we introduce the implementation of our design. Our implementation is based on ndnSIM [16], a NS-3 based simulator. The revised ndnSIM stack is shown in Figure 2. There are two major modifications in ndnSIM protocol stack.
(1)
Tdist is the defined maximum waiting time, Dmax is the vehicle’s transmission range, Dtransmitter is the distance from the vehicle to the last hop. Note that the farther this vehicle is from the last hop, the shorter it needs to wait for rebroadcasting Interest. Another criterium for selecting the expiration timers is to favor unexplored areas rather than farthest nodes, like in BLOOGO [20]. We will pursue this other option in future work. Phase 2 (the geo-routing phase) is triggered when a provider’s geographic location is found. In the geographic forwarding, vehicles closer to the content provider are
(2)
Figure 2 The protocol stack of ndnSIM
Interest: Name2? Last Encounter List Name1
V1
V3
Name2
V0
V1
V4
…… Location Table V0
(X0, Y0) + Age0
V1
(X1, Y1) + Age1
Forward to (X0, Y0)!
……
Figure 4 LEL implementation
ID by the specified content name in LastEncounterList and then finds the location corresponding to this vehicle ID from LocationTable. IV.
SIMULATION RESULTS
A. Simulation Setup We summarize the simulation parameters as follows: Figure 3 Forwarding implementation
First, we replace WiFiDeviceFace, which is the wireless interface implementation in original ndnSIM, by NetV2VDeviceFace. NetV2VDeviceFace handles the vehicleto-vehicle link and all ad-hoc forwarding functions in our system. The forwarding process is summarized in Figure 3. Note that both Interest and Data forwarding may be cancelled when corresponding data is received. Second, we implement the Last Encounter List (LEL) as a specialized application. To realize the content list exchange in the NDN pull-based model, all nodes broadcast LEL Interest packet periodically, and producers reply with LEL data packets carrying its content list. B. LEL applications The LEL records the known content providers (i.e. vehicles), their location information and the age of this information. We define that the location expires after Texpire seconds. The LEL is implemented as a two level mapping, as shown in Figure 4. The first level maps the content name to a list of content providers (LastEncounterList) and the second level maps a content provider to its location (LocationTable).
• MAC and PHY parameters: We use the 802.11 Adhoc WIFI module defined in ns-3. The wireless transmission range is set to be 150 meters. The maximum expiration time Tdist is 0.1 second. We use CORNER propagation model [17] to simulate signal loss in urban environments. • Urban environment and mobility model: The urban map we use is shown in Figure 5, which comes from TIGER/Line files [18] collected by Census Bureau. We generate a 120s mobility trace using SUMO [19] to simulate practical vehicle movements. • Application: For each trial, we randomly choose one vehicle as content consumer and one vehicle as content provider. The producer publishes prefix /prefix to its neighbors using LEL application. The consumer requests contents named /prefix/seq every 1 second, in which seq starts from 0 and increases by 1 each time. That is, the consumer sends Interests named /prefix/1, /prefix/2, …,
All nodes are both producer and consumer for our specialized LEL application. In other words, all nodes have the LEL producer application and LEL consumer application installed on them. The LEL producer application is responsible for two tasks: (1) adding the node’s contents to its LastEncounterList and (2) replying LEL Interests. The LEL consumer application, on the other hand, initiates LEL Interests periodically and merge the neighbors LEL reply into its own LEL. Figure 4 illustrates the procedure of content location lookup. To route an Interest, the relay first searches a provider’s vehicle
Figure 5 The urban map used in simulation
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Flooding
LER
Figure 7 Retrieval rate
(a)
change of LEL exchange frequency. LEL exchange frequency only affects the probability of finding a content provider; the actual number of forwarding hops mostly depends on the distance between the consumer and the selected provider. C. The Efficiency of LER In this section, we compare the traditional ICN floodingbased Interest forwarding with LER. We set LEL exchange period T to be 1s. Figure 7 shows the retrieval rate of both approaches. We define the retrieval rate as the number of successful retrievals to all attempts. We observe that without LER, only 47% of Interests can be successfully served, while 67% of Interests are successfully served with LER. Note that the major reason an Interest cannot be successfully served is due to the channel loss and the contention at MAC layer. In order to ensure reliable transmission, the Interest is retransmitted if not satisfied after a pre-defined interval. The retrieval rate is worse if this mechanism is not implemented. Consequently, LER improves retrieval rate over flooding-based forwarding since the contention is less with lower congestion level.
(b) Figure 6 The CDF of (a) the number of hops in flooding phase; (b) the total number of traversed hops
etc. All requests are served by the provider based on longest prefix matching.
The congestion level can be interpreted by Figure 8. Figure 8 presents the CDF of the total number of nodes forwarding data of each name. In general, LER requires less nodes participating the data forwarding since the Interests are forwarded by fewer relays.
B. The efficiency of Content Search We first study the relationship between LEL exchange frequency and the efficiency of content search. We set LEL exchange period T to be 1s, 5s and 10s, and examine (1) the number of flooding hops before switching to geo-routing and (2) the total number of hops an Interest traverses. The results are shown in Figure 6. In Figure 6(a), we can clearly see that when LEL exchange frequency increases, the number of Interest flooding hops during search phase decreases significantly: when T = 10s, 63% of Interests can find the provider within just one hop. When T = 1s, the probability of locating provider in one hop increases to 92%. This is because the more frequent vehicles exchange LEL with each other, the more information each vehicle collects, and therefore the more likely a provider can be located. On the other hand, as shown in Figure 6(b), the total number of traversed forwarding hops for retrieving a content mostly remains unchanged regardless of the
Figure 8 The CDF of total number of data forwarding
V.
CONCLUSIONS
We introduce LER, a content routing protocol designed for general-purpose data retrieval in ICN. LER integrates last encounter content discovery and geographical opportunistic forwarding to achieve low overhead and congestion level. Our simulation results show that LER can eliminate flooding overhead in 90% of our simulation when the broadcast interval is as short as 1 second. Moreover, the retrieval rate is improved by 42% when using LER instead of traditional flooding-based ICN routing. REFERENCES [1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
V. Jacobson, D. K. Smetters, J. D. Thornton, M. F. Plass, N. H. Briggs, and R. L. Braynard, “Networking named content,” in Proceedings of CoNEXT ’09, 2009. L. Wang, A. Afanasyev, R. Kuntz, R. Vuyyuru, R. Wakikawa, and L. Zhang, ”Rapid Traffic Information Dissemination Using Named Data”, 2012. Y.-T. Yu, X. Li, M. Gerla, and M. Y. Sanadidi, “Scalable VANET Content Routing Using Hierarchical BloomFilters,” in Proceedings of IWCMC’13, Jul. 2013. J. Härri, F. Filali, and C. Bonnet, "Performance comparison of AODV and OLSR in VANETs urban environments under realistic mobility patterns," in Med-Hoc-Net 2006, 5th Annual Mediterranean Ad Hoc Networking Workshop, IFIP, Jun. 2006. Karp, Brad, and Hsiang-Tsung Kung. "GPSR: Greedy perimeter stateless routing for wireless networks." Proceedings of the 6th annual international conference on Mobile computing and networking. ACM, 2000. Zorzi, Michele, and Ramesh R. Rao. "Geographic random forwarding (GeRaF) for ad hoc and sensor networks: multihop performance." Mobile Computing, IEEE Transactions on 2.4 (2003): 337-348. Lee, Kevin C., Uichin Lee, and Mario Gerla. "TO-GO: TOpology-assist geo-opportunistic routing in urban vehicular grids." Wireless On-Demand Network Systems and Services, 2009. WONS 2009. Sixth International Conference on. IEEE, 2009. Jiangzhe Wang, Ryuji Wakikawa, and Lixia Zhang. 2010. DMND: Collecting data from mobiles using named data. In Proceedings of 2010 Vehicular Networking Conference (VNC ’10). IEEE.
[9]
[10]
[11]
[12]
[13] [14]
[15]
[16] [17]
[18]
[19] [20]
Jihoon Lee, Daeyoub Kim, Myeong-Wuk Jang, and Byoung-Joon Lee. 2011. Proxy-based mobility management scheme in mobile content centric networking (CCN) environments. In Proceedings of 2011 IEEE International Conference on Consumer Electronics (ICCE ’11). IEEE. Matteo Varvello, Ivica Rimac, Uichin Lee, Lloyd Greenwald, and Volker Hilt. 2011. On the design of content-centric MANETs. In Proceedings of 2011 Eighth International Conference on Wireless On-Demand Network Systems and Services (WONS ’11). IEEE. Michael Meisel, Vasileios Pappas, and Lixia Zhang. 2010. Ad hoc networking via named data. In Proceedings of the fifth ACM international workshop on Mobility in the evolving internet architecture (MobiArch '10). ACM, New York, NY, USA, 3-8. M. Grossglauser and M. Vetterli, “Locating mobile nodes with ease: learning efficient routes from encounter histories alone,” IEEE/ACM Trans. Netw., 14(3):457–469, June 2006. N. Sarafijanovic-Djukic and M. Grossglauser, “Last encounter routing under random waypoint mobility,” In NETWORKING, 2004. U. Lee, J. Lee, J.-S. Park, and M. Gerla, “Fleanet: A virtual market place on vehicular networks,” Vehicular Technology, IEEE Transactions on, 59(1):344 –355, jan. 2010. S. Biswas and R. Morris, "Opportunistic routing in multi-hop wireless networks," SIGCOMM Comput. Commun. Rev., vol. 34, no. 1, pp. 6974, Jan. 2004. A. Afanasyev, I. Moiseenko, and L. Zhang, “ndnSIM: NDN simulator for NS-3,” NDN, Technical Report NDN-0005, 2012 Giordano, Eugenio, et al. "CORNER: a realistic urban propagation model for VANET." Wireless On-demand Network Systems and Services (WONS), 2010 Seventh International Conference on. IEEE, 2010. Miller, Catherine L. "TIGER/Line Files Technical Documentation. UA 2000. US Department of Commerce, Geography Division, US Census Bureau." Krajzewicz, Daniel, et al. "Sumo (simulation of urban mobility)." Proc. of the 4th Middle East Symposium on Simulation and Modelling. 2002. F. Angius, M. Gerla, and G. Pau. "BLOOGO: BLOOm filter based GOssip algorithm for wireless NDN." Proceedings of the 1st ACM workshop on Emerging Name-Oriented Mobile Networking DesignArchitecture, Algorithms, and Applications. ACM, 2012.
