A Novel Reputation Management Framework for Vehicular Ad Hoc Networks *

Transcription

1 A Novel Reputation Management Framework for Vehicular Ad Hoc Networks * Qing Ding, Xi Li School of Software Engineering, University of Science and Technology of China Hefei, China dingqing@ustc.edu.cn; llxx@ustc.edu.cn Abstract- Vehicular Ad hoc Network is a special type of MANET characterized by mobile nodes with high speed, and confined mobility. VANETs are received more and more researchers attention with their promising functions in road safety by exchanging real-time warning messages through vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. However, in VANETs, an inaccurate traffic warning message will impact drivers decisions, misguide drivers behavior, and even invoke serious car accidents. Keeping effective cooperation between nodes is prerequisite. In this paper, a novel reputation management framework is presented, which mainly works with the integration of entity-centric mechanism and event-centric mechanism to establish trust in VANETs. Based on the fact that in most of the time, the majority of people drive their vehicles locally for their daily commute, and hence, most vehicles have their predefined constant daily trajectories. We take advantage of RSUs to manage the long-term reputation values for these commute vehicles, and event-centric mechanism is adopted if the reputation value of a vehicle cannot be queried based on the fact that this vehicle just has a new route when the traffic event happens. This feature makes the proposed system very suitable for the actual situation of vehicular network. The simulation results show that our proposed system can effectively build trust and prevent false messages spreading on VANET environments. Keywords- VANET; Trust; Reputation I. INTRODUCTION Vehicular Ad Hoc Networks (VANETs) [1, 2] are a subclass of MANETs, which would perform crucial functions in road safety, such as detection of traffic accidents and reduction of traffic congestions. By exchanging real-time warning messages through vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication, VANETs present the capability of providing local information in near-realtime to enhance the safety of drivers and improve mobility and thus are received more and more researchers attention. However, VANETs applications also raise formidable research challenges. One of these challenges is security. More specifically, in VANETs, attackers can diffuse bogus information in the network to affect other drivers behaviors and cause intelligent collisions [3]. (e.g., to report a non-exist jamming ahead to gain a better driving condition). To determine whether the traffic event reported by a warning message is really occurred and prevent false traffic warning messages spreading on VANET, various secure communication protocols and trust systems have been proposed to ensure message authentication and evaluate the trustworthiness of the message content. In fact, trust establishment is a major challenge in VANETs, since VANETs have several distinctive properties compared to standard ad hoc networks, such as Dynamic topology, Short-lived contacts, Confined-mobility, Substantial computation power, etc. With these distinctive properties, a new solution need be haven for the VANETs. In this paper, we present a mixed reputation management framework. In our eyes, we cannot find any similar solution to this problem in current papers. The rest of this paper is structured as follows. The related work is introduced in Section 2. Section 3 introduces the VANET environment we are considering and some notations we use in the paper. In Section 4, we present the proposed reputation management framework. In Section 5, we show the initial simulation results, and finally conclude the paper in Section 6 and also discuss some future work on this research topic. II. RELATED WORK There are various types of trust models in ad hoc networks which differ in their architecture, trust establishment processes and flexibility. In [4], trust establishment is divided into two major categories: it can either statically rely on a security infrastructure or be built up dynamically in a self-organizing manner. The former process mainly relies on classical global certificate-based systems which can be used for message authentication and integrality check. The latter process lacks of this global knowledge and needs to take advantage of other trust supporting mechanisms, such as reputation system. Self organizing trust establishment can be based on direct information, indirect information, or hybrid. Existing trust establishment approaches for ad-hoc networks mainly work with the entity-centric mechanism or event-centric mechanism [5-14]. One of the most well-known entity-centric reputation mechanisms for ad hoc networks is the CONFIDANT protocol [5]. In order to detect uncooperative behaviors, WatchDog mechanism is designed to overhear the forwarding behaviors of the downstream neighboring nodes within the transmission range. In [6], bayesian statistics is proposed to improve the CONFIDANT protocol. In CORE [7], it is not * The Project Supported by Anhui Provincial Natural Science Foundation under Grant M

2 possible to spread negative reputation between the nodes, thus, the protocol avoids false reputation propagation classical problem. Haiying et al. presents an Account-based hierarchical Reputation Management system (ARM) in [8]. In [9], Ciszkowski et al. describes an enhanced distributed reputation system for anonymous authentication protocol in mobile ad hoc networks. There are also some reputation systems in VANETs. Vehicle Ad-Hoc Reputation System (VARS) [10] presents a message protocol that opinions are appended for packet forwarding, then everyone can makes use of these opinions to calculate reputation. Anand et al. [11] propose a context-aware reputation management system that provides a bootstrapping process to build trust relationships and stimulates proactive collaboration, to achieve a decentralized data management scheme that provides a high degree of reliability. Since the trust relationship between vehicles is established among entities through rounds of iterations using entity-centric mechanism, the process is relatively slow and complicated. Such relatively static reputation maintenance scheme is not suitable for the highly dynamic VANETs where with high probability a node interact with another node only once (in short duration) in his life time, thus the historical reputation cumulation of a vehicle is very time-consuming. Current reputation systems in VANETs are usually built by event-centric mechanism [12-13]. Maya et al. [12] proposed a data-centric trust establishment framework. The novel concept in [17] is to evaluate the trustiness of sensed data or received messages rather than the trust of individual vehicle. However, the authors did not consider the effect introduced by the dynamics of traffic events. Nai-Wei Lo et al. [13] also proposed an event-based reputation system architecture in VANETs. We can see most event-centric mechanisms have an implicit assumption that there are sufficient numbers of vehicles or else events. This assumption, however, is not always realistic. In addition, in an event-based reputation system, it is impossible to accumulate reputation of nodes. In this paper, a mixed reputation management framework is presented, in which both V2V and V2I communication mechanisms are adopted. This framework exploits the fact that in most of the time, the majority of people drive their vehicles locally for their daily commute (to schools, work places, etc), and hence, most vehicles have their predefined constant daily trajectories. Thus it is feasible to let the RSUs take charge of managing the long-term reputation values for these commute vehicles, and event-centric mechanism is adopted if the reputation value of a vehicle cannot be queried based on the fact that this vehicle just has a new route when the traffic event happens. This feature makes the proposed system very suitable for the the actual situation of vehicular network. III. A. Netwok Model MODEL AND ASSUMPTIONS In VANETs, the nodes are either vehicles or base stations. Vehicles and base stations can be private or public. Since the majority of the network nodes will consist of vehicles, the network dynamics will be characterized by confined mobility, high speeds, and very short connection times between vehicles. The scale of VANETs can be very large, but communication in this network will be mainly local. Traffic information is collected and distributed by each vehicle. Vehicles can travel freely, but in most of time they have a relatively fixed route, such as buses. B. Detection System We assume that Vehicles in VANETs are equipped with a detection mechanism to identify false information. This can be done in two ways. One is using tamper-proof device (TPD). We assume that each vehicle equips with a tamper-proof positioning device, such as GPS. Then its status data, such as speed, and direction, etc. are all carried out by this device. These data can be embedded to messages, and sent to another vehicle automatically by the beacon message. Another approach is using proactive detection. We assume that each vehicle can be equipped with the front and rear radar, which can detect neighboring cars within line of sight in a radius of 200 meters. The details of data collection techniques of radars are beyond the scope of this paper. We only assume that the status data of a detected vehicle also can be collected by these radars. C. Problem Formalization The problem in this paper in VANET can be formalized as a typical optimization problem represented by a quadruple (E, S, F, Ω): E is an event happened in time t 0 which will be propagated to all vehicles in a specific area. S is the strategy space containing all available behaviors for use by vehicles. F is an evaluation function that can be used to evaluate the correctness of vehicles future behavior when event E is received. Ω is a set of input information which can be used by vehicles to direct their behavior. Clearly, to produce best solution, correct data should be given high probability to be chosen and used when Ω is somewhat large. Now the question is transferred to how filter the error data. We let the traditional notion trust represent this probability: High trust of data inωindicates high probability to be chosen. Therefore, an appropriate trust system for VANETs is a good solution. IV. MIXED REPUTATION MANAGEMENT FRAMEWORK A. Role Definition First, depending on the different role vehicles played encounter the same traffic event, we categorize all players involved in an event message propagation as shown in Fig

3 Event Reporter (ER): we call a vehicle an event reporter, if it can perceive incident by equipped sensors, then send alarm messages to other neighboring vehicles. Event Observer (EO): within one hop of an event reporter, vehicles can observe the behavior of event reporter when they receive the event message from it. We call these vehicles event observers. Event Participant (EP): we call other vehicles beyond one hop of an event reporter as event participants, since they can receive and forward the event message, but it is impossible to identify the behavior of event reporter. We assume basic security and privacy protection approach are adopted in VANETs. That means beacon message which includes a vehicle s status, such as location, velocity, and direction and is broadcasted by this vehicle is encrypted and signed. Fig. 1 Mixed Reputation management framework B. Reputation Management Mechanism As a rule, when a vehicle encounters a traffic event, traffic-related data will be collected by sensors in this vehicle. If the value of gathered event data is over the predefined safety threshold, the information is sent to the traffic safety application in the vehicle. At the same time, the traffic warning messages will be broadcasted to neighboring vehicles (ie, the EO). Its own location information and the reputation value of the event are attached to this message. The message is also encrypted and signed by ER s privacy key. EOs receive the message, and it can decide if this message is from a ER by comparing the event location, vehicle location from warning message and the vehicle location from beacon message it received at the most recent time. Then it will send a reputation query message to the nearby RSU (messages can be forwarded by other vehicles), and a timer is started at the same time. Within Δt, all event messages with their reputation values sent or forwarded by other EOs and EPs are collected. The value of Δt seriously depends on the VANETs application type. For example, we can see the differences between Life-Critical Safety Applications and Safety Warning Applications. While the Life-Critical Safety Applications usually require the messages to be delivered to the nearby nodes within 100 milliseconds, the Safety Warning Applications can tolerate up to 1000 milliseconds. If the RSU returns the reputation value of ER during this time, this value will be directly used to determine if the message is bogus. If not, event-centric mechanism is adopted to calculate the reputation value of the event, and decide whether to forward the event again. Then according to the observation to ER s subsequent behavior, the reputation of ER is re-calculated and updated into RSU later. EPs and EOs have the basically same way to process messages, but they do not start the abnormal behavior analysis algorithm since they cannot observe ER s behavior. The whole alarm event message processing algorithm is shown below. Node i receive an alarm message AM ij if (AM ij is a new alarm message) { set Δt ; send a query message R j to RSU; } else { if (t current < Δt) store AM ij } Wait (Δt increase 0); If (R j exists) Adopt entity-centric mechanism; Else Adopt event-centric mechanism; Update R j ; C. Virtual Ring Storage The proposed reputation system framework relies on the support from roadside infrastructure. Based on performance considerations, it is desired that any car can obtain the required trust data of node directly from the nearest RSU when a traffic event is happened. To solve this problem, we presented a virtual ring technology to achieve the distributed storage of entities trust in all RSUs, the details are as follows: First, one RSU is assigned to a vehicle as its reputation management agent when it joins the VANET. The whole RSUs network is considered as a private cloud. Vehicles communicate with RSUs in its trajectory, and send information containing their own ID as well as the ID of all RSUs it has been passing through. We introduce basic concepts of ant colony algorithms which are suitable for tuple storage and retrieval. A small random factor is also included in those routing decisions, modeling the ant s inclination to follow pheromone paths. Then, after a period of time, as shown in Figure 2, all RSUs in one vehicle s trajectory will form a group for it, which is called as a virtual ring in this paper. Each vehicle moves RSU-by-RSU towards its destination every day. During the movement, this vehicle records each RSU s identifier of the followed path. Like a typical ant colony based routing protocol, after a period, a most desired path can be found for this vehicle. All RSUs in this path will be clustered as a virtual ring. RSUs in this ring

4 are organized through a DHT-based network with O(1) lookup algorithm. The reputation of this vehicle is stored distributedly in all RSUs belong to this ring. Then, when a vehicle sends a traffic event message, any other vehicle V i receives this message can acquire the current reputation value of this vehicle (single-key queries to neighboring nodes within the network) from the nearest RSU. If any RSU is beyond one hop from vehicle V i, the query request will be relayed to the nearest RSU by V2V communication. Once a reputation value of a vehicle is queried from a RSU, this value and vehicle ID is cached in it, since it has a big possibility that other EOs and EPs will query this value later. Then the whole access cost to a RSU is decreased greatly. Finally vehicles should update the reputation value of it based on their judgments about its behavior. Obviously, this data should be transferred to all RSUs in the ring reliablely with the same order. To ensure total ordered group message transfer, a token algorithm is adopted. There is only one special message with a unique sequence number passing between the RSUs in a ring, known as a token. A RSU has that token is allowed to send update message attached the sequence number. When finished, the token with the sequence number plus one is passed on to a next RSU. If a RSU having the token is not interested in sending the message, it simply passes it on. D. Reputation Update Fig. 2 Virtual ring storage There is only EO can update an ER s reputation after it observes the behavior of it. Define X(v, p), v as the speed of a vehicle, and p as the position of it. Then an EO can identify ER s behavior based on the function f(x) = Max(V p, L p ). Here V p denotes the probability of ER s misbehavior referred by the change of ER s speed, and L p denotes the probability of ER s misbehavior referred by the change of ER s position. Assume V t-1 is the speed of ER in time t-1, V tp is the actual speed of it in time t, and V texp is the expect speed of it in time t. Define, here V e is the desired speed about various traffic events, and 0 < β < 1 is the weight. We have: Assume H t-1 is the horizontal coordinates of the ER in time t-1, H tp is the actual horizontal coordinates of the ER in time t, and H texp is the expect horizontal coordinates of the ER in time t. Define, here H e is the the change rate of position, and 0 < α < 1is the weight. We have: V. SIMULATION RESULTS VANETs simulator TraNS [14] is used to evaluate system performance of the proposed event-based reputation system. The simulation scenario is set in a grid-typed street map. The map is constructed by street blocks and each is a 200 meters square. There is a RSU in each cross. We adopt SUMO as traffic event simulation model. The number of vehicles for each simulation varies from 50 to 200, and randomly placed on roads in the scenario map. Transmission range of vehicles is 100m. Both source nodes and relay nodes can be bad vehicles will try to send a misleading message. A bad source will send a bogus message every second and the number of total messages sent is 10. To reflect the dynamic status of a traffic event, the simulating event will freely occur at the 100th second to 500th second. Δt is defined as 500 milliseconds. Each measured result is an average number obtained from 100 replications of simulation runs. Our aim is to measure the efficiency of reputation update. We evaluate the time when data is written into RSUs if there are 10, 20, 40, 60, 80, and 100 RSUs in the route. A. Read Time Impact Figure 3 shows the cost for acquiring the reputation value of a specific vehicle when a traffic event happens. The merit of the mixed reputation management framework is highlighted by the fact that with the increasing number of RSUs in the commute route, reputation querying time of a specific vehicle does not change significantly. It is a much desired character for reputation system considering the actual situation of vehicular network. Fig. 3 Read time for different number of RSUs

5 B. Write Time Impact Fig. 4 shows the time required to store the update data of reputation from the client s standpoint. With the scale-up of the number of RSUs, significant increase of the time can be observed. But we can see the time increases basically linearly. It is rational since total ordered group message need to be ensured write time the number of RSUs VI. Fig. 4 Write time for different number of RSUs CONCLUSIONS AND FUTURE WORKS In this paper, a novel reputation management framework is presented to determine whether an incoming traffic message is significant and trustworthy to the driver. This framework takes advantage of the fact that in most of the time, the majority of people drive their vehicles locally for their daily commute (to schools, work places, etc), and hence, most vehicles have their predefined constant daily trajectories. So it is convenient and feasible to let the RSU take charge of managing the long-term reputation scores for these commute vehicles, and an event-centric reputation mechanism is also adopted as useful supplement. For the future work, first we are planning to introduce fuzzy theory into calculating reputation value of a vehicle. People naturally use linguistic expressions when they are asked to state their trust to others, such as high, medium, and low, etc. Hence, it seems that linguistic terms are superior to crisp numbers in describing trust. Moreover, a new event-centric mechanism which is more suitable for this mixed framework should be presented. VII. ACKNOWLEDGEMENTS The authors thank the editors and the anonymous referees for their valuable comments and suggestions. This work is supported by Anhui Provincial Natural Science Foundation under Grant M139. REFERENCE [1] J. Luo, J.P. Hubaux, A survey of inter-vehicle communication, Tech. Rep. IC/2004/24, EPFL, Lausanne, Switzerland, [2] J J. Blum, A. Eskandarian, and L.J. Huffman, Challenges of intervehicle ad hoc networks, IEEE Transactions on Intelligent Transportation Systems, vol. 5, no. 4, pp , [3] F. D otzer, M. Strassberger, and T. Kosch, Classification for traffic related inter-vehicle messaging, in Proceedings of the 5th IEEE International Conference on ITS Telecommunication (ITST 07), Brest, France, June [4] Philipp Wex, Jochen Breuer, Albert Held, Tim Leinmüller and Luca Delgrossi, Trust Issues for Vehicular Ad Hoc Networks, 67th IEEE Vehicular Technology Conference (VTC2008-Spring), Marina Bay, Singapore, May 11 14, [5] S. Buchegger, J.-Y.L. Boudec, Performance analysis of the CONFIDANT protocol (Cooperation of Nodes: Fairness In Dynamic Ad-hoc networks, in Proc of MobiHoc. ACM, 2002, pp [6] S. Buchegger, J.-Y.L. Boudec, The effect of rumor spreading in reputation systems for mobile ad-hoc networks, in Proc. of Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt 03), [7] P. Michiardi, R. Molva, Core: a collaborative reputation mechanism to enforce node cooperation in mobile ad hoc networks, in Proc. of the IFIP TC6/TC11 Sixth Joint Working Conference on Communications and Multimedia Security. Deventer, The Netherlands, The Netherlands: Kluwer, B.V., 2002, pp [8] Haiying Shen, Ze Li, ARM: An Account-Based Hierarchical Reputation Management System for Wireless Ad Hoc Networks, Distributed Computing Systems Workshops, ICDCS '08. 28th International Conference on, June 2008, On page(s): [9] Ciszkowski T, Kotulski Z, Distributed Reputation Management in Collaborative Environment of Anonymous MANETs, EUROCON, The International Conference on Computer as a Tool, 9-12 Sept. 2007, On page(s): [10] F. Dotzer, L. Fischer, and P. Magiera, VARS:AVehilceAd-hoc networkreputation System, sixth IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM 05), pp , [11] Patwardhan, Anand Joshi, Anupam Finin, Tim Yesha, Yelena, A Data Intensive Reputation Management Scheme for Vehicular Ad Hoc Networks, Mobile and Ubiquitous Systems - Workshops, rd Annual International Conference July 2006, On page(s): 1-8. [12] M. Raya, P. Papadimitratos, V.D. Gligor, and J.-P. Hubaux, On data-centric trust establishment in ephemeral ad hoc networks, in Proceedings of the 27th IEEE Conference on Computer Communications (INFOCOM 08), pp , April [13] Nai-Wei Lo, Hsiao-Chien Tsai, A Reputation Systemfor Traffic Safety Event on Vehicular Ad Hoc Networks, EURASIP Journal onwireless Communications and Networking Volume 2009, Article ID , 10 pages. [14] Michal Piorkowski, Maxim Raya,C., TraNS: Realistic Joint Traffic and Network Simulator for VANETs, ACM SIGMOBILE Mobile Computing and Communications Review, Volume 12, Issue 1 (January 2008)