INTER-VEHICLE AD-HOC COMMUNICATION PROTOCOL FOR ACQUIRING LOCAL TRAFFIC INFORMATION

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1 INTER-VEHICLE AD-HOC COMMUNICATION PROTOCOL FOR ACQUIRING LOCAL TRAFFIC INFORMATION Masashi Saito, Mayuko Funai, Takaaki Umedu, Teruo Higashino Graduate School of Information Science and Technology, Osaka University 1-5 Yamadaoka, Suita, Osaka, JAPAN Tel: Fax: {m-saito, m-funai, umedu, ABSTRACT In this paper, we propose an inter-vehicle ad-hoc communication protocol in order for each vehicle to acquire its local traffic information. Our protocol is based on dissemination and propagation of road information among moving vehicles. We have developed a mobile ad-hoc network simulator and combined it with a traffic flow simulator that decides traffic jams and vehicles' speeds/positions precisely. Based on those simulation results, our simulator calculates packet collision ratios and success rates of ad-hoc communications among vehicles per second, and evaluates how fast each vehicle can obtain its destination route information. Based on those results, we have proposed an inter-vehicle ad-hoc communication protocol with suitable dissemination intervals depending on vehicles' speeds and traffic density. INTRODUCTION According to recent progress of wireless LAN technology and diffusion of GPS (Global Positioning System) products, car navigation systems are going to equip GPS receivers and wireless LAN cards such as IEEE802.11b[3][4][5] to acquire the information about traffic jams, road surface conditions and free parking lots along the street in real time. If we install several wireless LAN base stations along streets to realize these services, considerable costs may be required. Such expenses may be reasonable for urban roads and highways, however, they are too expensive for back streets with light traffic. On the other hand, car drivers always want to access not only trunk road information but also back street road information. To solve these problems, we are doing research on disseminating and propagating road information using mobile inter-vehicle ad-hoc communication protocol. For example, suppose that a preceding car-a holds a set of its own speed, location, direction and surrounding facilities information for the past several minutes, and disseminates it to surrounding cars. Car-B, which is driving on the opposite lane, receives the information, moves to another place, 1

2 and re-disseminates car-a s information Move together with car-b's information. At that disseminate disseminate time another car-c may receives the car-a s incommunicable information. That means car-c can know its preceding traffic conditions and road surface Fig. 1 Inter-vehicle Ad-hoc Communication situations using inter-vehicle ad-hoc communication (Fig. 1). If each car saves the information from cars driving on its opposite lane and exchanges it with surrounding cars, most cars on roads can acquire necessary preceding road information with rather high probability. In this case, if cars are running at high speed, each car should send them more frequently because data exchange rate may be low. On the other hand, if cars in a traffic jam disseminate such information frequently, network collision reduces reception ratios. Moreover, we must define a suitable way to relay opposite lane information and decide which data we should discard when each car's buffer overflow occurs. So unlike general mobile ad-hoc and/or sensor networks, it is necessary to develop a suitable inter-vehicle communication protocol by considering traffic flow, traffic jams, car speeds, and so on. In this research, we have developed a mobile ad-hoc network simulator that produces inter-vehicle data propagation situation from the location information of all the cars and various kinds of statistics information such as packet collision ratios and reception message amount. This simulator uses traffic flow simulator NETSTREAM II [14] that is developed by an automaker and produces actual traffic flows. To simulate various cities traffic situations by combining our simulators with NETSTREAM II, we can develop an inter-vehicle communication protocol that does not depend on specific traffic conditions. In this paper, we propose an inter-vehicle communication protocol that can efficiently acquire information about the road and traffic conditions ahead. The protocol is derived via simulations based on a typical road and traffic flow in Japan. MOBILE AD-HOC NETWORK FOR INTER-VEHICLE COMMUNICATION We can acquire traffic jam information by receiving broadcasting media such as FM multiplex broadcasting [15], radio-wave beacons, and digital radio, and/or by inquiring a traffic information server using car navigation systems with cellular phones. On the other hand, the research to propagate and acquire such information using inter-vehicle communication is actively studied recently. For example, FleetNet Project [1] proposed the method to propagate information, which is acquired from a base station on a road, to other vehicles using inter-vehicle communication. Schwingenschlogl[12] proposed a multicast protocol for inter-vehicle ad-hoc communication, and Miller [8] proposed a peer-to-peer communication protocol to prevent collisions at a road crossing. Papadopouli [11] proposes an ad-hoc communication protocol between mobile terminals and 2

3 servers, which are placed at signals, kiosks at a station, or lounges at an airport. To build inter-vehicle mobile ad-hoc networks without base stations, some data dissemination methods have been proposed like [2] and [16]. However, in most of those methods, they assume that mobile terminals move randomly in order to simplify the problems. For each vehicle, the relevance to given road information depends on the distance and time from its origin, however, there exist some research results that do not consider such relevance. Xu [16] proposes an inter-vehicle communication protocol that takes account of time and movement of vehicles on a road map, however, it does not consider traffic jams and signal waiting. In addition, they assume collision free communication so that all the data can be received successfully. In actual traffic jam situations, broadcasting data from many vehicles may cause a broadcast storm [10] and too many collisions may prevent the inter-vehicle communication, so that we should take care to prevent the collisions. Khelil [7] proposes a protocol that changes the data dissemination rate corresponding to the sending node density, but they assume that mobile nodes move randomly. Our proposing inter-vehicle protocol takes account of collisions and vehicles movement on typical roads and the traffic flows, not random walk. TrafficView[9] and CB-AODV-Simulator[12] have been developed to monitor inter-vehicle mobile ad-hoc communication. Although they consider multiple lanes roads, they assume that roads themselves are straight simple ones. Our mobile ad-hoc network simulator cooperates with the traffic flow simulator NETSTREAM II, and considers actual road conditions and traffic flow situations. AD-HOC COMMUNICATION PROTOCOL FOR ACQUIRING TRAFFIC INFORMATION LOCATION DEPENDENT SERVICES AND INFORMATION EXCHANGE By diffusion of GPS systems and progress of car navigation systems, we will be able to use road information services interlocked with map information. Those information services are roughly classified into two as follows: 1) Global road information services: the service that provides traffic jams and road-repairing information of trunk roads and highways through radio broadcasting and/or inquiring by cellular phones, 2) Local road information services: the service which is used within a narrow region such as the service notifying an approaching vehicle at a crossing of other vehicles and walkers, back road traffic jam information service and free parking lots along the street service. When many vehicles moving to the same direction are stopping and/or moving at very low speeds, we can predict the traffic jam. If this information can be propagated by opposite lane s vehicles relay and if it is reached to the tail of the traffic jam, drivers just before the jammed 3

4 area can recognize the length of the queue of the traffic jam, and they may be able to avoid it by re-routing. Recent vehicle sensors help other situations. For example, freeze road surfaces can be detected by slide conditions of tires, and rainy conditions can be detected by movement of wipers. To propagate such information, other vehicles drivers can prepare danger situations such as advancing slowing down. So combining global and local road information services, we are able to have much safer driving environments. AD-HOC COMMUNICATION PROTOCOL As a method of performing inter-vehicle ad-hoc communication, we can use IEEE IBSS (Independent Basic Service Set)[3]. In IBSS, CSMA/CA collision avoidance algorithm is defined and broadcast communication is supported. In this paper, we simplify the IBSS and assume its network environment as follows. At first, vehicles within the circles of 100m s radius can communicate each other, however, data communication success probability decreases linearly according to the distance as shown in Fig.2. When the distance between two nodes becomes large, the receiving radio power becomes weaker, so that reception error ratios will Reception Probability be increased. When a vehicle can communicate with vehicles 0.98 within the range of 100m, it can communicate with the vehicles that are running at a speed of 60km/h (16.7 m/sec) on its opposite lane only for about 6 seconds. On the other hand, in case of both of lanes Distance between two nodes [m] are jammed and vehicles are waiting 10m intervals, Fig. 2 Success Ratio of Data Transmission the vehicle at the center may receive the road information from more than 40 vehicles simultaneously. Here, we assume that the bandwidth is 100 Kbytes/sec and exchange data size is 10Kbytes at the maximum. We also assume that one second is divided into 10 slots, and each data transmission occupies one slot. For this reason, when two vehicles send data at the same slot, we assume that any vehicle in the overlapped area of 100m radius circles of the two vehicles cannot receives the both of data because of collision. ROAD INFORMATION DISSEMINATION PROTOCOL In this paper, we use SDRP (Speed Dependent Random Protocol) for road information dissemination. In SDRP, according to the vehicle speed v, random transmission interval is calculated between the minimum value min(v) and the maximum value max(v). In case of traffic jams, since there are many vehicles around a vehicle, the transmission interval becomes large. In addition, in case of high speed driving, the interval becomes small to increase propagation probability. For example, we can define SDRP random interval as follows: 4

5 v is less than 30Km/h, min(v) is 3 seconds and max(v) is 5 seconds, v is more than 30Km/h, min(v) is 1 second and max(v) is 2 seconds. DEVELOPMENT OF THE MOBILE AD-HOC NETWORK SIMULATOR THE STRUCTURE AND THE FUNCTIONS OF THE SIMULATOR To evaluate road information propagation situations in an inter-vehicle mobile ad-hoc network setting, we have developed the network simulator. Fig. 3 shows the structure of our simulator. NETSTREAM II simulator [14] is a traffic flow simulator for performing the effective prediction for traffic jams and prior evaluation of ITS introduction. It defines traffic flow characteristics and the lengths of signals for all road links, and then calculates all vehicles actions for every second. For each road, each vehicle calculates its speed V as V=V max (1-K/K congestion ). Here let the distance between a front vehicle and itself be S and vehicle density K be K=1/S. V max is the speed when the traffic density is 0, that means free running. K congestion is the vehicle density when vehicles are stopping because of traffic jams. Therefore, in wide areas, such as the whole city, it can calculate the traffic flows with considerably high accuracy. Our developed mobile ad-hoc network simulator inputs NETSTREAM II log data, which include each vehicle's position, speed and direction in every second. We also give the attainment distance and bandwidth of wireless LAN, reception probability, data size, disseminating interval, each car's data holding method and equipping ratios of car navigation systems as simulation input parameters. Based on those parameter values, this simulator produces inter-vehicle data propagation situation from the location information of all the vehicles and records various kinds of statistics information such as packet collision ratios and reception message amount. The simulator s processing flow is as follows: 1) Setting the given network environment (attainment distance, communication bandwidth), 2) Defining car navigation equipped vehicles by the given equipping ratios, 3) Calculating the following NETSTREAM II vehicle log data for every second: a) Defining the vehicles to disseminate information by the given algorithm, b) For each transmitting vehicle: (i) Searching the vehicles within attainment distance and determining received vehicles based on their reception probabilities, (ii) Holding received vehicles data for next data transmission. 4) Calculating data propagation situation as follows: a) The ratios of receiving destination information by time for each vehicle, b) The ratios about how many vehicles have acquired destination information, c) The total amount of the receiving data of each vehicle, and so on. In the real world, after receiving the traffic jam information, each vehicle may dynamically 5

6 NETSTREAM II Simulator Mobile Ad-hoc Network Simulator Road Map Travel Time Route Network Environment Traffic Signal Each Vehicle's Speed/Position Origin/ Destination Route Traffic Flow Estimation Jam Length Traffic Volume Reception Ratio, Equip Ratio Propagation Algorithm Traffic Information Propagation Estimation Fig. 3 System Structure of Mobile Ad-hoc Network Simulator Each Vehicle Data Statistics Data change its route, but we do not take account of the influence in this evaluation for simplicity. SIMULATION ENVIRONMENT Using our mobile Ad-hoc network simulator, we have evaluated the inter-vehicle road information propagation with the following input parameters: Road Environment: 20km x 20km The number of signals is 198 (Fig. 4) Simulation Time: 60 minutes (the last 40 minutes data are used for evaluation) Location Information of Vehicles: Every second The Number of Vehicles: 8570 (The total number for 60 minutes) Equipping Ratios of Car Navigation Systems: 30%, 60%, 90% Network Environment: Attainment distance 100m, Bandwidth 100Kbytes/sec Dissemination Algorithm: SDRP (Speed Dependent Random Protocol) with two kinds of transmitting intervals, bordering on speed of 30km/h Receiving Probability: Linearly change based on the distance (see Fig. 2) We use simple simulation of IEEE MAC layer [3] as follows: 1) One second is divided into ten slots in order to 20Km transmit the 10Kbytes data to the bandwidth of 100 Kbytes/sec, 2) According to the transmitting interval of each 20Km vehicle, it selects the transmitting slot using random number independently, 3) When two or more vehicles transmit data at the same slot within the area of 100m radius circle, the transmission is failed because of packet collisions. For the influence of packet collision by two or more 6 Fig.4 Road Map for Mobile Ad-hoc Network Simulation

7 so-called ad-hoc networks hidden host problem for wireless LAN [13], and for the influence of the object and surrounding building along the street, we treat them within the approximation of reception probability. SIMULATION RESULT Transmitting data amount and collision At first we have measured the amount of transmitting data and network collision ratios for each equipping ratios and each SDRP protocol s transmitting interval. In SDRP protocol, the transmitting interval for speed v is defined as [min(v),max(v)]. Here, we assume that min(v)= max(v)/2, that is, [max(v)/2, max(v)] is used as the interval. Hereafter, we call max(v) the interval for the speed v as an abbreviation of [max(v)/2, max(v)]. Fig.5 and Fig.6 show the results of fixed transmitting intervals. When the transmitting intervals of SDRP are set as <A, B>, it means that A is the interval of less than 30Km/h and B is the interval of more than 30Km/h. Fig.5 and Fig.6 show the results by changing the value of <A, B> from <1, 1> to <16, 16>. Fig.7 and Fig.8 show the results for the cases that B is set to 1 second, and the value of A is Collision Ratio 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Data Amount (MBbytes) Dissemination Interval(sec) Dissemination Interval(sec) 30.0% 60.0% 90.0% Fig. 5 Dissemination Interval and Collision Ratio (A=B=1~16) 30% 60% 90% Fig. 6 Dissemination Interval and Total Data Amount (A=B=1~16) Collision Ratio 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Dissemination Interval of less than 30km/h (sec) 30.0% 60.0% 90.0% Fig. 7 Dissemination Interval and Collision Ratio (A =1~16, B=1) 7 Data Amount(MBytes) Dissemination Interval of less than 30km/h (sec) 60% 90% Fig. 8 Dissemination Interval and Total Data Amount (A =1~16, B=1)

8 varied from 1 to 16. When the equipping ratios are 90%, the total transmitting data amount is the maximum when A is 4 seconds, and it decreases if the value of A is far from 4 seconds. Similarly, when equipping ratios are 60%, it is the maximum when A is 2 seconds. The collision ratios at that time are 52% and 48% respectively, and the total transmitting data amounts are 46Gbytes and 26Gbytes, respectively. Road information acquisition time Next, we have measured the acquisition time for getting the information on the destination using SDRP. In this simulation, we decide that a vehicle has obtained the information on the destination if the vehicle indirectly/directly has the information from a vehicle whose location is within 500m circle and the direction angle is in 120 degree from the destination by propagation of the information using SDRP. Fig. 9 shows the acquired ratios of the number of vehicles using SDRP. It shows that 65% vehicles can acquire the information 1km beyond within 8 seconds and 50% ones can acquire 2km beyond ones within 30 seconds. In this simulation, we choose data from vehicles that satisfy the following conditions: 1) For each hour, more than 80 vehicles run the road near the destination, 2) Vehicles that run at least 5 other vehicles running on its opposite lane. In urban areas, this assumption is reasonable. For 2km s results, as far as each vehicle is moving, much more useful data are collected. 90 second is the time for each vehicle to move 1km because 41.5km/h is the average speed in our simulation, and about 70% vehicles can acquire the 1km preceding road information. Each vehicle need a bit more time to acquire 3km, 4km, and 5km preceding information. Fig.10 shows the data acquisition ratios of direct dissemination (two hops) and indirect dissemination (multi-hops) where we assume that each vehicle's destination is 3km far from its starting point. By using SDRP dissemination protocol, most of vehicles can obtain the preceding road information indirectly about 4 minutes advance than direct dissemination. Since each vehicle can drive about 1km if there is no traffic jams, this result means that each 100.0% 100.0% Acquire Ratio 80.0% 60.0% 40.0% 20.0% Acquire Ratio 80.0% 60.0% 40.0% 20.0% 0.0% % km 2km 3km 4km time(sec) 5km Direct Indirect Fig. 9 Preceding Road Information Acquire Ratio Fig. 10 Direct/Indirect Acquiring 8

9 vehicle can recognize its destination's information 1km before its destination by using multi-hop dissemination. This means that those vehicles can recognize its preceding road surface condition and parking lots information in advance. This helps safe and easy drive. Also, about 40% more vehicles always hold precedence road information at each time. That means SDRP dissemination protocol is efficient. EXMAMINATION OF SIMULATION RESULTS SERVICEABILITY Disseminated data amount Although many of related researches are not fully taking account of network collision in wireless communications, our research is based on much more realistic network environments with collisions. In general, when a vehicle is running at high speed, frequent information dissemination leads to high rate data propagation. However, when a vehicle is running at low speed or nearly stopping in a traffic jam region, most disseminated data may be the same one. So, it is enough to use a longer interval. In addition, frequent data dissemination in a traffic jam region causes collisions. Network collision ratios increase according to the short interval, but the total exchanged data amount increases until some extent. On the other hand, too many collisions cause the total exchange data being decreased. Therefore, we should find out appropriate collision ratios for the actual setting. Actually, our simulation result shows that even in the case of 61.2% collision ratios, 7700 vehicles can exchange 45Gbytes data in 40 minutes. In this simulation, average vehicle driving time is 15.6 minutes, so each vehicle exchanges data at 378Kbytes/min. In our setting, road information for each vehicle is 100 bytes so that about 4000 vehicles information can be acquired within a minute. We are sure that this data amount is enough to acquire local road information around several kilometers regions. Network collision ratios mainly depend on the data dissemination interval for low-speed vehicles. From the simulation results, we can examine how much transmitting interval is effective for inter-vehicle ad-hoc communication. Effective data reception In this simulation, when the data storage memory area becomes full by receiving disseminated data from other vehicles, a half of those data are discarded randomly. Although this is an easy technique, it works rather well. We can use other techniques to hold a constant number of information from each direction angle and/or to aggregate the similar data in order to take much more effect [9]. In fact as shown in Fig. 9, 4km and 5 km preceding data ratios do not grow as of 1km and 2km preceding data ratios. Because of random selection of disseminating data, it may be difficult to acquire such far beyond data. If we introduce the above devices, the information acquisition time will be reduced. 9

10 DISSEMINATION INTERVAL BASED ON THE NUMBER OF RECEPTION DATA In the real world, we do not know the equipping ratios as a priori knowledge. So nobody knows the dissemination interval which maximizes the total data amount. If we can calculate the interval using the number of reception data in fixed period, we can implement the information dissemination protocol for any equipping ratios. We call the protocol RMDP (Received Message Dependent Protocol). In RMDP, we assume that dissemination interval is in inverse proportion to the number of reception messages. Here, we explain the derivation way for obtaining the parameters to maximize the total data amount using simulation results. Let P be the dissemination interval and r be the number of reception data in a fixed time. P= 1 α β r ( α, β > 0) Here, let the fixed time be 30 seconds. From the simulation results: a) When the equipping ratios are 60%, the total data amount is maximized at lower speed dissemination intervals being 2 seconds. In this case r = b) When the equipping ratios are 90%, the total data amount is maximized at lower speed dissemination intervals being 4 seconds. In this case r = Derivate the formula which passes along two points. 2.0 = α 1 β 7.74 α, 4.0 = 1 β 8.80 From these simultaneous equations, α = 0.45, β = Therefore, it responds to the number of receiving message r, 0.45 P= r RMDP can perform suitable data dissemination by using this interval. Here, in the case of r 10, it is necessary to take a certain fixed value. Probably, it will be enough to consider as 9 seconds which is the twice in r= 9. Moreover, although this parameter depends on the attainment distance of wireless LAN and its bandwidth, it is calculable using the same technique. In our experiments, we have examined that these values are suitable for several equipping ratios. CONCLUDING REMARKS To devise the suitable inter-vehicle ad-hoc communication protocol, we have developed a mobile ad-hoc network simulator which reproduces the propagation situation of road information as precisely as possible based on location, speed, and direction angle in every second of each vehicle, and making it cooperate with the traffic flow simulator NETSTREAM 10

11 II which reproduces an actually near traffic flow. We also show that our protocol is very efficient to acquire the road information on the route. As a future subject, we are studying more efficient protocol like RMDP, data aggregation technique, and more detailed analysis especially for each vehicle by defining actual exchanged information. We also anticipate the simulation in the real world by implementing the protocol to car navigation systems. ACKNOWLEGEMENT In order to carry out this work, Toyota CRDL., Inc. kindly offers us the opportunity to use a traffic simulator NETSTREAM II for developing our mobile ad-hoc network simulator. Especially, we thank Dr. Eiji Teramoto and Mr. Hironobu Kitaoka (Toyota CRDL., Inc.) for their valuable comments and suggestions for improving our work. REFERENCES [1] H. Hartenstein, B. Bochow, A. Ebner, M. Lott, M. Radimirsch and D. Vollmer : "Position-Aware Ad-hoc Wireless Networks for Inter-Vehicle Communications: the Fleetnet Project", Proc. of 2001 ACM Int. Symp. on Mobile Ad-hoc Networking & Computing (MobiHoc), pp , [2] W. R. Heinzelman, J. Kulik, H. Balakrishnan : "Adaptive protocols for information dissemination in wireless sensor networks", Proc. of 5th Annual ACM/IEEE Int. Conf. on Mobile Computing and Networking (MobiCom'99), pp , [3] IEEE Standard : "Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications", ISO/IEC :1999, [4] IEEE802.11a Standard : "Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 1: High-speed Physical Layer in the 5 GHz band", ISO/IEC :1999/Amd 1:2000(E), [5] IEEE802.11b Standard : "Supplement to , Wireless LAN MAC and PHY specifications: Higher speed Physical Layer (PHY) extension in the 2.4 GHz band", ISO/IEC :1999, [6] W. Kellerer, C. Bettstetter, C. Schwingenschlogl, P. Sties, K-E Steinberg and H-J Vogel : "(Auto) Mobile Communication in a Heterogeneous and Converged World", IEEE Personal Communications, Vol. 8, No. 6, pp.41-47, [7] A. Khelil, C. Becker, J. Tian, K. Rothermel : "An epidemic model for information diffusion in MANETs", Proc. of 5th ACM Int. Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems (MSWiM'02), pp.54-60, [8] R. Miller, Q. Huang : "An Adaptive Peer-to-Peer Collision Warning System", Proc. of IEEE Vehicle Technology Conference(VTC), pp , [9] T. Nadeem, S. Dashtinezhad, C. Liao, and L. Iftode : "Traffic View: A Scalable Traffic 11

12 Monitoring System", Proc. of 2004 IEEE Int. Conf. on Mobile Data Management (MDM2004), pp.13-26, [10] S.Y. Ni, Y.C. Tseng, Y.S. Chen, and J.P. Sheu : "The Broadcast Storm Problem in a Mobile Ad-hoc Network", Proc. of 5th Annual ACM/IEEE Int. Conf. on Mobile Computing and Networking (MobiCom'99), pp , [11] M. Papadopouli, and H. Schulzrinne : "Effects of power conservation, wireless coverage and cooperation on data dissemination among mobile devices", Proc. of 2nd ACM Int. Symp. on Mobile Ad-hoc Networking & Computing (MobiHoc2001), pp , [12] C. Schwingenschlogl and T. Kosch : "Geocast Enhancements of AODV for Vehicular Networks", ACM SIGMOBILE Mobile Computing and Communications Review, Vol.6, No.3, pp.96-97, [13] A. S. Tanenbaum : "Computer Networks Forth Edition", Pearson Education Inc., [14] E. Teramoto, M. Baba, H. Mori, H. Kitaoka, I. Tanahashi, Y. Nishimura, et. al. : "Prediction of Traffic Conditions for the Nagano Olympic Winter Games Using Traffic Simulator : NETSTREAM", Proc. of 5th World Congress on Intelligent Transport Systems, Vol.4, pp , [15] [16] B. Xu, A. Ouksel and O. Wolfson : "Opportunistic Resource Exchange in Inter-vehicle Ad-hoc Networks", Proc. of 2004 IEEE Int. Conf. on Mobile Data Management (MDM2004), pp.4-12,