A geographical segment architecture for vehicular ad hoc networks

TOWARDS THE INTERNET OF VEHICLES: A GEOGRAPHICAL SEGMENT ARCHITECTURE FOR VANET AIDI HUANG (B.Eng., Huazhong University of Science and Technology) A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF ENGINEERING DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2015 Declaration I hereby declare that this thesis is my original work and it has been written by me in its entirety. I have duly acknowledged all the sources of information which have been used in the thesis. This thesis has also not been submitted for any degree in any university previously. _______________ AIDI HUANG 19 Jan 2015 I Acknowledgements I would like to dedicate this section to all who helped me in this project. I must thank my supervisor, A/P. Mehul Motani, for his guidance, invaluable time, and encouragement throughout the project. Every meeting with Prof. Mehul, his suggestion helps push my work forward. I am also grateful for the company of my fellow graduate students Cheng Huang and Anshoo Tandon. Cheng Huang provided valuable comments on the project and Anshoo Tandan recommended helpful references when I started this project. I always gain new findings discussing with them. II Table of Contents Acknowledgements II Table of Contents III Summary VII List of Tables VIII List of Figures . IX CHAPTER Introduction 1.1 Objective . 1.2 Challenges of Ad-hoc networking in the vehicular environment . 1.2.1 The High Mobility Environment . 1.2.2 The Channel Collision Problem in the High Density Network 1.2.3 The Network Fragmentation Problem in the Low Density Network . 1.3 Literature Survey . 1.3.1 Broadcasting Protocols 1.3.2 Routing Protocols . 1.3.3 Vehicular Cloud (VC) 1.3.4 Clustering Technologies in VANET . 10 1.3.5 VANET Standards . 11 1.4 Thesis Contribution . 12 1.5 Organization of this Thesis 14 III CHAPTER The Geographical Segment Architecture 15 2.1 The Structure of the Geographical Segment Architecture . 15 2.2 Representation of Segments and Vehicle Localization . 17 2.3 The Head Generation Algorithm for the Segment 19 2.4 Discussion on Abnormal Situations . 27 2.4.1 Failure of Receiving an HCM 27 2.4.2 Failure of Receiving an HRM 28 2.5 Information Carried by the Header . 29 CHAPTER Application of the Geo-Segment Architecture 30 3.1 Broadcasting Strategies 30 3.1.1 Directional Broadcasting 31 3.1.2 Intersection Broadcasting 31 3.1.3 Designated Area Broadcasting . 33 3.1.4 The Hidden Node Problem of Broadcasting 33 3.2 Routing Protocols 35 3.2.1 The GSA based Source Routing (GSA-SR) . 36 3.2.2 The GSA based Geographical Routing (GSA-GR) 41 3.2.3 The Infrastructure and GSA based Routing (IGSAR) 43 3.3 Optimization of Traffic Flows of the Vehicular Network 48 3.4 Vehicular Cloud . 49 IV CHAPTER Simulations .52 4.1 Simulation Setup . 52 4.1.1 Simulation Tools . 52 4.1.2 Simulation Parameters . 53 4.2 Simulation of the City Scenario . 55 4.2.1 Simulation Topology 55 4.2.2 Evaluation of Throughput 56 4.2.3 Evaluation of Packet Delivery Rate (PDR) 58 4.2.4 Evaluation of Packet End-to-End Delay 59 4.2.5 Evaluation of Protocol Overhead . 60 4.3 Simulation of the Rural Scenario 63 4.3.1 Simulation Topology 63 4.3.2 Evaluation of Throughput 64 4.3.3 Evaluation of Packet Delivery Rate (PDR) 65 4.3.4 Evaluation of Packet End-to-End Delay 66 4.3.5 Evaluation of Protocol Overhead . 67 4.4 Simulation of the Highway Scenario . 68 4.4.1 Simulation Topology 68 4.4.2 Evaluation of Throughput 68 4.4.3 Evaluation of Packet Delivery Rate (PDR) 69 V 4.4.4 Evaluation of Packet End-to-End Delay 70 4.4.5 Evaluation of Protocol Overhead . 71 4.5 Discussion on the Application of GSA based Protocols 72 CHAPTER Conclusions and Future Research 73 Bibliography .75 VI Summary Equipped with wireless transceivers, vehicles can be connected into a vehicular ad hoc network (VANET) to collect, share and utilize data for a variety of purposes, such as safety, navigation and entertainment. As diverse sensors are integrated into vehicles and large numbers of access points (AP) are deployed within cities, VANETs are being transformed into the Internet of Vehicles (IoV), where far more data can be collected and utilized to support smarter applications. However, the data explosion puts further strain on the broadcasting and routing protocols which are already challenged by the high mobility vehicular environment. In this thesis, a geographical segment architecture (GSA) which clusters vehicles based on their geographic locations is proposed. Leveraging on its two-tier architecture, the GSA can exploit cluster identifiers to develop efficient broadcasting strategies and routing protocols. Extensive simulations of vehicular networks in three road topology scenarios (city, rural, and highway) show that GSA based protocols can achieve high throughput, high packet delivery rate, low delay and low overhead. On the foundation of GSA based broadcasting and routing, we discuss that how the data collection and utilization can be further enhanced by (i) providing a solution to the hidden node problem in a broadcast scenario, (ii) facilitating the optimization of multiple network traffic flows and (iii) supporting the implementation of the vehicular cloud. VII List of Tables Table 4-1: Parameter of VANETMOBISIM 53 Table 4-2: Parameter of NS2 54 VIII List of Figures Fig. 2-1: Demonstration of the geographic segment architecture . 16 Fig. 2-2: The two-tier structure of the geographical segment architecture . 17 Fig. 2-3: Demonstration of Vehicle Localization for Simulation . 18 Fig. 2-4: Localization mistake in roads with curve shape . 18 Fig. 2-5: An example of localization mistake of inaccurate GPS . 19 Fig. 2-6: The flow of the head generation algorithm 20 Fig. 2-7: A vehicle enters an empty segment 21 Fig. 2-8: A vehicle enters a non-empty segment 22 Fig. 2-9: A head vehicle leaves a segment 25 Fig. 3-1: Demonstration of the directional broadcasting 31 Fig. 3-2: Demonstration of the intersection broadcasting . 32 Fig. 3-3: Demonstration of addressing the hidden node problem based on GSA . 35 Fig. 3-4: The destination discovery process to learn the E2E routing path 36 Fig. 3-5: A segment map at time point and . 38 Fig. 3-6: Demonstration of the packet routing based on GSA 40 Fig. 3-7: Demonstration of neighbor table updating based on GSA . 42 Fig. 3-8: Demonstration of a vehicle associating to an AP in multi-hop 45 Fig. 3-9: An example of a segment map and a routing path based on it . 46 Fig. 3-10: An example of vehicle density based algorithm 47 Fig. 3-11: Demonstration of managing vehicles as computation resource . 50 Fig. 4-1: The Road Topology for City Scenario . 55 IX 4.3.3 Evaluation of Packet Delivery Rate (PDR) Fig. 4-8: PDR Comparison for the Rural Scenario As shown in Fig. 4-8, GSA-GR and GSA-SR achieve much higher PDR than AODV. GSA-GR almost hits the upper-bound (i.e., 100%) and GSA-SR achieves about 80% of the upper-bound. It is difficult for GSA-SR to achieve as high throughput as GSA-GR. The routing path of GSA-SR is pre-learnt and updating an invalid path would take about 1s as we have previously discussed. So when a segment in the routing path contains no more nodes, the routing path will be interrupted. In this rural simulation scenario, an end-to-end routing path would usually take 2-4 hops. The possibility of a path becoming invalid should not be low. This hinders GSA-SR from approaching the upper-bound. But when more nodes (the 96-node case) are used, we can see that the PDR of GSA-SR is improved. 65 The PDR of AODV is low, which is for the same reason as its low throughput. GPSR obtains high PDR, but it is under the light traffic condition. Its PDR would decrease heavily when higher data rates are used. 4.3.4 Evaluation of Packet End-to-End Delay Fig. 4-9: Comparison of Packet End-to-End Delay for the Rural Scenario The comparison of the packet delay for the rural scenario (Fig. 4-9) is similar to that for the city scenario. One thing to point out is that the delay for GPSR is small in the light traffic condition. When we allow for larger data rates as for the case to test the throughput, the packet delay for GPSR would become much larger, even more than 10s. While for GSA based protocols, the packet delay would not increase a lot in heavy traffic condition. 66 4.3.5 Evaluation of Protocol Overhead Fig. 4-10: Comparison of Protocol Overhead for the Rural Scenario For the rural scenario, we can see that GSA-SR produces less overhead than that for the city scenario, as shown in Fig. 4-10. This is because longer segments are used that there are fewer segments to maintain. When GSA-SR discovers the routing path, the request packet will be re-broadcasted by fewer segment heads. AODV generates much more overhead since there are no building blocks to help limit the node density. 67 4.4 Simulation of the Highway Scenario 4.4.1 Simulation Topology Fig. 4-11: The Road Topology for the Highway Scenario For the highway scenario as shown in Fig. 4-11, we employ much longer roads which are 1800m for both directions. We set the speed to vary between 70km/h to 180km/h to simulate the extremely high dynamic environment, as shown in Table 4-1. Specific parameters for NS2 are similar to those for the rural scenario and can be found in Table 4-2. We place the source node and the sink node close to the two ends of the road, respectively. The distance between them is 1300m. This means the end-to-end routing path would commonly take 4-6 hops for GSA based protocols. 4.4.2 Evaluation of Throughput As shown in Fig. 4-12, GSA-GR performs much better than GSA-SR, GPSR and AODV. Comparing to GSA-GR, the throughput of GSA-SR is comparatively low, which is due to the similar reason as we have discussed in chapter 4.3.3. Actually, the situation is worse for GSA-SR in the highway scenario. As vehicles are 68 driving much faster than in the rural and the city areas, a routing path containing 4-6 hops is very vulnerable to the vehicle mobility. GSA-SR needs to frequently discover valid new paths. The low throughput of AODV is due to similar reason as for GSA-SR. But GSA-SR performs much better that AODV. GSA-SR shows better resistant to the dynamic environment, which suggests the capability of the GSA to handle the density and mobility problem. The reason for GPSR's low throughput is the same as for the rural scenario. The source node still tries to communicate with the sink node with one hop. Fig. 4-12: Throughput Comparison for the Highway Scenario 4.4.3 Evaluation of Packet Delivery Rate (PDR) The comparison of the PDR for the highway scenario (Fig. 4-13) is similar to that for the rural scenario. A difference is that the PDR of AODV is close to which 69 resembles its performance on the throughput. The reasons are due to the high density condition and high dynamic environment as already discussed. Fig. 4-13: PDR Comparison for the Highway Scenario 4.4.4 Evaluation of Packet End-to-End Delay Fig. 4-14: Comparison of Packet End-to-End Delay for the Rural Scenario 70 The comparison of the packet delay for the highway scenario shown in Fig. 4-14 is similar to that for the rural scenario. The delays for the highway scenario are larger than those for the city scenario, which is due to the longer distance between the source and the sink vehicle for the highway scenario. 4.4.5 Evaluation of Protocol Overhead Fig. 4-15: Comparison of Protocol Overhead for the Highway Scenario The comparison of the protocol overhead for the highway scenario (Fig. 4-15) is similar to that for the rural scenario. The overheads for highway scenario are lower than those for the city scenario. This is simply because the highway scenario has fewer clusters to maintain. 71 4.5 Discussion on the Application of GSA based Protocols Based on previous simulation results and discussion, we can see that GSA-GR can be a good option for routing in VANETs. For most cases, GSA-GR achieves high throughput, high packet delivery rate, low delay and low overhead. Furthermore, we can optimize the protocol design by combining the advantages of reactive routing and geographical routing, and exploit the infrastructure when it is readily available. We can employ the route discovery mechanism of GSA-SR to handle GSA-GR's dead-end problem. A possible design is to discover and monitor the connectivity among adjacent intersection segments. OAM packets can be sent among intersection segments to monitor the connectivity. The OAM packet is forwarded in a broadcasting manner by heads, thus other vehicles can receive the OAM and learn the connectivity status of the intersections. Therefore, a node can determine an appropriate intersection segment to forward the packet, making sure all potential paths are fully exploited. In addition, when the infrastructure is readily available, we can send an OAM packet to test the delay of the infrastructure path and decide whether to use the infrastructure to offload the traffic, or only utilize the infrastructure to implement the IGSAR. In conclusion, the GSA can provide a platform to realize smart strategies to improve the performance of broadcasting and routing protocols, and also help enhance the interoperability between VANETs and the infrastructure. 72 CHAPTER Conclusions and Future Research In this thesis, we proposed the geographical segment architecture to address the high mobility and the scalability issue of VANETs. Our purpose was to leverage on this architecture to deal with the data explosion of IoV. We proposed smart broadcasting strategies based on GSA that could fulfill various needs of applications in the vehicular environment. We also presented GSA-based routing protocols which can achieve high throughput for most vehicle densities. With these protocols, data sharing and collection would be efficient, and could further benefit from our solution proposed for the hidden node problem and the strategy planned to optimize the network traffic globally. Moreover, GSA could support the organization of vehicular cloud which provides a platform to process the massive data generated in the era of IoV. As VANETs being transformed into IoV, the co-working between VANETs and the infrastructure becomes increasingly pervasive and significant. An open issue pertinent to this process is how VANET can seamlessly cooperate with the infrastructure. Especially, how the time cost a vehicle associates to an AP can be reduced to minimum and how a vehicle can quickly switch among APs. Effort 73 should be made to optimize the authentication and association process while meet security requirements. Heterogeneous network can also be a promising option. 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Physical Review E 62.2 (2000): 1805. 80 [...]... clear to send (CTS) [39] mechanism among cluster heads The performance of multi-hop broadcasting protocol can be improved • The GSA can easily support implementing smart broadcasting protocols in a controlled manner, e.g., directional broadcasting, intersection broadcasting and restricted area broadcasting 12 • We propose and simulate a source routing protocol based on GSA The GSA can also help maintain... demonstrate that how GSA can be exploited to achieve efficient broadcasting and routing protocols We also discuss how GSA can facilitate handling of other significant issues: hidden node problem of broadcasting, network traffic optimization and vehicular cloud In chapter 4, we perform simulations to evaluate the overhead of GSA clustering and analyze the performance of GSA based protocols In chapter... vehicles and apply a time-estimation based contention algorithm to generate the cluster head In comparison to election-based method, our approach does not perform status monitoring, association, and election, which reduces overhead and increases scalability 10 1.3.5 VANET Standards Dedicated Short Range Communications (DSRC) [37] are a group of protocols and standards for communications in the vehicular. .. is adopted to handle the adjacent channel interference and Orthogonal Frequency Division Multiplexing (OFDM) is employed upon such channels Besides, the maximum communication range could reach up to 1000m The GSA is basically a clustered architecture, which is made necessary and feasible by the large communication radius With a large radius, a small group of vehicles (i.e., cluster heads) can be chained... help maintain a digital map with cluster information The map is called segment map, based on which the routing protocol can achieve good performance • We propose and simulate a geographical routing protocol based on GSA We reuse the protocol packet for GSA clustering to maintain the neighbor table We also utilize the segment map to tackle the dead-end problem • We propose and simulate an infrastructure... subsets repeatedly In this case, communication among vehicles will experience bad quality, e.g., large delay and low throughput 1.3 Literature Survey VANET has been a hot research area for years It promises a wide variety of innovative applications [1] For example, traffic alerts can be provided to drivers to help them bypass traffic congestions and warn them of potential road dangers Other examples include... with a positioning device, such as the global positioning system (GPS) We also assume that a digital map (we call it segment map) containing the segment information is pre-stored in every vehicle A segment was represented by its central point A vehicle can localize itself to a segment through comparing its distances to central points of adjacent segments As in Fig 2-3, the vehicle with a star can locate... or repair a routing path The protocol throughput will be low and the average packet delay will be large Targeting at the high dynamic vehicular environment, position based or geographical routing protocols [20]-[23] have attracted a lot of attention Geographical routing protocols route packets without discovering an end-to-end (E2E) path beforehand Routing decisions are made at the intermediate nodes... problem  The GSA-based broadcasting and routing protocols can further help implement the vehicular cloud (chapter 3.4) 13 1.5 Organization of this Thesis The remainder of this thesis is structured as follows We will explain the geographical segment architecture in chapter 2, including geographical segment representation, vehicle localization (into segment) and the head generation algorithm In chapter 3,... mistake of inaccurate GPS 2.3 The Head Generation Algorithm for the Segment A key issue for GSA clustering is how to generate and maintain valid segment heads with low overhead We propose a time-estimation based competition strategy for this purpose The idea is to measure vehicles’ lifetime (driving time) in a segment and set their timers based on the measured time to compete for the head position The . (city, rural, and highway) show that GSA based protocols can achieve high throughput, high packet delivery rate, low delay and low overhead. On the foundation of GSA based broadcasting and routing,. filtering and aggregating data was also discussed, prompted by the fact that data collected from sensors in adjacent vehicles is locally relevant. A key problem for VC, as in above scenarios,. Vehicles are regarded as storage and computation resources to form a flexible and immediately accessible platform to run applications of various needs. When a variety of sensors are integrated