1. 1 Chapter 7: Modeling of Intermittent Connectivity in Opportunistic Networks: The Case of Vehicular Ad hoc Networks 1 Anna Maria Vegni, 2 Claudia Campolo, 2 Antonella Molinaro, and 3 Thomas D.C. Little BOOK ON ROUTING IN OPPORTUNISTIC NETWORKS 1 University of Roma Tre 2 University Mediterannea of Reggio Calabria 3 Boston University

2. 2 Objectives of the Chapter  Analyze connectivity issues in Vehicular Ad hoc NETworks  Provide an overview of vehicular connectivity models in the literature  Discuss hybrid and opportunistic communication paradigms designed to improve connectivity in vehicular environments

3. 3 Outline  Opportunistic Networks  The Case of Vehicular Ad hoc Networks  VANETs: an Introduction  Connectivity in VANETs  Modeling Connectivity  Improving Connectivity  Conclusions and Discussions

4. 4 Opportunistic Networks  Definition: Opportunistic networks are one of the most interesting evolutions of Mobile Ad- hoc NETworks (MANETs)  The assumption of a complete path between the source and the destination is relaxed  Mobile nodes are enabled to communicate with each other even if a route connecting them may not exist or may break frequently

5. 5 Opportunistic Networks – Techniques  Opportunistic networking techniques allow mobile nodes to exchange messages by taking advantage of mobility and leveraging the store- carry-and-forward approach  A message can be stored in a node and forwarded over a wireless link as soon as a connection opportunity arises with a neighbour node  Opportunistic networks are then considered as a special kind of Delay Tolerant Network (DTN) [3], providing connectivity despite long link delays or frequent link breaks

6. 6 Opportunistic Networks – Types  Opportunistic networks include:  Mobile sensor networks [5]  Packet-switched networks [6]  Vehicular Ad hoc NETworks (VANETs) [7]

7. 7 VANETs  Definition: A VANET (Vehicular Ad hoc NETwork) is a special kind of MANET in which packets are exchanged between mobile nodes (vehicles) traveling on constrained paths

8. 8 VANETs  Like MANETs:  They self-organize over an evolving topology  They may rely on multi-hop communications  They can work without the support of a fixed infrastructure  Unlike MANETs:  They have been conceived for a different set of applications  They move at higher speeds (0-40 m/s)  They do not have battery and storage constraints

9. 9 VANETs  Communication modes:  Vehicle-to-Vehicle (V2V) among vehicles  Vehicle-to-Infrastructure (V2I), between vehicles and Road-Side Units (RSUs)  Vehicle-to-X (V2X), mixed V2V-V2I approach V2V V2I V2V V2I RSU

10. 10 VANETs  Applications:  Active Road-Safety Applications To avoid the risk of car accidents: e.g., cooperative collision warning, pre-crash sensing, lane change, traffic violation warning  Traffic efficiency and management applications To optimize flows of vehicles: e.g., enhanced route guidance/navigation, traffic light optimal scheduling, lane merging assistance  Comfort and Infotainment applications To provide the driver with information support and entertainment: e.g., point of interest notification, media downloading, map download and update, parking access, media streaming, voice over IP, multiplayer gaming, web browsing, social networking

11. 11 VANETs  VANETs applications exhibit very heterogeneous requirements  Safety applications require reliable, low-latency, and efficient message dissemination  Non-safety applications have very different communication requirements, from no special real- time requirements of traveler information support applications, to guaranteed Quality-of-Service needs of multimedia and interactive entertainment applications

12. 12 VANETs  Enabling communication technologies  Wi-MAX  Long Term Evolution (LTE)  IEEE 802.11  IEEE 802.11p Centralized V2I/I2V communications Ad hoc V2V and centralized V2I/I2V communications

13. 13 Connectivity in VANETs  There are three primary models for interconnecting vehicles based on: 1.Network infrastructure 2.Inter-vehicle communications 3.Hybrid configuration

14. 14 Connectivity in VANETs  Network infrastructure  Vehicles connect to a centralized server or a backbone network such as the Internet, through the road-side infrastructure, e.g., cellular base stations, IEEE 802.11 Access Points, IEEE 802.11p RSUs

15. 15 Connectivity in VANETs  Inter-vehicle communications  Use of direct ad-hoc connectivity among vehicles via multihop for applications requiring long-range communications (e.g., traffic monitoring), as well as short-range communications (e.g., lane merging)

16. 16 Connectivity in VANETs  Hybrid configuration  Use of a combination of V2V and V2I. Vehicles in range directly connect to the road-side infrastructure, while exploit multi-hop connectivity otherwise

17. 17 Connectivity in VANETs  Vehicles’ connectivity is determined by a combination of several factors, like:  Space and time dynamics of moving vehicles (i.e., vehicle density and speed)  Density of RSUs  Radio communication range Connectivity Communication range RSU Vehicular scenario Urban Highway Market penetration Vehicle density/speed Time of day

18. 18 Modeling V2V Connectivity in VANETs  Most of existing literature in VANET focuses on modeling the V2V connectivity probability  Common assumption: a vehicular network is partitioned into a number of clusters  Vehicles within a partition communicate either directly or through multiple hops, but no direct connection exists among partitions

19. 19 Modeling V2V Connectivity in VANETs  In a fragmented vehicular ad hoc network, under the DTN assumption and exponentially distributed inter-vehicle distances, the probability that two consecutive vehicles are disconnected is [28]  where X [m] is the inter-vehicle distance, λ [veh/m] is the distribution parameter for inter-vehicle distances and R [m] is the radio range

20. 20 Modeling V2V Connectivity in VANETs  Accurate predictions of the network connectivity can be made using percolation theory, describing the behavior of connected clusters in a random graph  In the stationary regime, assuming the spatial vehicles’ distribution as a Poisson process, the upper bound on the average fraction of vehicles that are connected to no other vehicles is [14]:  The vehicular network is at a state that the rate of vehicles entering the network is the same as the rate of vehicle leaving it

21. 21 Modeling V2V Connectivity in VANETs  The platoon size (i.e., the number of vehicles in each connected cluster), and the connectivity distance (i.e., the length of a connected path from any vehicle) are two metrics used to model V2V connectivity in VANETs [22]  When the traffic’s speed increases, the connectivity metrics decrease  If the variance of the speed’s distribution is increased, then, provided that the average speed remains fixed, the connectivity is improved

22. 22 Modeling V2I Connectivity in VANETs  More challenging w.r.t. V2V case  As vehicles move, connectivity is both fleeting, usually lasting only a few seconds at urban speeds, and intermittent, with gaps between a connection and the subsequent one  Different vehicle placement conditions influence the overall connectivity, while RSUs do not significantly improve connectivity in all scenarios  E.g., RSUs at intersections do not reduce the proportion of isolated vehicles, which are more likely to be in the middle of the road [14]

23. 23 Modeling V2I Connectivity in VANETs  The notion of intermittent coverage for mobile users provides the worst-case guarantees on the interconnection gap, while using significantly fewer RSUs  The interconnection gap is defined as the maximum distance, or expected travel time, between two consecutive vehicle-RSU contacts.  Such a metric is chosen because the delay due to mobility and disconnection affects messages delivery more than channel congestion [25]

24. 24 Modeling V2V-V2I Connectivity  List of the main common assumptions in connectivity models for VANET AssumptionAssumption Type Vehicle distributionPoisson Topology1D w/o traffic lights / intersections Underlying modelConnectivity graph Propagation modelUnit disk model RSUs’ distributionUniform

25. 25 Improving Connectivity in VANETs  Opportunistic approaches for connectivity support in VANETs  Opportunistic contacts, both among vehicles and from vehicles to available RSUs, can be used to instantiate and sustain both safety and non-safety applications  Opportunistic forwarding is the main technique adopted in DTN [55]  In VANETs, bridging technique links the partitioning that exists between clusters traveling in the same direction of the roadway

26. 26 Improving Connectivity in VANETs  The use of a vehicular grid together with an opportunistic infrastructure placed on the roads guarantees seamless connectivity in dynamic vehicular scenarios [59]-[61]  Hybrid communication paradigms for vehicular networking are used to limit intermittent connectivity  Vehicle-to-X (V2X) works in heterogeneous scenarios, where overlapping wireless networks partially cover the vehicular grid. It relies on the concept of multi-hop communication path

27. 27 Improving Connectivity in VANETs  In V2X approach, there is the vehicular partitioning with different connectivity phases:  Phase 1 (No connectivity) A vehicle is traveling alone in the vehicular grid (totally- disconnected traffic scenario). The vehicles are completely disconnected  Phase 2 (Short-range connectivity) A vehicle is traveling in the vehicular grid and forming a cluster with other vehicles. Only V2V connectivity is available  Phase 3 (Long-range connectivity) A vehicle is traveling in the vehicular grid with available neighboring RSUs. Only V2I connectivity is assumed to be available

28. 28 Improving Connectivity in VANETs  The probability that a vehicle lays in one of the three phases is expressed as the probability that a vehicle is:  Not connected (Phase 1)  Connected with neighbours (Phase 2)  Connected with RSUs (Phase 3)

29. 29 Improving Connectivity in VANETs (a) (b)  Probability of connected vehicles (a) vs. the vehicle traffic density (Phases 1–3), and (b) vs. the vehicle traffic density and the connectivity range (Phase 1).

30. 30 Improving Connectivity in VANETs  Satellite connectivity is used in VANETs for outdoor navigation and positioning services  As an opportunistic link, it is intended to augment short and medium-range communications to bridge isolated vehicles or clusters of vehicles, when no other mechanism is available

31. 31 Conclusions and Discussions  Connectivity issues in VANETs have been investigated  Road topology, traffic density, vehicle speed, market penetration of the VANET technology and transmission range strongly affect the network connectivity behavior

32. 32 Conclusions and Discussions  Analytical models deriving connectivity performance in VANETs have been discussed  They differ into the underlying assumptions and the considered connectivity metrics  Solutions improving connectivity in VANETs have been reviewed  Exploiting infrastructure nodes, relay-based techniques and even satellite communications to bridge isolated vehicles when no other mechanism is available

33. 33 Conclusions and Discussions  Analytical models play an important role in performance evaluation of VANETs and need to be significantly improved in terms of accurateness and realism  Further efforts are required to design solutions enabling V2V and V2I connectivity in different network conditions to sustain both safety and non-safety applications

34. 34 Thanks for your attention!