1 Anna Maria Vegni, 2 Claudia Campolo, 2 Antonella Molinaro, and 3 Thomas D.C. Little

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

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

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. 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. Opportunistic Networks – Types • Opportunistic networks include: • Mobile sensor networks [5] • Packet-switched networks [6] • Vehicular Ad hoc NETworks (VANETs) [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. 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. 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 RSU V2I V2I V2V RSU

10. VANETs • Applications: • Active Road-SafetyApplications • To avoid the risk of car accidents: e.g., cooperative collision warning, pre-crash sensing, lane change, traffic violation warning • Traffic efficiencyandmanagementapplications • To optimize flows of vehicles: e.g., enhanced route guidance/navigation, traffic light optimal scheduling, lane merging assistance • Comfortand 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. 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. 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. Connectivity in VANETs • There are three primary models for interconnecting vehicles based on: • Network infrastructure • Inter-vehicle communications • Hybrid configuration

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. 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. 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. 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 Vehicledensity/speed RSU Connectivity Time of day Communicationrange Market penetration • Vehicular scenario • Urban • Highway

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. 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. 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. 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. 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. 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. Modeling V2V-V2I Connectivity • List of the main common assumptions in connectivity models for VANET

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. 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. Improving Connectivity in VANETs • In V2X approach, there is the vehicular partitioning with different connectivity phases: • Phase 1 (No connectivity) • A vehicleistraveling alone in the vehiculargrid (totally-disconnectedtraffic scenario). The vehicles are completelydisconnected • 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 connectivityisassumed to beavailable

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. 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. 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. 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. 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. 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. Thanks for your attention!