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

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Chapter 7: Modeling of Intermittent

Connectivity in Opportunistic Networks: The Case of Vehicular

Ad hoc Networks1Anna Maria Vegni, 2Claudia Campolo, 2Antonella Molinaro,

and 3Thomas D.C. Little

BOOK ON ROUTING IN OPPORTUNISTIC NETWORKS

1University of Roma

Tre

2University Mediterannea

of Reggio Calabria

3Boston Universit

y

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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

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Outline

Opportunistic Networks The Case of Vehicular Ad hoc Networks

VANETs: an IntroductionConnectivity in VANETsModeling ConnectivityImproving ConnectivityConclusions and Discussions

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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

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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

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Opportunistic Networks – Types

Opportunistic networks include: Mobile sensor networks [5] Packet-switched networks [6] Vehicular Ad hoc NETworks (VANETs) [7]

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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

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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

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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

RSU

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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

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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

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VANETsEnabling 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

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Connectivity in VANETs

There are three primary models for interconnecting vehicles based on: 1. Network infrastructure2. Inter-vehicle communications3. Hybrid configuration

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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

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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)

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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

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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 Communicat

ion range

RSU

Vehicular scenario•Urban•Highway

Market penetration

Vehicle density/spee

d

Time of day

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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

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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

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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

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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

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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]

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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]

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Modeling V2V-V2I Connectivity

List of the main common assumptions in connectivity models for VANET

Assumption Assumption Type

Vehicle distribution Poisson

Topology 1D w/o traffic lights / intersections

Underlying model Connectivity graph

Propagation model Unit disk model

RSUs’ distribution Uniform

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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

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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

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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

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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)

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(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).

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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

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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

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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

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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

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Thanks for your attention!

Documents

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