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
1University
of Roma Tre
1
2University
Mediterannea
of Reggio Calabria
3Boston
University
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
2
Outline
Opportunistic Networks
 The Case of Vehicular Ad hoc Networks
VANETs: an Introduction
Connectivity in VANETs
Modeling Connectivity
Improving Connectivity
Conclusions and Discussions
3
Opportunistic Networks
Definition: Opportunistic networks are one of
the most interesting evolutions of Mobile Adhoc 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
4
Opportunistic Networks – Techniques
Opportunistic networking techniques allow
mobile nodes to exchange messages by taking
advantage of mobility and leveraging the storecarry-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
5
Opportunistic Networks – Types
Opportunistic networks include:
 Mobile sensor networks [5]
 Packet-switched networks [6]
 Vehicular Ad hoc NETworks (VANETs) [7]
6
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
7
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
8
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
9
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
10
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 realtime requirements of traveler information support
applications, to guaranteed Quality-of-Service needs
of multimedia and interactive entertainment
applications
11
VANETs
Enabling communication technologies
12
 Wi-MAX
 Long Term Evolution (LTE)
Centralized V2I/I2V
communications
 IEEE 802.11
 IEEE 802.11p
Ad hoc V2V and
centralized V2I/I2V
communications
Connectivity in VANETs
There are three primary models for
interconnecting vehicles based on:
1. Network infrastructure
2. Inter-vehicle communications
3. Hybrid configuration
13
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
14
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)
15
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
16
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
Vehicle
density/speed
Connectivity
Time of day
Market
penetration
17
Vehicular
scenario
• Urban
• Highway
RSU
Communication
range
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
18
Modeling V2V Connectivity in VANETs
P{X > R}= e- l R ¹ 0
Probability of Disconnected Vehicles
In a fragmented vehicular ad hoc network, under
the DTN assumption and exponentially
distributed inter-vehicle distances, the
probability that two consecutive vehicles are
1
disconnected is [28]
R=25
0.6
 where X [m] is the
0.4
inter-vehicle distance,
λ [veh/m] is the
0.2
distribution parameter
for inter-vehicle distances 00
and R [m] is the radio range
19
R=50
R=75
R=100
R=150
R=300
R=500
R=700
R=1000
0.8
20
40
60
80
Vehicular Density [veh/km]
100
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]:
E éëj (t) ùû = e-2 lr R
 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
20
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
21
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]
22
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]
23
Modeling V2V-V2I Connectivity
List of the main common assumptions in
connectivity models for VANET
24
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
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
25
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
26
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 (totallydisconnected 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
27
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)
28
Probability of Connected Vehicles
1
Phase 1
Phase 2
Phase 3
0.8
0.6
0.4
0.2
Probability of Connected Vehicles
Improving Connectivity in VANETs
1
0.8
0.6
0.5
0.4
0.2
0
50
100
0
0
0.02
0.04
0.06
0.08
Vehicle Traffic Density [veh/km]
(a)
0.1
100
Connectivity range [m]
150 0
50
Vehicle Traffic Density [veh/km]
( 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).
29
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
30
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
31
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
32
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
33
Thanks for your
attention!
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