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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
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
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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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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
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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)
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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
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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 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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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
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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
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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
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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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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
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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
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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)
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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).
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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
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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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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
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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