Vehicular ad hoc networks (Computer networks)

Vehicular Ad hoc Networks (VANET) is a wireless ad-hoc network that includes
two types of communications, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure
(V2I). In VANET there are two types of messages. The first type is the event-driven
messages that are only triggered in case of emergency. The second type is the periodical
messages named beacons that are exchanged frequently between vehicles. A
beacon message contains basic information about the sending vehicle such as id, location
and velocity. Beacons are frequently exchanged to increase the cooperative
awareness between vehicles. Increasing beacon frequency helps increasing neighborhood
awareness and improving information accuracy. However, this causes more
congestion in the network, specially when the number of vehicles increases. On the
other hand, reducing beacon frequency alleviates network congestion, but results in
out-dated information.
In this dissertation, we address the aforementioned challenges and propose a
number of smart beaconing protocols and evaluate their performance in di↵erent environments
and network densities. The four adaptive beaconing protocols are designed
to increase the cooperative awareness and information freshness, while alleviating the network congestion. All the proposed protocols take into account the most important
aspects, which are critical to beaconing rate adaptation. These aspects include channel
status, traffic conditions and link quality. The proposed protocols employ fuzzy
logic-based techniques to determine the congestion rank, which is used to adjust beacon
frequency.
The first protocol considers signal to interference-noise ratio (SINR), number
of neighboring nodes and mobility to determine the congestion rank and adjust the
beacon rate accordingly. This protocol works well in sparse conditions and highway
environments. The second protocol works well in sparse conditions and urban environments.
It uses channel busy time (CBT), mobility and packet delivery ratio
(PDR) to determine the congestion rank and adjust the beacon rate. The third protocol
utilizes CBT, SINR, PDR, number of neighbors and mobility as inputs for the
fuzzy logic system to determine the congestion rank and adjust the beacon rate. This
protocol works well in dense conditions in both highway and urban environments.
Through extensive simulation experiments, we established that certain input
parameters are more e↵ective in beacon rate adaptation for certain environments
and conditions. Based on this, we propose a high awareness and channel efficient
scheme that adapts to di↵erent environments and conditions. First, the protocol
estimates the network density using adaptive threshold function. Then, it looks at
the spatial distribution of nodes using the quadrat method to determine whether
the environment is highway or urban. Based on the density conditions and nodes
distribution, the protocol utilizes the appropriate fuzzy input parameters to adapt
the beaconing rate. In addition, the protocol optimizes the performance by adapting
the transmission power based on network density and nodes distribution.
Finally, an investigation of the impact of adaptive beaconing on broadcasting
is conducted. The simulation results confirm that our adaptive beaconing scheme
can improve performance of the broadcast protocols in terms of reachability and bandwidth consumption when compared to a fixed rate scheme.

The United States has been going through a road accident crisis for many
years. The National Safety Council estimates 40,000 people were killed and 4.57
million injured on U.S. roads in 2017. Direct and indirect loss from tra c congestion
only is more than $140 billion every year. Vehicular Ad-hoc Networks (VANETs) are
envisioned as the future of Intelligent Transportation Systems (ITSs). They have a
great potential to enable all kinds of applications that will enhance road safety and
transportation efficiency. In this dissertation, we have aggregated seven years of real-life tra c and
incidents data, obtained from the Florida Department of Transportation District 4.
We have studied and investigated the causes of road incidents by applying machine
learning approaches to this aggregated big dataset. A scalable, reliable, and automatic
system for predicting road incidents is an integral part of any e ective ITS. For this
purpose, we propose a cloud-based system for VANET that aims at preventing or at
least decreasing tra c congestions as well as crashes in real-time. We have created,
tested, and validated a VANET traffic dataset by applying the connected vehicle
behavioral changes to our aggregated dataset. To achieve the scalability, speed, and fault-tolerance in our developed system, we built our system in a lambda architecture
fashion using Apache Spark and Spark Streaming with Kafka.
We used our system in creating optimal and safe trajectories for autonomous
vehicles based on the user preferences. We extended the use of our developed system in
predicting the clearance time on the highway in real-time, as an important component
of the traffic incident management system. We implemented the time series analysis
and forecasting in our real-time system as a component for predicting traffic
flow.
Our system can be applied to use dedicated short communication (DSRC), cellular,
or hybrid communication schema to receive streaming data and send back the safety
messages.
The performance of the proposed system has been extensively tested on the
FAUs High Performance Computing Cluster (HPCC), as well as on a single node
virtual machine. Results and findings confirm the applicability of the proposed system
in predicting traffic incidents with low processing latency.

Vehicular Ad hoc Networks (VANETs) have the potential to enable various
kinds of applications aiming at improving road safety and transportation efficiency.
These applications require uni-cast routing, which remains a significant challenge due
to VANETs characteristics. Given VANET dynamic topology, geographic routing
protocols are considered the most suitable for such network due to their scalability
and low overhead. However, the optimal selection of next-hop nodes in geographic
routing is a challenging problem where the routing performance is highly affected by
the variable link quality and bandwidth availability.
In this dissertation, a number of enhancements to improve geographic routing
reliability in VANETs are proposed. To minimize packet losses, the direction and
link quality of next-hop nodes using the Expected Transmission Count (ETX) are
considered to select links with low loss ratios.
To consider the available bandwidth, a cross-layer enchantment of geographic
routing, which can select more reliable links and quickly react to varying nodes load
and channel conditions, is proposed. We present a novel model of the dynamic behavior of a wireless link. It considers the loss ratio on a link, in addition to transmission
and queuing delays, and it takes into account the physical interference e ect on the
link.
Then, a novel geographic routing protocol based on fuzzy logic systems, which
help in coordinating di erent contradicting metrics, is proposed. Multiple metrics
related to vehicles' position, direction, link quality and achievable throughput are
combined using fuzzy rules in order to select the more reliable next-hop nodes for
packet forwarding.
Finally, we propose a novel link utility aware geographic routing protocol,
which extends the local view of the network topology using two-hop neighbor information.
We present our model of link utility, which measures the usefulness of a
two-hop neighbor link by considering its minimum residual bandwidth and packet
loss rate. The proposed protocol can react appropriately to increased network tra c
and to frequent topology dis-connectivity in VANETs.
To evaluate the performance of the proposed protocols, extensive simulation
experiments are performed using network and urban mobility simulation tools. Results
confirm the advantages of the proposed schemes in increased traffic loads and
network density.

This work presents the implementation of the the IEEE 1609.2 WAVE Security
Services Standard. This implementation provides the ability to generate a message
signature, along with the capability to verify that signature for wave short messages
transmitted over an unsecured medium. Only the original sender of the message can sign
it, allowing for the authentication of a message to be checked. As hashing is used during
the generation and verification of signatures, message integrity can be verified because a
failed signature verification is a result of a compromised message. Also provided is the
ability to encrypt and decrypt messages using AES-CCM to ensure that sensitive
information remains safe and secure from unwanted recipients. Additionally this
implementation provides a way for the 1609.2 specific data types to be encoded and
decoded for ease of message transmittance. This implementation was built to support the
Smart Drive initiative’s VANET testbed, supported by the National Science Foundation
and is intended to run on the Vehicular Multi-technology Communication Device
(VMCD) that is being developed. The VMCD runs on the embedded Linux operating
system and this implementation will reside inside of the Linux kernel.

This work presents the development of the Context-Aware Hybrid Data Dissemination
protocol for vehicular networks. The importance of developing vehicular networking data
dissemination protocols is exemplified by the recent announcement by the U.S. Department of Transportation (DOT) National Highway Traffic Safety Administration (NHTSA) to enable vehicle-to-vehicle (V2V) communication technology. With emphasis on safety, other useful applications of V2V communication include but are not limited to traffic and routing, weather, construction and road hazard alerts, as well as advertisement and entertainment. The core of V2V communication relies on the efficient dispersion of relevant data through wireless broadcast protocols for these varied applications. The challenges of vehicular networks demand an adaptive broadcast protocol capable of handling diverse applications. This research work illustrates the design of a wireless broadcast protocol that is context-aware and adaptive to vehicular environments taking into consideration vehicle density, road topology, and type of data to be disseminated. The context-aware hybrid data dissemination scheme combines store-and-forward and multi-hop broadcasts, capitalizing on the strengths of both these categories and mitigates the weaknesses to deliver data with maximum efficiency to a widest possible reach. This protocol is designed to work in both urban and highway mobility models. The behavior and performance of the hybrid data dissemination scheme is studied by varying the broadcast zone radius, aggregation ratio, data message size and frequency of the broadcast messages. Optimal parameters are determined and the protocol is then formulated to become adaptive to node density by keeping the field size constant and increasing the number of nodes. Adding message priority levels to propagate safety messages faster and farther than non-safety related messages is the next context we add to our adaptive protocol. We dynamically
set the broadcast region to use multi-hop which has lower latency to propagate
safety-related messages. Extensive simulation results have been obtained using realistic vehicular network scenarios. Results show that Context-Aware Hybrid Data Dissemination Protocol benefits from the low latency characteristics of multi-hop broadcast and low bandwidth consumption of store-and-forward. The protocol is adaptive to both urban and highway mobility models.