Mobile computing.

A Vehicular Ad-hoc Network (VANET) is a wireless ad-hoc network that
provides communications among vehicles with on-board units and between vehicles
and nearby roadside units. The success of a VANET relies on the ability of a
routing protocol to ful ll the throughput and delivery requirements of any applications
operating on the network. Currently, most of the proposed VANET routing protocols
focus on urban or highway environments. This dissertation addresses the need for an
adaptive routing protocol in VANETs which is able to tolerate low and high-density
network tra c with little throughput and delay variation.
This dissertation proposes three Geographic Ad-hoc On-Demand Distance
Vector (GEOADV) protocols. These three GEOADV routing protocols are designed
to address the lack of
exibility and adaptability in current VANET routing protocols.
The rst protocol, GEOADV, is a hybrid geographic routing protocol. The second
protocol, GEOADV-P, enhances GEOADV by introducing predictive features. The
third protocol, GEOADV-PF improves optimal route selection by utilizing fuzzy logic
in addition to GEOADV-P's predictive capabilities.
To prove that GEOADV and GEOADV-P are adaptive their performance is demonstrated by both urban and highway simulations. When compared to existing
routing protocols, GEOADV and GEOADV-P lead to less average delay and a
higher average delivery ratio in various scenarios. These advantages allow GEOADV-
P to outperform other routing protocols in low-density networks and prove itself
to be an adaptive routing protocol in a VANET environment. GEOADV-PF is
introduced to improve GEOADV and GEOADV-P performance in sparser networks.
The introduction of fuzzy systems can help with the intrinsic demands for
exibility
and adaptability necessary for VANETs.
An investigation into the impact adaptive beaconing has on the GEOADV
protocol is conducted. GEOADV enhanced with an adaptive beacon method is
compared against GEOADV with three xed beacon rates. Our simulation results
show that the adaptive beaconing scheme is able to reduce routing overhead, increase
the average delivery ratio, and decrease the average delay.

Image Processing and Computer Vision solutions have become commodities
for software developers, thanks to the growing availability of Application Program-
ming Interfaces (APIs) that encapsulate rich functionality, powered by advanced al-
gorithms. To understand and create an e cient method to process faces in images
by computers, one must understand how the human visual system processes them.
Face processing by computers has been an active research area for about 50
years now. Face detection has become a commodity and is now incorporated into
simple devices such as digital cameras and smartphones.
An iOS app was implemented in Objective-C using Microsoft Cognitive Ser-
vices APIs, as a tool for human vision and face processing research. Experimental
work on image compression, upside-down orientation, the Thatcher e ect, negative
inversion, high frequency, facial artifacts, caricatures and image degradation were
completed on the Radboud and 10k US Adult Faces Databases along with other
images.

This work presents the implementations of three adaptive broadcast protocols for vehicular ad hoc networks (VANET) using the Network Simulator 3 (Ns-3). Performing real life tests for VANET protocols is very costly and risky, so simulation becomes a viable alternative technique. Ns-3 is one of the most advanced open source network simulators. Yet Ns-3 lacks implementations of broadcast protocols for VANET. We first implement the Distance to Mean (DTM) protocol, which uses the distance to mean to determine if a node should rebroadcast or not. We then implement the Distribution-Adaptive Distance with Channel Quality (DADCQ) protocol, which uses node distribution, channel quality and distance to determine if a node should favor rebroadcasting. The third protocol, Statistical Location-Assisted Broadcast protocol (SLAB), is an improvement of DADCQ which automates the threshold function design using machine learning. Our NS-3 implementations of the three protocols have been validated against their JiST/SWANS implementations.

Vehicular Ad hoc NETworks (VANETs) are a subclass of Mobile Ad hoc NETworks
and represent a relatively new and very active field of research. VANETs will enable in
the near future applications that will dramatically improve roadway safety and traffic
efficiency. There is a need to increase traffic efficiency as the gap between the traveled
and the physical lane miles keeps increasing. The Dynamic Traffic Assignment problem
tries to dynamically distribute vehicles efficiently on the road network and in accordance
with their origins and destinations. We present a novel dynamic decentralized and
infrastructure-less algorithm to alleviate traffic congestions on road networks and to fill
the void left by current algorithms which are either static, centralized, or require
infrastructure. The algorithm follows an online approach that seeks stochastic user
equilibrium and assigns traffic as it evolves in real time, without prior knowledge of the traffic demand or the schedule of the cars that will enter the road network in the future.
The Reverse Online Algorithm for the Dynamic Traffic Assignment inspired by Ant
Colony Optimization for VANETs follows a metaheuristic approach that uses reports from
other vehicles to update the vehicle’s perceived view of the road network and change route
if necessary. To alleviate the broadcast storm spontaneous clusters are created around
traffic incidents and a threshold system based on the level of congestion is used to limit
the number of incidents to be reported. Simulation results for the algorithm show a great
improvement on travel time over routing based on shortest distance. As the VANET
transceivers have a limited range, that would limit messages to reach at most 1,000 meters,
we present a modified version of this algorithm that uses a rebroadcasting scheme. This
rebroadcasting scheme has been successfully tested on roadways with segments of up to
4,000 meters. This is accomplished for the case of traffic flowing in a single direction on
the roads. It is anticipated that future simulations will show further improvement when
traffic in the other direction is introduced and vehicles travelling in that direction are
allowed to use a store carry and forward mechanism.