Wireless communication systems--Technological innovations.

Multi-hop broadcast is one of the main approaches to disseminate data in
VANET. Therefore, it is important to design a reliable multi-hop broadcast protocol,
which satis es both reachability and bandwidth consumption requirements.
In a dense network, where vehicles are very close to each other, the number of
vehicles needed to rebroadcast the message should be small enough to avoid a broad-
cast storm, but large enough to meet the reachability requirement. If the network
is sparse, a higher number of vehicles is needed to retransmit to provide a higher
reachability level. So, it is obvious that there is a tradeo between reachability and
bandwidth consumption.
In this work, considering the above mentioned challenges, we design a number
of smart broadcast protocols and evaluate their performance in various network den-
sity scenarios. We use fuzzy logic technique to determine the quali cation of vehicles
to be forwarders, resulting in reachability enhancement. Then we design a band-
width e cient fuzzy logic-assisted broadcast protocol which aggressively suppresses
the number of retransmissions. We also propose an intelligent hybrid protocol adapts
to local network density. In order to avoid packet collisions and enhance reachability, we design a cross layer statistical broadcast protocol, in which the contention window
size is adjusted based on the local density information.
We look into the multi-hop broadcast problem with an environment based
on game theory. In this scenario, vehicles are players and their strategy is either
to volunteer and rebroadcast the received message or defect and wait for others to
rebroadcast. We introduce a volunteer dilemma game inspired broadcast scheme to
estimate the probability of forwarding for the set of potential forwarding vehicles. In
this scheme we also introduce a fuzzy logic-based contention window size adjustment
system.
Finally, based on the estimated spatial distribution of vehicles, we design a
transmission range adaptive scheme with a fuzzy logic-assisted contention window
size system, in which a bloom lter method is used to mitigate overhead.
Extensive experimental work is obtained using simulation tools to evaluate the
performance of the proposed schemes. The results con rm the relative advantages of
the proposed protocols for di erent density scenarios.

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.

Cognitive radio technology that enables dynamic spectrum access has been
a promising solution for the spectrum scarcity problem. Cognitive radio networks
enable the communication on both licensed and unlicensed channels, having the potential
to better solve the interference and collision issues. Channel assignment is of
great importance in cognitive radio networks. When operating on licensed channels,
the objective is to exploit spectrum holes through cognitive communication, giving
priority to the primary users. In this dissertation, we focus on the development of efficient
channel assignment algorithms and protocols to improve network performance
for cognitive radio wireless networks. The first contribution is on channel assignment
for cognitive radio wireless sensor networks aiming to provide robust topology control,
as well as to increase network throughput and data delivery rate. The approach
is then extended to specific cognitive radio network applications achieving improved
performances.

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.