Computer algorithms.

Identifying and tracking individuals affected by this virus in densely
populated areas is a unique and an urgent challenge in the public health sector.
Currently, mapping the spread of the Ebola virus is done manually, however with
the help of social contact networks we can model dynamic graphs and predictive
diffusion models of Ebola virus based on the impact on either a specific person or
a specific community.
With the help of this model, we can make more precise forward
predictions of the disease propagations and to identify possibly infected
individuals which will help perform trace – back analysis to locate the possible
source of infection for a social group. This model will visualize and identify the
families and tightly connected social groups who have had contact with an Ebola
patient and is a proactive approach to reduce the risk of exposure of Ebola
spread within a community or geographic location.

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.

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.