Wireless sensor networks

Mobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movement – where people want to go, how they get there, and the challenges they face along the way. Today, local governments can automate the acquisition of such data using video surveillance to understand the potential impact of investment and policy decisions. However, public disapproval of computer vision due to privacy concerns opens opportunities for research into alternative tools built with privacy constraints at the core of the design. WiFi sensing emerges as a promising solution. Modern mobile devices ubiquitously support the 802.11 standard and regularly emit WiFi probe requests for network discovery. We can passively monitor this traffic to estimate the levels of congestion in public spaces.
In this dissertation, we address three fundamental research problems pertaining to developing streetscape-scale mobility intelligence: scalable infrastructure for WiFi signal capture, passive device localization, and device re-identification.

The success of deep learning has renewed interest in applying neural networks and other machine learning techniques to most fields of data and signal processing, including communications. Advances in architecture and training lead us to consider new modem architectures that allow flexibility in design, continued learning in the field, and improved waveform coding. This dissertation examines neural network architectures and training methods suitable for demodulation in power-limited communication systems, such as those found in wireless sensor networks. Such networks will provide greater connection to the world around us and are expected to contain orders of magnitude more devices than cellular networks. A number of standard and proprietary protocols span this space, with modulations such as frequency-shift-keying (FSK), Gaussian FSK (GFSK), minimum shift keying (MSK), on-off-keying (OOK), and M-ary orthogonal modulation (M-orth). These modulations enable low-cost radio hardware with efficient nonlinear amplification in the transmitter and noncoherent demodulation in the receiver.

Wireless sensor networks (WSNs) provide rapid, untethered access to information, eliminating the barriers of distance, time, and location for many applications in national security, civilian search and rescue operations, surveillance, border monitoring, and many more. Sensor nodes are resource constraint in terms of power, bandwidth, memory, and computing capabilities. Sensor nodes are typically battery powered and depending on the application, it may be impractical or even impossible to recharge them. Thus, it is important to develop mechanisms for WSN which are energy efficient, in order to reduce the energy consumption in the network. Energy efficient algorithms result in an increased network lifetime. Data gathering is an important operation in WSNs, dealing with collecting sensed data or event reporting in a timely and efficient way. There are various scenarios that have to be carefully addressed. In this dissertation we propose energy efficient algorithms for data gathering. We propose a novel event-based clustering mechanism, and propose several efficient data gathering algorithms for mobile sink WSNs and for spatio-temporal events. Border surveillance is an important application of WSNs. Typical border surveillance applications aim to detect intruders attempting to enter or exit the border of a certain region. Deploying a set of sensor nodes on a region of interest where sensors form barriers for intruders is often referred to as the barrier coverage problem. In this dissertation we propose some novel mechanisms for increasing the percentage of events detected successfully. More specifically, we propose an adaptive sensor rotation mechanism, which allow sensors to decide their orientation angle adaptively, based on the location of the incoming events. In addition, we propose an Unmanned Aerial Vehicle UAV aided mechanism, where an UAV is used to cover gaps dynamically, resulting in an increased quality of the surveillance.

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.

Channel assignment in multi-radio networks is a topic of great importance because
the use of multiple channels and multiple radios reduces interference and increases the
network throughput. The goal of our research is to design algorithms that maximize the
use of available resources while providing robustness to primary users that could reclaim
one or more channels. Our algorithms could be used in ad hoc networks, mesh networks,
and sensor networks where nodes are equipped with multiple radios. We design
algorithms for channel assignment which provide robustness to primary users without
assuming an accurate primary user behavior model. We also compute bounds for capacity
in grid networks and discuss how the capacity of a network changes when multiple
channels are available. Since preserving energy is very important in wireless networks,
we focus on algorithms that do not require powerful resources and which use a reduced
number of messages.

Fourier telescopy imaging is a recently-developed imaging method that relies on active
structured-light illumination of the object. Reflected/scattered light is measured by a large
“light bucket” detector; processing of the detected signal yields the magnitude and phase
of spatial frequency components of the object reflectance or transmittance function. An
inverse Fourier transform results in the image.
In 2012 a novel method, known as time-average Fourier telescopy (TAFT), was
introduced by William T. Rhodes as a means for diffraction-limited imaging through
ground-level atmospheric turbulence. This method, which can be applied to long
horizontal-path terrestrial imaging, addresses a need that is not solved by the adaptive
optics methods being used in astronomical imaging.
Field-experiment verification of the TAFT concept requires instrumentation that is not
available at Florida Atlantic University. The objective of this doctoral research program is thus to demonstrate, in the absence of full-scale experimentation, the feasibility of
time-average Fourier telescopy through (a) the design, construction, and testing of smallscale
laboratory instrumentation capable of exploring basic Fourier telescopy datagathering
operations, and (b) the development of MATLAB-based software capable of
demonstrating the effect of kilometer-scale passage of laser beams through ground-level
turbulence in a numerical simulation of TAFT.

Time Average Fourier Telescopy (TAFT) has been proposed as a means for obtaining high-resolution, diffraction-limited images over large distances through ground-level horizontal-path atmospheric turbulence. Image data is collected in the spatial-frequency, or Fourier, domain by means of Fourier Telescopy; an inverse two dimensional Fourier transform yields the actual image. TAFT requires active illumination of the distant object by moving interference fringe patterns. Light reflected from the object is collected by a “light-bucket” detector, and the resulting electrical signal is digitized and subjected to a series of signal processing operations, including an all-critical averaging of the amplitude and phase of a number of narrow-band signals.

The Transmission Control Protocol (TCP) is one of the core protocols of the Internet protocol suite. In the wired network, TCP performs remarkably well due to its scalability and distributed end-to-end congestion control algorithms. However, many studies have shown that the unmodified standard TCP performs poorly in networks with large bandwidth-delay products and/or lossy wireless links. In this thesis, we analyze the problems TCP exhibits in the wireless communication and develop TCP congestion control algorithm for mobile applications. We show that the optimal TCP congestion control and link scheduling scheme amounts to window-control oriented implicit primaldual solvers for underlying network utility maximization. Based on this idea, we used a scalable congestion control algorithm called QUeueIng-Control (QUIC) TCP where it utilizes queueing-delay based MaxWeight-type scheduler for wireless links developed in [34]. Simulation and test results are provided to evaluate the proposed schemes in practical networks.

Sensors are shaping many activities in our society with an endless array of potential applications in military, civilian, and medical application. They support different real world applications ranging from common household appliances to complex systems. Technological advancement has enabled sensors to be used in medical applications, wherein they are deployed to monitor patients and assist disabled patients. Sensors have been invaluable in saving lives, be it a soldier's life in a remote battlefield or a civilian's life in a disaster area or natural calamities. In every application the sensors are deployed in a pre-defined manner to perform a specific function. Understanding the basic structure of a sensor node is essential as this would be helpful in using the sensors in devices and environments that have not been explored. In this research, patterns are used to present a more abstract view of the structure and architecture of sensor nodes and wireless sensor networks. This would help an application designer to choose from different types of sensor nodes and sensor network architectures for applications such as robotic landmine detection or remote patient monitoring systems. Moreover, it would also help the network designer to reuse, combine or modify the architectures to suit more complex needs. More importantly, they can be integrated with complete IT applications. One of the important applications of wireless sensor networks in the medical field is a remote patient monitoring system. In this work, patterns were developed to describe the architecture of patient monitoring system.