Wireless Sensor Networks (WSNs) are emerging technologies that have the ability to sense, process, communicate, and transmit information to a destination, and they are expected to have significant impact on the efficiency of many applications in various fields. The resource constraint such as limited battery power, is the greatest challenge in WSNs design as it affects the lifetime and performance of the network. An energy efficient, secure, and trustworthy system is vital when a WSN involves highly sensitive information. Thus, it is critical to design mechanisms that are energy efficient and secure while at the same time maintaining the desired level of quality of service. Inspired by these challenges, this dissertation is dedicated to exploiting optimization and game theoretic approaches/solutions to handle several important issues in WSN communication, including energy efficiency, latency, congestion, dynamic traffic load, and security. We present several novel mechanisms to improve the security and energy efficiency of WSNs. Two new schemes are proposed for the network layer stack to achieve the following: (a) to enhance energy efficiency through optimized sleep intervals, that also considers the underlying dynamic traffic load and (b) to develop the routing protocol in order to handle wasted energy, congestion, and clustering. We also propose efficient routing and energy-efficient clustering algorithms based on optimization and game theory. Furthermore, we propose a dynamic game theoretic framework (i.e., hyper defense) to analyze the interactions between attacker and defender as a non-cooperative security game that considers the resource limitation. All the proposed schemes are validated by extensive experimental analyses, obtained by running simulations depicting various situations in WSNs in order to represent real-world scenarios as realistically as possible. The results show that the proposed schemes achieve high performance in different terms, such as network lifetime, compared with the state-of-the-art schemes.

The airborne nature of wireless transmission offers a potential target for attackers to compromise IEEE 802.11 Wireless Local Area Network (WLAN). In this dissertation, we explore the current WLAN security threats and their corresponding defense solutions. In our study, we divide WLAN vulnerabilities into two aspects, client, and administrator. The client-side vulnerability investigation is based on examining the Evil Twin Attack (ETA) while our administrator side research targets Wi-Fi Protected Access II (WPA2). Three novel techniques have been presented to detect ETA. The detection methods are based on (1) creating a secure connection to a remote server to detect the change of gateway's public IP address by switching from one Access Point (AP) to another. (2) Monitoring multiple Wi-Fi channels in a random order looking for specific data packets sent by the remote server. (3) Merging the previous solutions into one universal ETA detection method using Virtual Wireless Clients (VWCs). On the other hand, we present a new vulnerability that allows an attacker to force the victim's smartphone to consume data through the cellular network by starting the data download on the victim's cell phone without the victim's permission. A new scheme has been developed to speed up the active dictionary attack intensity on WPA2 based on two novel ideas. First, the scheme connects multiple VWCs to the AP at the same time-each VWC has its own spoofed MAC address. Second, each of the VWCs could try many passphrases using single wireless session. Furthermore, we present a new technique to avoid bandwidth limitation imposed by Wi-Fi hotspots. The proposed method creates multiple VWCs to access the WLAN. The combination of the individual bandwidth of each VWC results in an increase of the total bandwidth gained by the attacker. All proposal techniques have been implemented and evaluated in real-life scenarios.

Oral story telling has become a lost art of family histories because social media and technology have taken over the personal interactions that once passed on the important stories and facts from generation to generation. This dissertation presents and evaluates a method of generating a narrative with input from the listener without actually forcing him or her to become an actual character in the narrative. This system is called CAMPFIRE Story Telling System (STS) and employs a contextual approach to story generation. This system uses the Cooperating Context Method (CCM) to generate and tell dynamic stories in real time and can be modified by the listener. CCM was created to overcome the weaknesses found in other contextual approaches during story generation while still meeting the design criteria of 1) being able to plan out a story; 2) being able to create a narrative that is entertaining to the listener; and 3) being able to modify the story that could incorporate the listener's request in the story. The CCM process begins by creating a list of tasks by analyzing the current situation. A list of contexts is narrowed down through a series of algorithms into two lists: high priority and low priority lists. These lists are analyzed and a set of context best suited to handle the tasks are selected. The CAMPFIRE STS was rigorously assessed for its functionality, novelty, and user acceptance as well as the time needed to modify the knowledge base. These evaluations showed that the CAMPFIRE STS has the ability to create novel stories using the same knowledge base. A group of 38 test subjects used and evaluated CAMPFIRE STS with respect to its use for children, story entertainment, story creativity and the system's ease of use answering a extensive survey of 54 questions. The survey showed that CAMPFIRE STS can create stories appropriate for bedtime stories with some minor modifications and that the generated stories are novel and entertaining stories, and that it was an easy system to use.

This research proposes to enhance some Evolutionary Algorithms in order to obtain optimal and adaptive network configurations. Due to the richness in technologies, low cost, and application usages, we consider Heterogeneous Wireless Mesh Networks. In particular, we evaluate the domains of Network Deployment, Smart Grids/Homes, and Intrusion Detection Systems. Having an adaptive network as one of the goals, we consider a robust noise tolerant methodology that can quickly react to changes in the environment. Furthermore, the diversity of the performance objectives considered (e.g., power, coverage, anonymity, etc.) makes the objective function non-continuous and therefore not have a derivative. For these reasons, we enhance Particle Swarm Optimization (PSO) algorithm with elements that aid in exploring for better configurations to obtain optimal and sub-optimal configurations. According to results, the enhanced PSO promotes population diversity, leading to more unique optimal configurations for adapting to dynamic environments. The gradual complexification process demonstrated simpler optimal solutions than those obtained via trial and error without the enhancements. Configurations obtained by the modified PSO are further tuned in real-time upon environment changes. Such tuning occurs with a Fuzzy Logic Controller (FLC) which models human decision making by monitoring certain events in the algorithm. Example of such events include diversity and quality of solution in the environment. The FLC is able to adapt the enhanced PSO to changes in the environment, causing more exploration or exploitation as needed. By adding a Probabilistic Neural Network (PNN) classifier, the enhanced PSO is again used as a filter to aid in intrusion detection classification. This approach reduces miss classifications by consulting neighbors for classification in case of ambiguous samples. The performance of ambiguous votes via PSO filtering shows an improvement in classification, causing the simple classifier perform better the commonly used classifiers.

Because of the ever increasing demand wireless data in the modern era, the Radio Frequency (RF) spectrum is becoming more congested. The remaining RF spectrum is being shrunk at a very heavy rate, and spectral management is becoming more difficult. Mobile data is estimated to grow more than 10 times between 2013 and 2019, and due to this explosion in data usage, mobile operators are having serious concerns focusing on public Wireless Fidelity (Wi-Fi) and other alternative technologies. Visible Light Communication (VLC) is a recent promising technology complementary to RF spectrum which operates at the visible light spectrum band (roughly 400 THz to 780 THz) and it has 10,000 times bigger size than radio waves (roughly 3 kHz to 300 GHz). Due to this tremendous potential, VLC has captured a lot of interest recently as there is already an extensive deployment of energy efficient Light Emitting Diodes (LEDs). The advancements in LED technology with fast nanosecond switching times is also very encouraging. In this work, we present hybrid RF/VLC architecture which is capable of providing simultaneous lighting and communication coverage in an indoor setting. The architecture consists of a multi-element hemispherical bulb design, where it is possible to transmit multiple data streams from the multi-element hemispherical bulb using LED modules. We present the detailed components of the architecture and make simulations considering various VLC transmitter configurations. Also, we devise an approach for an efficient bulb design mechanism to maintain both illumination and communication at a satisfactory rate, and analyze it in the case of two users in a room. The approach involves formulating an optimization problem and tackling the problem using a simple partitioning algorithm. The results indicate that good link quality and high spatial reuse can be maintained in a typical indoor communication setting.

DC-DC power converters play an important role in powering telecom and computing systems. Complex systems, including power electronics systems, are increasingly using digital controllers because of the major advancements in digital controllers and DSP as well as there ability to perform sophisticated and enhanced control schemes. In this thesis, the digital controller is investigated for DC-DC converters in high current low voltage applications. For an optimal design of a regulated DC-DC converter, it is necessary to derive a valid model. The current doubler rectified half bridge (CDRHB) DC-DC converter is suitable for high current low voltage applications. In this thesis, the topology operations are analyzed and then the unified state space model, analog small signal model and digital small signal model are derived. Then the digital compensator design is discussed as well as the analog-digital converter (ADC) and the digital pulse-width-modulator (DPWM) design rules. In addition, voltage driving optimization is proposed for the benefit of the digital controller. Finally, experimental results based on the CDRHB are presented and analyzed.

MapReduce, designed by Google, is widely used as the most popular distributed programming model in cloud environments. Hadoop, an open-source implementation of MapReduce, is a data management framework on large cluster of commodity machines to handle data-intensive applications. Many famous enterprises including Facebook, Twitter, and Adobe have been using Hadoop for their data-intensive processing needs. Task stragglers in MapReduce jobs dramatically impede job execution on massive datasets in cloud computing systems. This impedance is due to the uneven distribution of input data and computation load among cluster nodes, heterogeneous data nodes, data skew in reduce phase, resource contention situations, and network configurations. All these reasons may cause delay failure and the violation of job completion time. One of the key issues that can significantly affect the performance of cloud computing is the computation load balancing among cluster nodes. Replica placement in Hadoop distributed file system plays a significant role in data availability and the balanced utilization of clusters. In the current replica placement policy (RPP) of Hadoop distributed file system (HDFS), the replicas of data blocks cannot be evenly distributed across cluster's nodes. The current HDFS must rely on a load balancing utility for balancing the distribution of replicas, which results in extra overhead for time and resources. This dissertation addresses data load balancing problem and presents an innovative replica placement policy for HDFS. It can perfectly balance the data load among cluster's nodes. The heterogeneity of cluster nodes exacerbates the issue of computational load balancing; therefore, another replica placement algorithm has been proposed in this dissertation for heterogeneous cluster environments. The timing of identifying the straggler map task is very important for straggler mitigation in data-intensive cloud computing. To mitigate the straggler map task, Present progress and Feedback based Speculative Execution (PFSE) algorithm has been proposed in this dissertation. PFSE is a new straggler identification scheme to identify the straggler map tasks based on the feedback information received from completed tasks beside the progress of the current running task. Straggler reduce task aggravates the violation of MapReduce job completion time. Straggler reduce task is typically the result of bad data partitioning during the reduce phase. The Hash partitioner employed by Hadoop may cause intermediate data skew, which results in straggler reduce task. In this dissertation a new partitioning scheme, named Balanced Data Clusters Partitioner (BDCP), is proposed to mitigate straggler reduce tasks. BDCP is based on sampling of input data and feedback information about the current processing task. BDCP can assist in straggler mitigation during the reduce phase and minimize the job completion time in MapReduce jobs. The results of extensive experiments corroborate that the algorithms and policies proposed in this dissertation can improve the performance of data-intensive applications running on cloud platforms.

The revolution in chip manufacturing processes spanning five decades has proliferated high performance and energy-efficient nano-electronic devices across all aspects of daily life. In recent years, CMOS technology scaling has realized billions of transistors within large-scale VLSI chips to elevate performance. However, these advancements have also continually augmented the impact of Single-Event Transient (SET) and Single-Event Upset (SEU) occurrences which precipitate a range of Soft-Error (SE) dependability issues. Consequently, soft-error mitigation techniques have become essential to improve systems' reliability. Herein, first, we proposed optimized soft-error resilience designs to improve robustness of sub-micron computing systems. The proposed approaches were developed to deliver energy-efficiency and tolerate double/multiple errors simultaneously while incurring acceptable speed performance degradation compared to the prior work. Secondly, the impact of Process Variation (PV) at the Near-Threshold Voltage (NTV) region on redundancy-based SE-mitigation approaches for High-Performance Computing (HPC) systems was investigated to highlight the approach that can realize favorable attributes, such as reduced critical datapath delay variation and low speed degradation. Finally, recently, spin-based devices have been widely used to design Non-Volatile (NV) elements such as NV latches and flip-flops, which can be leveraged in normally-off computing architectures for Internet-of-Things (IoT) and energy-harvesting-powered applications. Thus, in the last portion of this dissertation, we design and evaluate for soft-error resilience NV-latching circuits that can achieve intriguing features, such as low energy consumption, high computing performance, and superior soft errors tolerance, i.e., concurrently able to tolerate Multiple Node Upset (MNU), to potentially become a mainstream solution for the aerospace and avionic nanoelectronics. Together, these objectives cooperate to increase energy-efficiency and soft errors mitigation resiliency of larger-scale emerging NV latching circuits within iso-energy constraints. In summary, addressing these reliability concerns is paramount to successful deployment of future reliable and energy-efficient CMOS logic and spintronic memory architectures with deeply-scaled devices operating at low-voltages.

Underwater sensor networks are deployed in marine environments, presenting specific challenges compared to sensor networks deployed in terrestrial settings. Among the major issues that underwater sensor networks face is communication medium limitations that result in low bandwidth and long latency. This creates problems when these networks need to transmit large amounts of data over long distances. A possible solution to address this issue is to use mobile sinks such as autonomous underwater vehicles (AUVs) to offload these large quantities of data. Such mobile sinks are called data mules. Often it is the case that a sensor network is deployed to report events that require immediate attention. Delays in reporting such events can have catastrophic consequences. In this dissertation, we present path planning algorithms that help in prioritizing data retrieval from sensor nodes in such a manner that nodes that require more immediate attention would be dealt with at the earliest. In other words, the goal is to improve the Quality of Information (QoI) retrieved. The path planning algorithms proposed in this dissertation are based on heuristics meant to improve the Value of Information (VoI) retrieved from a system. Value of information is a construct that helps in encoding the valuation of an information segment i.e. it is the price an optimal player would pay to obtain a segment of information in a game theoretic setting. Quality of information and value of information are complementary concepts. In this thesis, we formulate a value of information model for sensor networks and then consider the constraints that arise in underwater settings. On the basis of this, we develop a VoI-based path planning problem statement and propose heuristics that solve the path planning problem. We show through simulation studies that the proposed strategies improve the value, and hence, quality of the information retrieved. It is important to note that these path planning strategies can be applied equally well in terrestrial settings that deploy mobile sinks for data collection.

Free-Space-Optical (FSO) communication has the potential to play a significant role in future generation wireless networks. It is advantageous in terms of improved spectrum utilization, higher data transfer rate, and lower probability of interception from unwanted sources. FSO communication can provide optical-level wireless communication speeds and can also help solve the wireless capacity problem experienced by the traditional RF-based technologies. Despite these advantages, communications using FSO transceivers require establishment and maintenance of line-of-sight (LOS). We consider autonomous mobile nodes (Unmanned Ground Vehicles or Unmanned Aerial Vehicles), each with one FSO transceiver mounted on a movable head capable of scanning in the horizontal and vertical planes. We propose novel schemes that deal with the problems of automatic discovery, establishment, and maintenance of LOS alignment between these nodes with mechanical steering of the directional FSO transceivers in 2-D and 3-D scenarios. We perform extensive simulations to show the effectiveness of the proposed methods for both neighbor discovery and LOS maintenance. We also present a prototype implementation of such mobile nodes with FSO transceivers. The potency of the neighbor discovery and LOS alignment protocols is evaluated by analyzing the results obtained from both simulations and experiments conducted using the prototype. The results show that, by using such mechanically steerable directional transceivers and the proposed methods, it is possible to establish optical wireless links within practical discovery times and maintain the links in a mobile setting with minimal disruption.