Doctor of Philosophy

dissertationWe are seeing an extensive proliferation of wireless devices including various types and forms of sensor nodes that are increasingly becoming ingrained in our daily lives. There has been a significant growth in wireless devices capabilities as well. This proliferation and rapid growth of wireless devices and their capabilities has led to the development of many distributed sensing and computing applications. In this dissertation, we propose and evaluate novel, efficient approaches for localization and computation offloading that harness distributed sensing and computing in wireless networks. In a significant part of this dissertation, we exploit distributed sensing to create efficient localization applications. First, using the sensing power of a set of Radio frequency (RF) sensors, we propose energy efficient approaches for target tracking application. Second, leveraging the sensing power of a distributed set of existing wireless devices, e.g., smartphones, internet-of-things devices, laptops, and modems, etc., we propose a novel approach to locate spectrum offenders. Third, we build efficient sampling approaches to select mobile sensing devices required for spectrum offenders localization. We also enhance our sampling approaches to take into account selfish behaviors of mobile devices. Finally, we investigate an attack on location privacy where the location of people moving inside a private area can be inferred using the radio characteristics of wireless links that are leaked by legitimate transmitters deployed inside the private area, and develop the first solution to mitigate this attack. While we focus on harnessing distributed sensing for localization in a big part of this dissertation, in the remaining part of this dissertation, we harness the computing power of nearby wireless devices for a computation offloading application. Specially, we propose a multidimensional auction for allocating the tasks of a job among nearby mobile devices based on their computational capabilities and also the cost of computation at these devices with the goal of reducing the overall job completion time and being beneficial to all the parties involved

On delay-sensitive communication over wireless systems

This dissertation addresses some of the most important issues in delay-sensitive communication over wireless systems and networks. Traditionally, the design of communication networks adopts a layered framework where each layer serves as a “black box” abstraction for higher layers. However, in the context of wireless networks with delay-sensitive applications such as Voice over Internet Protocol (VoIP), on-line gaming, and video conferencing, this layered architecture does not offer a complete picture. For example, an information theoretic perspective on the physical layer typically ignores the bursty nature of practical sources and often overlooks the role of delay in service quality. The purpose of this dissertation is to take on a cross-disciplinary approach to derive new fundamental limits on the performance, in terms of capacity and delay, of wireless systems and to apply these limits to the design of practical wireless systems that support delay-sensitive applications. To realize this goal, we consider a number of objectives. 1. Develop an integrated methodology for the analysis of wireless systems that support delay-sensitive applications based, in part, on large deviation theory. 2. Use this methodology to identify fundamental performance limits and to design systems which allocate resources efficiently under stringent service requirements. 3. Analyze the performance of wireless communication networks that takes advantage of novel paradigms such as user cooperation, and multi-antenna systems. Based on the proposed framework, we find that delay constraints significantly influence how system resources should be allocated. Channel correlation has a major impact on the performance of wireless communication systems. Sophisticated power control based on the joint space of channel and buffer states are essential for delaysensitive communications

Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications

From its introduction to its quindecennial, network coding has built a strong reputation for enhancing packet recovery and achieving maximum information flow in both wired and wireless networks. Traditional studies focused on optimizing the throughput of the system by proposing elaborate schemes able to reach the network capacity. With the shift toward distributed computing on mobile devices, performance and complexity become both critical factors that affect the efficiency of a coding strategy. Instantly decodable network coding presents itself as a new paradigm in network coding that trades off these two aspects. This paper review instantly decodable network coding schemes by identifying, categorizing, and evaluating various algorithms proposed in the literature. The first part of the manuscript investigates the conventional centralized systems, in which all decisions are carried out by a central unit, e.g., a base-station. In particular, two successful approaches known as the strict and generalized instantly decodable network are compared in terms of reliability, performance, complexity, and packet selection methodology. The second part considers the use of instantly decodable codes in a device-to-device communication network, in which devices speed up the recovery of the missing packets by exchanging network coded packets. Although the performance improvements are directly proportional to the computational complexity increases, numerous successful schemes from both the performance and complexity viewpoints are identified

Control of transport dynamics in overlay networks

Transport control is an important factor in the performance of Internet protocols, particularly in the next generation network applications involving computational steering, interactive visualization, instrument control, and transfer of large data sets. The widely deployed Transport Control Protocol is inadequate for these tasks due to its performance drawbacks. The purpose of this dissertation is to conduct a rigorous analytical study on the design and performance of transport protocols, and systematically develop a new class of protocols to overcome the limitations of current methods. Various sources of randomness exist in network performance measurements due to the stochastic nature of network traffic. We propose a new class of transport protocols that explicitly accounts for the randomness based on dynamic stochastic approximation methods. These protocols use congestion window and idle time to dynamically control the source rate to achieve transport objectives. We conduct statistical analyses to determine the main effects of these two control parameters and their interaction effects. The application of stochastic approximation methods enables us to show the analytical stability of the transport protocols and avoid pre-selecting the flow and congestion control parameters. These new protocols are successfully applied to transport control for both goodput stabilization and maximization. The experimental results show the superior performance compared to current methods particularly for Internet applications. To effectively deploy these protocols over the Internet, we develop an overlay network, which resides at the application level to provide data transmission service using User Datagram Protocol. The overlay network, together with the new protocols based on User Datagram Protocol, provides an effective environment for implementing transport control using application-level modules. We also study problems in overlay networks such as path bandwidth estimation and multiple quickest path computation. In wireless networks, most packet losses are caused by physical signal losses and do not necessarily indicate network congestion. Furthermore, the physical link connectivity in ad-hoc networks deployed in unstructured areas is unpredictable. We develop the Connectivity-Through-Time protocols that exploit the node movements to deliver data under dynamic connectivity. We integrate this protocol into overlay networks and present experimental results using network to support a team of mobile robots

Distributed Operation of Uncertain Dynamical Cyberphysical Systems

In this thesis we address challenging issues that are faced in the operation of important cyber-physical systems of great current interest. The two particular systems that we address are communication networks and the smart grid. Both systems feature distributed agents making decisions in dynamic uncertain environments. In communication networks, nodes need to decide which packets to transmit, while in the power grid individual generators and loads need to decide how much to pro-duce or consume in a dynamic uncertain environment. The goal in both systems, which also holds for other cyber-physical systems, is to develop distributed policies that perform efficiently in uncertain dynamically changing environments. This thesis proposes an approach of employing duality theory on dynamic stochastic systems in such a way as to develop such distributed operating policies for cyber-physical systems. In the first half of the thesis we examine communication networks. Many cyber-physical systems, e.g., sensor networks, mobile ad-hoc networks, or networked control systems, involve transmitting data over multiple-hops of a communication network. These networks can be unreliable, for example due to the unreliability of the wireless medium. However, real-time applications in cyber-physical systems often require that requisite amounts of data be delivered in a timely manner so that it can be utilized for safely controlling physical processes. Data packets may need to be delivered within their deadlines or at regular intervals without large gaps in packet deliveries when carrying sensor readings. How such packets with deadlines can be scheduled over networks is a major challenge for cyber-physical systems. We develop a framework for routing and scheduling such data packets in a multi-hop network. This framework employs duality theory in such a way that actions of nodes get decoupled, and results in efficient decentralized policies for routing and scheduling such multi-hop communication networks. A key feature of the scheduling policy derived in this work is that the scheduling decisions regarding packets can be made in a fully distributed fashion. A decision regarding the scheduling of an individual packet depend only on the age and location of the packet, and does not require sharing of the queue lengths at various nodes. We examine in more detail a network in which multiple clients stream video packets over shared wireless networks. We are able to derive simple policies of threshold type which maximize the combined QoE of the users. We turn to another important cyber-physical system of great current interest – the emerging smarter grid for electrical power. We address some fundamental problems that arise when attempting to increase the utilization of renewable energy sources. A major challenge is that renewable energy sources are unpredictable in their availability. Utilizing them requires adaptation of demand to their uncertain availability. We address the problem faced by the system operator of coordinating sources of power and loads to balance stochastically time varying supply and demand while maximizing the total utilities of all agents in the system. We develop policies for the system operator that is charged with coordinating such distributed entities through a notion of price. We analyze some models for such systems and employ a combination of duality theory and analysis of stochastic dynamic systems to develop policies that maximize the total utility function of all the agents. We also address the issue of how the size of energy storage facilities should scale with respect to the stochastic behavior of renewables in order to mitigate the unreliability of renewable energy sources

Distributed detection and estimation in wireless sensor networks: resource allocation, fusion rules, and network security

This thesis addresses the problem of detection of an unknown binary event. In particular, we consider centralized detection, distributed detection, and network security in wireless sensor networks (WSNs). The communication links among SNs are subject to limited SN transmit power, limited bandwidth (BW), and are modeled as orthogonal channels with path loss, flat fading and additive white Gaussian noise (AWGN). We propose algorithms for resource allocations, fusion rules, and network security. In the first part of this thesis, we consider the centralized detection and calculate the optimal transmit power allocation and the optimal number of quantization bits for each SN. The resource allocation is performed at the fusion center (FC) and it is referred as a centralized approach. We also propose a novel fully $distributed$ algorithm to address this resource allocation problem. What makes this scheme attractive is that the SNs share with their neighbors just their individual transmit power at the current states. Finally, the optimal soft fusion rule at the FC is derived. But as this rule requires a-priori knowledge that is difficult to attain in practice, suboptimal fusion rules are proposed that are realizable in practice. The second part considers a fully distributed detection framework and we propose a two-step distributed quantized fusion rule algorithm where in the first step the SNs collaborate with their neighbors through error-free, orthogonal channels. In the second step, local 1-bit decisions generated in the first step are shared among neighbors to yield a consensus. A binary hypothesis testing is performed at any arbitrary SN to optimally declare the global decision. Simulations show that our proposed quantized two-step distributed detection algorithm approaches the performance of the unquantized centralized (with a FC) detector and its power consumption is shown to be 50% less than the existing (unquantized) conventional algorithm. Finally, we analyze the detection performance of under-attack WSNs and derive attacking and defense strategies from both the Attacker and the FC perspective. We re-cast the problem as a minimax game between the FC and Attacker and show that the Nash Equilibrium (NE) exists. We also propose a new non-complex and efficient reputation-based scheme to identify these compromised SNs. Based on this reputation metric, we propose a novel FC weight computation strategy ensuring that the weights for the identified compromised SNs are likely to be decreased. In this way, the FC decides how much a SN should contribute to its final decision. We show that this strategy outperforms the existing schemes