Algorithms for the Analysis of Spatio-Temporal Data from Team Sports

Modern object tracking systems are able to simultaneously record trajectories—sequences of time-stamped location points—for large numbers of objects with high frequency and accuracy. The availability of trajectory datasets has resulted in a consequent demand for algorithms and tools to extract information from these data. In this thesis, we present several contributions intended to do this, and in particular, to extract information from trajectories tracking football (soccer) players during matches. Football player trajectories have particular properties that both facilitate and present challenges for the algorithmic approaches to information extraction. The key property that we look to exploit is that the movement of the players reveals information about their objectives through cooperative and adversarial coordinated behaviour, and this, in turn, reveals the tactics and strategies employed to achieve the objectives. While the approaches presented here naturally deal with the application-specific properties of football player trajectories, they also apply to other domains where objects are tracked, for example behavioural ecology, traffic and urban planning

Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems

The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem

Robust deployment and control of sensors in wireless monitoring networks

Advances in Micro Electro-Mechanical Systems (MEMS) technology, including MEMS sensors, have allowed the deployment of small, inexpensive, energy-efficient sensors with wireless networking capabilities. The continuing development of these technologies has given rise to increased interest in the concept of wireless sensor networks (WSNs). A WSN is composed of a large number (hundreds, even thousands) of sensor nodes, each consisting of sensing, data processing, and communication components. The sensors are deployed onto a region of interest and form a network to directly sense and report on physical phenomena. The goal of a monitoring wireless sensor network is to gather sensor data from a specified region and relay this information to a designated base station (BSt). In this study, we focus on deploying and replenishing wireless sensor nodes onto an area such that a given mission lifetime is met subject to constraints on cost, connectivity, and coverage of the area of interest. The major contributions of this work are (1) a technique for differential deployment (meaning that nodes are deployed with different densities depending on their distance from the base station); the resulting clustered architecture extends lifetime beyond network lifetime experienced with a uniform deployment and other existing differential techniques; (2) a characterization of the energy consumption in a clustered network and the energy remaining after network failure, this characterization includes the overhead costs associated with creating hierarchies and retrieving data from all sensors ; (3) a characterization of the effects and costs associated with hop counts in the network; (4) a strategy for replenishing nodes consisting of determining the optimal order size and the allocation over the deployment region. The impact of replenishment is also integrated into the network control model using intervention analysis. The result is a set of algorithms that provide differential deployment densities for nodes (clusterhead and non-clusterhead) that maximize network lifetime and minimize wasted energy. If a single deployment is not feasible, the optimal replenishment strategy that minimizes deployment costs and penalties is calculated.Ph.D., Electrical Engineering -- Drexel University, 201

Role of Interference and Computational Complexity in Modern Wireless Networks: Analysis, Optimization, and Design

Owing to the popularity of smartphones, the recent widespread adoption of wireless broadband has resulted in a tremendous growth in the volume of mobile data traffic, and this growth is projected to continue unabated. In order to meet the needs of future systems, several novel technologies have been proposed, including cooperative communications, cloud radio access networks (RANs) and very densely deployed small-cell networks. For these novel networks, both interference and the limited availability of computational resources play a very important role. Therefore, the accurate modeling and analysis of interference and computation is essential to the understanding of these networks, and an enabler for more efficient design.;This dissertation focuses on four aspects of modern wireless networks: (1) Modeling and analysis of interference in single-hop wireless networks, (2) Characterizing the tradeoffs between the communication performance of wireless transmission and the computational load on the systems used to process such transmissions, (3) The optimization of wireless multiple-access networks when using cost functions that are based on the analytical findings in this dissertation, and (4) The analysis and optimization of multi-hop networks, which may optionally employ forms of cooperative communication.;The study of interference in single-hop wireless networks proceeds by assuming that the random locations of the interferers are drawn from a point process and possibly constrained to a finite area. Both the information-bearing and interfering signals propagate over channels that are subject to path loss, shadowing, and fading. A flexible model for fading, based on the Nakagami distribution, is used, though specific examples are provided for Rayleigh fading. The analysis is broken down into multiple steps, involving subsequent averaging of the performance metrics over the fading, the shadowing, and the location of the interferers with the aim to distinguish the effect of these mechanisms that operate over different time scales. The analysis is extended to accommodate diversity reception, which is important for the understanding of cooperative systems that combine transmissions that originate from different locations. Furthermore, the role of spatial correlation is considered, which provides insight into how the performance in one location is related to the performance in another location.;While it is now generally understood how to communicate close to the fundamental limits implied by information theory, operating close to the fundamental performance bounds is costly in terms of the computational complexity required to receive the signal. This dissertation provides a framework for understanding the tradeoffs between communication performance and the imposed complexity based on how close a system operates to the performance bounds, and it allows to accurately estimate the required data processing resources of a network under a given performance constraint. The framework is applied to Cloud-RAN, which is a new cellular architecture that moves the bulk of the signal processing away from the base stations (BSs) and towards a centralized computing cloud. The analysis developed in this part of the dissertation helps to illuminate the benefits of pooling computing assets when decoding multiple uplink signals in the cloud. Building upon these results, new approaches for wireless resource allocation are proposed, which unlike previous approaches, are aware of the computing limitations of the network.;By leveraging the accurate expressions that characterize performance in the presence of interference and fading, a methodology is described for optimizing wireless multiple-access networks. The focus is on frequency hopping (FH) systems, which are already widely used in military systems, and are becoming more common in commercial systems. The optimization determines the best combination of modulation parameters (such as the modulation index for continuous-phase frequency-shift keying), number of hopping channels, and code rate. In addition, it accounts for the adjacent-channel interference (ACI) and determines how much of the signal spectrum should lie within the operating band of each channel, and how much can be allowed to splatter into adjacent channels.;The last part of this dissertation contemplates networks that involve multi-hop communications. Building on the analytical framework developed in early parts of this dissertation, the performance of such networks is analyzed in the presence of interference and fading, and it is introduced a novel paradigm for a rapid performance assessment of routing protocols. Such networks may involve cooperative communications, and the particular cooperative protocol studied here allows the same packet to be transmitted simultaneously by multiple transmitters and diversity combined at the receiver. The dynamics of how the cooperative protocol evolves over time is described through an absorbing Markov chain, and the analysis is able to efficiently capture the interference that arises as packets are periodically injected into the network by a common source, the temporal correlation among these packets and their interdependence

Advances in Graph-Cut Optimization: Multi-Surface Models, Label Costs, and Hierarchical Costs

Computer vision is full of problems that are elegantly expressed in terms of mathematical optimization, or energy minimization. This is particularly true of low-level inference problems such as cleaning up noisy signals, clustering and classifying data, or estimating 3D points from images. Energies let us state each problem as a clear, precise objective function. Minimizing the correct energy would, hypothetically, yield a good solution to the corresponding problem. Unfortunately, even for low-level problems we are confronted by energies that are computationally hard—often NP-hard—to minimize. As a consequence, a rather large portion of computer vision research is dedicated to proposing better energies and better algorithms for energies. This dissertation presents work along the same line, specifically new energies and algorithms based on graph cuts. We present three distinct contributions. First we consider biomedical segmentation where the object of interest comprises multiple distinct regions of uncertain shape (e.g. blood vessels, airways, bone tissue). We show that this common yet difficult scenario can be modeled as an energy over multiple interacting surfaces, and can be globally optimized by a single graph cut. Second, we introduce multi-label energies with label costs and provide algorithms to minimize them. We show how label costs are useful for clustering and robust estimation problems in vision. Third, we characterize a class of energies with hierarchical costs and propose a novel hierarchical fusion algorithm with improved approximation guarantees. Hierarchical costs are natural for modeling an array of difficult problems, e.g. segmentation with hierarchical context, simultaneous estimation of motions and homographies, or detecting hierarchies of patterns

Reliable and energy efficient resource provisioning in cloud computing systems

Cloud Computing has revolutionized the Information Technology sector by giving computing a perspective of service. The services of cloud computing can be accessed by users not knowing about the underlying system with easy-to-use portals. To provide such an abstract view, cloud computing systems have to perform many complex operations besides managing a large underlying infrastructure. Such complex operations confront service providers with many challenges such as security, sustainability, reliability, energy consumption and resource management. Among all the challenges, reliability and energy consumption are two key challenges focused on in this thesis because of their conflicting nature. Current solutions either focused on reliability techniques or energy efficiency methods. But it has been observed that mechanisms providing reliability in cloud computing systems can deteriorate the energy consumption. Adding backup resources and running replicated systems provide strong fault tolerance but also increase energy consumption. Reducing energy consumption by running resources on low power scaling levels or by reducing the number of active but idle sitting resources such as backup resources reduces the system reliability. This creates a critical trade-off between these two metrics that are investigated in this thesis. To address this problem, this thesis presents novel resource management policies which target the provisioning of best resources in terms of reliability and energy efficiency and allocate them to suitable virtual machines. A mathematical framework showing interplay between reliability and energy consumption is also proposed in this thesis. A formal method to calculate the finishing time of tasks running in a cloud computing environment impacted with independent and correlated failures is also provided. The proposed policies adopted various fault tolerance mechanisms while satisfying the constraints such as task deadlines and utility values. This thesis also provides a novel failure-aware VM consolidation method, which takes the failure characteristics of resources into consideration before performing VM consolidation. All the proposed resource management methods are evaluated by using real failure traces collected from various distributed computing sites. In order to perform the evaluation, a cloud computing framework, 'ReliableCloudSim' capable of simulating failure-prone cloud computing systems is developed. The key research findings and contributions of this thesis are: 1. If the emphasis is given only to energy optimization without considering reliability in a failure prone cloud computing environment, the results can be contrary to the intuitive expectations. Rather than reducing energy consumption, a system ends up consuming more energy due to the energy losses incurred because of failure overheads. 2. While performing VM consolidation in a failure prone cloud computing environment, a significant improvement in terms of energy efficiency and reliability can be achieved by considering failure characteristics of physical resources. 3. By considering correlated occurrence of failures during resource provisioning and VM allocation, the service downtime or interruption is reduced significantly by 34% in comparison to the environments with the assumption of independent occurrence of failures. Moreover, measured by our mathematical model, the ratio of reliability and energy consumption is improved by 14%

Social evolution : opinions and behaviors in face-to-face networks

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 133-143).Exposure to new ideas and opinions, and their diffusion within social networks, are important questions in education, business, and government. However until recently there has been no method to automatically capture fine-grained face-to-face interactions between people, to better model the diffusion process. In this thesis, we describe the use of colocation and communication sensors in 'socially aware' mobile phones to model the spread of opinions and behaviors of 78 residents of an undergraduate residence hall for an entire academic year, based on over 320,000 hours of behavior data. Political scientists (Huckfeldt and Sprague, APSR, 1983) have noted the problem of mutual causation between face-to-face networks and political opinions. During the last three months of the 2008 US presidential campaigns of Barack Obama and John McCain, we find that political discussants have characteristic interaction patterns that can be used to recover the self-reported 'political discussant' ties within the community. Automatically measured mobile phone features allow us to estimate exposure to different types of opinions in this community. We propose a measure of 'dynamic homophily' which reveals surprising short-term, population-wide behavior changes around external political events such as election debates and Election Day. To our knowledge, this is the first time such dynamic homophily effects have been measured. We find that social exposure to peers in the network predicts individual future opinions (R 2 ~ 0.8, p < 0.001). The use of mobile phone based dynamic exposure increases the explained variance for future political opinions by up to 30%. It is well known that face-to-face networks are the main vehicle for airborne contagious diseases (Elliott, Spatial Epidemiology, 2000). However, epidemiologists have not had access to tools to quantitatively measure the likelihood of contagion, as a function of contact/exposure with infected individuals, in realistic scenarios (Musher, NEJM, 2003), since it requires data about both symptoms and social interactions between individuals. We use of co-location and communication sensors to understand the role of face-to-face interactions in the contagion process. We find that there are characteristic changes in behavior when individuals become sick, reflected in features like total communication, temporal structure in communication (e.g., late nights and weekends), interaction diversity, and movement entropy (both within and outside the university). These behavior variations can be used to infer the likelihood of an individual being symptomatic, based on their network interactions alone, without the use of health-reports. We use a recently-developed signal processing approach (Nolte, Nature, 2008) to better understand the temporal information flux between physical symptoms (i.e., common colds, influenza), measured behavior variations and mental health symptoms (i.e., stress and early depression). Longitudinal studies indicate that health-related behaviors from obesity (Christakis and Fowler, 2007) to happiness (Fowler and Christakis, 2008) may spread through social ties. The effects of social networks and social support on physical health are well-documented (Berkman, 1994; Marmot and Wilkinson, 2006). However, these studies do not quantify actual face-to-face interactions that lead to the adoption of health-related behaviors. We study the variations in BMI, weight (in lbs), unhealthy eating habits, diet and exercise, and find that social exposure measured using mobile phones is a better predictor of BMI change over a semester, than self-report data, in stark contrast to previous work. From a smaller pilot study of social exposure in face-to-face networks and the propagation of viral music, we find that phone communication and location features predict the sharing of music between people, and also identify social ties that are 'close friends' or 'casual acquaintances'. These interaction and music sharing features can be used to model latent influences between participants in the music sharing process.by Anmol Madan.Ph.D

Fault Diagnosis Algorithms for Wireless Sensor Networks

The sensor nodes in wireless sensor networks (WSNs) are deployed in unattended and hostile environments. The ill-disposed environment affects the monitoring infrastructure that includes the sensor nodes and the links. In addition, node failures and environmental hazards cause frequent topology change, communication failure, and network partition. This in turn adds a new dimension to the fragility of the WSN topology. Such perturbations are far more common in WSNs than those found in conventional wireless networks. These perturbations demand efficient techniques for discovering disruptive behavior in WSNs. Traditional fault diagnosis techniques devised for wired interconnected networks, and conventional wireless networks are not directly applicable to WSNs due to its specific requirements and limitations. System-level diagnosis is a technique to identify faults in distributed networks such as multiprocessor systems, wired interconnected networks, and conventional wireless networks. Recently, this has been applied on ad hoc networks and WSNs. This is performed by deduction, based on information in the form of results of tests applied to the sensor nodes. Neighbor coordination-based system-level diagnosis is a variation of this method, which exploits the spatio-temporal correlation between sensor measurements. In this thesis, we present a new approach to diagnose faulty sensor nodes in a WSN, which works in conjunction with the underlying clustering protocol and exploits spatio-temporal correlation between sensor measurements. An advantage of this method is that the diagnostic operation constitutes real work performed by the system, rather than a specialized diagnostic task. In this way, the normal operation of the network can be used for the diagnosis and resulting less time and message overhead. In this thesis, we have devised and evaluated fault diagnosis algorithms for WSNs considering persistence of the faults (transient, intermittent, and permanent), faults in communication channels and in one of the approaches, we attempt to solve the issue of node mobility in diagnosis. A cluster based distributed fault diagnosis (CDFD) algorithm is proposed where the diagnostic local view is obtained by exploiting the spatially correlated sensor measurements. We derived an optimal threshold for effective fault diagnosis in sparse networks. The message complexity of CDFD is O(n) and the number of bits exchanged to diagnose the network are O(n log2 n). The intermittent fault diagnosis is formulated as a multiobjective optimization problem based on the inter-test interval and number of test repetitions required to diagnose the intermittent faults. The two objectives such as detection latency and energy overhead are taken into consideration with a constraint of detection errors. A high level (> 95%) of detection accuracy is achieved while keeping the false alarm rate low (< 1%) for sparse networks. The proposed cluster based distributed intermittent fault diagnosis (CDIFD) algorithm is energy efficient because in CDIFD, diagnostic messages are sent as the output of the routine tasks of the WSNs. A count and threshold-based mechanism is used to discriminate the persistence of faults. The main characteristics of these faults are the amounts of time the fault disappears. We adopt this state-holding time to discriminate transient from intermittent or permanent faults. The proposed cluster based distributed fault diagnosis and discrimination (CDFDD) algorithm is energy efficient due to the improved network lifetime which is greater than 1150 data-gathering rounds with transient fault rates as high as 20%. A mobility aware hierarchal architecture is proposed which is to detect hard and soft faults in dynamic WSN topology assuming random movements of nodes in the WSN. A test pattern that ensures error checking of each functional block of a sensor node is employed to diagnose the network. The proposed mobility aware cluster based distributed fault diagnosis (MCDFD) algorithm assures a better packet delivery ratio (> 80%) in highly dynamic networks with a fault rate as high as 30%. The network lifetime is more than 900 data-gathering rounds in a highly dynamic network with a fault rate as high as 20%