Collaborative Communication And Storage In Energy-Synchronized Sensor Networks

In a battery-less sensor network, all the operation of sensor nodes are strictly constrained by and synchronized with the fluctuations of harvested energy, causing nodes to be disruptive from network and hence unstable network connectivity. Such wireless sensor network is named as energy-synchronized sensor networks. The unpredictable network disruptions and challenging communication environments make the traditional communication protocols inefficient and require a new paradigm-shift in design. In this thesis, I propose a set of algorithms on collaborative data communication and storage for energy-synchronized sensor networks. The solutions are based on erasure codes and probabilistic network codings. The proposed set of algorithms significantly improve the data communication throughput and persistency, and they are inherently amenable to probabilistic nature of transmission in wireless networks. The technical contributions explore collaborative communication with both no coding and network coding methods. First, I propose a collaborative data delivery protocol to exploit the optimal performance of multiple energy-synchronized paths without network coding, i.e. a new max-flow min-variance algorithm. In consort with this data delivery protocol, a localized TDMA MAC protocol is designed to synchronize nodes\u27 duty-cycles and mitigate media access contentions. However, the energy supply can change dynamically over time, making determined duty cycles synchronization difficult in practice. A probabilistic approach is investigated. Therefore, I present Opportunistic Network Erasure Coding protocol (ONEC), to collaboratively collect data. ONEC derives the probability distribution of coding degree in each node and enable opportunistic in-network recoding, and guarantee the recovery of original sensor data can be achieved with high probability upon receiving any sufficient amount of encoded packets. Next, OnCode, an opportunistic in-network data coding and delivery protocol is proposed to further improve data communication under the constraints of energy synchronization. It is resilient to packet loss and network disruptions, and does not require explicit end-to-end feedback message. Moreover, I present a network Erasure Coding with randomized Power Control (ECPC) mechanism for collaborative data storage in disruptive sensor networks. ECPC only requires each node to perform a single broadcast at each of its several randomly selected power levels. Thus it incurs very low communication overhead. Finally, I propose an integrated algorithm and middleware (Ravine Stream) to improve data delivery throughput as well as data persistency in energy-synchronized sensor network

Perfect Matchings, Tilings and Hamilton Cycles in Hypergraphs

This thesis contains problems in finding spanning subgraphs in graphs, such as, perfect matchings, tilings and Hamilton cycles. First, we consider the tiling problems in graphs, which are natural generalizations of the matching problems. We give new proofs of the multipartite Hajnal-Szemeredi Theorem for the tripartite and quadripartite cases. Second, we consider Hamilton cycles in hypergraphs. In particular, we determine the minimum codegree thresholds for Hamilton l-cycles in large k-uniform hypergraphs for l less than k/2. We also determine the minimum vertex degree threshold for loose Hamilton cycle in large 3-uniform hypergraphs. These results generalize the well-known theorem of Dirac for graphs. Third, we determine the minimum codegree threshold for near perfect matchings in large k-uniform hypergraphs, thereby confirming a conjecture of Rodl, Rucinski and Szemeredi. We also show that the decision problem on whether a k-uniform hypergraph with certain minimum codegree condition contains a perfect matching can be solved in polynomial time, which solves a problem of Karpinski, Rucinski and Szymanska completely. At last, we determine the minimum vertex degree threshold for perfect tilings of C_4^3 in large 3-uniform hypergraphs, where C_4^3 is the unique 3-uniform hypergraph on four vertices with two edges

An Energy-Efficient Distributed Algorithm for k-Coverage Problem in Wireless Sensor Networks

Wireless sensor networks (WSNs) have recently achieved a great deal of attention due to its numerous attractive applications in many different fields. Sensors and WSNs possesses a number of special characteristics that make them very promising in many applications, but also put on them lots of constraints that make issues in sensor network particularly difficult. These issues may include topology control, routing, coverage, security, and data management. In this thesis, we focus our attention on the coverage problem. Firstly, we define the Sensor Energy-efficient Scheduling for k-coverage (SESK) problem. We then solve it by proposing a novel, completely localized and distributed scheduling approach, naming Distributed Energy-efficient Scheduling for k-coverage (DESK) such that the energy consumption among all the sensors is balanced, and the network lifetime is maximized while still satisfying the k-coverage requirement. Finally, in related work section we conduct an extensive survey of the existing work in literature that focuses on with the coverage problem

The Playful Audience: Professional Wrestling, Media Fandom, and the Omnipresence of Media Smarks

This dissertation posits a new model for understanding media audiences, bringing the scholarship of game studies to the critical analysis of audience practices. The concept of play proves beneficial for understanding the complex processes of media audiences, as they are able to traverse dichotomous categories when engaging media content. The genre of professional wrestling proves a perfect case study for examining these playful audience practices, and this study is an ethnographic account of the practices of wrestling fans. Focusing on the behaviors of fans at live wrestling events, in online contexts, and in the subcultural setting of a card game entitled Champions of the Galaxy, this study demonstrates the necessity of the concept of play for understanding what media audiences do when they engage media content. These practices, however, are always negotiated by the hegemonic power of the rules that structure how audiences are encouraged to engage content, resulting in ideological constraints on the possibilities play offers

Innovative Algorithms and Evaluation Methods for Biological Motif Finding

Biological motifs are defined as overly recurring sub-patterns in biological systems. Sequence motifs and network motifs are the examples of biological motifs. Due to the wide range of applications, many algorithms and computational tools have been developed for efficient search for biological motifs. Therefore, there are more computationally derived motifs than experimentally validated motifs, and how to validate the biological significance of the ‘candidate motifs’ becomes an important question. Some of sequence motifs are verified by their structural similarities or their functional roles in DNA or protein sequences, and stored in databases. However, biological role of network motifs is still invalidated and currently no databases exist for this purpose. In this thesis, we focus not only on the computational efficiency but also on the biological meanings of the motifs. We provide an efficient way to incorporate biological information with clustering analysis methods: For example, a sparse nonnegative matrix factorization (SNMF) method is used with Chou-Fasman parameters for the protein motif finding. Biological network motifs are searched by various clustering algorithms with Gene ontology (GO) information. Experimental results show that the algorithms perform better than existing algorithms by producing a larger number of high-quality of biological motifs. In addition, we apply biological network motifs for the discovery of essential proteins. Essential proteins are defined as a minimum set of proteins which are vital for development to a fertile adult and in a cellular life in an organism. We design a new centrality algorithm with biological network motifs, named MCGO, and score proteins in a protein-protein interaction (PPI) network to find essential proteins. MCGO is also combined with other centrality measures to predict essential proteins using machine learning techniques. We have three contributions to the study of biological motifs through this thesis; 1) Clustering analysis is efficiently used in this work and biological information is easily integrated with the analysis; 2) We focus more on the biological meanings of motifs by adding biological knowledge in the algorithms and by suggesting biologically related evaluation methods. 3) Biological network motifs are successfully applied to a practical application of prediction of essential proteins

Data Collection and Capacity Analysis in Large-scale Wireless Sensor Networks

In this dissertation, we study data collection and its achievable network capacity in Wireless Sensor Networks (WSNs). Firstly, we investigate the data collection issue in dual-radio multi-channel WSNs under the protocol interference model. We propose a multi-path scheduling algorithm for snapshot data collection, which has a tighter capacity bound than the existing best result, and a novel continuous data collection algorithm with comprehensive capacity analysis. Secondly, considering most existing works for the capacity issue are based on the ideal deterministic network model, we study the data collection problem for practical probabilistic WSNs. We design a cell-based path scheduling algorithm and a zone-based pipeline scheduling algorithm for snapshot and continuous data collection in probabilistic WSNs, respectively. By analysis, we show that the proposed algorithms have competitive capacity performance compared with existing works. Thirdly, most of the existing works studying the data collection capacity issue are for centralized synchronous WSNs. However, wireless networks are more likely to be distributed asynchronous systems. Therefore, we investigate the achievable data collection capacity of realistic distributed asynchronous WSNs and propose a data collection algorithm with fairness consideration. Theoretical analysis of the proposed algorithm shows that its achievable network capacity is order-optimal as centralized and synchronized algorithms do and independent of network size. Finally, for completeness, we study the data aggregation issue for realistic probabilistic WSNs. We propose order-optimal scheduling algorithms for snapshot and continuous data aggregation under the physical interference model

Formal Object Interaction Language: Modeling and Verification of Sequential and Concurrent Object-Oriented Software

As software systems become larger and more complex, developers require the ability to model abstract concepts while ensuring consistency across the entire project. The internet has changed the nature of software by increasing the desire for software deployment across multiple distributed platforms. Finally, increased dependence on technology requires assurance that designed software will perform its intended function. This thesis introduces the Formal Object Interaction Language (FOIL). FOIL is a new object-oriented modeling language specifically designed to address the cumulative shortcomings of existing modeling techniques. FOIL graphically displays software structure, sequential and concurrent behavior, process, and interaction in a simple unified notation, and has an algebraic representation based on a derivative of the π-calculus. The thesis documents the technique in which FOIL software models can be mathematically verified to anticipate deadlocks, ensure consistency, and determine object state reachability. Scalability is offered through the concept of behavioral inheritance; and, FOIL’s inherent support for modeling concurrent behavior and all known workflow patterns is demonstrated. The concepts of process achievability, process complete achievability, and process determinism are introduced with an algorithm for simulating the execution of a FOIL object model using a FOIL process model. Finally, a technique for using a FOIL process model as a constraint on FOIL object system execution is offered as a method to ensure that object-oriented systems modeled in FOIL will complete their processes based activities. FOIL’s capabilities are compared and contrasted with an extensive array of current software modeling techniques. FOIL is ideally suited for data-aware, behavior based systems such as interactive or process management software

Improving Feature Selection Techniques for Machine Learning

As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built by learning algorithms. Feature selection techniques have been widely employed in a variety of applications, such as genomic analysis, information retrieval, and text categorization. Researchers have introduced many feature selection algorithms with different selection criteria. However, it has been discovered that no single criterion is best for all applications. We proposed a hybrid feature selection framework called based on genetic algorithms (GAs) that employs a target learning algorithm to evaluate features, a wrapper method. We call it hybrid genetic feature selection (HGFS) framework. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for the target algorithm. The experiments on genomic data demonstrate that ours is a robust and effective approach that can find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm. A common characteristic of text categorization tasks is multi-label classification with a great number of features, which makes wrapper methods time-consuming and impractical. We proposed a simple filter (non-wrapper) approach called Relation Strength and Frequency Variance (RSFV) measure. The basic idea is that informative features are those that are highly correlated with the class and distribute most differently among all classes. The approach is compared with two well-known feature selection methods in the experiments on two standard text corpora. The experiments show that RSFV generate equal or better performance than the others in many cases

Sign Pattern Matrices That Require Almost Unique Rank

A sign pattern matrix is a matrix whose entries are from the set {+,-, 0}. For a real matrix B, sgn(B) is the sign pattern matrix obtained by replacing each positive respectively, negative, zero) entry of B by + (respectively, -, 0). For a sign pattern matrixA, the sign pattern class of A, denoted Q(A), is defined as { B : sgn(B)= A }. The minimum rank mr(A)(maximum rank MR(A)) of a sign pattern matrix A is the minimum (maximum) of the ranks of the real matrices in Q(A). Several results concerning sign patterns A that require almost unique rank, that is to say, the sign patterns A such that MR(A)= mr(A)+1 are established. In particular, a complete characterization of these sign patterns is obtained. Further, the results on sign patterns that require almost unique rank are extended to sign patterns A for which the spread is d =MR(A)-mr(A)

Upper Bound Analysis and Routing in Optical Benes Networks

Multistage Interconnection Networks (MIN) are popular in switching and communication applications. It has been used in telecommunication and parallel computing systems for many years. The new challenge facing optical MIN is crosstalk, which is caused by coupling two signals within a switching element. Crosstalk is not too big an issue in the Electrical Domain, but due to the stringent Bit Error Rate (BER) constraint, it is a big major concern in the Optical Domain. In this research dissertation, we will study the blocking probability in the optical network and we will study the deterministic conditions for strictly non-blocking Vertical Stacked Optical Benes Networks (VSOBN) with and without worst-case scenarios. We will establish the upper bound on blocking probability of Vertical Stacked Optical Benes Networks with respect to the number of planes used when the non-blocking requirement is not met. We will then study routing in WDM Benes networks and propose a new routing algorithm so that the number of wavelengths can be reduced. Since routing in WDM optical network is an NP-hard problem, many heuristic algorithms are designed by many researchers to perform this routing. We will also develop a genetic algorithm, simulated annealing algorithm and ant colony technique and apply these AI algorithms to route the connections in WDM Benes network