Dynamics of Information Diffusion and Social Sensing

Statistical inference using social sensors is an area that has witnessed remarkable progress and is relevant in applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This chapter presents a tutorial description of four important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models and risk averse social learning is considered with applications in finance and online reputation systems. Third, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. Finally, the interaction of social sensors with YouTube channel owners is studied using time series analysis methods. All four topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this chapter stem from recent developments in network science, economics and signal processing. At a deeper level, this chapter considers mean field dynamics of networks, risk averse Bayesian social learning filtering and quickest change detection, data incest in decision making over a directed acyclic graph of social sensors, inverse optimization problems for utility function estimation (revealed preferences) and statistical modeling of interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112

High Level Synthesis Evaluation of Tools and Methodology

The advances in silicon technology, as well as competitive time to market, in the recent decade have forced the design tools and methodologies to progress towards higher levels of abstraction. Raising the level of abstraction shortens the design cycle via elimination of details in design specification. One such new methodology is High Level Synthesis (HLS). HLS tools accept the behavioral design in the abstract level as the input and generate the detailed Register Transfer Level (RTL) code. In this thesis project, the HLS methodology is introduced in the design flow and its advantages are outlined. We then evaluate and compare three HLS tools developed by market leading vendors, namely, C-to-Silicon, CatapultC and Synphonycc. To compare the HLS tools, an HLS input is developed for one of the Ericsson’s designs and the generated RTL is compared with the hand-written RTL based on several performance criteria. Thereof, we discuss the choice of the best tool so as to facilitate adoption of HLS in Ericsson’s design flow. At last, capability of the HLS tools in the synthesis of designs with pure control flow is investigated

Reinforcement learning in non-stationary games

The unifying theme of this thesis is the design and analysis of adaptive procedures that are aimed at learning the optimal decision in the presence of uncertainty. The first part is devoted to strategic decision making involving multiple individuals with conflicting interests. This is the subject of non-cooperative game theory. The proliferation of social networks has led to new ways of sharing information. Individuals subscribe to social groups, in which their experiences are shared. This new information patterns facilitate the resolution of uncertainties. We present an adaptive learning algorithm that exploits these new patterns. Despite its deceptive simplicity, if followed by all individuals, the emergent global behavior resembles that obtained from fully rational considerations, namely, correlated equilibrium. Further, it responds to the random unpredictable changes in the environment by properly tracking the evolving correlated equilibria set. Numerical evaluations verify these new information patterns can lead to improved adaptability of individuals and, hence, faster convergence to correlated equilibrium. Motivated by the self-configuration feature of the game-theoretic design and the prevalence of wireless-enabled electronics, the proposed adaptive learning procedure is then employed to devise an energy-aware activation mechanism for wireless-enabled sensors which are assigned a parameter estimation task. The proposed game-theoretic model trades-off sensors' contribution to the estimation task and the associated energy costs. The second part considers the problem of a single decision maker who seeks the optimal choice in the presence of uncertainty. This problem is mathematically formulated as a discrete stochastic optimization. In many real-life systems, due to the unexplained randomness and complexity involved, there typically exists no explicit relation between the performance measure of interest and the decision variables. In such cases, computer simulations are used as models of real systems to evaluate output responses. We present two simulation-based adaptive search schemes and show that, by following these schemes, the global optimum can be properly tracked as it undergoes random unpredictable jumps over time. Further, most of the simulation effort is exhausted on the global optimizer. Numerical evaluations verify faster convergence and improved efficiency as compared with existing random search, simulated annealing, and upper confidence bound methods.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

Distributed dynamic coalition formation for bearings-only localization in wireless sensor networks

Lifetime maximization is a key challenge in the design of sensor-network-based tracking applications. In this dissertation, formation of optimal coalitions of nodes is investigated for data acquisition in bearings-only target localization such that the average sleep times allocated to the nodes are maximized. Targets are assumed to be localized with a pre-defined accuracy where the determinant of the Bayesian Fisher information matrix (B-FIM) is used as the metric for estimation accuracy. Cooperative game theory is utilized as a tool to devise a distributed dynamic coalition formation algorithm in which nodes autonomously decide which coalition to join, while maximizing their feasible sleep times. Nodes in the sleep mode do not record any measurements; hence, save power in both sensing and transmitting the sensed data. The proposed scheme reduces the number of sensor measurements by capturing the spatio-temporal correlation of the information provided by the sensors from one side and bounding the localization accuracy to the pre-defined value from the other side. It is proved that if each node operates according to this algorithm, the average sleep time for the entire network converges to its maximum feasible value. In numerical examples, we illustrate the inherent trade-off between the localization accuracy and the average sleep time allocated to the nodes and demonstrate the superior performance of the proposed algorithm via Monte Carlo simulations.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat