As data become more pervasive and computing power increases, the opportunity for transformative use of data grows. Collecting data from individuals can be useful to the individuals (by providing them with personalized predictions) and the data collectors (by providing them with information about populations). However, collecting these data is costly: answering survey items, collecting sensed data, and computing values of interest deplete finite resources of time, battery, life, money, etc. Dynamically ordering the items to be collected, based on already known information (such as previously collected items or paradata), can lower the costs of data collection by tailoring the information-acquisition process to the individual. This thesis presents a framework for an iterative dynamic item ordering process that trades off item utility with item cost at data collection time. The exact metrics for utility and cost are application-dependent, and this frame- work can apply to many domains. The two main scenarios we consider are (1) data collection for personalized predictions and (2) data collection in surveys. We illustrate applications of this framework to multiple problems ranging from personalized prediction to questionnaire scoring to government survey collection. We compare data quality and acquisition costs of our method to fixed order approaches and show that our adaptive process obtains results of similar quality at lower cost. For the personalized prediction setting, the goal of data collection is to make a prediction based on information provided by a respondent. Since it is possible to give a reasonable prediction with only a subset of items, we are not concerned with collecting all items. Instead, we want to order the items so that the user provides information that most increases the prediction quality, while not being too costly to provide. One metric for quality is prediction certainty, which reflects how likely the true value is to coincide with the estimated value. Depending whether the prediction problem is continuous or discrete, we use prediction interval width or predicted class probability to measure the certainty of a prediction. We illustrate the results of our dynamic item ordering framework on tasks of predicting energy costs, student stress levels, and device identification in photographs and show that our adaptive process achieves equivalent error rates as a fixed order baseline with cost savings up to 45%. For the survey setting, the goal of data collection is often to gather information from a population, and it is desired to have complete responses from all samples. In this case, we want to maximize survey completion (and the quality of necessary imputations), and so we focus on ordering items to engage the respondent and collect hopefully all the information we seek, or at least the information that most characterizes the respondent so imputed values will be accurate. One item utility metric for this problem is information gain to get a “representative” set of answers from the respondent. Furthermore, paradata collected during the survey process can inform models of user engagement that can influence either the utility metric ( e.g., likelihood therespondent will continue answering questions) or the cost metric (e.g., likelihood the respondent will break off from the survey). We illustrate the benefit of dynamic item ordering for surveys on two nationwide surveys conducted by the U.S. Census Bureau: the American Community Survey and the Survey of Income and Program Participation.

Benefiting from the constant and significant advancement of wireless communication technologies and networking protocols, Wireless Ad hoc NETwork (WANET) has played a more and more important role in modern communication networks without relying much on existing infrastructures. The past decades have seen numerous applications adopting ad hoc networks for service provisioning. For example, Wireless Sensor Network (WSN) can be widely deployed for environment monitoring and object tracking by utilizing low-cost, low-power and multi-function sensor nodes. To realize such applications, the design and evaluation of communication protocols are of significant importance. Meanwhile, the network performance analysis based on mathematical models is also in great need of development and improvement. This dissertation investigates the above topics from three important and fundamental aspects, including data collection protocol design, protocol modeling and analysis, and physical interference modeling and analysis. The contributions of this dissertation are four-fold. First, this dissertation investigates the synchronization issue in the duty-cycled, pipelined-scheduling data collection of a WSN, based on which a pipelined data collection protocol, called PDC, is proposed. PDC takes into account both the pipelined data collection and the underlying schedule synchronization over duty-cycled radios practically and comprehensively. It integrates all its components in a natural and seamless way to simplify the protocol implementation and to achieve a high energy efficiency and low packet delivery latency. Based on PDC, an Adaptive Data Collection (ADC) protocol is further proposed to achieve dynamic duty-cycling and free addressing, which can improve network heterogeneity, load adaptivity, and energy efficiency. Both PDC and ADC have been implemented in a pioneer open-source operating system for the Internet of Things, and evaluated through a testbed built based on two hardware platforms, as well as through emulations. Second, Linear Sensor Network (LSN) has attracted increasing attention due to the vast requirements on the monitoring and surveillance of a structure or area with a linear topology. Being aware that, for LSN, there is few work on the network modeling and analysis based on a duty-cycled MAC protocol, this dissertation proposes a framework for modeling and analyzing a class of duty-cycled, multi-hop data collection protocols for LSNs. With the model, the dissertation thoroughly investigates the PDC performance in an LSN, considering both saturated and unsaturated scenarios, with and without retransmission. Extensive OPNET simulations have been carried out to validate the accuracy of the model. Third, in the design and modeling of PDC above, the transmission and interference ranges are defined for successful communications between a pair of nodes. It does not consider the cumulative interference from the transmitters which are out of the contention range of a receiver. Since most performance metrics in wireless networks, such as outage probability, link capacity, etc., are nonlinear functions of the distances among communicating, relaying, and interfering nodes, a physical interference model based on distance is definitely needed in quantifying these metrics. Such quantifications eventually involve the Nodal Distance Distribution (NDD) intrinsically depending on network coverage and nodal spatial distribution. By extending a tool in integral geometry and using decomposition and recursion, this dissertation proposes a systematic and algorithmic approach to obtaining the NDD between two nodes which are uniformly distributed at random in an arbitrarily-shaped network. Fourth, with the proposed approach to NDDs, the dissertation presents a physical interference model framework to analyze the cumulative interference and link outage probability for an LSN running the PDC protocol. The framework is further applied to analyze 2D networks, i.e., ad hoc Device-to-Device (D2D) communications underlaying cellular networks, where the cumulative interference and link outage probabilities for both cellular and D2D communications are thoroughly investigated. / Graduate / 0984 / 0544 / tong1987fei@163.com