Storage-Centric Wireless Sensor Networks for Smart Buildings

In the first part of the dissertation, we propose a model-based systems design framework, called WSNDesign, to facilitate the design and implementation of wireless sensor networks for Smart Buildings. We apply model-based systems engineering principles to enhance model reusability and collaboration among multiple engineering domains. Specifically, we describe a hierarchy of model libraries to model various behaviors and structures of sensor networks in the context of Smart Buildings, and introduce a system design flow to compose both continuous-time and event-triggered modules to develop applications with support for performance evaluation. WSNDesign can obtain early feedback and high-confidence evaluation of a design without requiring any intrusive and costly deployment. In addition, we develop a graphical tool that exposes a sequence of design choices to system designers, and provides instant feedback about the influence of a design decision on the complexity of system analysis. Our tool can facilitate comprehensive analysis and bring competitive advantage to the systems design workflow by reducing costly unanticipated behaviors. One of the main challenges to design efficient sensor networks is to collect and process the data generated by various sensor motes in Smart Buildings efficiently. To make this task easier, we provide an abstraction for data collection and retrieval in the second part of the dissertation. Specifically, we design and implement a distributed database system, called HybridDB, for application development. HybridDB enables sensors to store large-scale datasets in situ on local NAND flash using a novel resource-aware data storage system, and can process typical queries in sensor networks extremely efficiently. In addition, HybridDB supports incremental $\epsilon$-approximate querying that enables clients to retrieve a just-sufficient set of sensor data by issuing refinement and zoom-in sub-queries to search events and analyze sensor data efficiently. HybridDB can always return an approximate dataset with guaranteed maximum absolute ($L_\infty$-norm) error bound, after applying temporal approximate locally on each sensor, and spatial approximate in the neighborhood on the proxy. Furthermore, HybridDB exploits an adaptive error distribution mechanism between temporal approximate and spatial approximate for trade-offs of energy consumption between sensors and the proxy, and response times between the current sub-query and the following sub-queries. The implementation of HybridDB in TinyOS 2.1 is transformed and imported to WSNDesign as a part of the model libraries

Linked Open Data - Creating Knowledge Out of Interlinked Data: Results of the LOD2 Project

Database Management; Artificial Intelligence (incl. Robotics); Information Systems and Communication Servic

Microarchitectural techniques to reduce energy consumption in the memory hierarchy

This thesis states that dynamic profiling of the memory reference stream can improve energy and performance in the memory hierarchy. The research presented in this theses provides multiple instances of using lightweight hardware structures to profile the memory reference stream. The objective of this research is to develop microarchitectural techniques to reduce energy consumption at different levels of the memory hierarchy. Several simple and implementable techniques were developed as a part of this research. One of the techniques identifies and eliminates redundant refresh operations in DRAM and reduces DRAM refresh power. Another, reduces leakage energy in L2 and higher level caches for multiprocessor systems. The emphasis of this research has been to develop several techniques of obtaining energy savings in caches using a simple hardware structure called the counting Bloom filter (CBF). CBFs have been used to predict L2 cache misses and obtain energy savings by not accessing the L2 cache on a predicted miss. A simple extension of this technique allows CBFs to do way-estimation of set associative caches to reduce energy in cache lookups. Another technique using CBFs track addresses in a Virtual Cache and reduce false synonym lookups. Finally this thesis presents a technique to reduce dynamic power consumption in level one caches using significance compression. The significant energy and performance improvements demonstrated by the techniques presented in this thesis suggest that this work will be of great value for designing memory hierarchies of future computing platforms.Ph.D.Committee Chair: Lee, Hsien-Hsin S.; Committee Member: Cahtterjee,Abhijit; Committee Member: Mukhopadhyay, Saibal; Committee Member: Pande, Santosh; Committee Member: Yalamanchili, Sudhaka

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p