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Sensor networks have dynamically expanded our ability to monitor and study the world. Their presence and need keep increasing, and new hardware configurations expand the range of physical stimuli that can be accurately recorded. Sensors are also no longer simply recording the data, they process it and transform into something useful before uploading to the cloud. However, building sensor networks is costly and very time consuming. It is difficult to build upon other people’s work and there are only a few open-source solutions for integrating different devices and sensing modalities. We introduce REIP, a Reconfigurable Environmental Intelligence Platform for fast sensor network prototyping. REIP’s first and most central tool, implemented in this work, is an open-source software framework, an SDK, with a flexible modular API for data collection and analysis using multiple sensing modalities. REIP is developed with the aim of being user-friendly, device-agnostic, and easily extensible, allowing for fast prototyping of heterogeneous sensor networks. Furthermore, our software framework is implemented in Python to reduce the entrance barrier for future contributions. We demonstrate the potential and versatility of REIP in real world applications, along with performance studies and benchmark REIP SDK against similar systems. 

Advances in technology coupled with the availability of low-cost sensors have resulted in the continuous generation of large time series from several sources. In order to visually explore and compare these time series at different scales, analysts need to execute online analytical processing (OLAP) queries that include constraints and group-by's at multiple temporal hierarchies. Effective visual analysis requires these queries to be interactive. However, while existing OLAP cube-based structures can support interactive query rates, the exponential memory requirement to materialize the data cube is often unsuitable for large data sets. Moreover, none of the recent space-efficient cube data structures allow for updates. Thus, the cube must be re-computed whenever there is new data, making them impractical in a streaming scenario. We propose Time Lattice, a memory-efficient data structure that makes use of the implicit temporal hierarchy to enable interactive OLAP queries over large time series. Time Lattice is a subset of a fully materialized cube and is designed to handle fast updates and streaming data. We perform an experimental evaluation which shows that the space efficiency of the data structure does not hamper its performance when compared to the state of the art. In collaboration with signal processing and acoustics research scientists, we use the Time Lattice data structure to design the Noise Profiler, a web-based visualization framework that supports the analysis of noise from cities. We demonstrate the utility of Noise Profiler through a set of case studies.