In this paper, we discuss the applicat[ons ant! implications of the Programmable Bricks -s tiny, portable computer embecfded~nside a LE~Q'J brick, capable of interacting with the phys!cal world in a large variety of ways. We describe how Programmable Bricks make possible a wide range of new design activities for children, and we discuss experiences in using Programmable Bricks in three types of applications: autonomous creatures, active environments, and personal science experiments.

Developing information technology to democratize scientific knowledge and support citizen empowerment is a challenging task. In our case, a local community suffered from air pollution caused by industrial activity. The residents lacked the technological fluency to gather and curate diverse scientific data to advocate for regulatory change. We collaborated with the community in developing an air quality monitoring system which integrated heterogeneous data over a large spatial and temporal scale. The system afforded strong scientific evidence by using animated smoke images, air quality data, crowdsourced smell reports, and wind data. In our evaluation, we report patterns of sharing smoke images among stakeholders. Our survey study shows that the scientific knowledge provided by the system encourages agonistic discussions with regulators, empowers the community to support policy making, and rebalances the power relationship between stakeholders.

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Abstract-As sensor networks gain traction and begin to scale, we will be increasingly faced with challenges associated with managing large-scale time-series data. In this paper, we present a cloud-to-edge partitioned architecture called Respawn that is capable of serving large amounts of time-series data from a continuously updating datastore with access latencies low enough to support interactive real-time visualization. Respawn targets sensing systems where resource-constrained edge node devices may only have limited or intermittent network connections linking them to a cloud-backend. The cloud-backend provides aggregate storage and transparent dispatching of data queries to edge node devices. Data is downsampled as it enters the system creating a multi-resolution representation capable of lowlatency range-base queries. Lower-resolution aggregate data is automatically migrated from edge nodes to the cloud-backend both for improved consistency and caching. In order to further mask latency from users, edge nodes automatically identify and migrate blocks of data that contain statistically interesting features. We show through simulation and micro-benchmarking that Respawn is able to run on ARM-based edge node devices connected to a cloud-backend with the ability to serve thousands of clients and terabytes of data with sub-second latencies.

The increasing size of cosmological simulations has led to the need for new visualization techniques. We focus on Smoothed Particle Hydrodynamical (SPH) simulations run with the GADGET code and describe methods for visually accessing the entire simulation at full resolution. The simulation snapshots are rastered and processed on supercomputers into images that are ready to be accessed through a web interface (GigaPan). This allows any scientist with a web-browser to interactively explore simulation datasets in both in spatial and temporal dimensions, datasets which in their native format can be hundreds of terabytes in size or more. We present two examples, the first a static terapixel image of the MassiveBlack simulation, a P-GADGET SPH simulation with 65 billion particles, and the second an interactively zoomable animation of a different simulation with more than one thousand frames, each a gigapixel in size. Both are available for public access through the GigaPan web interface. We also make our imaging software publicly available.