This paper describes the Dubuque Electricity Portal, a city-scale system aimed at supporting voluntary reductions of electricity consumption. The Portal provided each household with fine-grained feedback on its electricity use, as well as using incentives, comparisons, and goal setting to encourage conservation. Logs, a survey and interviews were used to evaluate the user experience of the Portal during a 20-week pilot with 765 volunteer households. Although the volunteers had already made a wide range of changes to conserve electricity prior to the pilot, those who used the Portal decreased their electricity use by about 3.7%. They also reported increased understanding of their usage, and reported taking an array of actions - both changing their behavior and their electricity infrastructure. The paper discusses the experience of the system's users, and describes challenges for the design of ECF systems, including balancing accessibility and security, a preference for time-based visualizations, and the advisability of multiple modes of feedback, incentives and information presentation.

Broad usage of Smartphones and mobile apps enables both individual trip summary and regional travel demand analysis. In this paper, we describe a trip analysis system, as part of smarter transit service. This trip analysis system consists of mobile apps and a centralized analyzer. It identifies the travel mode and purpose of the trips sensed by mobile devices, provides trip summaries and insights to mobile subscribers, and generates meaningful patterns to support traffic operation planning and transit system design. It is developed and deployed to the Smartphones of the volunteers in Dubuque, IA, to serve both the volunteers and the transit agencies. Preliminary evaluation has demonstrated the applicability of the design.

In this paper we describe the multi-resolution water consumption prediction based on the SmarterWater system. This prediction service provides household consumption projection and regional demand forecasting for both short-term and med-term. Water consumption prediction, together with the other functions in the SmarterWater service, has been deployed to Dubuque, IA. Consumption behavior change after accessing the service has been observed.

In this paper, we focus on a network of rich sensors that are geographically distributed and argue that the design of such networks poses very different challenges from traditional mote-class sensor network design. We identify the need to handle the diverse requirements of multiple users to be a major design challenge, and propose a utility-driven approach to maximize data sharing across users while judiciously using limited network and computational resources. Our utility-driven architecture addresses three key challenges for such rich multi-user sensor networks: how to define utility functions for networks with data sharing among end-users, how to compress and prioritize data transmissions according to its importance to end-users, and how to gracefully degrade end-user utility in the presence of bandwidth fluctuations. We instantiate this architecture in the context of geographically distributed wireless radar sensor networks for weather, and present results from an implementation of our system on a multi-hop wireless mesh network that uses real radar data with real end-user applications. Our results demonstrate that our progressive compression and transmission approach achieves an order of magnitude improvement in application utility over existing utility-agnostic non-progressive approaches, while also scaling better with the number of nodes in the network.

The emerging of rich sensor networks poses very different design challenges from traditional "mote-class" sensor networks. One important challenge is that these networks are designed to handle the diverse requirements of multiple users. In this work, we demonstrate how multiple end user needs are handled in rich sensor networks using a utility-driven architecture. We instantiate this architecture in the context of geographically distributed wireless radar sensor networks for weather, and demonstrate the real-time operation of the prototype on a radar testbed in Okalahoma.