This work presents a virtual sensing framework by exploiting operating system events for energy efficient context inference. Specifically, we present a novel set of features that can be extracted from virtual sensors and used to infer the logical status of mobile users, such as isWorking, isSocial, and isStressful. The preliminary results indicate promising inference performance and suggest a wide range of applications of the proposed framework.

As mobile apps become more closely integrated into our everyday lives, mobile app interactions ought to be rapid and responsive. Unfortunately, even the basic primitive of launching a mobile app is sorrowfully sluggish: 20 seconds of delay is not uncommon even for very popular apps.

Mobile phones are becoming increasingly sophisticated with a rich set of on-board sensors and ubiquitous wireless connectivity. However, the ability to fully exploit the sensing capabilities on mobile phones is stymied by limitations in multimedia processing techniques. For example, search using cellphone images often encounters high error rate due to low image quality.

Crowdsourcing is a new paradigm for utilizing the power of "crowds" of people to facilitate large scale tasks that are costly or time consuming with traditional methods. Crowdsourcing has enormous potential that can be truly unleashed when extended to sensor-rich mobile devices, such as smart phones. In this paper, we demonstrate mCrowd, an iPhone based mobile crowdsourcing platform that enables mobile users to post and work on sensor-related crowdsourcing tasks. mCrowd enables mobile users to fully utilize the rich sensors equipped with iPhone to participate and accomplish crowdsourcing tasks at fingertips, including geolocation-aware image collection, image tagging, road traffic monitoring, and others.

This paper presents the design and implementation of a dual-camera sensor network that can be used as a memory assistant tool for assisted living. Our system performs energy-efficient object detection and recognition of commonly misplaced objects. The novelty in our approach is the ability to tradeoff between recognition accuracy and computational efficiency by employing a combination of low complexity but less precise color histogram-based image recognition together with more complex image recognition using SIFT descriptors. In addition, our system can seamlessly integrate feedback from the user to improve the robustness of object recognition. Experimental results reveal that our system is computation-efficient and adaptive to slow changes of environmental conditions.

Recent advances in sensor networks permit the use of a large number of relatively inexpensive distributed computational nodes with camera sensors linked in a network and possibly linked to one or more central servers. We argue that the full potential of such a distributed system can be realized if it is designed as a distributed search engine where images from different sensors can be captured, stored, searched and queried. However, unlike traditional image search engines that are focused on resource-rich situations, the resource limitations of camera sensor networks in terms of energy, bandwidth, computational power, and memory capacity present significant challenges. In this paper, we describe the design and implementation of a distributed search system over a camera sensor network where each node is a search engine that senses, stores and searches information. Our work involves innovation at many levels including local storage, local search, and distributed search, all of which are designed to be efficient under the resource constraints of sensor networks. We present an implementation of the search engine on a network of iMote2 sensor nodes equipped with low-power cameras and extended flash storage. We evaluate our system for a dataset comprising book images, and demonstrate more than two orders of magnitude reduction in the amount of data communicated and up to 5x reduction in overall energy consumption over alternate techniques.

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.