The Internet of Things envisions a web-connected infrastructure of billions of sensors and actuation devices. However, the current state-of-the-art presents another reality: monolithic end-to-end applications tightly coupled to a limited set of sensors and actuators. Growing such applications with new devices or behaviors, or extending the existing infrastructure with new applications, involves redesign and redeployment. We instead propose a modular approach to these applications, breaking them into an equivalent set of functional units (context engines) whose input/output transformations are driven by general-purpose machine learning, demonstrating an improvement in compute redundancy and computational complexity with minimal impact on accuracy. In conjunction with formal data specifications, or ontologies, we can replace application-specific implementations with a composition of context engines that use common statistical learning to generate output, thus improving context reuse. We implement interconnected context-aware applications using our approach, extracting user context from sensors in both healthcare and grid applications. We compare our infrastructure to single-stage monolithic implementations with single-point communications between sensor nodes and the cloud servers, demonstrating a reduction in combined system energy by 22-45%, and multiplying the battery lifetime of power-constrained devices by at least 22x, with easy deployment across different architectures and devices.
This paper describes a new software package, SHMTools, for prototyping algorithms for various structural health
monitoring (SHM) applications. The software includes a set of standardized MATLAB routines covering three
main stages of SHM: data acquisition, feature extraction, and feature classification for damage identification. A
subset of the software in SHMTools is embeddable, which consists of Matlab functions that can be cross-compiled
into generic "C" programs to be run on a target hardware. The software is designed to accommodate multiple
sensing modalities, including piezoelectric active-sensing, which have become widely used in SHM practice. The
software package, standardized datasets, and detailed documentation are publicly available for use by the SHM
community. The details of this software will be discussed, along with several example processes to demonstrate
Wireless communication today supports heterogeneous wireless devices with a number of different wireless network interfaces (WNICs). A large fraction of communication is infrastructure based, so the wireless access points and hotspot servers have become more ubiquitous. Battery lifetime is still a critical issue, with WNICs typically consuming a large fraction of the overall power budget in a mobile device. In this work we present a new technique for managing power consumption and QoS in diverse wireless environments using Hotspot servers. We introduce a resource manager module at both Hotspot server and the client. Resource manager schedules communication bursts between it and each client. The schedulers decide what WNIC to employ for communication, when to communicate data and how to minimize power dissipation while maintaining an acceptable QoS based on the application needs. We present two new scheduling policies derived from well known earliest deadline first (EDF) and rate monotonic (RM)  algorithms. The resource manager and the schedulers have been implemented in the HP's Hotspot server . Our measurement and simulation results show a significant improvement in power dissipation and QoS of Bluetooth and 802.11b for applications such as MP3, MPEG4, WWW, and email.
Conference Committee Involvement (1)
Multimedia Computing and Networking 2006
18 January 2006 | San Jose, California, United States