Donald J. Patterson

Cacophony: Building a Resilient Internet of Things

Cacophony: Building a resilient Internet of things by John Brock and Donald J. Patterson

This is one of two workshop papers that received a promotion to journal publications as part of this special issue of First Monday:

This month: August 2015
Special issue: LIMITS 2015 — First workshop on computing within limits
Today’s society is increasingly dependent upon and enmeshed with computing and technology. In parallel with advancements in computing, we have seen scientific developments in our understanding of the fundamental limits with which societies must cope. How can computing serve society in the future as the consequences of these limits? Papers from this workshop in this special issue consider new ideas and perspectives from researchers representing a number of different computer science disciplines. Overall, these papers represent a shift in thinking about computing, where societal and ecological needs become our highest priorities.


Paper Abstract:

The proliferation of sensors in the world has created increased opportunities for context-aware applications. However, it is often cumbersome to capitalize on these opportunities due to the difficulties inherent in collecting, fusing, and reasoning with data from a heterogeneous set of distributed sensors. The fabric that connects sensors lacks resilience and fault tolerance in the face of infrastructure intermittency. To address these difficulties, we introduce Cacophony, a network of peer-to-peer nodes (CNodes), where each node provides real-time predictions of a specified set of sensor data. The predictions from each of the Cacophony prediction nodes can be used by any application with access to the Web. Creating a new CNode involves three steps: (1) Developers and domain-knowledge experts, via a simple Web UI, specify which sensor data they care about. Possible sources of sensor data include stationary sensors, mobile sensors, and the real-time Web; (2) The CNode automatically aggregates data from the relevant sensors in real time using a JXTA-based peer-to-peer network; and, (3) The CNode uses the aggregated data to train a prediction model via the Weka machine-learning library (Hall, et al., 2009). Real-time predictions made by the CNode are then made publicly available to applications that wish to use data from a CNode’s particular set of sensors. The real-time predictions themselves can also be used recursively as sensor data, enabling the creation of CNodes that make predictions based on other CNodes.(permanentlocal copy)

Published in First Monday

C.V.: JR-13

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