- Mining of Massive Datasets (PDF) — book by Stanford profs, focuses on data mining of very large amounts of data, that is, data so large it does not fit in main memory. Because of the emphasis on size, many of our examples are about the Web or data derived from the Web. Further, the book takes an algorithmic point of view: data mining is about applying algorithms to data, rather than using data to “train” a machine-learning engine of some sort.
- Lessons from Iceland’s Failed Crowdsourced Constitution (Slate) — Though the crowdsourcing moment could have led to a virtuous deliberative feedback loop between the crowd and the Constitutional Council, the latter did not seem to have the time, tools, or training necessary to process carefully the crowd’s input, explain its use of it, let alone return consistent feedback on it to the public.
- Thread a ZigBee Killer? — Thread is Nest’s home automation networking stack, which can use the same hardware components as ZigBee, but which is not compatible, also not open source. The Novell NetWare of Things. Nick Hunn makes argument that Google (via Nest) are taking aim at ZigBee: it’s Google and Nest saying “ZigBee doesn’t work”.
ENTRIES TAGGED "iot"
Buildings are ready to be smart — we just need to collect and monitor the data.
Buildings, like people, can benefit from lessons built up over time. Just as Amazon.com recommends books based on purchasing patterns or doctors recommend behavior change based on what they’ve learned by tracking thousands of people, a service such as Clockworks from KGS Buildings can figure out that a boiler is about to fail based on patterns built up through decades of data.
I had the chance to be enlightened about intelligent buildings through a conversation with Nicholas Gayeski, cofounder of KGS Buildings, and Mark Pacelle, an engineer with experience in building controls who has written for O’Reilly about the Internet of Things. Read more…
Range, power consumption, scalability, and bandwidth dominate technology decisions.
Three types of networking topologies are utilized in the Internet-of-Things: point-to-point, star, and mesh networking. To provide a way to explore the attributes and capabilities of each of these topologies, we defined a hypothetical (but realistic) application in the building monitoring and energy management space and methodically defined its networking requirements.
Let’s pull it all together to make a network selection for our building monitoring application. As described previously, the application will monitor, analyze, and optimize energy usage throughout the user’s properties. To accomplish this, monitoring and control points need to be deployed throughout each building, including occupancy and temperature sensors. Sensor data will be aggregated back to a central building automation panel located in each building. A continuous collection of data will provide a higher resolution of temperature and occupancy information, thus rendering better insight into HVAC performance and building utilization patterns. Comparison of energy utilization throughout the portfolio of properties allows lower performing buildings to be flagged.
Internet of Things, local energy sources, and online collaboration underlie the Zero Marginal Cost Society.
Government sensor networks can streamline processes, cut labor costs, and improve services.
It’s not news to anyone who works in government that we live in a time of ever-tighter budgets and ever-increasing needs. The 2013 federal shutdown only highlighted this precarious situation: government finds it increasingly difficult to summon the resources and manpower needed to meet its current responsibilities, yet faces new ones after each Congressional session.
Sensor networks are an important emerging technology that some areas of government already are implementing to bridge the widening gap between the demand to reduce costs and the demand to improve services. The Department of Defense, for instance, uses RFID chips to monitor its supply chain more accurately, while the U.S. Geological Survey employs sensors to remotely monitor the bacterial levels of rivers and lakes in real time. Additionally, the General Services Administration has begun using sensors to measure and verify the energy efficiency of “green” buildings (PDF), and the Department of Transportation relies on sensors to monitor traffic and control traffic signals and roadways. All of which is productive, but more needs to be done. Read more…
A suitable network topology for building automation.
Editor’s note: this article is part of a series exploring the role of networking in the Internet of Things.
Today we are going to consider the attributes of wireless mesh networking, particularly in the context of our building monitoring and energy application.
A host of new mesh networking technologies came upon the scene in the mid-2000s through start-up ventures such as Millennial Net, Ember, Dust Networks, and others. The mesh network topology is ideally suited to provide broad area coverage for low-power, low-data rate applications found in application areas like industrial automation, home and commercial building automation, medical monitoring, and agriculture.
Business users are becoming more comfortable with graph analytics.
The rise of sensors and connected devices will lead to applications that draw from network/graph data management and analytics. As the number of devices surpasses the number of people — Cisco estimates 50 billion connected devices by 2020 — one can imagine applications that depend on data stored in graphs with many more nodes and edges than the ones currently maintained by social media companies.
This means that researchers and companies will need to produce real-time tools and techniques that scale to much larger graphs (measured in terms of nodes & edges). I previously listed tools for tapping into graph data, and I continue to track improvements in accessibility, scalability, and performance. For example, at the just-concluded Spark Summit, it was apparent that GraphX remains a high-priority project within the Spark1 ecosystem.