The Industrial Internet
The Industrial Internet is a term coined by GE  and refers to the integration of complex physical machinery with networked sensors and software.
The Google driverless car takes in environmental data from roof-mounted LIDAR, uses machine-vision techniques to identify road geometry and obstacles, and controls the car’s throttle, brakes and steering mechanism in real-time.
The Union Pacific Railroad mounts infrared thermometers, microphones and ultrasound scanners alongside its tracks. These sensors scan every train as it passes and send readings to the railroad’s data centers, where pattern-matching software identifies equipment at risk of failure. Falling prices for computing power and networked sensors mean that similar techniques can be applied to small, common devices like machine tools.
- Leber, Jessica (2012-11-28). “General Electric’s San Ramon Software Center Takes Shape | MIT Technology Review”. Technologyreview.com. Retrieved 2013-08-18.
- Steve Lahor. “The Internet Gets Physical”. The New York Times. Retrieved 2013-08-18.
- Chris Murphy (2012-08-08). “Union Pacific Delivers Internet Of Things Reality Check – Global Cio”. Informationweek.com. Retrieved 2013-08-18.
- Chris Murphy (2012-12-07). “Silicon Valley Needs To Get Out More – Global Cio – Executive”. Informationweek.com. Retrieved 2013-08-18.
- Jon Bruner (2012-10-29). “Listening for tired machinery – O’Reilly Radar”. Radar.oreilly.com. Retrieved 2013-08-18.
The Internet of Things
The Internet of Things (or IoT for short) refers to uniquely identifiable objects and their virtual representations in an Internet-like structure. The term Internet of Things was proposed by Kevin Ashton in 1999. The concept of the Internet of Things first became popular through the Auto-ID Center at MIT and related market analysis publications. Radio-frequency identification (RFID) was seen as a prerequisite for the Internet of Things in the early days. If all objects and people in daily life were equipped with identifiers, they could be managed and inventoried by computers. Besides using RFID, the tagging of things may be achieved through such technologies as near field communication, barcodes, QR codes and digital watermarking.
Equipping all objects in the world with minuscule identifying devices or machine-readable identifiers could be transformative of daily life.
For instance, business may no longer run out of stock or generate waste products, as involved parties would know which products are required and consumed. One’s ability to interact with objects could be altered remotely based on immediate or present needs, in accordance with existing end-user agreements.
- Ashton, Kevin (22 June 2009). “That ‘Internet of Things’ Thing, in the real world things matter more than ideas”. RFID Journal.
- Analyst Geoff Johnson interviewed by Sue Bushell in Computerworld, on 24 July 2000 (“M-commerce key to ubiquitous internet”).
- P. Magrassi, T. Berg, A World of Smart Objects, Gartner research report R-17-2243, 12 August 2002.
- Commission of the European Communities (18 June 2009). “Internet of Things — An action plan for Europe” (PDF). COM(2009) 278 final.
- Techvibes. From M2M to The Internet of Things: Viewpoints From Europe. 7 July 2011.
- Dr. Lara Sristava, European Commission Internet of Things Conference in Budapest, 16 May 2011. The Internet of Things – Back to the Future (Presentation).
- P. Magrassi, A. Panarella, N. Deighton, G. Johnson, Computers to Acquire Control of the Physical World, Gartner research report T-14-0301, 28 September 2001.
- Casaleggio Associati. The Evolution of Internet of Things. 2011.
Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to “spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions.”
- White, Tom (10 May 2012). Hadoop: The Definitive Guide. O’Reilly Media. p. 3.
- “MIKE2.0, Big Data Definition”.
- Kusnetzky, Dan. “What is “Big Data?”. ZDNet.
- Vance, Ashley (22 April 2010). “Start-Up Goes After Big Data With Hadoop Helper”. New York Times Blog.
- “Data, data everywhere”. The Economist. 25 February 2010. Retrieved 9 December 2012.
- “E-Discovery Special Report: The Rising Tide of Nonlinear Review”. Hudson Global. Retrieved 1 July 2012. by Cat Casey and Alejandra Perez.
- “What Technology-Assisted Electronic Discovery Teaches Us About The Role Of Humans In Technology — Re-Humanizing ”. Forbes. Retrieved 1 July 2012.
Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data.
For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders.
Machine to Machine (m2m) Communication
Machine to machine (M2M) refers to technologies that allow both wireless and wired systems to communicate with other devices of the same type.
M2M can include the case of industrial instrumentation – comprising a device (such as a sensor or meter) to capture an event (such as temperature, inventory level, etc.) that is relayed through a network (wireless, wired or hybrid) to an application (software program) that translates the captured event into meaningful information (for example, items need to be restocked). 
- “Machine-to-Machine (M2M) Communication Challenges Established (U)SIM Card Technology” – GD
- “Machine to Machine (M2M) Technology in Demand Responsive Commercial Buildings”
- “M2M: The Internet of 50 Billion Devices”, WinWin Magazine, January 2010.