Internet of Things
November 17, 2016
When we discuss Web or Internet interconnected “Things”, or IoT, we’ve become accustomed to recognize such devices as our desktops, tablets, cars (i.e., Telematics), TVs (e.g. LG Smart OLED TVs) and our phones — all being connected to the World Wide Web. And, new devices have arrived such as Amazon’s Echo, Oculus Rift, Samsung’s Gear VR (powered by Oculus, by the way) and Google Home, in addition to our microwaves, washers and dryers, lighting (e.g., Hue), thermostats (e.g., Nest) and refrigerators (even lawn sprinklers, like Rachio) offering connectivity via the Web; and, IoT is increasingly becoming pervasive across HealthCare, Automotive, Manufacturing, and Agriculture industries. The Internet of Things has certainly advanced from the days when IoT was termed as persistent computing, ubiquitous computing, the industrial Internet, the nested network, and certainly from the days of Arpanet, the start of TCP/IP22, and Tim Berners-Lee‘s first web page in 1990.

“Learning is any process by which a system improves performance from experience.” Source: Herbert Simon, Nobel Economist

As to Machine to Machine Learning (M2M) it is widely accepted in both data and IT communities that this field of computation had its genesis though the efforts of Mr. Aurthur Lee Samuel while he was at IBM and then at Stanford University. But, what is Machine to Machine Learning (M2M)? Well, that’s how search engines learn the meaning and intent of published web pages and web sites. M2M is the technology that underpins what’s grown to be known as the Semantic Web, led by interpretation (Artificial IntelligenceAI; see ‘Advancing our Ambition to Democratize Artificial Intelligence‘) technologies such as Google’s TensorFlow coupled with RankBrain, both of which have spawned the new Google Assistant; Microsoft’s New Experiences and Technologies (NExT), tied to the Microsoft Concept Graph, as delivered by Bing.com and Cortana; and by industry-specific/enterprise M2M platforms such as Torch, Tractica and Theano. Lastly, there are open-source M2M AI solutions, notably OpenAI, funded by Tesla‘s Elon Mush and Sam Altman8, and FaceBook‘s open-sourced Open Compute Big Sur machine learning platform. Lastly, there is the W3C IoT Web of Things Schema.org Community working group that’s free to join.

One key element to Machine to Machine Learning, on the Web, began in 2011 with the introduction of Schema.org, a Web-standards collaboration among Google, Bing, Yahoo and Yandex in the form of Schema.org (e.g., see the attributes and entities associated with Thing as an example). Additionally, Google has built an IoT online resource, its Internet of Things Guides site, as have Microsoft, Verizon (ThingSpace), NokiaSamsung and others.

The Gartner Group projects that by the end of this year 6.4 billion devices will be “connected things”. And, with the enormous push by Google with its Google Home product, Caliber Media Group, a leading digital marketing agency, anticipates that the number of “things connected on the Internet will actually be much larger, for device interconnectivity. Also, for those who’ve used Google Home’s Google Assistant, there will be a great increase in Machine to Machine Learning — as demonstrated by all of the connected devices and applications in this demonstration of Google Home.

What’s in the future for IoT and M2M? Well, the Information Technology and Innovation Foundation’s Center for Data Innovation (ITIF) shed light, earlier this year, on the creation of the DIGIT Internet of Things Act, a bipartisan bill in the U.S. Senate which will create an IoT and M2M working group to accelerate the adoption rates throughout the U.S. Government for Internet of Things and Machine-to-Machine technologies.

blog
November 16, 2016

Internet of Things | The Search of Things | IoT

The Internet of Things (IoT), codified in 2013 by Internet of Things Global Standards Initiative (IoT-GSI; now led by the ITU-T SG20), has such a wide range of applications that it can grow beyond our expectations. IoT is the internetworking of physical devices, vehicles, structures and other items embedded with electronics, software, sensors and other network connectivity that allows these objects to collect and exchange data. IoT provides network connectivity, connectivity management, data analytics, management and automation, security, embedded networks and other applications. An example of IoT would be “smart” thermostats that learns what temperature you prefer and builds a schedule around yours. It uses sensors and real-time weather forecasts along with the actual activity in the home throughout the day to keep your room temperature comfortable and save in energy bills during the process.

IOT Search

The possibilities for IoT applications are endless: Industries that have adopted IoT have used it for geographic specific infrastructure monitoring and predictive maintenance (i.e., Smart City IoT, Microsoft’s Azure cloud), asset tracking, automated vehicles, sustainable development, international aid (e.g., IoT in Africa), sports industry and sporting goods, real estate, seismic monitoring, et al. Water leakages along pipes can be alerted/noticed before such public safety hazards become a problem; vehicle auto-diagnosis can bring real time alarms to emergency services and provide advice to drivers — IoT for traffic congestion can monitor vehicles and pedestrians to optimize driving and walking routes (e.g., Hum.com). Air pollution monitoring, water quality monitoring and forest fire detection are some of the other many applications for IoT. “Smart” industries using IoT include manufacturing with connected factories, connected machines and connected supply chains. Also, “smart energy” has provided solutions for utilities and smart grids, oil and gas, and field area networking. In transportation, solutions in IoT include aviation, mass transit, maritime, rail, roadways, vehicles and cars. Remote monitoring and remote action by machines will contribute greatly to the efficiency of intended usage.

Although IoT has been springing up in private partnerships, the open-source platform is gaining more momentum. Recently, GE and Bosh utilized the open source platform through the Eclipse Foundation to make IoT software components work together. The two companies want to establish a core IoT software stack based on open-source software. They will work on several open-source projects such as creating code for messaging, user authentication, access control and device descriptions.

The open source platform tends to be more advantageous than a private platform. For instance, the Google Cloud Platform is integrating its public cloud, which offers greater scale and better security than private in-house IoT platforms. Public clouds provide more advanced analytics, with often times, less hardware for software than private platforms. The full suite of Google Cloud services includes Prediction API for machine learning, the Cloud Datastore NoSQL database, and the BigQuery data warehouse.

There have been six distinct types of applications emerging in two broad categories: information and analysis, and automation and control. Information and analysis includes tracking behavior, such as shopping behaviors and preferences; enhanced real-time situational awareness of the environment, such as detecting the location of a sniper shooting; and sensor analytics, such as continuous monitoring of chronic diseases to help doctors evaluate treatments. Automation and control includes process optimization, such as automated control of manufacturing lines; optimized resource consumption, such as smart meters and energy grids that match load capacity to lower costs; and complex autonomous systems, such as collision avoidance systems to sense objects and automatically apply brakes: The many applications of IoT across all sectors of industries will make the IoT platform beyond our imagination. Having an open-source platform for developers to join and not have disparate, private IoT implementations, will assist the IoT in its further growth, much like the early days of the Internet.

The usage of IoT applications in particular has been in the fleet trucking and automotive industry. For instance, the OBD-II (On-board diagnostics) provides diagnostics of repairs through its digital signaling. It monitors DTCs (Diagnostic trouble codes) that a technician can query on the on-board computer on any vehicle, since the codes are standardized. Most manufacturers have made the Data Link Connector or the main connector of all systems in the vehicle to diagnose and reprogram. Custom parameters can also be met for more specific data.

 

Caliber Media Group, your digital marketing and web-assets production agency, in Orange County, CA, is on the cutting edge of providing digital marketing services to companies implementing IoT, Please contact Caliber Media Group for services at 714-867-1601.

References:
1. Lawson, Stephen. “GE, Bosch and Open Source Could Bring More IoT Tools.” PCWorld from IDG. IDG Consumer & SMB, 26 Sept. 2016. Web. 29 Sept. 2016. <http://www.pcworld.com/article/3124749/ge-bosch-and-open-source-could-bring-more-iot-tools.html>.
2. Lawson, Stephen. “Bringing IoT Data Into Public Clouds Is Getting Easier.“PCWorld from IDG. IDG Consumer & SMB, 27 Sept. 2016. Web. 29 Sept. 2016. <http://www.pcworld.com/article/3125004/bringing-iot-data-into-public-clouds-is-getting-easier.html>.
3. By Michael Chui, Markus Löffler, and Roger Roberts. “The Internet of Things.” McKinsey & Company. MicKinsey Quarterly, Mar. 2010. Web. 29 Sept. 2016. <http://www.mckinsey.com/industries/high-tech/our-insights/the-internet-of-things>.

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