So what really is the Internet of Things? It is made up of physical objects (“things”) that have chips, sensors embedded in them that allow the sensing, capturing and communication of all types of data. These devices are then linked through both wired and wireless networks to the Internet. Advanced “things” have actuators embedded into them as well, giving them the capability to interact with other devices, computing systems and the external environment, including people.
IoT takes this one step further – Actuation
Quantity of data and quality of solution (actuation)
Sensors have existed for a long time, think how many sensors you need to send a rocket into space, but today this is not rocket science, what is happening is that sensors are becoming commodities, leading to adoption on a massive scale, enabling new applications to be possible e.g. placing large numbers of sensors in agricultural fields to measure soil humidity and nutrient levels
Big data versus huge data
IoT data : typically sensor readings and associated data together with timestamps
Why so big ?
1) Many more networked devices than networked humans – and growing fast
2) Associated data can be video, audio and social networking data – yes things will join social networks like humans do
=> Going to be biggest big data
Video, audio, images can also be IoT data
According to new research from International Data Corporation (IDC), the worldwide Internet of Things market will grow from $655.8 billion in 2014 to $1.7 trillion in 2020 with a compound annual growth rate (CAGR) of 16.9%.
http://www.idc.com/getdoc.jsp?containerId=prUS25658015
http://www.emc.com/leadership/digital-universe/2014iview/internet-of-things.htm
Sensors record speed and intensity of traffic
Open data
Swift highly scalable and low cost, in comparison to using a database
Essential for keeping IoT data long term
What kind of data ?
Together with traffic speed and intensity data we also have camera images – less suitable for DB
Need some kind of indexing to make it efficient
Learn from historical data to respond in real time to live data
Actuate:
CEP – respond according to thresholds
Possible actions: reroute buses, alter traffic light behavior, alert citizens, etc.
Action to be taken is application specific
Need for real time and historical – real time is green arrows
Adding historical dimension
Left cycle is real time only, we added larger cycle
Stopwatch graphic is from Wikimedia commons
Object storage (openstack swift) as a long term repository for IoT data
Scalable and relatively low cost
By adding metadata to describe what is contained in each object and metadata search we can access it efficiently
Databases are often overkill for what is needed by analytics
Partitioning needs to be done statically, difficult to change later on
Difficult to change the schema
Partitioning must be hierarchical – advantages to selecting on columns up high in hierarchy
HDFS versus Swift for analytics
-Swift more scalable and reliable (Namenode SPOF issue?)
-Supports metadata
-Supports stronger security model
-More natural for storing some types of data e.g. traffic images
Can depend on other elements of context like weather etc.
Note: table is for one location only
Importance of responding in time
Value of actuation in real time e.g. pipe leakage
Potential to learn new insights by tapping in to the historical data e.g. as you heard in a previous talk by Intel, in healthcare improve quality of healthcare for patients with Parkinsons disease
Insurance – pay as you drive and pay how you drive models – pay where you drive ? New business models
I’ve been thinking a lot about the last one since I arrived in Amsterdam, I think its ironic that I lost my things on the way to a conference where I’m talking about the internet of things, airlines please work on this ;-)
IBM Bluemix is IBM’s Platform as a Service offering. Based on Cloud Foundry. Bluemix has services for most of the components we used, for example there are Spark and object storage services as well as a MessageHub service based on Kafka. Together with a team led by Naeem Altaf, we ported this use case to run on Bluemix to give customers an example of an IoT use case that can be built on the platform. This work was demoed yesterday in Las Vegas at IBM’s Insight conference.