The Internet of Things, commonly known as IoT, is spreading at a much faster rate than what I initially thought it would about a year ago. I have been curious to learn about what IoT is all about and encountered a large community of contributors and followers in the two dominant open source forums, Arduino and RaspberryPi. Both are working hard to transform the lives of people, organisations and cities around world with the power of digital.
Agile Analytics of Things with prototyping
I experimented with Arduino projects by prototyping a few sensors to understand implications for large scale data collection, data management and analytics. Here are some of the lessons that I learned.
Things want to speak! Are you listening?
There is an incredibly large number of organisations such as ThingSpeak that are providing open platforms in the cloud that leverage micro-controllers and sensors from the likes of Arduino and Raspberry Pi to collect and share sensor data and analytics from around the world, which is a good resource for experimenting and learning about IoT.
What is Internet of Things (IoT) anyway?
IoT is a major development that promises to extend the digitally connected world in myriads of ways to connect things and people to offer new ways of monitoring events and situations to learn and respond in real-time using analytics.
I use the Arduino Mega 2560 micro-controller to write programs called Sketch to monitor and collect sensor data and manage them over embedded communication networks such WiFi, Ethernet, GSM SIM card, Bluetooth etc.
With a passive infrared motion sensor I am able to track foot traffic in strategic locations that is useful in marketing, retail and town planning. When combined with the photo-electric sensor data, I can determine how the overcast weather affects traffic movement in the same location. In addition, the RFID reader allows me to verify and track proximity of things that have embedded tags.
My favourite is the Accelerometer which when placed in my car with a GPS sensor allows me to track the precise locations of bumps and pot holes on the road which can help town planners and transport authorities with traffic planning and commuter safety. These sensors also continuously measure acceleration and lane changes to determine driving behaviours which insurance companies can use to provide incentives or penalise policy holders on insurance premiums.
IoT comes to life with actuator
Sensors are one thing but actuators make IoT actionable. Weather sensors can monitor rain water levels which can be analysed to predict trigger time for remote activation of irrigation pumps to meet the needs of crops.
Alphabet soup of IoT
As I experimented with IoT, I am learning that “Internet of Things” is a misnomer. It is not a single, unified network of connected devices but rather a set of related technologies and systems, including the use of sensors, RFID chips, communication networks that work in coordination together. For instance, moisture sensors in remote agricultural fields and acoustic sensors in distance rainforests are likely to be using solar power, low maintenance sensors with long-life batteries, multi-kilometer reach with secure communication network to continually collect and transmit data.
In fact, industrial IoT is more likely to use whatever communication networks (e.g. Infrared, ZigBee, RFID, Bluetooth, LoRa, WiMax, NB-IoT, Coax, Fibre, 4G/LTE) are suitable for the application depending on the remote communication distance range and reach, bandwidth need, cost and power source / battery life of the sensors. “Communication of Things” or “Network of Things” may be more meaningful but people are using whatever acronyms to suit their area of focus – “Analytics of Things” and “Internet of Everything” are some of its variations that are coming to take roots.
Irrespective of the differences in acronyms used and communication technologies deployed, the internet protocol and web services are likely to dominate in the minds of IoT application development communities across various industry sectors. In fact, my Arduino prototype is a web server that communicates sensor readings from the micro-controller which can be viewed in a standard web browser and/or be integrated with Apache NiFi.
Business versus Engineering focus of IoT –
While sensor metrics such as temperature, relative humidity, motion etc. are well understood in the business community, when it comes to industrial IoT where sensors are embedded in machines and equipment, most metrics will require scientific and engineering knowledge and industry expertise to interpret the meaning of the sensor data to develop actionable insights in optimising the performance of equipment and machines.
A brief look at the word-cloud below will provide a glimpse of the partial range of sensors and measures made possible by the open source IoT community. This list does not include a wide range of biometric sensors that are used in the healthcare industry which expands the range of possible sensors.
From sketch to scale with Big Data
Each type of sensors produces one type of metric which is often not that helpful in itself in making decisions about possible remedial action. Data context and semantic consistencies, with data being collected from other sensors which operate in the same environment, is required in order to develop an integrated view of the situation impacting the environment.
As sensors continually monitor their environment they generate data continuously, often with same value repeated over extended periods of time until a change occurs in the environment. This results in huge volumes of data with high levels of redundancy that is too expensive to be transported over the communication network. Elimination of redundancy requires local processing of the data closer to the point of data collection and therefore requires consideration of a distributed architecture (e.g. hub and spoke architecture) for efficient data management.
While open source IoT platforms such as Arduino and RaspberryPi allow prototype development of sketch for sensor data collection and actioning of the actuators remotely, it will greatly benefit by leveraging and integrating with technologies from the Big Data open source communities such as Apache Hadoop. As I started to integrate my Arduino prototype sensor data with Apache NiFi for central data transformation, processing and correlation, I learnt that the RaspberryPi community is well on its way to embed Apache NiFi in its microcontroller board. This is a big step in enabling distributed and local processing by leveraging the two open source technologies.
Machine learning and predictive analytics
Sensor data collected from my Arduino prototype offers exciting opportunities for machine learning and predictive analytics with Aster Analytics in the Teradata Unified Data Architecture (UDA). The prediction score and insights gained from Aster Analytics can be combined with the customer and product data in the Integrated Data Warehouse, in the UDA it can be used to trigger remote activation of the actuators.
If you want to know more about ‘Analytics of Things then start here.
Latest posts by Sundara Raman (see all)
- Making Smart City projects smarter with Smart Organisations - March 29, 2017
- IoT will accelerate industry convergence and structural disruption - October 25, 2016
- Internet of Things – Lessons from an IoT prototype project - August 22, 2016
- How Come NPS (Net Promoter Score) Data Doesn’t Rate Ben Affleck Movies? - August 17, 2016
- Which Open Source technologies are suitable for your Big Data roadmap? - June 27, 2016