- Tracking behavior for real-time marketing;
- Enhanced situational awareness;
- Sensor-driven decision analytics;
- Process optimization;
- Optimized resource consumption; and
- Instantaneous control and response in complex autonomous systems.
Associated with these benefits (and others) are the major driving forces that are pushing us at an increasing pace toward full IoT development and deployment. These forces include (at least) the following 8 motivating factors:
personal Wi-Fi on your mobile phone and on every other device. Everyone (and everything) wants and needs to be connected.
we want all of our devices, phones, televisions, music players, vehicles, etc. to keep track of what we are doing, viewing, reading, and listening to as we move through our day, from place to place – the handoffs from device to device are already happening.
on everything. It is already here – the Internet of Everything and the wearables revolution.
4.Intelligence at the periphery of the network
Jim Gray, the visionary database guru from Microsoft, envisioned smart sensors acting as a mini-database with embedded machine learning algorithms. Here is how he said it (10 years ago): “Intelligence is moving to the periphery of the network. Each disk and each sensor will be a competent database machine.”
5.Analytics as a Service
the API and App economies are already vast and growing – this enables any “thing” to “do something interesting” as long as it can connect to an API or invoke an App that performs a network-based service. The “thing” is a data generator and/or collector that also learns from, makes predictions, and maybe even takes data-driven actions in response to the data that are collected (through the versatility and convenience of an App or API call).
mobile customer engagement, geolocation, Apple’s iBeacon, etc. are all creating a network of knowledge about customers’ locations, intentions, preferences, and buying patterns. Of course, this degree of location-based knowledge needs to strike the right balance between user privacy and the timely delivery of useful products and services to that user.
7.Supply Chain Analytics
delivering just-in-time products at the point of need (including the use of RFID-based tracking). Essentially, everything is a customer (including machines, automobiles, manufacturing plants, ATM machines, etc.), and the IoT is monitoring, watching, and waiting for a product need to arise.
There is a huge hiring gap in manufacturing, which is pushing toward more automation, robotics, M2M (Machine-to-Machine), machine log mining, 3-D printing, predictive and prescriptive analytics in the machines that are doing that work for us. As the classic rock song “2525” predicted would happen in the year 5555: “some machine is doing that for you.”
One of the major developers of IoT in the industrial environment is GE – check out the excellent recent article on “GE’s Vision for the Industrial Internet of Things”. Several big data platforms are beginning to investigate the data challenges, communication standards, analytics requirements, and technology responses that the Internet of Things will bring to operational analytics and supply chain environments, but very few are architected to handle IoT. The data challenges include: high input rate, streaming (time-series) data, many small files, and the need for fast micro-adjustments in the operational environment. The biggest technology challenge will be the integration of everything: big data, cloud, billions of devices (IoT and M2M), and the network fabric.