ANALYTICS AND THE INTERNET OF THINGS
The opportunity for IoT is interoperable IoT applications with analytics
Some impressive facts on the use of IoT applications today and what we can expect in the next years
The majority of people (87%) have not heard of the term ‘Internet of Things’.
ATMs are considered some of the first IoT objects, and went online as far back as 1974.
Back in 2008, there were already more objects connected to the Internet than people.
This year, we will have 4.9 billion connected things.
And some predict that by 2020, the number of Internet-connected things will reach or even exceed 50 billion.
In 2015, over 1.4 billion smart phones will be shipped and by 2020 we will have a staggering 6.1 billion smartphone users.
The IoT will connect many of the devices we have in our homes, from smart thermostats to smart fridges. Companies like Google GOOGL +0.80% and Samsung understand this. Google bought smart thermostat maker, Nest Labs, for $3.2 billion, and Samsung purchased connected home company SmartThings for $200 million.
By 2020, a quarter of a billion vehicles will be connected to the Internet, giving us completely new possibilities for in-vehicle services and automated driving.
In fact, we already have cars that can drive on their own – Google’s self-driving cars currently average about 10,000 autonomous miles per week.
The global market for wearable devices has grown 223% in 2015, with Fitbit shipping 4.4 million devices and Apple AAPL +0.90% selling 3.6 million Apple Watches.
And yes, Internet-connected clothing is coming. Estimates predict that 10.2 million units of smart clothing will ship by 2020, compared to a meagre 140K units in 2013.
Today, the market for Radio Frequency Identification (RFID) tags, used for transmitting data to identify and track objects, is worth $11.1 billion. This is predicted to rise to $21.9 billion in 2020.
Machine-to-machine (M2M) connections will grow from 5 billion at the beginning of this year to 27 billion by 2024, with China taking a 21% share and the U.S. 20%.
GE believes that the “Industrial Internet” (their term for IoT) will add $10 to $15 trillion to global GDP in the next 20 years.
According to estimations by the McKinsey Global Institute, the IoT will have a total economic impact of up to $11 trillion by 2025.
Having a connected kitchen could save the food and beverage industry as much as 15% annually.
CISCO believes the IoT could generate $4.6 trillion over the next ten years for the public sector, and $14.4 trillion for the private sector.
IoT and Sensors
According to Cisco, “The fundamental problem posed by the Internet of Things is that network power remains very centralized. Even in the era of the cloud, when you access data and services online you’re mostly communicating with a relative few massive datacenters that might not be located particularly close to you. That works when you’re not accessing a ton of data and when latency isn’t a problem, but it doesn’t work in the Internet of Things, where you could be doing something like monitoring traffic at every intersection in a city to more intelligently route cars and avoid gridlock. In that instance, if you had to wait for that chunk of data to be sent to a datacenter hundreds of miles away, processed, and then commands sent back to the streetlights, it would already be too late — the light would have already needed to change.
Cisco says that the solution is to do more computing closer to the sensors (fog computing) that are gathering the data in the first place, so that the amount of data that needs to be sent to the centralized servers is minimized and the latency is mitigated. Cisco says that this data crunching capability should be put on the router. This, however, is only part of the story. Getting the right data from the right device at the right time is not just about hardware and sensors, instead it is about data intelligence. If you can understand data and only distribute what is important, at the application level, this is more powerful than any amount of hardware you throw at the problem.
This prioritization of data should be done at the application level where there is logic. Combine this with data caching at the network edge and you have a solution that reduces latency.
IoT and Mobile Data
Smartphones certainly play a role in collecting some of this data and providing a user interface for accessing IoT applications, but they’re ill-suited to play a more central role. Consider the example of home automation: It hardly makes sense for critical home-monitoring and security applications, such as those that protect an elderly resident against an accident or illness, to rely upon a smartphone as its decision-making hub. What happens when that person travels and his smartphone goes into airplane mode? Does his home security get interrupted, or home electricity shut down?
Such examples make it clear that the IoT will, with a few exceptions (such as “wearable” technology and bio-monitoring systems) and some automobile-related applications, rely mostly upon dedicated gateways and remote processing solutions—not on smartphones and mobile apps.
Today, without any IoT services, more than 80% of the traffic over LTE networks goes through Wi-Fi access points. What happens when that data increases by 22 times? In addition, cellular networks and communication devices have serious drawbacks in areas such as cost, power consumption, coverage and reliability.
So, will the Internet of Things have a place for smartphones and cellular communications? Absolutely. But in terms of performance, availability, cost, bandwidth, power consumption and other key attributes, the Internet of Things will require a much more diverse and innovative variety of hardware, software and networking solutions.
IoT and the Volume of Data
The IoT is going to produce a lot of data – an avalanche. As a result, some IoT experts believe that we will never be able to keep up with the ever-changing and ever-growing data being generated by the IoT because it’s just not possible to monitor it all. Amongst all the data that is produced by the IoT, not all of it needs to be communicated to end-user apps such as real-time operational intelligence apps. This is because a lot of the chatter generated by devices is useless and does not represent any change in state. The apps are only interested in state changes, e.g. a light being on or off, a valve being open or shut, a traffic lane being open or closed. Rather than bombarding the apps with all of the device updates, apps should only be updated when the state changes.
IoT and Datacenters
Some argue that the datacenter is where all the magic happens for IoT. The datacenter is absolutely an important factor for the IoT; after all this is where the data will be stored. But the myth here is that the datacenter is where the magic happens. What about the network? After all, IoT is nothing without the Internet actually supporting the distribution of information. So you might be able to store it or analyze it in a datacenter, but if the data cannot get there in the first place, is too slow in getting there or you cannot respond back in real time, there is no IoT.
IoT is a Future Technology
The Internet of Things is simply the logical next step in an evolutionary process. The truth is that the technological building blocks of the IoT—including microcontrollers, microprocessors, environmental and other types of sensors, and short range and long range networking communications—are in wide-spread use today. They have become far more powerful, even as they get smaller and less expensive to produce.
The Internet of Things, as we define it, while evolving the existing technologies further, simply adds one additional capability—a secured service infrastructure—to this technology mix. Such a service infrastructure will support the communication and remote control capabilities that enable a wide variety of Internet-enabled devices to work together. (freescale)
IoT and Current Interoperability Standards
Everybody involved in the standards-making process knows that one size will not fit all— multiple (and sometimes overlapping) standards are a fact of life when dealing with evolving technology. At the same time, a natural pruning process will encourage stakeholders to standardize and focus on a smaller number of key standards. Standards issues pose a challenge, but these will be resolved as the standards process continues to evolve.
The Internet of Things will eventually include billions of interconnected devices. It will involve manufacturers from around the world and countless product categories. All of these devices must communicate, exchange data and perform closely coordinated tasks—and they must do so without sacrificing security or performance.
This sounds like a recipe for mass confusion. Fortunately, the building blocks to accomplish many of these tasks are already in place. Global standards bodies such as IEEE, International Society of Automation (ISA), the World Wide Web Consortium (W3C), OMA, IETF and IPSO alliance (to name a few) bring together manufacturers, technology vendors, policymakers and other interested stakeholders. As a result, while standards issues pose a short-term challenge for building the Internet of Things, the long-term process for resolving these challenges is already in place.
IoT and Privacy & Security
Security and privacy are major concerns—and addressing these concerns is a top priority. These are legitimate concerns. New technology often carries the potential for misuse and mischief, and it’s vital to address the problem before it hinders personal privacy and security, innovation or economic growth. Manufacturers, standards organizations and policy-makers are already responding on several levels.
At the device level, security researchers are working on methods to protect embedded processors that, if compromised, would halt an attacker’s ability to intercept data or compromise networked systems. At the network level, new security protocols will be necessary to ensure end-to-end encryption and authentication of sensitive data, and since with the Internet of Things the stakes are higher than the Internet, the industry is looking at full system level security and optimization.
IoT utilized for better competition results
Nice and practical cases showing what IoT sensors can do, of course can be found in sports. We have all seen how the German soccer team leveraged IoT in getting World Champion. An example that is way closer to my personal life, is the SAP Sailing simulator. It allows the SAP professional sailing team to real-time monitor and simulate their performance on the water; comparing their online choices and courses against the competition. I am a regatta sailor myself, and believe me if I say this is great and advanced tehnique. Below is a video demonstrating the application.
IoT and Typology of Analytics
If we focus on the analytics part of IoT, the challenge is lees in the volume, but more in handling the variety of data. In these days we primarily focus on the descriptive element of analytics; we describe what is in the data. This is a great help to isolate or narrow down the data to proportions or focus areas we want to investigate. However, there are way more valuable propostions to use analytics for IoT. It is veru nicely described in this article. From the article, we can identify following types of analytics for IoT:
Descriptive analytics for IoT:
Diagnostic analytics for IoT
Predictive analytics for IoT: focussing on extracting the relevant information from IoT
Prescriptive analytics for IoT: focussing on the recommendations that come from the predictive analytics
Especially in North America insurance companies are very keen on preventing leakage issues in houses and properties. Most of the houses and properties there are 100% made of bricks, and their core materials tend to be very sensitive to water-leakage or moisture-issues; they affect the core of the property quickly and could cause dramatic damage or even make the property collapse. These leakage thus cause huge costs for the Insurance companies.
SAP got in touch with them and we made following: at very limited costs, the insurance companies have sensors placed in their insured properties permanently spotting leakage. This information is brought into a SAP Hana Cloud Platform (HCP) application which alerts both the insurer as the inhabitant when leakage appears. The platform is also connected to the SAP Ariba network allowing to immediately bring in third party maintenance people to repair the leakage. But the innovation is way beyond that; the insurance companies utilize the big data and analytics capabilities of HCP to further optimize:
They generate insights on types of properties, regions or specific areas where leakage tends to occur more. Think of the age or location of properties that have greater risk on leakage. It allows them to optimize their insurance policies for this segmented group, using SAP Lumira and Design Studio dashboards utilizing real-time information from the HANA Cloud Platform.
The insurance companies deploy predictive models allowing to optimize their insurance policy offerings to any new habitant of a property depending on the risk of a leakage. Properties with less risk can have better conditions. New properties that become within the insured policies are parsed through the predictive models, and the insurers can now predict the risk of leakage depending on parameters they got insight into from the properties that already are in their “inventory”
The SAP Ariba network is fully utilized making SLA’s with maintenance companies and negotiating conditions
The above shows how Digital Transformation – and enabling a permanent link between the Insurer and the Insured Object – can better both the conditions for the insurer as the habitant of properties. Here is how it works:
Shop Performance and Shelf optimization
Of course the Retail Industry is a good candidate for new innovations on process and performance optimization. The overwhelming power of it's opportunities for analytics is obvious, as well as is its potential for IoT related innovations. The industry is highly customer centric, extremely agile and extremely sensitive to market fluctuations. It is also an industry that is very well suited to getting closer to its customers based upon user experience and immediate customer involvement.
Impressive innovations are being created for customers regarding shop and shelf optimization.for retail. A first example about getting better insights in real-time on customer behavior in the shop. By placing small camera's on every shelf in a shop, it is possible to:
use face detection technology finding the age and gender of the customer looking though the products on the shelf
the technology also monitors in real-time how long a customer stays with the shelf. It allows to create exact heat maps of the most intense visited places on the shop floor. Business Analytics help the shop manager to verify how visitor-density evolves by changing shelfs or changing the type of products being put on the shelf. Using predictive analytics, the shop manager can start simulating what the customer density per shelf will be, when changing order or products
Below is a video showing the picture the face detection camera's are capturing when somebody visits a shelf in a retail store. On the right hand you notice the real-time data being collected and stored in the HCP platform.
A similar example where SAP worked together with Powershelf shows how connected-shelfs can be monitored and leveraged in favor of the customer. The connected-shelf recognizes the customer and is capable of offering tailored offerings. The distributor has real-time insights on all of his connected-shelfs with concatenated data and uses business intelligence and predictive algorithms to re-allocate stock, run basket analyses, optimize product-mixes and compare regional performance using GEOSpatials. It is all facilitated in real-time, again leveraging the in-memory HCP platform.
You thought Lego was only for kids? Have a look at below video where our Lego robot fixes the famous Rubik cube real quickly. You'd probably say "nice, but seen that before". I know, but yet we need to re-think about the technology behind it. Working with supplier of color-detection-sensors, SAP developed a color-recognition algorithm that can be applied in numerous use cases (think pharmaceuticals or agriculture). Our Lego object is being directed by the real-time data stream coming from the color-detection-sensor. Passing an algorithm for unlocking the Rubik cube solution, it drives the Lego object to solve the puzzle. It doesn't take too much imagination that by replacing the Lego-vehicle by an industrial machine (i.e. harvest sorting machine in agriculture), a use case for optimizing industrial processes is there. The power is of course in the scalability and the real-time insights on both analytics as predictive algorythmls it brings. By using the HCP platform, the gateway ois open to other networks like Ariba. How about for example a agriculture machine that recognizes and automatically sorts the harvest of a fruit-grower, while automatically looking for the best offer of a logistics firm shipping the sorted fruits to different distributors.
the next generation IoT platform
Connected Products includes solutions that support product development and its supply chain.
Connected Assets is about supporting and running the assets (machines and equipment) that are owned by a company and typically used in a factory.
Connected Fleet contains any “thing” that moves, like cars, trucks, trains and planes.
Connected Infrastructure covers buildings, pipelines and energy grids.
Connected Markets is for commercial spaces including cities, retail, farms and other public environments.
Connected People supports humans for health, sports and in their homes.