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“There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days.”
- Eric Schmidt, Ex Technical advisor at Alphabet and Strategic advisor at Chainlink Labs
We are dealing with an abundance of data. It is only a matter of time before enterprise clouds, growing slowly at an annual rate of thousands, become overwhelmed by enormous volumes of datasets. And these volumes are not digestible. To respond to time-critical events quickly and efficiently, companies will need to optimize and monitor processes. The cloud has always existed, but waiting for feedback from the cloud can result in a delayed response, making devices less likely to accomplish tasks in real-time. Increasingly, companies are incorporating AI solutions into their existing systems to solve issues of data overload and response lag.
From IoT to AIoT - The need of the Hour
As an umbrella term, the Internet of Things (IoT) refers to any physical or remote device, ranging from household objects to sophisticated industrial tools, which are equipped with sensors & software for connecting and exchanging data over the internet with other systems. Artificial Intelligence of things (AIoT) combines Artificial Intelligence with IoT infrastructure to create smarter operating systems, improve human-machine interactions, and enhance data management.
It's no longer a hypothetical situation. With the advent of the Internet of Things, industries are digitally transforming. As of now, IoT has been helping users to provide data from their remote devices. However, this is no longer enough. Expectations are rising every day. This is where AIoT comes to the rescue, making devices more efficient and reliable. In the future, they will be able to act in real time without requiring human interpretations. Where IoT generates & analyses data and human reactions to them, AIoT adds actions to it by converting analyses to insights and reactions to proactions.
Devices are getting smarter – The Edge Computing Era
Data collection remains the same. Data was previously delivered to the cloud for processing. The Internet of Things has helped embed algorithms on devices. Unfortunately, this did not work in situations where there was a narrow data pipeline, time constraints, or a quick response was needed. Deep neural networks and raw data are now accessible to provide intelligence and processing where they are most needed. Hence, AI has added the capability to process high dimension data, perform predictive analysis and take proactive measures.
That brings us to the next and perhaps the much-needed concept – The Edge Computing. Yes, it’s possible now to process the data at the location of its production before it gets to the cloud. Local devices are now able to process time-sensitive data as close to its source as possible, rather than having to send the data to a centralized control server for analysis. This eliminates latency and enables local devices to respond instantly. Also, by filtering raw data near the source, edge computing can significantly reduce the amount of data to be sent to the enterprise cloud, alleviating both bandwidth usage and analytical burden.
AIoT – Human Centered Design POV
On AIoT devices, information is analyzed & processed on the device using AI algorithms that ensure real-time notifications as soon as the event happens. Deep learning deployed at the edge teaches computers to learn complex patterns from image data in order to detect and identify objects in photos and videos – in a similar way that the human brain does. This allows a computer to recognize intricate patterns much faster and with greater accuracy, in many cases surpassing human-level performance. It is also a highly data-driven technology because a deep neural network must take in tremendous amounts of training data in order to increase inference accuracy.
So, how do we know if the implemented concept is the right service to invest in?
Looking at Artificial intelligence of things from the point of view of Human-Centered design, we see a process that solves the problems of the consumers. Three questions to ask before running for any project.
User Desirability & Usability
Do this concept and the solution make users desire to act on it? Is the user interface experience amazing? Is it usable? Does it solve the core problems and underlying issues and not just the symptoms?
Technical Feasibility
Is the concept technically possible? Is there any barrier? Do we have solutions at hand for them? Will & can the organization support this physically and technically?
Business Viability
Is the concept & the processing within the company’s budget requirement? Will it bring value to the business? Is it a great venture to generate revenue?
If the concept this sensitive & smart is desirable, usable, technically feasible and adds value to your business, then you have a perfect space to invest on AIoT.
“Do not fall for the IoT vs AIoT headlines. There is no Brain vs Nervous system.”
Let’s see how AIoT transforms user experiences
Water Management & AIoT
Water management, conservation, and equitable access to water are key to sustainable development. A third of the global population lacks access to safe drinking water. Water is a critical resource for residential, commercial, and industrial activities and the world must manage it effectively as it recovers from the repercussions of the pandemic. Global warming was disrupting the water ecosystem even before the COVID-19 pandemic.
Case 1:
Pipe breakage and valve malfunction along the distribution pipelines from reservoirs to pumping and purification stations and then further to user premises result in significant water leakage due to high flow rates and hydraulic pressure. An approach like the measurement of pressure and flow rates in water distribution networks will allow the network to be optimized and its integrity to be maintained. It is also possible to detect leaks beyond the threshold from overhead tanks and pipes by using vision systems.
The distribution network can transmit flow rate, pressure, rate of change, and time wise patterns across points using rugged outdoor sensors and communication modules. From an analytical perspective, historical leakage trends and telemetry data patterns sent to Cloud applications could be integrated with weather data. This would enable the system to predict likely failure times for various distribution networks.
Using computer vision-based pipelines on known bottlenecks in the network, i.e., spots where leaks have been localized on multiple occasions, can prevent major leaks. Depending on the demand and seasonality of the distribution network, these video cameras could also move along the pipeline length to capture video feeds.
Case 2:
In commercial and residential premises, unattended water flow from outlets leads to water waste. While measuring and monitoring every water usage endpoint is not feasible, a smart meter and valve can be enhanced with intelligence. This is so that a set of endpoints can be regulated in a technologically and economically feasible way.
Monitoring water flow rates can be used to detect water wastage on-premises. By detecting periodic trends in water flow rates, smart meters can facilitate this process. With the help of machine learning models, one or more simultaneous leaks can be detected, such as a leaking shower head, a malfunctioning flush tank, or an open kitchen sink faucet.
The use of connected vision or sensor data-based machine learning solutions can be used to detect incorrect configurations of deployed equipment, deteriorated critical components, or sustained violations of standard operating procedures by the operator or user. Corrective action based on these sensor data-based insights helps prevent ineffective or broken processing equipment from occurring.
Across the housing, commercial, and industrial value chains, AIoT technologies improve water utilization. This has the potential to contribute significantly to achieving the sustainable development and conservation of water resources.
Improving Health Outcomes – The Berg Balance Scale
Patients undergoing stroke rehabilitation or treatment for Parkinson's disease or arthritis are routinely assessed for balance and mobility. The berg balance scale is used to evaluate a patient's balance and mobility during 14 specific movement-related tasks, each of which receives a score of zero to four. Despite its longevity as the standard, it is flawed. It's also subjectively and qualitatively based, relying heavily on caregivers' personal observations. In addition, the test is a highly manual process that takes more than 20 minutes and requires assessment. An AIoT approach can make assessments simpler by using fewer steps while ensuring that the test is quantitative, consistent, and reproducible.
Researchers at SAS have created advanced, AIoT-enabled motion analytics tools for digitizing routine balance and motion assessments. This solution marshals IoT capabilities, motion monitoring tools, machine learning, and advanced analytics to deliver a more accurate assessment of patients’ movements much faster than current methods.
Benefits include
A co-operate drive for greater accuracy and consistency in measuring both individuals and groups is proposed.
Build on a foundation of patient-specific and system-level clinical data.
Record and keep track of information that the human eye cannot see.
Focus on higher-value aspects of patient care can be enabled for clinicians and other care providers.
To identify new clinical assessment endpoints.
Develop methods to quantify and evaluate pain, discomfort, and disequilibrium treatment efficacy.
Wrapping it up with
Creating the right digital product may require developing a new and unique user experience. Adding AI to IoT may be just what's required to allow for new user experiences—experiences that would otherwise be impossible. At this moment, this is cutting-edge technology, but if you want to ride the exponential digital operating model value curve, you may need cutting-edge technology to propel you and your company forward.
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