I work at ValueFirst Digital Media Private Ltd. I am a Product Marketer in the Surbo Team. Surbo is Chatbot Generator Platform owned by Value First. ...Full Bio
I work at ValueFirst Digital Media Private Ltd. I am a Product Marketer in the Surbo Team. Surbo is Chatbot Generator Platform owned by Value First.
Success story of Haptik
961 days ago
Who is afraid of automation?
961 days ago
What's happening in AI, Blockchain & IoT
962 days ago
3 million at risk from the rise of robots
962 days ago
Artificial Intelligence Timeline: Infographic
Edge computing is going to change how the government operates
Just consider the fact, In an average winter, Kentucky's Department of Transportation spend somewhere between $45 million to $75 million on snow removal & road salting. However, during the harsh winter of 2014-15, the spending of the state exceeded projections, and it put them one storm away from finishing their entire snow removal budget. One of the factors contributing to this were the inefficiencies in how the department used it plows and spread anti-icing agents.
In order to fix the problem, they turned to big data, analytics & edge computing. They used sensors in roads and on snowplows as well as data from Waze, a popular driving app and combined it with information from weather reports, Twitter, staff & road crews to develop a more precise and better managed process for snow removal.
That process included algorithms that analyzed in how fast cars traveled over roads, the number of vehicles on a road at a given time and other factors, such as temperature, wind speed and cloud cover. As a result, the department was able to use anti-icing and plowing schedules more precisely, which resulted in less waste and saved the department from 5 to 10 percent on snow and ice removal.
Every 60 seconds, more than 80,000 records of information are fed into the department's computers, where engineers are able to deal with snow on roads more efficiently and in real time.
These advances are part of a broad trend where analytics, machine learning and edge computing are starting to benefit government agencies in ways not seen before, according to Dave Shuman, industry leader for IoT and manufacturing at Cloudera, the analytics firm that developed Kentucky's snow removal program. "There's been a big focus on using algorithms for machine learning in advanced analytics," he said. "The tipping point occurs when you have lots of data and want to apply lots of compute to the problem."
Besides snow removal, versions of these kinds of data rich, analytical IoT solutions are popping up in a number of government functions, from the use of ankle monitors to improve prisoner and parolee rehabilitation and reduce recidivism, to body and dashboard cameras that improve law enforcement forensics to a host of infrastructure applications that include parking, lighting, traffic, transit and even garbage removal.
A big reason this kind of robust, data-driven solution has become more popular is the maturation of edge computing. The consulting firm McKinsey projects IoT devices to reach 30 billion units by 2020 and government adoption to outgrow that of the private sector by 2019. While many of the sensors will simply gather data, they are also enhancing the ability for computing - and decision-making - to take place in cars, trucks, buildings and other government infrastructure, rather than back at a central IT location or in the cloud.
"Processing data at the edge allows for low-level latency on your decision-making processes," said Shuman. "So, you are able to have devices that have enough intelligence, compute capacity and models to make the decisions that need to be made."
Every day, more examples of edge computing emerge, from building elevators that know to stop and lock their doors open if there's an earthquake, to water valves that can shut down if there's a leak somewhere. Snapchat, the popular smartphone app, is another example of edge computing that crunches petabytes of data to create funny images of a person's face. More significantly, autonomous vehicles operate with edge computing in order to make split-second decisions on how to steer through an urban environment.
In recent years, edge computing has generated significant attention. Gartner, the IT research company, believes edge computing will bring value where localized computing power is necessary. As IoT decentralizes an organization's computing infrastructure, edge computing will become an advantage over cloud or centralized computing.
But state and local government IT executives aren't ready to embrace the trend quite yet, certainly not going as far as to say edge will replace the cloud. Former Boston CIO Jascha Franklin-Hodge thinks edge computing is a bit overrated. "What cloud infrastructure has taught us over the last 10 years is that centralized, high-efficiency computing infrastructure in most use cases is going to outperform distributed, lower-efficiency systems in price, performance, scalability, resiliency and all the other things we value," he told Government Technology.
Rather than viewing edge computing as an "either/or" choice, Shuman believes a hybrid approach will prevail, thanks partly to the vast amount of legacy technology that still operates in the public sector. "While there are certain things where it makes sense to take advantage of edge compute analytics, other things will need to apply more significant context to make the right decisions," he said.
The type of hybrid combinations Shuman sees unfolding are platforms where sensors are combined to drive CRM, ERP and supply chain systems in government agencies.
However, it is still important for government CIOs and agency heads to conduct due diligence before they embark on these kinds of data driven & analytics processes. Shuman warns that there is a possibility of built-in biases in computer models that are meant to solve a problem. He feels that we must be sure that there is an open and transparent method to it. Since governments rely not just on their own data, but also on what comes from data brokers, they must watch out for the introduction of new biases that could possibly affect the outcomes negatively.
He also said that it is less about gathering data and more so about you are going to eventually do with it. To do at that scale, we would need machine learning, advanced analytics and host of combinations that drive these new cases forward.