How IoT can cool down a hot problem

In October 2015, a refrigerator thermostat at Stanford Children’s Health failed, spoiling ten different types of vaccines and causing 1,500 kids to receive ineffective vaccinations. Unfortunately, failed thermostats are not an unusual issue and can cause ruined blood samples, vaccines and other important tests, costing millions of dollars and placing lives at risk.

To prevent lost bio samples, hospitals are now turning to IoT-monitoring platforms which deploy sensors to alert hospital and lab administrators of temperature irregularities in real-time, helping them fix the problem before it starts.

One solution to losing bio samples: connected thermostats

DataToWeb is an IoT monitoring platform from the company based in Montreal, Canada, that reduces the risk of losses, facilitate the management of regulation complacency and reduce workload related for the management of temperatures in hospitals, laboratories and pharmacies by automating temperature readings and alerting when there is a deviation.

I spoke to their CEO, Hakim Rouab, about their services. Like many startups, temperature regulation through IoT was not their original intention; rather, they pivoted from home energy monitoring.

Roubac was prosaic in admitting that it was great to being in a sector where we can provide a business solution to an identified problem. “We’re bootstrapped. we are very happy because we struggled with energy monitoring and now we are very lucky to have found a business where customers pay for the service that we provide,” he said.

In discussing the need for temperature monitoring, he said:

“We’ve heard so many bad stories about what happens when a laboratory or hospital’s fridges and freezers are not at the right temperature. 80% of our customers previous to our services, were having a person go around several times a day (usually between two and four) and manually check the temperature of each fridge or freezer.

 There are significant consequences if hospitals fail to meet basic monitoring standards, and they cannot operate if they do not monitor their supplies accurately. There’s also the need in many instances to monitor room temperatures and humidity. Every time there is a big issue in hospitals, it is typically just before national holidays, we heard of an example when someone in maintenance just shut down the entire supply of Co2 for their incubators throughout the entire hospital.”

 

It’s easy to predict a media outcry if patients realized the need for “do-over” tests due to incorrectly stored samples. Roubac noted:

“When someone has a liver biopsy or blood sample, you don’t really want to ask ‘Can you come back for another test, we had the throw the first one out!'”

DatatoWeb also has the advantage of being within the health industry but not subject to the restrictions of regulation due to the nature of their data. “We don’t store patient data, only machine data, so we don’t require all of the regulation around privacy of people” he explained. “Our system is safe to hacking, people cannot access data from our server.”

I asked what would happen if their service failed, as technology is wont to do very occasionally.

“We’ve used the services of  Compose since 2015, we had problems with our previous databases, so we now use mongo DB,” he replied. “Because this is an alert system, we really don’t want to have downtime. We had downtime once with our previous database and it wasn’t great. Luckily, our customers typically use back-up.”

The next step for DatatoWeb is to scale their business outside of Canada and expand their customer base.

US government CIO calls for “self-aware” systems for IoT

As the Internet of Things (IoT) grows to an enormous scale, new security and scalability challenges are bound to emerge. According to the chief information officer (CIO) of the federal government, Tony Scott, the issues may be fixed by implemented self aware systems and lowering the amount of data analysed.

Speaking at the ICIT Forum 2016, Scott — who previously worked for Microsoft and VMWare under the same title — said that IoT components lack a few critical features that may lead to major issues.

The first missing feature is self awareness; the ability for the component to ask questions like “Am I healthy? Am I still operating the way I was designed to? Have I been compromised? Can I call for help?” Self aware components might even be able to self diagnose, providing a report every few hours on its health to a centralized server.

Self awareness is not artificial intelligence, but provides the chip with enough “brain power” to know when it’s unhealthy or vulnerable to attack. This could save money by lowering the amount of analysis done on an IoT network.

This leads into Scott’s next point on the over analysation of a network. He calls for systems to be created that send only the most important pieces of information, rather than an entire network of info.

Security an “intractable problem?”

“I don’t believe we can collect logs and analyze them for everything that’s going to be a participant in the Internet of Things,” said Scott. “I don’t think there’s enough compute power or enough data science to do that effectively at really large scale. It’s just an intractable kind of problem.”

Scott believes that developers of IoT systems need to look at new ways of designing systems that differ from previous platforms. He suggests that developers rethink how a system is built and if every component is necessary to make IoT functional and secure.

Automation of systems is being tested by a few companies like Google and IBM, which want to use artificial intelligence to lower the amount of human input, but it is still in the early days. Other systems like the blockchain might help towards a more computer-orientated system of trust and security.

Can telcos rewire the data silos of tomorrow’s smart cities?

While the Internet of Things (IoT) may connect our devices, smart cities are launching increasingly disparate applications that need help connecting together the mess of data being generated.

In a commentary on The Stack, Comptel IoT specialist Veli-Pekka Luoma argues that telecommunications operators, or telcos, are perfectly suited to play the role of great communicator between the disconnected IoT applications that are proliferating across connected cities.

“Operators have the chance to be the glue that stitches data from separate IoT applications together,” said Luoma. “Telcos can be the catalyst that spurs knowledge sharing, benchmarking and the development of best practices across IoT initiatives,”

He says it is vital that these IoT platforms better share resources in order for smart infrastructure to reach its efficiency potential.

“An internet-enabled suite of services, supported by real-time data analytics, could improve life for citizens in the areas of health, transportation, energy efficiency and more,” he said.

Luoma says it’s possible to develop effective strategies to get disparate smart city initiatives working in concert, and to tap the insights their harmony will produce

“Take a few steps back and you’ll quickly see the wide swath of vertical markets already engaging with IoT applications across global cities: health, energy, education, public safety and governance,” he said. “Imagine if each of those applications were connected, and cities had an opportunity to aggregate and analyze all of that information. What kinds of insights could be drawn, to the benefit of the entire city?”

Data – not the connection – is ultimately the future opportunity

However, he says the smart city opportunities of the future will not be just a connectivity play, but will be found in the powerful data that will emerge.

“Real-time data analysis and management is at the heart of the operator opportunity in the IoT, but especially in smart cities,” Luoma said. “After all, IoT-enabled devices are just like every other source of data, such telco networks or mobile apps, which operators already tap for customer insights.”

He sees telcos as being familiar with the concept of “horizontal platforms” that enable management of smart city technology, as they are similar to the service platforms operators currently use to look at data across different sources.

“Applied at the scale of a smart city, these urban operating systems rely on real-time data analytics to reveal the patterns of life within a city,” he said.  “Authorities could then look across real-time contextual data from vertical IoT applications to optimize and improve city life.”

“Ultimately, operators can serve as managers of the platforms cities use to aggregate and analyze disparate IoT data in real-time, to the benefit of everyday citizens.”

At Carnegie Mellon, using tech to make teachers more engaging

Amy Ogan, an educational technologist at Carnegie Mellon University, calls herself a “CMU lifer” and for good reason. She nabbed both her undergraduate degree and Ph.D from the school. For the last couple of years, she has also worked at the university as an assistant professor, where she’s primarily focused on making classrooms, both online and offline, far more engaging.

Earlier this week, we talked with Ogan about how her team of researchers is right now trying to make good old-fashioned university settings more compelling through what they call “sensing.” Our chat has been edited for length.

TC: What inspired you to examine real-world classroom engagement?

AO: One of the motivations of this work is that with [online courses], we can collected data on what students are doing all the time, but there’s a lot that you can’t see, and a lot of those things demonstrate learning and give us better feedback. So we’re taking the idea back to the physical classroom first.

TC: You’re collecting talk data to start. What kinds of patterns are you looking for?

AO: The first thing is to just look at who is talking in the classroom, which is a major indicator of how students are doing — and how the teacher is doing, too. In many university classrooms, it turns out that not a lot of active learning is happening.

TC: What tech are you using to collect this data?

AO: We’re using sensors that aren’t expensive and can be set up in a variety of classrooms. You don’t need to build a million-dollar, state-of-the-art classroom. It’s microphones and cameras.

TC: What in the talk data tells you that students are engaged?

AO: We’re looking at the frequency of questions, but also how often the instructor is pausing. It’s been demonstrated that if you wait just three seconds after asking a question, you get orders of magnitude more participation from students in the class.

In fact, one approach we’re trying is when we detect that the teacher has been talking too long without interruption, we flash a big red screen on his or her laptop that sort of breaks that lecture mode and gets them to stop and solicit student participation.

TC: How do they react?

AO: They’ve been reacting favorably to it. The trick is not to interrupt the teaching in a way they can’t handle.

TC: And what are you doing with this analysis?

AO: We’ve figured out that the most productive approach is to incorporate some of this information into weekly training exercises for teachers and teaching assistants. One training exercise might prompt them to ask open questions that let students explore ideas, versus closed questions that check whether students have read materials. Then we look at week’s end to see whether they asked those open-ended questions.

TC: Who, or what, is poring over all this data?

AO: A research team here is working on it every day. But we’re working to  verify that you can use machine learning to do this in a completely automated way, as if a human was analyzing the data.

TC: This is a research project, but if and when it’s time to commercialize it, what do you think the product might look like?

AO: It would be easy for any teacher to set up; they’d receive feedback in an automated fashion. They’d receive training exercises on a weekly basis that help them gauge how they’re doing. And they’d hopefully improve their own teaching and get better over time.

While we’re focusing on talk first, we’ll study facial reactions, too. A number of studies has shown how posture is related to student learning experiences, as well as how many people have their hands raised.

TC: You’re not alone in trying to measure student engagement. What’s your biggest differentiator?

AO: I’ve seen a number of desktop -based systems that feature teacher dashboards, so teachers can look at the work that students have done across classrooms and determine, say, maybe the most common error that students are making. The idea is a similar one [to ours] in that it allows you to collect data so you can make improvements. But [those existing systems] don’t show data about the teachers to better support learning. That’s really the piece that we’re trying to tackle — changing the way people approach teaching to turn lectures into more active participatory learning environments.

Data suggests Uber is trailing far behind rival Ola in India

New data has suggested that Ola, the $5 billion valued taxi on-demand service, may be more than twice as large as Uber in India.

The information comes courtesy of Truecaller, which today released a snapshot of data from the 130 million registered users of its smart calling app. Truecaller operates a community-based directory that helps label spam callers, but more broadly assign IDs to otherwise random phone numbers that might call you. Truecaller used its database of call tagging to look at the volume of calls coming from numbers that it knows to be Uber and Ola drivers to get a glimpse at the communication volume between drivers and its users, and thus a snapshot into the potential marketshare of the two rivals.

Truecaller found that between January and March this year, 4.1 percent of all calls that it identified as being made for the purpose of getting a taxi where made to Ola or Ola drivers. By contrast, Uber’s share comes in at 1.6 percent. Ola users were found to have racked up 59.5 million outgoing calls and 42.5 million incoming calls over that same period. Uber users call volumes were 24.5 million outgoing and 13.6 million incoming.

 

 

Reliable information addressing the marketshare for taxi on-demand companies is hard to come by, and figures released by both Uber and Ola differ wildly, as Mint recently pointed out. That makes this information from Truecaller all the more interesting but, of course, there are caveats — it applies to Truecaller users only (we don’t know how many active users the service has in India), while multiple calls between a driver and passenger are common but can’t be broken out from the Truecaller data. Nonetheless, the information does seem to validate what other such studies have shown. WhichApp, a service that lets friends share their favorite apps with each other, similarly found that Ola usage was double that of Uber, but that sample size was likely far smaller than Truecaller’s given that Which App counts just 90,000 users.

What is perhaps most interesting about this data is the room for expansion that both Ola and Uber have, according to Truecaller’s findings.

The app firm, which is based in Sweden but has a strong focus on emerging markets, found that 94 percent of all calls for taxis or private cars were made independent of Uber, Ola or other booking apps like Meru or Ola-owned TaxiForSure. There may be some error here, but, even adding an unlikely 10 percent error rate, it appears that most users who are savvy enough to download a smart dialer app like Truecaller don’t use taxi on-demand services.

“As you can see, these new app-based services are still just a drop in the bigger ocean that is the Indian taxi industry. Even if the market keeps growing at anywhere near the pace it currently is, then there is abundant room for more than one service to operate,” Truecaller said in a statement.

While Ola got an earlier start on the market by launching its service before Uber arrived in August 2013, Ola’s lead may also be down to the sheer breadth of services that it offers. Last week, the SoftBank-backed company claimed that its ‘Micro’ vehicle service alone is bigger than Uber, covering 75 cities and over a million daily rides. The acquisition of TaxiForSure for $200 million last year will also have helped.

This is the first report of its kind for Truecaller and it is designed to help showcase the company’s monetization push. Truecaller has begun making money via something that it describes as ‘call intent’ — that’s to say that, thanks to its tagging data, it can tell what number or service a phone owner wants when they bring up a number and before they hit dial. That information — which the company stressed is anonymized to protect users — is then offered to companies which can place messages such as promotions or deals.

So, for example, if I am about to call a bank, or receive a call from a bank, there’s intent that companies offering financial products may be interested in pursuing. I might find myself receiving a bank or loan-based message before or after I phone my bank.