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Science and the Internet of Things

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Imagine a lab that smoothly, automatically conducts experiments and collects data. A lab that saves time for your scientists (and business) by automating routine laboratory procedures.

A lab that battles the reproducibility crisis by keeping impossibly detailed records of every moment of a procedure.

The Internet of Things (IoT) refers to the interconnectedness of physical devices, which use embedded sensors and connectivity to communicate with each other through the internet.

With the increasing surge of wearable technology, the Internet of Things is becoming more popular. Its popularity is not limited to fitness tracking; IoT is coming to the life sciences, ushering in an age of more connected science.

What Is the State of the Internet of Things?

Current State

The term “Internet of Things” was coined by British futurist Kevin Ashton in 1999. Although the term itself is relatively new, IoT has existed in some form since the introduction of ATMs in 1974.

As the internet took off, more objects began to become connected to each other and the cloud. As of 2008, there were more internet-connected devices than there were people. A visualization from Cisco predicts that there will be 50 billion connected devices by 2020.

The Internet of Things includes a variety of everyday devices, including smart phones, smart watches, fitness trackers, other wearable devices, smart thermostats, smart appliances, and self-driving cars.

Despite the prevalence of and increasing communication between connected devices, 87% of people do not recognize the term “Internet of Things.”

Trends

Proponents of the Internet of Things argue that its true value is demonstrated when more and more devices become connected to each other.

In a 2014 Wired article, technology forecaster Daniel Burrus argued that the Internet of Things will eventually be connected to city infrastructure. Smart sensors within concrete can measure the load and strain on bridges, detect when the bridge is icy, and even send signals to cars passing overhead. As he writes:

“What can you achieve when a smart car and a smart city grid start talking to each other? We’re going to have traffic flow optimization, because instead of just having stoplights on fixed timers, we’ll have smart stoplights that can respond to changes in traffic flow. Traffic and street conditions will be communicated to drivers, rerouting them around areas that are congested, snowed-in, or tied up in construction.”

Cisco presents a similar picture of connectivity. When a sleeping employee receives an email saying the first appointment of the day is pushed back 45 minutes, the email automatically adjusts the employee’s alarm clock. The clock, in turn, signals the coffee pot to begin brewing a cup of coffee and the car to begin defrosting the windows at the appropriate time.

As a result of this increasing connectivity, brokers in the IoT market will need to make acquisitions and partnerships. IoT is complex, and producing IoT products—not to mention multiple connected ones—is an ambitious undertaking.

The smart home market is currently stagnant, as backward compatibility and excessive complexity of products cause problems. However, IoT is gaining popularity in industry.

Finally, the Internet of Things is becoming more and more about data and analytics, rather than actual objects. Analyzing and using “big data” can allow manufacturers to design more valuable IoT products.

Similarly, the increased adoption of connected objects is resulting in unparalleled data collection, which can then be used to explore questions that used to be impossible to answer.

Key Challenges

The future of the Internet of Things is promising, but there are also serious concerns. Most of these concerns relate to security and privacy.

According to one study, 77 percent of IT professionals believe that IoT devices do not have enough security. The increasing number of IoT devices also means an increasing number of access points to networks.

Coupled with the lack of security upgrades and effective authentication systems on these devices, the Internet of Things may soon face a security crisis. A major 2016 distributed denial of service (DDoS) attack took swaths of the internet offline, highlighting the need for increased security before placing critical operations purely in machine hands.

With security concerns come privacy concerns. The increasing collection of personal data has raised concerns about individual privacy (although big data analysis is typically conducted en masse).

Sheila Colclasure, VP Global Executive for Privacy and Public Policy at Acxiom and LiveRamp, warns marketers about the ethical use of data:

“There are several key questions that marketers in particular should be asking when they work with data: What is the purpose of my data-driven project? What is the data provenance? Under what privacy promise did the data originate? What was the consumer expectation when it was collected? Was the consumer given meaningful notice and choice? Will the outcome of my data-driven project and impact deliver articulate-able value to all stakeholders, including the consumer, or does it violate a line that may be undefined, but could be discerned against all the facts with good judgment?”

Privacy issues touch on a moral gray area. Is it okay for a fitness tracker to predict and respond to your heart attack, notifying the authorities of your information and location? Colclasure presents several such scenarios, which indicate the thorny nature of privacy in an increasingly connected world.

What Is the State of the Internet of Things in the Life Sciences?

Value of a Life Sciences IoT

The most prominent voices on the Internet of Things in the life sciences talk about it in relation to the reproducibility crisis.

Vikram Dhillon argues that “the lack of replicability [in science] can be attributed to two broad reasons: a lack of consensus on protocols being used in research labs and the level of access to tools and equipment required for performing experiments.”

Protocols often differ across labs, and it is impossible for physical or electronic lab notebooks to encapsulate 100 percent of the information related to a given protocol. In the case of physical lab notebooks, it is easy for data to be lost forever.

Some have argued that the complete automation of lab work would be instrumental in reducing irreproducibility. Others take a more moderate perspective, arguing that increasing the connectivity of equipment, and putting in place big data systems capable of handling the vast amount of information acquired in the process, will allow for the more precise measurement and cross-lab communication of protocols.

Based on this argument, the chief benefit of the Internet of Things in the life sciences is the ability to collect, share, and analyze larger quantities of data.

Limited Adoption in Science

Unfortunately, adoption of IoT devices in the life sciences has been slow. Managing the amount of data that comes from cloud-connected equipment is a tall task that the IoT space is still dealing with at large.

Some Silicon Valley startups have begun working to automate basic experiments; although these efforts provide a glimpse into a possible future, they have yet to become widely popular.

Some companies are beginning to connect at the scale of individual pieces of equipment. Cloud connected sensors enable monitoring, notifications, and adjustments, as well as increasingly detailed data collection. However, multi-device communication is not yet a reality.

As with the general Internet of Things, changes to R&D and lab practices seem more likely once entire labs become integrated and connected. There are companies working toward fully smart laboratories, but price of equipment may limit these adoption rates for the time being.

One key challenge to smart laboratories is communication across manufacturers. Equipment from different manufacturers must be able to communicate with each other to create a fully connected lab. The complexity of IoT necessitates partnerships, and the added complexity of scientific processes is likely to make this especially true in the life sciences.

Finally, there is some time before full laboratory automation becomes reality. Machines carry out instructions precisely, and therefore require increasingly precise instructions. Data collected from IoT technology will have to be used to clearly define protocols before fully automated labs are possible.

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