Evolution of the Internet of Things

The Internet of Things (IoT) exploits the use of rapid and nearly contemporaneous flow of information that is possible with communication technology such as Wi-Fi and Bluetooth. These technologies are expanded further by the continual synching of data across a range of social media platforms. Recently, I had the chance to interview Jessica Lingel, an assistant professor at the Annenberg School for Communication at the University of Pennsylvania, on the IoT for chemists and life scientists.

RLS: The Internet of Things seems to be a very hot topic today, especially in the consumer sector. What is the motivating vision for the laboratory?

JL: Connectivity is a key vision of technology in 2018, particularly connecting various objects with others through flows of data. Objects can be physical or data, current or old. Many of these interconnections do not deliver useful associations, but a few do. It is the potential utility of the associations that makes the IoT valuable and hence interesting. People are recognizing that, as a set of relationships, the IoT can conveniently provide current and useful information. This is true generally, but the lab has special considerations.

RLS: What is the IoT and where did it come from?

JL: The IoT is not a new concept. For example, in 1969, Honeywell introduced the concept of the computer for the kitchen. The idea was to use ubiquitous computing (which is what academics and industry people used to call the IoT) to automate kitchen workflows, mostly around recipes. The product is often held up as an example of a mismatch between how a technology is designed and how it’s used. The Honeywell kitchen computer ad included the line, “If only she can cook as well as Honeywell can compute.” The computer’s operating system was not user-friendly. Cost was also excessive, at $10,000 in 1969 dollars. Even after introduction of hard drives a decade later, these were the size of a dishwasher and cost of over $100,000 for one megabyte. There was a huge disconnect between what the engineer envisioned and what the customer could use and afford. But what’s interesting is how often the idea of the IoT is still focused on the home (smart refrigerators; devices like Alexa) when there are arguably more significant implications for the workplace or the classroom.

RLS: You mentioned that there are similarities between the kitchen and the laboratory. Please explain.

JL: The concept of the smart kitchen has similarities to chemistry and life science laboratories. Some technologists are promoting IoT technology, much as Honeywell did for the kitchen and ubiquitous computing. Some reported applications seem unconvincing. With few proven applications, it is probably better to start with the need and work back to the answer, and then critically review the cost and benefit. Is it worth it? Then look for unintended consequences, particularly in terms of privacy and information control. Who has access to the data? How is that data safeguarded? How much prerogative will people have to opt out of or at least be informed of data surveillance?

In a lab setting, I’m most optimistic about the IoT as a means of monitoring supplies and equipment. For example, labs can use help in automating inventory control of reagents, reference and working standards, operating supplies, samples, waste identification, segregation, and proper disposal, etc. Some items have use-by dates, and samples often have maximum hold times. Making laboratory shelves “smart” can help automate these processes.

Cannabis laboratories are an interesting example. The street price of cannabis can be ~$300/oz or $5000/lb. In California, the upper limit of batch size is 10.0 lb. Failed batches cannot be sold and must be disposed of. But disposed of how? Send it to agricultural recycling? The possibility for highly profitable diversion of failed batches to the illicit market channel is obvious.

Some labs limit access by unauthorized people. Simple IoT monitors can supplement the card-swiping systems to assure high security, perhaps particularly around IP concerns. Plus, personal IoT devices can easily record time spent in the lab. California’s sampling requirements for cannabis assays require strict adherence to the sample security protocols. The sample log must record the identity of the sampler, plus any associated staff during the sampling process and transport.

RLS: These sound interesting, but what is the state-of-the-art today?

JL: Currently, deployment examples of IoT in labs are often limited to monitoring temperature and humidity of the lab and critical instruments such as incubators. Monitoring utilities such as liquid or gas supplies or levels in waste reservoirs are less common, but important. This is basic deployment, but as the variety of sensors grows, deployment will expand. I expect that instruments, such as chromatographs, spectrometers, and even balances, will soon start to include IoT features probably implemented with combinations of Wi-Fi and Bluetooth technology.

RLS: Where and how would you start?

JL: Too often, people seek to measure first and ask questions about how or whether the data matters later. I would start by identifying stakeholders and then mapping their interests and needs. For example, human resources may be concerned about compliance with applicable wage/hour laws, especially for hourly employees. Egregious violations can be costly in terms of a company’s reputation, not to mention employee morale. Other laws deal with professional qualifications such as running complex clinical diagnostics. Contract labs charge according to the qualifications of the staff and hours spent on the project. A log from an IoT log would be useful in verifying the accuracy of the invoice.

RLS: What about laboratory safety? How can the IoT help?

JL: Safety of employees is probably the biggest concern of employers. It is also the topic that seems to be getting the most innovative attention with development of robust, wearable sensors. Karen Levy has done some really interesting work on how this is playing out in the trucking industry, for example.1

In drug discovery and development labs, the staff may be dealing with novel materials that obviously have not been characterized. Libraries of poorly characterized materials are screened against various diseases to create structure–activity relationships. Today’s good laboratory practices (GLP) seem to off er good protection, but accidents happen. For example, chemists are creating new variants of fentanyl and cannabis. Some have usual strong potency and toxicity.

RLS: You mentioned sleep deprivation and fatigue as a target. This has been an issue with transportation workers, but what about the lab?

JL: Generally speaking, the gig economy is growing rapidly. And in some ways, it’s less about the number of people working for Uber or TaskRabbit than it is to think about the normalization of gig work as a style of employment: a lack of social connection between employees replaced by constant technological connection.

More structurally, increasing income inequality and (particularly on the coasts) high costs of living mean more and more folks are being pushed into informal employment.

Meanwhile, there is a shortage of skilled or trained workers, which leads to overtime and double shifting, particularly for labs in the healthcare sector.

I expect that fatigue sensing and other forms of health monitoring will be an important application for IoT. It’s also one of the most concerning from a privacy perspective. In the U.S., we have historically had much stronger protections for health data than other forms of data, so when it comes to monitoring health data, people tend to be much more sensitive.

Let me add, researchers in physiology are studying the effects of sleep deprivation using wearable helmets that measure neurological and physiological performance. These sensors will soon be reduced in size and incorporated into a hat, shirt, or perhaps even a watch or belt that would be worn continuously in the lab to catch problems with fatigue or toxic exposure.

RLS: Forensics labs are another lab segment, which deals with “unknown unknowns.”

JL: The other side of the toxicity problem is that symptoms of diseases such as the flu present with similar symptoms. Nearly every stakeholder will want an accurate diagnosis. Location-wide monitoring would help differentiate between contagious illnesses compared to toxic exposure. Outside the lab, I foresee that clinical trials may use similar monitoring devices to track patient response and protocol compliance. The watch or belt format would probably be the least obtrusive.

RLS: What about pushback to IoT?

JL: Pushback is a real issue. Some will see the device as infringing on their privacy, which it is. In general, I have a lot of skepticism about the ability of companies to anticipate serious privacy concerns, and we’ve seen serious missteps by major industry players. However, there are reasons and benefits (above) that can outweigh the loss, and I think there are particularly strong arguments in the context of the laboratory and the health sector.

Computers and high granularity data are convenient and reliable, so their continued use is unavoidable. However, designers need to be sensitive to privacy issues. The key is to make the benefits visible and valuable, while assuring that the information is protected. Each application should be transparent. Phil Howard has made some important arguments on this point.2

It will help if case histories of the installations are broadcast, including publishing and posting, to get buy-in from employees. To me, IoT problems start when there’s a total imbalance of visibility and access in the data being gathered.

RLS: Do you think that the IoT can reduce the number of cases of drylabbing in forensic labs? These have had a terrible impact on society and law enforcement.

JL: Yes, I think so. The sample load of many labs is fairly uniform, such as blood alcohols, rape kits, or identification of drugs and metabolites. These should have workflows and outputs that can be monitored as part of an IoT program. Thus, if one analyst shows unusual productivity, the manager can see this, and ask why. It could be that the analyst is very organized and productive, or is cutting corners, or fabricating data. With the IoT, the manager would have hard data to support the investigation, as he/she should.

Should fraud be discovered, the IoT file should quickly lead to identification of samples were potentially mishandled. We’re already starting to see wearable data being used in court cases, but again, the key is making sure that the people being monitored are aware of the systems gathering data about them.

RLS: Do you see any dangers from the widespread adoption of the IoT?

JL: Yes! People can and do misuse information. Just look at the recent hacking jobs such as Target, Equifax, Yahoo, Uber …. The list grows almost daily. Data security will only become more of a concern as an increasing amount of sensitive information is gathered. Another issue is that industry folks have largely been reluctant to engage seriously with research on privacy. Julie Cohen and Helen Nissenbaum are two legal and philosophy thinkers who have made some really relevant arguments about privacy in the context of data networks.3,4

Again, focus on the lab: Biosimilars in the U.S.A. require showing that the new product is highly similar to the licensed therapeutic. Developing an assay could be much easier if the developer of a biosimilar could tell what technology, instruments, and reagents were used in by the innovator’s lab.

The power of object programming is that one can associate properties of objects. So, if one has the lab number in a particular facility, one can potentially find out who works there and what the lab uses. Thus, isotopically labeled proteins from a contract vendor might identify the protein under development and perhaps even specific structure.

Firms will need to consider what information is proprietary, and try to protect such information as they do for supply chain, business plans, and key documents, including trade secrets. They will be aided by the fact that there is so much information being gathered and archived that making sense of it from the outside actually becomes an obstacle.

Summary

The Internet of Things is a technology that is in the early-adopter phase of business development. Compelling applications are starting to appear, particularly in the laboratory segment. However, to cross the chasm, early adopters need to report compelling case histories.5

References

  1. Levy, K.E.C. The contexts of control: information, power, and truckdriving work. The Information Society 2015, 31.2, 160–74.
  2. Howard, P.N. Pax Technica: How the Internet of Things May Set Us Free or Lock Us Up. Yale University Press: New Haven, CT, 2015.
  3. Cohen, J.E. Configuring the Networked Self: Law, Code, and the Play of Everyday Practice. Yale University Press: New Haven, CT, 2012.
  4. Nissenbaum, H. Privacy in Context: Technology, Policy, and the Integrity of Social Life. Stanford University Press: Palo Alto, CA, 2009.
  5. Moore, G.A. Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream. HarperCollins: New York, NY, 2014.

Robert L. Stevenson, Ph.D., is Editor Emeritus, American Laboratory/Labcompare; email: [email protected]

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