3 experts discuss how to navigate the challenges of IoT

IoT will continue to improve people's lives

Tom Coughlin, IEEE fellow and president of Coughlin Associates: IoT began before the internet. In the early 1980s, students at Carnegie Mellon University hooked a circuit board to a Coke machine that would sense if it was out of Coke and connected it to the Advanced Research Projects Agency Network. Since then, TCP/IP protocols made the internet possible. Applying internet connectivity to radios made it possible for wireless internet connections, and circuit miniaturization made small ship sets possible with small processors and radios. The development of AI has made trained functions, such as speech recognition, possible and given devices the ability to respond to voices.

Tom Coughlin

IoT is a key in factory automation and building better machine and human industry collaboration. IoT has become important in health monitoring and treatment, and it is creating new interfaces for home entertainment, transportation and urban infrastructure. IoT makes lives better, but it also generates an enormous amount of information about the people who use it. This information could be put to use serving users, but it could also be used to invade users' privacy and even put them in danger if that information falls into the wrong hands.

IoT will become even more powerful with new developments in nanotechnology, better power management, longer-lasting power sources, new and dispersed processor technologies, and new wireless communication technologies, such as 5G. Enhanced security -- including strong encryption -- and clear rules on the use and ownership of human data generated by IoT will be making certain that IoT makes life better rather than creating new dangers and threatening human freedom. If it is done right, IoT and the related technology can help people live longer, more productive and fulfilled lives.

Shawn Chandler

Organizations must address three challenges of IoT

Shawn Chandler, IEEE senior member and director of IT at PacifiCorp: Sensors are used today to deliver feedback from tens of thousands of systems across many industries and uses. Energy storage economics have driven a massive increase in field device placement, permitting the distribution of sensors and mobile electronics everywhere and permitting tracking of nearly anything that can be measured. Computer processing has advanced generally as expected by Moore's law to handle the computational overhead and compute as fast as, or faster than, any human counterpart. Advances in machine learning and AI have contributed to computer processing improvements. Finally, nearly anything can be linked together with wireless communications. Considering these advancements as a whole, IoT technology is living up to its potential.

Privacy is an important consideration when deploying sensors. Many IoT applications require a broad amount of information, and some of the data may not be owned by the entity performing the capture, for example, in a smart city. If imaging is used, is permission needed to gather the images? Broadly examining the regulatory aspects of IoT applications is an important consideration.

Important challenges of IoT include communications bandwidth, the frequency of sensing and relating storage of information. Many sensing applications can use real-time data but do not require it to be effective or successful. The challenge is to scale back the sensing, communication transport and data storage to the minimum necessary for the applications, because each is an ongoing cost. With the advent of inexpensive electronic storage, the easy choice has been to communicate and then store everything. Notably, global storage of information is increasing at a staggering rate, measuring more than 50% annually.

Security is an important challenge for IoT. At the sensor where IT pros must maintain a secure connection, they must reduce vulnerabilities and resist cyberattack. In addition, information should be encrypted at rest and in transport. For smart devices, it is important to consider if the microprocessor or embedded hardware is vulnerable to attack when the device is removed and altered.

IoT integration with complex systems is vital. IoT activities with smart cities, for example, and applications delivering value between public and private partners, have the potential to transform the IoT-based experience, decrease costs, improve efficiencies and drive greater business agility to respond to change. For example, smart buildings that provide population mobility patterns can lead organizations to transform traffic systems in real time and plan oriented time horizons. Smart streetlights can be an effective tool to reduce light pollution and improve regional security with gunshot detection, natural disaster tracking and event monitoring. Smart water networks can identify leaks in real time, decreasing environmental affects and improving resource conservation.

However, if society isn't supportive of these systems, all the planning and upfront investment for the communication, sensing and integration activity may be in vain. Unlocking value from these technologies requires more than blind investments in systems. To cause a tipping point in IoT technology requires focus on marketing the performance advantages coupled with standards implementation to drive success and increase adoption.

Machine learning will bring out the best in IoT data

Carmen Fontana, IEEE member and cloud and emerging tech practice lead at Centric Consulting: One of the critical challenges of IoT is avoiding death by data. IoT generates tremendous volume and velocity of information, easily swamping most homegrown data collection systems. Additionally, organizations find it difficult to sift through the data to find actionable insights in a way that is both scalable and forward-looking.

The first hurdle is surviving the onslaught of incoming data. In recent years, this barrier has been dramatically reduced thanks to advancements in streaming and storage from major cloud providers.

Carmen Fontana

Once the mechanics of ingesting and storing are mastered, the real challenge begins: How do organizations breathe life into the data?

A common knee-jerk next step is to develop dashboards, dashboards and more dashboards. There is nothing wrong with building out a strong suite of analytics, but caution needs to be heeded. IT pros can use modern visualization tools to slice and dice data at ease. They also make it easy to get sucked into a vortex of pie charts and bar graphs that look pretty but provide only incremental knowledge. Just because IoT gives all of the data, does not mean IT pros need to use all the data. Be thoughtful: What is the heartbeat of what the IoT project is trying to accomplish?

Extending the metaphor to a healthcare example, having sensors throughout a hospital gives healthcare providers the ability to track everything from temperature changes in rooms to how often the trash is taken out. But will creating elaborate dashboards on all those data points improve patient care? Likely not. But surfacing something like the frequency and length of patient and caregiver interactions -- a known driver of improved outcomes -- will. By culling the extraneous data and reports, users avoid being overwhelmed and, more importantly, surface the most valuable insights.

That said, IoT truly comes to life when paired with machine learning. Machine learning pushes IoT to transition from reactive to predictive analytics, forecasting future outcomes and cutting through the fog of data. Machine learning also thrives on scale, making it a natural pairing for voluminous IoT data.

Extending the hospital example, adding in machine learning transitions the conversation from, "How many patient and caregiver interactions were there last month?" to, "How will next month's staffing levels affect patient care?"

However, machine learning is not inherently easy to execute. Building and deploying custom models for IoT data requires a strong understanding of both big data and data science. Thus, organizations often delay or outright skip machine learning. Recent advancements in this space are promising, but IoT will not reach its full potential until machine learning is more accessible.