You’ve probably heard about how the Internet of Things (IoT) is the future of how we will interact with technology. From cars to refrigerators, more and more of our devices will connect to the internet in the not-too-distant future. Given the massive amount of data likely to be produced, how can relevant parties process, analyze and leverage the exponential increase in data?
Enter edge computing, which is essentially taking data collected from an IoT device (machine in a factory, an engine, smartwatch, cell phone, etc.) and processing it close to the source, saving numerous resources. It would be tremendously costly and unproductive for a Smart TV to continuously send information back to the cloud at all times. The data requirements are immense, tieing up both man-hours and computing resources on the back end. Instead, the television could be set up to only relay back information when the user was switching between apps, watching certain shows, if there was an internal error, or another event desired to be monitored. This is the essence of edge computing; analysis at or close to the endpoint.
Given the obvious intersection of IoT, edge computing and real-time analysis, what are some of the trends in the nexus and what do those who are looking to leverage the tech need to be aware of?
- IoT management does not exist in one space, but rather in an ecosystem.
In order to best use IoT data, there are many steps and factors that need to be taken into consideration: capture, utilization, analysis, security, governance, compliance, etc. The complexity and relative infancy of the IoT space mean that there is no one player who can yet “do it all.” Instead, there are multiple actors with their own niches, spread across the marketplace. Companies looking to put IoT tech into their wheelhouse will need to find at least one partner, and probably more.
- IoT using edge computing is increasingly connecting related players in real time.
Consider receiving an email at the end of the week with your driving patterns, both good and bad. This email would come not from an overly nosy neighbor, but rather from a device plugged into your car. This connectivity could potentially be linked to insurance companies to determine the types of individuals they are covering. Though it all sounds a bit Orwellian, remember that Progressive Insurance has been doing something similar with their Snapshot program for years. The healthcare sector is another market with vast applications; imagine the benefits of being able to monitor a patient’s vital signs in real-time, linking patient to provider and system. While IoT items (through design or application) can link consumer and producer, edge computing allows for the opening of additional, and real-time, links.
- IoT and edge computing data is generally not centralized, but spread across spaces.
As an example let’s look at the wind turbine, an item often linked to the IoT and often leverages edge computing. The edge computing parameters may have the turbine send data to a cloud server at designated points for analysis purposes. Easy enough, right? Now consider two turbines, or three, or the entire wind farm, or even the entire grid. The nature of edge computing means that information is not centrally located, but housed across servers, cloud systems, or even different partners. Because of the diversified nature of edge computing data, proper use requires combining historical and perishable insights, gleaned from multiple datasets to be able to deliver actionable intelligence before it turns stale.
- This is a relatively new space, and there are emerging challenges, both regulatory and technical.
- Volume & Velocity – The automated nature of edge computing means that the data flows will not only be be enormous, but will also be delivered quickly, as would be expected for real-time analysis. The ability to (or find a partner who can) process the flows is absolutely critical.
- Variety – The combination of edge computing and IoT means that the data itself will be incredibly diverse, covering different devices, protocols and the like. There is as yet no “one size fits all” approach to this type of analysis, requiring well-defined project design.
- Security – A problem in any space, but edge computing offers a second level of vulnerability, as information can be hacked at either the device level or the data level.
- Governance & Compliance – While the laws remain behind the tech (as is often the case) national governments are beginning to catch up. The European Union recently released a new data privacy law, The General Data Protection Regulation (GDPR). The GDPR, though still fairly ambiguous from a technical standpoint, reflects a growing realization of the need to regulate how harvested data is used.
The challenges for players in the marketspace are numerous, including managing partnerships as well as data, recognition of vulnerabilities, and fluid compliance regulations, just to name a few. That said, the benefits of properly leveraging edge computing with IoT are potentially incredible, providing real-time analysis capabilities on a host of applications.
We at Praescient provide government insight into relevant technology trends and recommendations on how to navigate evolving policies with organizational forward movement. Although edge computing and IoT have real challenges to overcome, much like blockchain and AI, these advancements are here to stay. Further, as with the majority of key technology progresses, the useful adoption of these features will be achieved with private public partnerships working together.
Thank you to Dinesh Chandrasekhar, Director of Product Marketing at Hortonworks and Eric Kavanagh, CEO of The Bloor Group. Their webinar on IoT and edge computing provided helpful insights into the topic.