Issue link: https://www.e-digitaleditions.com/i/1081695
March/April 2019 I 33 From acting on real time air quality measurements to lighting, building automation systems (BAS) are inching closer and closer to the edge. of data that is continuously streamed across all the different sensors that measure these loads. Everything from human activity to solar radiation and precipitation can affect these loads and fluctuate throughout the day, such as when the building is too cold in the morning but too hot in the afternoon. Think of the occupancy rate compared to temperature, humidity and precipitation levels. Not only can the systems be tuned in real time, but they can start predicting and detecting anomalies, which might not show up in a system until something fails. HVAC is truly expensive and by adopting these machine learning approaches, HVAC energy consumption and demand can be greatly reduced. A FEW BIG PROBLEMS LATENCY Latency is a big problem. Latency is how fast one computer can talk to another spread out over a physical distance, and it is a problem because if that distance is too far then decisions cannot be made fast enough. For instance, consider self-driving cars. There is software that can now tell the difference between a child running in front of a car versus a dog. Nobody wants to hit either, but asking the cloud to make that decision is too slow. The software onboard the car needs to make a decision immediately about what to do— slow down, slam on the brakes, veer sharp left, is the car running into another car? Latency becomes a big problem even though the benefits of applying a lot of this software are clearly valuable. Tesla cars have GPUs in them and run Linux, but what about the semi-trucks that may eventually replace 3.5 million workers in the U.S.?No one wants an 18-wheeler barreling down the highway in California waiting to get a decision from some data center in Virginia on when it decides it wants the truck to change lanes. However, latency is not the only problem. Bandwidth throughput is also a problem when streaming high definition video from a few thousand cameras in an IP surveillance or smart city application. Being able to analyze information in real time presents challenges if the intent is to offload to software in the cloud. SECURIT Y Data security is one of those industries that seems forever locked in the dark ages as it has not only remained relatively the same over the past 25-30 years since the Internet, but it has arguably gotten worse. One might question how to secure edge compute deployments when it is known that traditional IoT and connected devices have such a horrible security rap, and not even Google or Facebook are immune to the whimsical desires of hackers. Uber was recently sued by every single state's attorney general in the U.S. including the District of Columbia for covering up a data breach. The company is paying $148 million in the national settlement; it originally thought it would only cost them approximately $100 thousand. Facebook is looking at a $1.6 billion fine for a recent data breach. Google shut down its social platform because its security was incredibly broken, thereby exposing private data in as many as 500,000 accounts; it is now confronting possible legal repercussions. The emperor has no clothes. Traditional IoT and connected devices have routinely been used to build the largest botnets. Unsecured printers, routers, and IP connected cameras have been mass "rooted," which is hacker parlance for completely taking over and using them as giant cannons against unsuspecting websites to knock them offline, sometimes for days at a time. There is a very large chance that companies and enterprises have connected devices in one or more of their buildings that are a part of a botnet.