In previous blog posts, I’ve spent a lot of time sharing info about precision agriculture. This month and in the months to come, I’ll be focusing on design patterns relevant to IoT. When it comes to solving problems in new domains or with new technologies, one often benefits by framing the problem in the context of problems already solved. Mathematicians are notorious for taking a seemingly new and challenging problem and applying a well-known technique to solve it. For example, going through integral calculus will lead a student to be exposed to both u-substitution and trig substitution. In both cases, we are taking what appears to be a hard problem and turning it into an easier problem we’ve previously solved. We are also using a type of design pattern - substitution in this case - to tackle hard problems. This exact same approach happens in software engineering as we apply well established design patterns when we work with new technologies, languages, and domains.
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For those of you that have read any of my previous blog posts, you know I have a passion for precision agriculture. While I promise to explore other topics in upcoming posts, I wanted to present at least one more post to drive home the many areas IoT can impact precision ag.
In keeping up with technology in precision agriculture, I often read articles mentioning both drones and IoT as distinct areas of study. While they are certainly two topics that deserve their own focus, this all too often comes at the expense of understanding how drones fit into the IoT space now and in the future.
With the beginning of a new year it’s often fun to review what predictions from the previous year proved to be true and which turned out to be wrong. If one combs through the predictions of recent history, it’s easy to find some of the more interesting one: