If you try to do everything, you always end up doing nothing. Which is why Gray’s laws suggest searching for the twenty “big questions” in a field and then focusing-in the first five as the ones that’ll generate the biggest return on the effort invested. So what are the five biggest open issues in programming for sensorised systems?
Data-intensive science (or “web science” as it is sometimes known) has received a major boost from the efforts of Googlle and others, with the availability of enormous data sets against which we can learn. It’s easy to see that such large-scale data affects experimental science, but there are lessons further down the scale too.
Most sensor systems are programmed using C: compact and well-known, but low-level and tricky to get right when things get compact and complex. There have been several proposals for alternative languages from across the programming language research spectrum. I haven’t heard anyone mention Forth, though, and it’s worth considering — even if only as a target for other languages.
Technology always advances, and in most areas the rate of change is also increasing all the time. But there are some areas where technological changes either happen only slowly, or even go into reverse. Not something we’re used to in computer science, but it’s a feature of sensor network programming: what are the challenges that technology won’t solve for us?