An interesting concept: I suppose it is the converse of the old expression "you can't get there from here". Knowing which nodes first encounter a threat means that certain paths in a network are likely and other ones are ruled out. Certain source locations become prime candidates for further investigation.
The Sparse Inference algorithm sounds too good to be true, since it appears to be a remedy for what the researchers term a "seemingly impossible task." But the heart of the story is that with a careful selection process, all possible nodes of a network do not have to be evaluated in order to make accurate inferences. The real work, however, lies ahead for these researchers, as they try to use Sparse Inference to make predictions about unknown source-locations that actually pan out. For that, they will need to refine a methodology for picking those key nodes and prove that accurate predictions were actually made from their selection. To read a their paper (for free) try:
with some supplemental material here:
Drones are, in essence, flying autonomous vehicles. Pros and cons surrounding drones today might well foreshadow the debate over the development of self-driving cars. In the context of a strongly regulated aviation industry, "self-flying" drones pose a fresh challenge. How safe is it to fly drones in different environments? Should drones be required for visual line of sight – as are piloted airplanes? Join EE Times' Junko Yoshida as she moderates a panel of drone experts.