Optimizing Timely Coverage in Communication-Constrained Collaborative Sensing Systems

In this article, we consider a collection of distributed sensor nodes periodically exchanging information to achieve real-time situational awareness in a communication-constrained setting, e.g., collaborative sensing among vehicles to improve safety-critical decisions. Nodes may be both consumers and producers of sensed information. Consumers express interest in information about particular locations, e.g., obstructed regions and/or road intersections, while producers broadcast updates on what they are currently able to see. Accordingly, we introduce and explore optimizing tradeoffs between the coverage and the space-time interest weighted average “age” of the information available to consumers. We consider two settings that capture the fundamental character of the problem. The first addresses selecting a subset of producers that maximizes the coverage of the consumers preferred regions and minimizes the average age of these regions given that producers provide updates at a fixed rate. The second addresses the minimization of the interest weighted average age achieved by a fixed subset of producers with possibly overlapping coverage by optimizing their update rates. The first problem is shown to be submodular and, thus, amenable to greedy optimization, while the second has a nonconvex/nonconcave cost function, which is amenable to effective optimization using the Frank–Wolfe algorithm. Numerical results exhibit the benefits of context-dependent optimization information sharing among obstructed sensing nodes.