Optimizing Timely Coverage in Communication Constrained Collaborative Sensing Systems

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 amongst vehicles to enable 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, whilst producers provide updates on what they are currently able to see. Accordingly, we introduce and explore optimizing trade-offs between the coverage and the space-time average of the “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 which optimizes a weighted sum of the coverage and the average age given that producers provide updates at a fixed rate. The second addresses the minimization of the weighted average age achieved by a fixed subset of producers with possibly overlapping coverage by optimizing their update rates. The former is shown to be submodular and thus amenable to greedy optimization while the latter has a non-convex/non-concave cost function which is amenable to effective optimization using tools such as the Frank-Wolfe algorithm. Numerical results exhibit the benefits of context dependent optimization information exchanges among obstructed sensing nodes in a communication constrained environment.