We consider a collection of distributed sensor nodes periodically exchanging information to achieve real-time situa- tional 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.
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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 improve safety-critical decisions. Nodes may be both con- sumers and producers of sensed information. Consumers express interest in information about particular locations, e.g., obstructed regions and/or road intersections, whilst producers broadcast updates on what they are currently able to see. Accordingly, we introduce and explore optimiz- ing trade-offs between the coverage and the space-time in- terest 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 cover- age 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 mini- mization of the interest weighted average age achieved by a fixed subset of producers with possibly overlapping cov- erage by optimizing their update rates. The first problem is shown to be submodular and thus amenable to greedy op- timization while the second has a non-convex/non-concave 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.
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- Award ID(s):
- 1809327
- NSF-PAR ID:
- 10379452
- Date Published:
- Journal Name:
- IEEE Transactions on Control of Network Systems
- ISSN:
- 2372-2533
- Page Range / eLocation ID:
- 1 to 12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TCNS.2022.3203806
