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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Optimizing Networked Situational Awareness
This paper proposes a framework to explore the op- timization of applications where a distributed set of nodes/sensors, e.g., automated vehicles, collaboratively exchange information over a network to achieve real-time situational-awareness. To that end we propose a reasonable proxy for the usefulness of possibly delayed sensor updates and their sensitivity to the network re- sources devoted to such exchanges. This enables us to study the joint optimization of (1) the application-level update rates, i.e., how often and when sensors update other nodes, and (2), the transmission resources allocated to, and resulting delays associated with, exchanging updates. We first consider a network scenario where nodes share a single resource, e.g., an ad hoc wireless setting where a cluster of nodes, e.g., platoon of vehicles, share information by broadcasting on a single collision domain. In this setting we provide an explicit solution characterizing the interplay between network congestion and situational awareness amongst heterogeneous nodes. We then extend this to a setting where such clusters can also exchange information via a base station. In this setting we characterize the optimal solution and develop a natural distributed algorithm based on exchanging congestion prices associated with sensor nodes’ update rates and associated network transmission rates. Preliminary numerical evaluation provides initial insights on the trade-offs associated with optimizing situational awareness and the proposed algorithm’s convergence.
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- Award ID(s):
- 1809327
- PAR ID:
- 10096036
- Date Published:
- Journal Name:
- IEEE WiOPT RAWNET Workshop
- Page Range / eLocation ID:
- 1-8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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