  We investigate the problem of co-designing computation and communication in a
multi-agent system (e.g. a sensor network or a multi-robot team). We consider
the realistic setting where each agent acquires sensor data and is capable of
local processing before sending updates to a base station, which is in charge
of making decisions or monitoring phenomena of interest in real time. Longer
processing at an agent leads to more informative updates but also larger
delays, giving rise to a delay-accuracy-tradeoff in choosing the right amount
of local processing at each agent. We assume that the available communication
resources are limited due to interference, bandwidth, and power constraints.
Thus, a scheduling policy needs to be designed to suitably share the
communication channel among the agents. To that end, we develop a general
formulation to jointly optimize the local processing at the agents and the
scheduling of transmissions. Our novel formulation leverages the notion of Age
of Information to quantify the freshness of data and capture the delays caused
by computation and communication. We develop efficient resource allocation
algorithms using the Whittle index approach and demonstrate our proposed
algorithms in two practical applications: multi-agent occupancy grid mapping in
time-varying environments, and ride sharing in autonomous vehicle networks. Our
experiments show that the proposed co-design approach leads to a substantial
performance improvement (18-82% in our tests).
