Computation-Communication Trade-offs and Sensor Selection in Real-time Estimation for Processing Networks
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This paper investigates the use of a networked system (e.g., swarm of robots, smart grid, sensor network) to monitor a time-varying phenomenon of interest in the presence of communication and computation latency. Recent advances on edge computing are enabling processing to be performed at each sensor, hence we investigate the fundamental latency-accuracy trade-off, arising when a sensor in the network has to decide whether to transmit raw data (incurring a computational delay) or transmit it (incurring communication delays) in order to compute an accurate estimate of the state of the phenomenon of interest. We propose two key contributions. First, we formalize the notion of processing network. Contrarily to sensor and communication networks, where the designer is concerned with the design of a suitable communication policy, in a processing network one can also control when and where the computation occurs in the network. The second contribution is to provide analytical results on the optimal communication latency (i.e., the optimal time spent on processing at each node) for the case with a single sensor and multiple homogeneous sensors. Finally, we extend the problem to heterogeneous networks and design greedy algorithms for sensor selection and delay optimization. Numerical results substantiate our claims that accounting for computation latencies (both at sensor and estimator side) and communication delays can largely impact the estimation accuracy.
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