Remote Monitoring of Markov Sources over Random Access Channels: False Alarm and Detection Probability.
Asilomar Conference on Signals, Systems and Computers(2023)
Abstract
We study the problem of remote source monitoring in an Internet of Things (IoT) system where a set of devices share a wireless channel to a common receiver. Each device observes an independent two-state Markov chain, with one of the states visited sporadically (modeling a critical event), and may transmit the current source value following a slotted ALOHA contention. We focus on protocols that set the transmission probability over a slot based on the value of the monitored process over the current and past slot. In turn, the receiver estimates the source state leveraging the channel outputs leaning either on a simple decode and hold approach, which requires no knowledge of the source statistics, or a maximum a posteriori estimator. For both approaches, we derive an analytical characterization of the system behavior in terms of false alarm and detection probability, deriving interesting insights and highlighting protocol design hints that depart from those commonly employed for throughput or age of information optimization.
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Key words
False Discovery Rate,False Alarm,Detection Probability,Remote Monitoring,Random Access,Channel Access,Random Access Channel,Markov Sources,Propensity,Markov Chain,Internet Of Things,Age Of Information,Current Source,Output Channels,Transmission Probability,Maximum A Posteriori,Internet Of Things Systems,Statistical Knowledge,Critical Conditions,Reaction Solution,Posterior Mode,Random Strategy,Hidden Markov Model,False Alarm Rate,Transition Probabilities,Interesting Source,Source Conditions,Access Control,Average Probability,State Process
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