Transmission Power Control for Remote State Estimation in Industrial Wireless Sensor Networks

Abstract

Novel low-power wireless technologies and IoT applications open the door to the Industrial Internet of Things (IIoT). In this new paradigm, Wireless Sensor Networks (WSNs) must fulfil, despite energy and transmission power limitations, the challenging communication requirements of advanced manufacturing processes and technologies. In industrial networks, this is possible thanks to the availability of network infrastructure and the presence of a network coordinator that efficiently allocates the available radio resources. In this work, we consider a WSN that simultaneously transmits measurements of Networked Control Systems' (NCSs) dynamics to remote state estimators over a shared packet-erasure channel. We develop a minimum transmission power control (TPC) policy for the coordination of the wireless medium by formulating an infinite horizon Markov decision process (MDP) optimization problem. We compute the policy using an approximate value iteration algorithm and provide an extensive evaluation of its parameters in different interference scenarios and NCSs dynamics. The evaluation results present a comprehensive characterization of the algorithm's performance, proving that it can flexibly adapt to arbitrary use cases.

Transmission Power Allocation for Remote Estimation with Multi-packet Reception Capabilities

“…This scenario has been specifically considered e.g. by [30], [17], [7], [39]. We consider the case where each system is provided with only a (possibly multi-dimensional) sensor, as the case reported in Fig.…”

“…The work [15] provides some properties of the one-stepahead optimal policy for remote estimation when the transmission power may take continuous unbounded values. Power allocation with multiple receivers has been recently studied [39] where multiple systems communicate at each time instant to a dedicated remote estimator. Powers are then allocated in order to match minimum arrival probabilities for each system so that a minimum estimation quality is enforced.…”

“…Essentially, if the SINR exceeds a certain threhsold, the packet is received and vice versa. The proposed cost function is similar to the one of [39] but in our case we explicitly consider a single remote estimator. Moreover, unlike [39], we do not assume that the SINR always meets the required thresholds as we allow the channels to randomly vary according to typical channel fading distributions, thus allowing for outages (and hence packet losses) when the SINR falls below the required threshold, which are realistic events in a typical wireless communication scenario.…”