312,279 research outputs found
An Assessment on the Use of Stationary Vehicles as a Support to Cooperative Positioning
In this paper, we consider the use of stationary vehicles as tools to enhance
the localisation capabilities of moving vehicles in a VANET. We examine the
idea in terms of its potential benefits, technical requirements, algorithmic
design and experimental evaluation. Simulation results are given to illustrate
the efficacy of the technique.Comment: This version of the paper is an updated version of the initial
submission, where some initial comments of reviewers have been taken into
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Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems
In many Cyber-Physical Systems, we encounter the problem of remote state
estimation of geographically distributed and remote physical processes. This
paper studies the scheduling of sensor transmissions to estimate the states of
multiple remote, dynamic processes. Information from the different sensors have
to be transmitted to a central gateway over a wireless network for monitoring
purposes, where typically fewer wireless channels are available than there are
processes to be monitored. For effective estimation at the gateway, the sensors
need to be scheduled appropriately, i.e., at each time instant one needs to
decide which sensors have network access and which ones do not. To address this
scheduling problem, we formulate an associated Markov decision process (MDP).
This MDP is then solved using a Deep Q-Network, a recent deep reinforcement
learning algorithm that is at once scalable and model-free. We compare our
scheduling algorithm to popular scheduling algorithms such as round-robin and
reduced-waiting-time, among others. Our algorithm is shown to significantly
outperform these algorithms for many example scenarios
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