A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks - Afifi, Haitham; Ramaswamy, Arunselvan; Karl, Holger (2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) (CCNC 2021))
Two of the most important metrics when developing Wireless Sensor Networks (WSNs) applications are the Quality of Information (QoI) and Quality of Service (QoS). The former is used to specify the quality of the collected data by the sensors (e.g., measurements error or signal's intensity), while the latter defines the network's performance and availability (e.g., packet losses and latency). In this paper, we consider an example of wireless acoustic sensor networks, where we select a subset of microphones for two different objectives. First, we maximize the recording quality under QoS constraints. Second, we apply a trade-off between QoI and QoS. We formulate the problem as a constrained Markov Decision Problem (MDP) and solve it using reinforcement learning (RL). We compare the RL solution to a baseline model and show that in case of QoS-guarantee objective, the RL solution has an optimality gap up to 1%. Meanwhile, the RL solution is better than the baseline with improvements up to 23%, when using the trade-off objective.
Keywords: reinforcement learning; wireless sensor networks; resource allocation; acoustic sensor networks

Server-based execution of periodic tasks on dynamically reconfigurable hardware - Danne, Klaus; Mühlenbernd, Roland; Platzner, Marco (IET Computers Digital Techniques)
Keywords: reconfigurable architectures; resource allocation; device reconfiguration time; dynamic hardware reconfiguration; dynamically reconfigurable hardware; light-weight runtime system; merge server distribute load; periodic real-time tasks; runtime system overheads; schedulability analysis; scheduling technique; server-based execution; synthesis tool flow