@inproceedings{25278, abstract = {{Using Service Function Chaining (SFC) in wireless networks became popular in many domains like networking and multimedia. It relies on allocating network resources to incoming SFCs requests, via a Virtual Network Embedding (VNE) algorithm, so that it optimizes the performance of the SFC. When the load of incoming requests -- competing for the limited network resources -- increases, it becomes challenging to decide which requests should be admitted and which one should be rejected. In this work, we propose a deep Reinforcement learning (RL) solution that can learn the admission policy for different dependencies, such as the service lifetime and the priority of incoming requests. We compare the deep RL solution to a first-come-first-serve baseline that admits a request whenever there are available resources. We show that deep RL outperforms the baseline and provides higher acceptance rate with low rejections even when there are enough resources.}}, author = {{Afifi, Haitham and Sauer, Fabian Jakob and Karl, Holger}}, booktitle = {{2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS'21)}}, keywords = {{reinforcement learning, admission control, wireless sensor networks}}, title = {{{Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding}}}, year = {{2021}}, } @inproceedings{21479, abstract = {{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.}}, author = {{Afifi, Haitham and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{2021 IEEE 18th Annual Consumer Communications \& Networking Conference (CCNC) (CCNC 2021)}}, keywords = {{reinforcement learning, wireless sensor networks, resource allocation, acoustic sensor networks}}, title = {{{A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks}}}, year = {{2021}}, } @inproceedings{10780, author = {{Guettatfi, Zakarya and Hübner, Philipp and Platzner, Marco and Rinner, Bernhard}}, booktitle = {{12th International Symposium on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC)}}, keywords = {{embedded systems, image sensors, power aware computing, wireless sensor networks, Zynq-based VSN node prototype, computational self-awareness, design approach, platform levels, power consumption, visual sensor networks, visual sensor nodes, Cameras, Hardware, Middleware, Multicore processing, Operating systems, Runtime, Reconfigurable platforms, distributed embedded systems, performance-resource trade-off, self-awareness, visual sensor nodes}}, pages = {{1--8}}, title = {{{Computational self-awareness as design approach for visual sensor nodes}}}, doi = {{10.1109/ReCoSoC.2017.8016147}}, year = {{2017}}, } @article{11886, abstract = {{Today, we are often surrounded by devices with one or more microphones, such as smartphones, laptops, and wireless microphones. If they are part of an acoustic sensor network, their distribution in the environment can be beneficially exploited for various speech processing tasks. However, applications like speaker localization, speaker tracking, and speech enhancement by beamforming avail themselves of the geometrical configuration of the sensors. Therefore, acoustic microphone geometry calibration has recently become a very active field of research. This article provides an application-oriented, comprehensive survey of existing methods for microphone position self-calibration, which will be categorized by the measurements they use and the scenarios they can calibrate. Selected methods will be evaluated comparatively with real-world recordings.}}, author = {{Plinge, Axel and Jacob, Florian and Haeb-Umbach, Reinhold and Fink, Gernot A.}}, issn = {{1053-5888}}, journal = {{IEEE Signal Processing Magazine}}, keywords = {{Acoustic sensors, Microphones, Portable computers, Smart phones, Wireless communication, Wireless sensor networks}}, number = {{4}}, pages = {{14--29}}, title = {{{Acoustic Microphone Geometry Calibration: An overview and experimental evaluation of state-of-the-art algorithms}}}, doi = {{10.1109/MSP.2016.2555198}}, volume = {{33}}, year = {{2016}}, } @article{17663, abstract = {{In this paper, we define and study a new problem, referred to as the Dependent Unsplittable Flow Problem (D-UFP). We present and discuss this problem in the context of large-scale powerful (radar/camera) sensor networks, but we believe it has important applications on the admission of large flows in other networks as well. In order to optimize the selection of flows transmitted to the gateway, D-UFP takes into account possible dependencies between flows. We show that D-UFP is more difficult than NP-hard problems for which no good approximation is known. Then, we address two special cases of this problem: the case where all the sensors have a shared channel and the case where the sensors form a mesh and route to the gateway over a spanning tree.}}, author = {{Cohen, R. and Nudelman, I. and Polevoy, Gleb}}, issn = {{1063-6692}}, journal = {{Networking, IEEE/ACM Transactions on}}, keywords = {{Approximation algorithms, Approximation methods, Bandwidth, Logic gates, Radar, Vectors, Wireless sensor networks, Dependent flow scheduling, sensor networks}}, number = {{5}}, pages = {{1461--1471}}, title = {{{On the Admission of Dependent Flows in Powerful Sensor Networks}}}, doi = {{10.1109/TNET.2012.2227792}}, volume = {{21}}, year = {{2013}}, }