Smart Home Energy Trading With Deep Reinforcement Learning
This python library storest code for a conference paper about smart home energy management using Deep Reinforcement Learning. It is possible for the user to train their own DRL agent, as well as load pre-trained models and test the agent’s performance.
Backreference:
- M. Pokorn, et al., “Smart Home Energy Cost Minimisation Using Energy Trading with Deep Reinforcement Learning”, 2023, (ACM BuildSys'23)
CCWEBAPP
This tool helps estimate the computational complexity of neural networks. It implements the computations according to the methodology explained in the IEEE ICC paper linked below. It supports fully connected, convolutional, and pooling layers.
Backreference:
- A. Pirnat, et al., “Towards Sustainable Deep Learning for Wireless Fingerprinting Localization”, 2022, (IEEE ICC'22), (arXiv:2201.09071)
BLE Fingerprints Dataset
The available dataset contains received signal strength (RSS) measurements made with Bluetooth Low Energy (BLE) technology, which can be used for outdoor fingerprinting based localization applications. The dataset was collected with 25 nodes of the LOG-a-TEC testbed positioned at the campus of the Jozef Stefan Institute, Ljubljana.
Backreference:
- B. Bertalanič, et al., “LOG-a-TEC Testbed Outdoor Localization using BLE Beacons”, 2022, (BalkanCom'22)
Data-Driven Link Quality Estimation
This repository hosts the source code used in a comprehensive tutorial and survey paper focused on link quality estimation research. The materials contained herein provide a detailed overview and practical applications of various methodologies and technologies employed in the study of link quality estimation. This repository serves as a valuable resource for researchers and practitioners in the field, offering insights and tools developed as part of this significant research effort.
Backreference:
- G. Cerar, et al., “Machine Learning for Wireless Link Quality Estimation: A Survey”, 2021, (IEEE COMST)
- M. Kulin, et al., “Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial”, 2016, (MDPI Sensors)
Kubitect
Kubitect is an open source project that aims to simplify the deployment and subsequent management of Kubernetes clusters. It provides a CLI tool written in Golang that lets you set up, upgrade, scale, and destroy Kubernetes clusters. Under the hood, it uses Terraform along with terraform-libvirt-provider to deploy virtual machines on target hosts running libvirt. Kubernetes is configured on the deployed virtual machines using Kubespray, the popular open source project.
Backreference:
- C. Fortuna, et al., “On-Premise Artificial Intelligence as a Service for Small and Medium Size Setups”, 2024, (arXiv:2210.06956)
UWB Localization Dataset
UWB localization data set contains measurements from four different indoor environments. The data set contains measurements that can be used for range-based localization evaluation in different indoor environments using 9 DW1000 UWB transceivers (DWM1000 modules) connected to the networked RaspberryPi computer using in-house radio board SNPN_UWB. 8 nodes were used as localization anchor nodes with fixed locations in individual indoor environment and one node was used as a mobile localization tag.
Backreference:
- K. Bregar, M. Mohorcic, “Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices”, 2018, (IEEE Access)
LPWAN Trace Set
A dataset containing 24 hours of continuous spectrum measurements. A proprietary spectrum sensing device placed on top of a building in a mid-sized European city recorded 5 PSD measurements per second using 1024 FFT bins in a 192 kHz wide band inside the unlicensed European 868 MHz SRD band.
Backreference:
- C. Fortuna, et al., “Automatic Detection and Query of Wireless Spectrum Events from Streaming Data”, 2018, (arXiv:1804.05019)
- T. Gale, et al., “Automatic Detection of Wireless Transmissions”, 2022, (IEEE Access)
LOG-a-TEC
LOG-a-TEC is a diverse testbed used for research purposes. It started in 2016 and evolved overtime into its third iteration. It covers ultra narrow band and ultra wide band, packet based experimentation, clean slate protocol design, composable and modular protocol stacks, custom and advanced spectrum sensing and signal generating functions in sub-GHz spectrum.
