38 research outputs found
On Designing a Machine Learning Based Wireless Link Quality Classifier
Ensuring a reliable communication in wireless networks strictly depends on
the effective estimation of the link quality, which is particularly challenging
when propagation environment for radio signals significantly varies. In such
environments, intelligent algorithms that can provide robust, resilient and
adaptive links are being investigated to complement traditional algorithms in
maintaining a reliable communication. In this respect, the data-driven link
quality estimation (LQE) using machine learning (ML) algorithms is one of the
most promising approaches. In this paper, we provide a quantitative evaluation
of design decisions taken at each step involved in developing a ML based
wireless LQE on a selected, publicly available dataset. Our study shows that,
re-sampling to achieve training class balance and feature engineering have a
larger impact on the final performance of the LQE than the selection of the ML
method on the selected data.Comment: accepted in PIMRC 2020. arXiv admin note: text overlap with
arXiv:1812.0885
Towards Sustainable Deep Learning for Multi-Label Classification on NILM
Non-intrusive load monitoring (NILM) is the process of obtaining
appliance-level data from a single metering point, measuring total electricity
consumption of a household or a business. Appliance-level data can be directly
used for demand response applications and energy management systems as well as
for awareness raising and motivation for improvements in energy efficiency and
reduction in the carbon footprint. Recently, classical machine learning and
deep learning (DL) techniques became very popular and proved as highly
effective for NILM classification, but with the growing complexity these
methods are faced with significant computational and energy demands during both
their training and operation. In this paper, we introduce a novel DL model
aimed at enhanced multi-label classification of NILM with improved computation
and energy efficiency. We also propose a testing methodology for comparison of
different models using data synthesized from the measurement datasets so as to
better represent real-world scenarios. Compared to the state-of-the-art, the
proposed model has its carbon footprint reduced by more than 23% while
providing on average approximately 8 percentage points in performance
improvement when testing on data derived from REFIT and UK-DALE datasets
UWB Radio-Based Motion Detection System for Assisted Living
Because of the ageing population, the demand for assisted living solutions that can help prolonging independent living of elderly at their homes with reduced interaction with caregivers is rapidly increasing. One of the most important indicators of the users’ well-being is their motion and mobility inside their homes, used either on its own or as contextual information for other more complex activities such as cooking, housekeeping or maintaining personal hygiene. In monitoring users’ mobility, radio frequency (RF) communication technologies have an advantage over optical motion detectors because of their penetrability through the obstacles, thus covering greater areas with fewer devices. However, as we show in this paper, RF links exhibit large variations depending on channel conditions in operating environment as well as the level and intensity of motion, limiting the performance of the fixed motion detection threshold determined on offline or batch measurement data. Thus, we propose a new algorithm with an online adaptive motion detection threshold that makes use of channel impulse response (CIR) information of the IEEE 802.15.4 ultra-wideband (UWB) radio, which comprises an easy-to-install robust motion detection system. The online adaptive motion detection (OAMD) algorithm uses a sliding window on the last 100 derivatives of power delay profile (PDP) differences and their statistics to set the threshold for motion detection. It takes into account the empirically confirmed observation that motion manifests itself in long-tail samples or outliers of PDP differences’ probability density function. The algorithm determines the online threshold by calculating the statistics on the derivatives of the 100 most recent PDP differences in a sliding window and scales them up in the suitable range for PDP differences with multiplication factors defined by a data-driven process using measurements from representative operating environments. The OAMD algorithm demonstrates great adaptability to various environmental conditions and exceptional performance compared to the offline batch algorithm. A motion detection solution incorporating the proposed highly reliable algorithm can complement and enhance various assisted living technologies to assess user’s well-being over long periods of time, detect critical events and issue warnings or alarms to caregivers