Radiation Aware Mobility Paths in Wirelessly Powered Communication Networks

Wireless power transfer (WPT) is an emerging technology that is used in ad hoc networks of battery-powered devices, to deliver energy and keep the network functional. Existing state-of-the-art studies have mainly focused on applying this technology, but the potential risk of electromagnetic radiation (EMR) exposure is really overlooked by them. This still holds for the general case of the RF Wireless Communication networks. Hence, we consider The Minimum Radiation Path Problem of finding the lowest radiation trajectory of an agent moving from a source to a destination point in a network plane. Different from previous works, we attempt to study (for the first time in the state-of-the-art) path radiation under a more realistic WPT model than the usual one-dimensional models, that have been used in the past and cannot capture interesting superadditive and cancellation effects between distinct electromagnetic sources. In the light of the above, we design and evaluate both an algorithm and a heuristic that achieve different trade-offs between radiation and trajectory length of a moving agent. Document type: Conference objec

Agnostic Learning for Packing Machine Stoppage Prediction in Smart Factories

The cyber-physical convergence is opening up new business opportunities for industrial operators. The need for deep integration of the cyber and the physical worlds establishes a rich business agenda towards consolidating new system and network engineering approaches. This revolution would not be possible without the rich and heterogeneous sources of data, as well as the ability of their intelligent exploitation, mainly due to the fact that data will serve as a fundamental resource to promote Industry 4.0. One of the most fruitful research and practice areas emerging from this data-rich, cyber-physical, smart factory environment is the data-driven process monitoring field, which applies machine learning methodologies to enable predictive maintenance applications. In this paper, we examine popular time series forecasting techniques as well as supervised machine learning algorithms in the applied context of Industry 4.0, by transforming and preprocessing the historical industrial dataset of a packing machine's operational state recordings (real data coming from the production line of a manufacturing plant from the food and beverage domain). In our methodology, we use only a single signal concerning the machine's operational status to make our predictions, without considering other operational variables or fault and warning signals, hence its characterization as ``agnostic''. In this respect, the results demonstrate that the adopted methods achieve a quite promising performance on three targeted use cases

Placement Optimization in Wireless Charging Systems under the Vector Model

—This paper addresses the optimization of power provisioning in systems of wireless energy transfer. In this context, a vectorial representation of wireless waves recently becomes a precious tool; being more reliable and precise thanone-dimensional models, it enables an increased potential for power maximization and control that before seemed impossible. We study the deployment of nodes and chargers for power maximization, for the first time under the vector model. In particular, we present both offline and approximation protocols and provide an evaluation of their performance. The main idea of our approach is to take advantage of the high precision offered by the vector model of WPT waves, in order to fine-tune the exact positioning of wireless chargers. The results of the conducted simulations demonstrate the advantages of our approaches in terms of power maximization; interestingly our findings suggest that even some slight optimization in the exact placement of chargers can significantly improved received power. Index Terms—Wireless Power Transfer, Vector Model, Ad-hoc Wireless Networks, Charger Placeme

An Impact Localization Solution Using Embedded Intelligence—Methodology and Experimental Verification via a Resource-Constrained IoT Device

Recent advances both in hardware and software have facilitated the embedded intelligence (EI) research field, and enabled machine learning and decision-making integration in resource-scarce IoT devices and systems, realizing “conscious” and self-explanatory objects (smart objects). In the context of the broad use of WSNs in advanced IoT applications, this is the first work to provide an extreme-edge system, to address structural health monitoring (SHM) on polymethyl methacrylate (PPMA) thin-plate. To the best of our knowledge, state-of-the-art solutions primarily utilize impact positioning methods based on the time of arrival of the stress wave, while in the last decade machine learning data analysis has been performed, by more expensive and resource-abundant equipment than general/development purpose IoT devices, both for the collection and the inference stages of the monitoring system. In contrast to the existing systems, we propose a methodology and a system, implemented by a low-cost device, with the benefit of performing an online and on-device impact localization service from an agnostic perspective, regarding the material and the sensors’ location (as none of those attributes are used). Thus, a design of experiments and the corresponding methodology to build an experimental time-series dataset for impact detection and localization is proposed, using ceramic piezoelectric transducers (PZTs). The system is excited with a steel ball, varying the height from which it is released. Based on TinyML technology for embedding intelligence in low-power devices, we implement and validate random forest and shallow neural network models to localize in real-time (less than 400 ms latency) any occurring impacts on the structure, achieving higher than 90% accuracy