Seamless Link-level Redundancy to Improve Reliability of Industrial Wi-Fi Networks

The adoption of wireless communications and, in particular, Wi-Fi, at the lowest level of the factory automation hierarchy has not increased as fast as expected so far, mainly because of serious issues concerning determinism. Actually, besides the random access scheme, disturbance and interference prevent reliable communication over the air and, as a matter of fact, make wireless networks unable to support distributed real-time control applications properly. Several papers recently appeared in the literature suggest that diversity could be leveraged to overcome this limitation effectively. In this paper a reference architecture is introduced, which describes how seamless link-level redundancy can be applied to Wi-Fi. The framework is general enough to serve as a basis for future protocol enhancements, and also includes two optimizations aimed at improving the quality of wireless communication by avoiding unnecessary replicated transmissions. Some relevant solutions have been analyzed by means of a thorough simulation campaign, in order to highlight their benefits when compared to conventional Wi-Fi. Results show that both packet losses and network latencies improve noticeably.Comment: preprint, 13 pages (Winner of the "2017 Best Paper Award for the IEEE Transactions on Industrial Informatics"

Experimental Evaluation of Techniques to Lower Spectrum Consumption in Wi-Red

Seamless redundancy layered atop Wi-Fi has been shown able to tangibly increase communication quality, hence offering industry-grade reliability. However, it also implies much higher network traffic, which is often unbearable as the wireless spectrum is a shared and scarce resource. To deal with this drawback the Wi-Red proposal includes suitable duplication avoidance mechanisms, which reduce spectrum consumption by preventing transmission on air of inessential frame duplicates. In this paper, the ability of such mechanisms to save wireless bandwidth is experimentally evaluated. To this purpose, specific post-analysis techniques have been defined, which permit to carry out such an assessment on a simple testbed that relies on plain redundancy and do not require any changes to the adapters' firmware. As results show, spectrum consumption decreases noticeably without communication quality is impaired. Further saving can be obtained if a slight worsening is tolerated for latencies.Comment: preprint, 13 page

Adapting Hybrid ANN/HMM to Speech Variations

A technique is proposed for the adaptation of automatic speech recognition systems using Hybrid models combining Artificial Neural Networks with Hidden Markov Models. We investigated in this paper the extension of the classical approach consisting in applying linear transformations not only to the input features, but also to the outputs of the internal layers. The motivation is that the outputs of an internal layer represent a projection of the input pattern into a space where it should be easier to learn the classification or transformation expected at the output of the network. To reduce the risk that the network focuses on new data only, loosing its generalization capability (catastrophic forgetting), an original solution, Conservative Training is proposed. We illustrate the problem of catastrophic forgetting using an artificial test-bed, and apply our techniques to a set of adaptation tasks in the domain of Automatic Speech Recognition (ASR) based on Artificial Neural Networks. We report on the adaptation potential of different techniques, and on the generalization capability of the adapted networks. The results show that the combination of the proposed approaches mitigates the catastrophic forgetting effects, and always outperforms the use of the classical linear transformation in the feature space. 1

Adaptation of Hybrid ANN/HMM Models using Linear Hidden Transformations and Conservative Training

International audienceA technique is proposed for the adaptation of automatic speech recognition systems using Hybrid models combining Artificial Neural Networks with Hidden Markov Models. The application of linear transformations not only to the input features, but also to the outputs of the internal layers is investigated. The motivation is that the outputs of an internal layer represent a projection of the input pattern into a space where it should be easier to learn the classification or transformation expected at the output of the network. A new solution, called Conservative Training, is proposed that compensates for the lack of adaptation samples in certain classes. Supervised adaptation experiments with different corpora and for different adaptation types are described. The results show that the proposed approach always outperforms the use of transformations in the feature space and yields even better results when combined with linear input transformations

Wireless Sensor Networks and TSCH: a compromise between Reliability, Power Consumption and Latency

7siReliability, power consumption, and latency are the three main performance indicators of wireless sensor networks. Time slotted channel hopping (TSCH) is a promising technique introduced in the IEEE 802.15.4 standard that performs some steps ahead in the direction of the final dream to meet all the previous requirements at the same time. In this article, a simple and effective mathematical model is presented for TSCH that, starting from measurements performed on a real testbed, permits to characterize both the network and the surrounding environment. To better characterize power consumption, an experimental measurement campaign was purposely performed on OpenMote B devices. The model, which was checked against a real 6TiSCH implementation, can be employed to predict network behaviour when configuration parameters are varied, in such a way to satisfy different application contexts. Results show that, when one of the three above indices is privileged, unavoidably there is a worsening of the others.openopenScanzio, Stefano; Vakili, Mohammad Ghazi; Cena, Gianluca; Demartini, Claudio Giovanni; Montrucchio, Bartolomeo; Valenzano, Adriano; Zunino, ClaudioScanzio, Stefano; Vakili, Mohammad Ghazi; Cena, Gianluca; Demartini, Claudio Giovanni; Montrucchio, Bartolomeo; Valenzano, Adriano; Zunino, Claudi