Lightweight error correction technique in industrial IEEE802.15.4 networks

Industrial Wireless Sensor Networks (IWSNs) are nowadays becoming more and more popular thanks to their flexibility and pervasive monitoring capabilities to support process automation and remote maintenance applications. In such a scenario, channel errors due to the wireless medium can result in data packet losses, and consequently in unreliable IWSN services. To mitigate the above reported problem, this paper presents a lightweight error correction scheme specially developed for IEEE802.15.4-based IWSNs. By adding error correction and detection information inside the IEEE802.15.4 MAC data frame, the proposed FEC scheme is able to guarantee a backward compatibility with the standard while providing advanced capabilities in recovering data packets affected by bit errors. In the paper the benefits of the proposed technique are first evaluated through simulated loss traces, then they are validated in a real environment by considering real loss traces collected in an electricity power plant. The proposed error correction scheme is able to recover around 50% of the data packets that would be lost in case of a standard communication without any error correction capability

Encapsulation Techniques and Traffic Characterisation of an Ethernet-Based 5G Fronthaul

This paper first overviews how, in the 5G Next Generation Radio Access Network (NG-RAN), the Next generation NodeB (gNB) functions are split into Distributed Unit (DU) and Central Unit (CU). Then it describes the proposed fronthaul transport solutions, such as Common Packet Radio Interface (CPRI), eCPRI, IEEE P1914.3 and their relationship with the Ethernet protocol. Finally, a characterisation of the traffic generated by the fronthaul is presented. Such characterisation may guide in the selection of the right network for fronthaul transport.This work has been partially funded by the EU H2020 “5G-Transformer” Project (grant no. 761536)

Impact of Virtualization Technologies on Virtualized RAN Midhaul Latency Budget: A Quantitative Experimental Evaluation

In the Next Generation Radio Access Network (NGRAN) defined by 3GPP for the fifth generation of mobile communications (5G), the next generation NodeB (gNB) is split into a Radio Unit (RU), a Distributed Unit (DU), and a Central Unit (CU). RU, DU, and CU are connected through the fronthaul (RU-DU) and midhaul (DU-CU) segments. If the RAN is also virtualised RAN (VRAN), DU and CU are deployed in virtual machines or containers. Different latency and jitter requirements are demanded on the midhaul according to the distribution of the protocol functions between DU and CU. This study shows that, in VRAN, the virtualisation technologies, the functional split option, and the number of elements deployed in the same computational resource affect the latency budget available for the midhaul. Moreover, it provides an expression for the midhaul allowable latency as a function of the aforementioned parameters. Finally, it shows that, the virtualised DUs featuring a lower layer split option shall be deployed not in the sameThis work has been partially funded by the EC H2020 “5G-Transformer” Project (grant no. 761536)

Remote Control of a Robot Rover Combining 5G, AI, and GPU Image Processing at the Edge

This paper has been presented at 2020 Optical Fiber Communications Conference and Exhibition (OFC)The demo shows the effectiveness of a low latency remote control based on 5G and image processing at the edge exploiting artificial intelligence and GPUs to make a robot rover slalom between posts.This work has been partially supported by TIM under the Cooperation Agreement with Scuola Superiore Sant’Anna for the 5G MISE Trial in Bari and Matera 2018-2022 and the EU Commission through the 5GROWTH project (grant agreement no. 856709)

Predictive maintenance using cox proportional hazard deep learning

Predictive maintenance (PdM) has become prevalent in the industry in order to reduce maintenance cost and to achieve sustainable operational management. The core of PdM is to predict the next failure so corresponding maintenance can be scheduled before it happens. The purpose of this study is to establish a Time-Between-Failure (TBF) prediction model through a data-driven approach. For PdM, data sparsity is regarded as a critical issue which can jeopardize algorithm performance for the modelling based on maintenance data. Meanwhile, data censoring has imposed another challenge for handling maintenance data because the censored data is only partially labelled. Furthermore, data sparsity may affect algorithm performance of existing approaches when addressing the data censoring issue. In this study, a new approach called Cox proportional hazard deep learning (CoxPHDL) is proposed to tackle the aforementioned issues of data sparsity and data censoring that are common in the analysis of operational maintenance data. The idea is to offer an integrated solution by taking advantage of deep learning and reliability analysis. To start with, an autoencoder is adopted to convert the nominal data into a robust representation. Secondly, a Cox proportional hazard model (Cox PHM) is researched to estimate the TBF of the censored data. A long-short-term memory (LSTM) network is then established to train the TBF prediction model based on the pre-processed maintenance data. Experimental studies using a sizable real-world fleet maintenance data set provided by a UK fleet company have demonstrated the merits of the proposed approach where the algorithm performance based on the proposed LSTM network has been improved respectively in terms of MCC and RMSE

The economic impact of moderate stage Alzheimer's disease in Italy: Evidence from the UP-TECH randomized trial

Background: There is consensus that dementia is the most burdensome disease for modern societies. Few cost-of-illness studies examined the complexity of Alzheimer's disease (AD) burden, considering at the same time health and social care, cash allowances, informal care, and out-of-pocket expenditure by families. Methods: This is a comprehensive cost-of-illness study based on the baseline data from a randomized controlled trial (UP-TECH) enrolling 438 patients with moderate AD and their primary caregiver living in the community. Results: The societal burden of AD, composed of public, patient, and informal care costs, was about �20,000/yr. Out of this, the cost borne by the public sector was �4,534/yr. The main driver of public cost was the national cash-for-care allowance (�2,324/yr), followed by drug prescriptions (�1,402/yr). Out-of-pocket expenditure predominantly concerned the cost of private care workers. The value of informal care peaked at �13,590/yr. Socioeconomic factors do not influence AD public cost, but do affect the level of out-of-pocket expenditure. Conclusion: The burden of AD reflects the structure of Italian welfare. The families predominantly manage AD patients. The public expenditure is mostly for drugs and cash-for-care benefits. From a State perspective in the short term, the advantage of these care arrangements is clear, compared to the cost of residential care. However, if caregivers are not adequately supported, savings may be soon offset by higher risk of caregiver morbidity and mortality produced by high burden and stress. The study has been registered on the website www.clinicaltrials.org (Trial Registration number: NCT01700556). Copyright � International Psychogeriatric Association 2015

Socioeconomic Predictors of the Employment of Migrant Care Workers by Italian Families Assisting Older Alzheimer's Disease Patients: Evidence from the Up-Tech Study

Background: The availability of family caregivers of older people is decreasing in Italy as the number of migrant care workers (MCWs) hired by families increases. There is little evidence on the influence of socioeconomic factors in the employment of MCWs. Method: We analyzed baseline data from 438 older people with moderate Alzheimer's disease (AD), and their family caregivers enrolled in the Up-Tech trial. We used bivariate analysis and multilevel regressions to investigate the association between independent variables - education, social class, and the availability of a care allowance - and three outcomes - employment of a MCW, hours of care provided by the primary family caregiver, and by the family network (primary and other family caregivers). Results: The availability of a care allowance and the educational level were independently associated with employing MCWs. A significant interaction between education and care allowance was found, suggesting that more educated families are more likely to spend the care allowance to hire a MCW. Discussion: Socioeconomic inequalities negatively influenced access both to private care and to care allowance, leading disadvantaged families to directly provide more assistance to AD patients. Care allowance entitlement needs to be reformed in Italy and in countries with similar long-term care and migration systems. � 2015 The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved

WB3.2-Exploiting Programmable and Reconfigurable Hardware in 5G (Invited)

International audienc