Defenses Against Perception-Layer Attacks on IoT Smart Furniture for Impaired People

[EN] Internet of Things (IoT) is becoming highly supportive in innovative technological solutions for assisting impaired people. Some of these IoT solutions are still in a prototyping phase ignoring possible attacks and the corresponding security defenses. This article proposes a learning-based approach for defending against perception-layer attacks performed on specific sensor types in smart furniture for impaired people. This approach is based on the analysis of time series by means of dynamic time warping algorithm for calculating similarity and a novel detector for identifying anomalies. This approach has been illustrated by defending against simulated perception-layer magnetic attacks on a smart cupboard with door magnetic sensors. The results show the performance of the proposed approach for properly identifying these attacks. In particular, these results advocate an accuracy about 95.5% per day.This work was supported in part by the research project Utilisation of IoT and Sensors in Smart Cities for Improving Quality of Life of Impaired People under Grant 52-2020, in part by the Ciudades Inteligentes Totalmente Integrales, Eficientes Y Sotenibles (CITIES) funded by the Programa Iberoamericano de Ciencia y Tecnologia para el Desarrollo (CYTED) under Grant 518RT0558, in part by the Diseno Colaborativo Para La Promocion Del Bienestar En Ciudades Inteligentes Inclusivas under Grant TIN2017-88327-R funded by the Spanish Council of Science, Innovation and Universities from the Spanish Government, and in part by the Ministerio de Economia y Competitividad in the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento under Grant TIN2017-84802-C2-1-P.Nasralla, MM.; García-Magariño, I.; Lloret, J. (2020). Defenses Against Perception-Layer Attacks on IoT Smart Furniture for Impaired People. IEEE Access. 8:119795-119805. https://doi.org/10.1109/ACCESS.2020.3004814S119795119805

A Repository of Method Fragments for Agent-Oriented Development of Learning-Based Edge Computing Systems

[EN] The upcoming avenue of IoT, with its massive generated data, makes it really hard to train centralized systems with machine learning in real time. This problem can be addressed with learning-based edge computing systems where the learning is performed in a distributed way on the nodes. In particular, this work focuses on developing multi-agent systems for implementing learning-based edge computing systems. The diversity of methodologies in agent-oriented software engineering reflects the complexity of developing multi-agent systems. The division of the development processes into method fragments facilitates the application of agent-oriented methodologies and their study. In this line of research, this work proposes a database for implementing a repository of method fragments considering the development of learning-based edge computing systems and the information recommended by the FIPA technical committee. This repository makes method fragments available from different methodologies, and computerizes certain metrics and queries over the existing method fragments. This work compares the performance of several combinations of dimensionality reduction methods and machine learning techniques (i.e., support vector regression, k-nearest neighbors, and multi-layer perceptron neural networks) in a simulator of a learning-based edge computing system for estimating profits and customers.The authors acknowledge PSU Smart Systems Engineering Lab, project "Utilisation of IoT and sensors in smart cities for improving quality of life of impaired people" (ref. 52-2020), CYTED (ref. 518RT0558), and the Spanish Council of Science, Innovation and Universities (TIN2017-88327-R).García-Magariño, I.; Nasralla, MM.; Lloret, J. (2021). A Repository of Method Fragments for Agent-Oriented Development of Learning-Based Edge Computing Systems. IEEE Network. 35(1):156-162. https://doi.org/10.1109/MNET.011.2000296S15616235

An intelligent fuzzy logic-based content and channel aware downlink scheduler for scalable video over OFDMA wireless systems

The recent advancements of wireless technology and applications make downlink scheduling and resource allocations an important research topic. In this paper, we consider the problem of downlink scheduling for multi-user scalable video streaming over OFDMA channels. The video streams are precoded using a scalable video coding (SVC) scheme. We propose a fuzzy logic-based scheduling algorithm, which prioritises the transmission to different users by considering video content, and channel conditions. Furthermore, a novel analytical model and a new performance metric have been developed for the performance analysis of the proposed scheduling algorithm. The obtained results show that the proposed algorithm outperforms the content-blind/channel aware scheduling algorithms with a gain of as much as 19% in terms of the number of supported users. The proposed algorithm allows for a fairer allocation of resources among users across the entire sector coverage, allowing for the enhancement of video quality at edges of the cell while minimising the degradation of users closer to the base station

Protective effect of resveratrol on acrylamide induced renal impairment

Background: Acrylamide (ACR) has a wide range of uses. It possesses a renal impairment effect. The work aimed to study the possible protecting role of resveratrol (RVS) over the ACR-mediated renal impairment in rats. The suggested underlying mechanisms participating in such protection were investigated. Materials and methods: Thirty Sprague-Dawley adult albino rats were divided into 3 groups, control, ACR, and RVS. After 4 weeks, the kidney was removed, and prepared for histological, immunohistochemical, and biochemical studies. The activity of tissue oxidative (MDA) and anti-oxidative (GSH) markers were assessed.   Results: ACR induced glomerular renal affection in the form of shrinkage and distortion of the glomeruli with wrinkling of their basement membranes and widening of the urinary spaces. Degenerative tubular changes were markedly present in the PCT. The necrotic tubular cells exhibited cytoplasmic vacuolation with desquamated epithelial cells within the tubular lumen. ACR increases the deposition of collagen fibers in the basement membrane of the glomerular capillaries and induced thickening of the basement membranes of the renal corpuscles and renal tubules. The administration of RVS affords high protection to the kidney. The glomeruli and renal tubules were nearly normal. The content of collagen fibers and the PAS reaction of the basement membrane of the renal tubules were 70 % and 19% lower linked to the ACR group. The creatinine and urea levels decreased by 51%, 47%. RVS induced such a protective role through its antioxidant effect as the MDA level decreased by 45%, while the GSH level increased by 83% compared with the ACR group. Conclusions: ACR displays the structural and functional affection of the kidney. It induces kidney affection through oxidative stress and apoptosis. With the use of RVS, normal kidney architecture was preserved with little structural affection. Adding, functional kidney test became normal. RVS exerts its protective effect through its anti-apoptotic and antioxidant features

Concomitant administration of sitagliptin and rutin improve the adverse hepatic alterations in streptozotocin-induced diabetes mellitus in albino rats, an overlook on the role of alpha smooth muscle actin

Background: Diabetes mellitus (DM), one of the commonest worldwide metabolic conditions, recognized to persuade oxidant/antioxidant discrepancies. Sitagliptin is an oral anti-hyperglycemic remedy that blocks dipeptidyl peptidase 4 (DPP4). Rutin is a polyphenolic natural flavonoid which owns antioxidant and anti-proliferative activity. The aim of the present work is to elucidate the concomitant effect of Sitagliptin and rutin on the deleterious alterations in the liver of experimentally induced diabetes in rats. Materials and methods: 50 adult male albino rats, weighing 170-200 g were used. Rats were randomly divided into 5 groups (n=10).  Group 1 (control group), the other 4 groups (Groups II, III, IV and V) received a single i.p. injection of STZ, 65 mgKg-1 body weight to induce diabetes; group II (diabetic), group III (diabetic and rutin administered), group IV (diabetic and sitagliptin administered), and group V (diabetic with sitagliptin and rutin concomitantly administered).  H&E, masson trichrome, PAS, immune-histochemical; α-smooth muscle actin (α-SMA), histomorphometric analysis, liver enzymes and  oxidatants / anti-oxidatants; malondialdehyde (MDA)/ glutathione (GSH) and were done. Results: Distorted hepatic architecture, dilatation, congestion of sinusoids and central veins as well as cytoplasmic vacuolations were remarkable changes in the diabetic group. There was extravasation of blood, diffuse fibrous tissue formation, increase in the mean values of liver enzymes, oxidative markers and α-SMA expression in the same group. The aforementioned changes were ameliorated in groups III and IV. Concomitant administration of sitagliptin and rutin resulted in marked enhancement of these hepatic alterations. Conclusions: Combination of sitagliptin and rutin has an ameliorating effect on the hepatic deterioration induced by diabetes, which   is better than either sitagliptin or rutin alone

Multilayer perceptron neural network-based QoS-aware, content-aware and device-aware QoE prediction model : a proposed prediction model for medical ultrasound streaming over small cell networks

This paper presents a QoS-aware, content-aware and device-aware non-intrusive medical QoE (m-QoE) prediction model over small cell networks. The proposed prediction model utilises a Multilayer Perceptron (MLP) neural network to predict m-QoE. It also acts as a platform to maintain and optimise the acceptable diagnostic quality through a device-aware adaptive video streaming mechanism. The proposed model is trained for an unseen dataset of input variables such as QoS, content features, and display device characteristics, to produce an output value in the form of m-QoE (i.e. MOS). The efficiency of the proposed model is validated through subjective tests carried by medical experts. The prediction accuracy obtained via the correlation coefficient and Root Mean-Square-Error (RMSE) indicates that the proposed model succeeds in measuring m-QoE closer to the visual perception of the medical experts. Furthermore, we have addressed the following two main research questions: (1) How significant is ultrasound video content type in determining m-QoE? and (2) How much of a role does the screen size and device resolution play in medical experts’ diagnostic experience? The former is answered through the content classification of ultrasound video sequences based on their spatio-temporal features, by including these features in the proposed prediction model, and validating their significance through medical experts’ subjective ratings. The latter is answered by conducting a novel subjective experiment of the ultrasound video sequences across multiple devices

Swarm of UAVs for Network Management in 6G: A Technical Review

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs. Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile networks. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.Comment: 19,

Management of patients at the hepatopancreatobiliary unit of a London teaching hospital during the COVID-19 pandemic

To mitigate COVID-19-related shortage of treatment capacity, the hepatopancreatobiliary (HPB) unit of the Royal Free Hospital London (RFHL) transferred its practice to independent hospitals in Central London through the North Central London Cancer Alliance. The aim of this study was to critically assess this strategy and evaluate perioperative outcomes. Prospectively collected data were reviewed on all patients who were treated under the RFHL HPB unit in six hospitals between November 2020 and October 2021. A total of 1541 patients were included, as follows: 1246 (81%) at the RFHL, 41 (3%) at the Chase Farm Hospital, 23 (2%) at the Whittington Hospital, 207 (13%) at the Princess Grace Hospital, 12 (1%) at the Wellington Hospital and 12 (1%) at the Lister Hospital, Chelsea. Across all institutions, overall complication rate were 40%, major complication (Clavien-Dindo grade ≥ 3a) rate were 11% and mortality rates were 1.4%, respectively. In COVID-19-positive patients (n = 28), compared with negative patients, complication rate and mortality rates were increased tenfold. Outsourcing HPB patients, including their specialist care, to surrounding institutions was safe and ensured ongoing treatment with comparable outcomes among the institutions during the COVID-19 pandemic. Due to the lack of direct comparison with a non-pandemic cohort, these results can strictly only be applied within a pandemic setting