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

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

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

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,

An investigation into the roles of chlorides and sulphate salts on the performance of low salinity injection in sandstone reservoirs : experimental approach

Numerous studies have been carried out to ascertain the mechanisms of low salinity and smart water flooding technique for improved oil recovery. Focus were often on brine composition and, specifically the cationic content in sandstone reservoirs. Given the importance of the salt composition and concentration, tweaking the active ions which are responsible for the fluids-rock equilibrium will bring into effect numerous mechanisms of displacement which have been extensively debated. This experimental study, however, was carried out to evaluate the extent of the roles of chloride and sulphate-based brines in improved oil recovery. To carry this out, 70,000 ppm sulphates and chloride-based brines were prepared to simulate formation water and 5,000ppm brines of the same species as low salinity displacement fluids. Core flooding process was used to simulate the displacement of oil by using four (4) native sandstones core samples, obtained from Burgan oil field in Kuwait, at operating conditions of 1500 psig and 50oC. The core samples were injected with 70,000 ppm chloride and sulphates and subsequently flooded with the 5,000 ppm counterparts in a forced imbibition process. Separate evaluations of chloride and sulphate-based brines were carried out to investigate the displacement efficiencies of each brine species. The results showed that the in both high and low salinity displacement tests, the SO4 brine presented better recovery of up to 89% of the initial oil saturation (Soi). Several mechanisms of displacement were observed to be responsible for improved recovery during SO4 brine displacement. IFT measurement experiments also confirmed that there was reduction in IFT at test conditions between SO4 brine and oil and visual inspection of the effluent showed a degree emulsification of oil and brines. Changes in pH were observed in the low salinity flooding and negligible changes were noticed in the high salinity floods. These results provide an insight into the roles of chloride and sulphate ions in the design of smart “designer” water and low salinity injection scenarios

An Innovative JavaScript-Based Framework for Teaching Backtracking Algorithms Interactively

Algorithm fundamentals are useful to learn at different levels engineering education. One of the most difficult concepts to teach and understand is backtracking algorithms with proper bounding functions. This article proposes a framework to implement interactive online tools showing examples of backtracking algorithms in which students can graphically observe execution step-by-step. This approach is illustrated with the n-queens problem with students from Prince Sultan University, Saudi Arabia, and Complutense University of Madrid, Spain. The results show 6.67% increased learning on a backtracking exercise in the experimental group over the control group, in which the algorithms were automatically validated with DOMjudge software (an automated system used to run programming contests). The proposed framework was evaluated as easy to use, with a score of 74.5% in the validated System Usability Scale (SUS); easy to learn, with a score of 6.22 out of 7 in the validated Usefulness, Satisfaction, and Ease-of-Use (USE) scale; and with a general satisfaction of 5.97 out of 7 in the validated USE scale

Sustainable Virtual Reality Patient Rehabilitation Systems with IoT Sensors Using Virtual Smart Cities

To develop sustainable rehabilitation systems, these should consider common problems on IoT devices such as low battery, connection issues and hardware damages. These should be able to rapidly detect any kind of problem incorporating the capacity of warning users about failures without interrupting rehabilitation services. A novel methodology is presented to guide the design and development of sustainable rehabilitation systems focusing on communication and networking among IoT devices in rehabilitation systems with virtual smart cities by using time series analysis for identifying malfunctioning IoT devices. This work is illustrated in a realistic rehabilitation simulation scenario in a virtual smart city using machine learning on time series for identifying and anticipating failures for supporting sustainability

Evaluation of the Influence of 6G Networks on Smart Cities: An Approach Towards Sustainability

With the extensive proliferation of virtual reality and smart channels, the need for higher-level wireless communication networks also elevates. The revolution of 6G networks is an imperative addition to this prototype that enables fast-speed mobility. This cutting-edge innovation also has greater implications for the emergence of smart cities. Thus, in order to delve deeper into this regard, the present research intends to assess the influence of 6G networks on smart cities. The study entails a review approach and explores the current findings pertaining to the given topic. It has been endeavored to overview the prerequisites of the 6G networks and their influence in building smart cities