Intelligent Transportation Systems based on Internet-Connected Vehicles: Fundamental Research Areas and Challenges

ABSTRACT Information and Communication Technologies (ICT) rapidly migrate towards the Future Internet (FI) era, which is characterized, among others, by powerful and complex network infrastructures and innovative applications, services and content. An application area that attracts immense research interest is transportation. In particular, traffic congestions, emergencies and accidents reveal inefficiencies in transportation infrastructures, which can be overcome through the exploitation of ICT findings, in designing systems that are targeted at traffic / emergency management, namely Intelligent Transportation Systems (ITS). This paper considers the potential connection of vehicles to form vehicular networks that communicate with each other at an IP-based level, exchange information either directly or indirectly (e.g. through social networking applications and web communities) and contribute to a more efficient and green future world of transportation. In particular, the paper presents the basic research areas that are associated with the concept of Internet of Vehicles (IoV) and outlines the fundamental research challenges that arise there from

ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-time Constraints

Remote health monitoring is becoming indispensable, though, Internet of Things (IoTs)-based solutions have many implementation challenges, including energy consumption at the sensing node, and delay and instability due to cloud computing. Compressive sensing (CS) has been explored as a method to extend the battery lifetime of medical wearable devices. However, it is usually associated with computational complexity at the decoding end, increasing the latency of the system. Meanwhile, mobile processors are becoming computationally stronger and more efficient. Heterogeneous multicore platforms (HMPs) offer a local processing solution that can alleviate the limitations of remote signal processing. This paper demonstrates the real-time performance of compressed ECG reconstruction on ARM's big.LITTLE HMP and the advantages they provide as the primary processing unit of the IoT architecture. It also investigates the efficacy of CS in minimizing power consumption of a wearable device under real-time and hardware constraints. Results show that both the orthogonal matching pursuit and subspace pursuit reconstruction algorithms can be executed on the platform in real time and yield optimum performance on a single A15 core at minimum frequency. The CS extends the battery life of wearable medical devices up to 15.4% considering ECGs suitable for wellness applications and up to 6.6% for clinical grade ECGs. Energy consumption at the gateway is largely due to an active internet connection; hence, processing the signals locally both mitigates system's latency and improves gateway's battery life. Many remote health solutions can benefit from an architecture centered around the use of HMPs, a step toward better remote health monitoring systems.Peer reviewedFinal Published versio

Remote Elderly Monitoring Systems on a Human-centric Perspective

Information systems based on the Internet of Things (IoT) are driving revolutionary solutions in innumerable domains, as the sensitive domain of healthcare. Indicatively, remote monitoring of patients and real-time diagnosis are anticipated as complex systems, offering various services to the associated humans (e.g. patients and caregivers). While researchers focus on the technology necessary to implement remote healthcare systems, such as Remote Elderly Monitoring System (REMS), human concerns restricting their wider adoption are often neglected. Such concerns are transformed into criticalities, that should be considered during system design. In this work, a human-centric perspective on REMS design is explored. Following this perspective, supported tasks are decodified, human concerns associated to REMS usage are identified and revealed criticalities, that stem from human concerns, are extracted. Furthermore, existing REMS implementations are examined, based on the tasks supported and criticalities addressed, resulting in the identification of ways to further improve such systems

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl

Special issue: Empowering eHealth with smart internet of things (IoT) medical devices

[No abstract available]Scopu

Techno-economic assessment of building energy efficiency systems using behavioral change: A case study of an edge-based micro-moments solution

Energy efficiency based on behavioral change has attracted increasing interest in recent years, although, solutions in this area lack much needed techno-economic analysis. That is due to the absence of both prospective studies and consumer awareness. To close such gap, this paper proposes the first techno-economic assessment of a behavioral change-based building energy efficiency solution, to the best of the authors' knowledge. From the one hand, the technical assessment is conducted through (i) introducing a novel edge-based energy efficiency solution; (ii) analyzing energy data using machine learning tools and micro-moments, and producing intelligent, personalized, and explainable action recommendations; and (iii) proceeding with a technical evaluation of four application scenarios, i.e., data collection, data analysis and anomaly detection, recommendation generation, and data visualization. On the other hand, economic assessment is performed by examining the marketability potential of the proposed solution via a market and research analysis of behavioral change-based systems for energy efficiency applications. Also, various factors impacting the commercialization of the final product are investigated before providing recommended actions to ensure its potential marketability via conducting a Go/No-Go evaluation. In conclusion, the proposed solution is designed at a low cost and can save up to 28%-68% of the consumed energy, which results in a Go decision to commercialize the technology. 2021 Elsevier LtdThis paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

THz emission from Fe/Pt spintronic emitters with L10_{0}-FePt alloyed interface

Recent developments in nanomagnetism and spintronics have enabled the use of ultrafast spin physics for terahertz (THz) emission. Spintronic THz emitters, consisting of ferromagnetic FM / non-magnetic (NM) thin film heterostructures, have demonstrated impressive properties for the use in THz spectroscopy and have great potential in scientific and industrial applications. In this work, we focus on the impact of the FM/NM interface on the THz emission by investigating Fe/Pt bilayers with engineered interfaces. In particular, we intentionally modify the Fe/Pt interface by inserting an ordered L1$_{0}$-FePt alloy interlayer. Subsequently, we establish that a Fe/L1$_{0}$-FePt (2\,nm)/Pt configuration is significantly superior to a Fe/Pt bilayer structure, regarding THz emission amplitude. The latter depends on the extent of alloying on either side of the interface. The unique trilayer structure opens new perspectives in terms of material choices for the next generation of spintronic THz emitters

achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations

Excessive domestic energy usage is an impediment towards energy efficiency. Developing countries are expected to witness an unprecedented rise in domestic electricity in the forthcoming decades. a large amount of research has been directed towards behavioral change for energy efficiency. Thus, it is prudent to develop an intelligent system that combines the proper use of technology with behavior change research in order to sustainably transform end-user behavior at a large scale. This paper presents an overview of our aI-based energy efficiency framework for domestic applications and explains how micro-moments can provide an accurate understanding of user behavior and lead to more effective recommendations. Micro-moments are short-term events at which an energy-saving recommendation is presented to the consumer. They are detected using a variety of sensing modules placed at prominent locations in the household. a supervised machine learning classifier is then used to analyze the acquired micro-moments, identify abnormalities, and formulate a list of energy-saving recommendations. Each recommendation is presented through the end-user mobile application. The results so far include a mobile application in the front-end and a set of sensing modules in the backend that comprise, an ensemble bagging-trees micro-moment classifier (achieving up to 99.64% accuracy and 98.8% F-score), and a recommendation engine. 2013 IEEE.The statements made herein are solely the responsibility of the authors. This work was supported in part by the National Priorities Research Program (NPRP) from the Qatar National Research Fund (a member of Qatar Foundation) under Grant 10-0130-170288.Scopu

The seed germination properties of two hyperaccumulator plant species with the potential for Ni agromining

The aim of this study is to investigate the effect of different nickel concentrations and light in combination with storage conditions and storage time on the seed germination ability of two serpentine-endemic nickel hyperaccumulating species (Bornmuellera emarginata and B. tymphaea). The seeds of both species were collected from natural populations in the Pindus Mountain range, Greece in early July and stored in a refrigerator (4°C) and in laboratory conditions (22°C). The seeds were exposed to a range of nickel concentrations typical of non-ultramafic ‒ ultramafic gradient in two light environments (12 h photoperiod and continuous darkness). The nickel concentration only had a significant effect on B. emarginata, decreasing its seed germination rate with increasing Ni concentrations. The storage temperature significantly affected the germination percentage of both species and it was higher at 4°C compared to 22°C. A higher germination rate (> 60%) was observed for 5‒8-month-old seeds, but both species generally showed significantly higher germination rates in the tests conducted seven months after seed ripening in the field. A higher germination rate was observed in a 12-hour photoperiod than in continuous darkness only for B. tymphaea. This study provides guidelines on the germination capacity of two obligate nickel hyperaccumulators with a potential for use in agromining systems