110 research outputs found
Quality Estimation for Scarce Scenarios within Mobile Crowdsensing Systems
No full textMobile crowdsensing (MCS) is a paradigm that exploits the presence of a crowd of moving human participants to acquire, or generate, data from their environment. As a part of the Internet-of-Things (IoT) paradigm, MCS serves the quest for a more efficient operation of a smart city. Big data techniques employed on this data produce inferences about the participants' environment, the smart city. However, sufficient amounts of data are not always available. Sometimes, the available data are scarce as it is obtained at different times, locations, and from different MCS participants who may not be present. As a consequence, the scale of data acquired may be small and susceptible to errors. In such scenarios, the MCS system requires techniques that acquire reliable inferences from such limited data sets. To that end, we resort to small data (SD) techniques that are relevant for scarce and erroneous scenarios. In this article, we discuss SD and propose schemes to tackle the problems associated with such limited data sets, in the context of the smart city. We propose two novel quality metrics: 1) MAD quality metric (MAD-Q) and 2) MAD bootstrap quality metric (MADBS-Q), to deal with SD, focusing on evaluating the quality of a data set within MCS. We also propose an MCS-specific coverage metric that combines the spatial dimension with MAD-Q and MADBS-Q. We show the performance of all the presented techniques through closed-form mathematical expressions, with which simulation results were found to be consistent.Manuscript received January 10, 2020; revised April 11, 2020; accepted April 27, 2020. Date of publication May 14, 2020; date of current version November 12, 2020. This work was supported by the Natural Sciences and Engineering Research Council of Canada under Grant RGPIN-2019-05667.(Corresponding author: Nizar Zorba.) Sherif B. Azmy is with the Department of Electrical and Computer Engineering, Queen's University, Kingston, ON K7L 3N6, Canada (e-mail: [email protected]).Scopu
Canon Medicinae: Preliminary Edition of Book One (Fann One) of Avicenna's Canon of Medicine (Original Arabic text and Latin translation by Gerard of Cremona). القانون في الطب
No full textPreliminary edition of Book One, Fann One of Ibn Sīnā's al-Qānūn fī al-Ṭibb and of Gerard of Cremona's Latin translation. The edition has been carried out as part of Nicola Carpentieri's Research Project LATQAN (2020-2022). The text has been co-edited by Dr. Isaac Lampurlanes Farre (University of Padua) and Dr. Sherif Masry (Bibliotheca Alexandrina).
The Arabic manuscripts used for the Arabic text are:
B: BNF 2895 (Canon_d'Avicenne_Premier_livre_2895)
E: Escorial 822 (ESC. Ms. árabe 822)
L: Laurenziana 56 (Laurenziana- Orient 195).
The Latin manuscripts used for the edition are:
A: Milan, Biblioteca Ambrosiana, C292 Inf., fols 1ra-49rb (ca. 1200)
B: Angers, Bibliotheque municipale, 458, fols 1ra-111vb (ca. 1250)
L: Florence, Biblioteca Medicea Laurenziana, ms. Plut. 73-14, fols 1ra-61vb (1230-1240)
M: Madrid, Biblioteca Nacional de España, MSS/928, fols 1ra-34ra (1248-1250)
O: Oxford, Bodleian Library, Lat. Misc. c. 73, fols 79ra-91va (ca. 1240)
P: Paris, Bibliotheque Nationale de France, lat. 16186, fols 9ra-45vb (XIII century)
V: Vatican City, Biblioteca Apostolica Vaticana, Vat. lat. 2412, fols 1ra-72rb (1258)
Y: Munich, Bayerische Staatsbibliothek, clm 13017, fols 1ra-52vb (1246)
Z: Munich, Bayerische Staatsbibliothek, clm 14, fols 3ra-76rb (1250)
The LATQAN Project obtained the digital copies of manuscripts containing Book One of the Canon of Avicenna from twenty different libraries in countries around the world, thus gathering as completely as possible all copies of manuscripts containing this crucial historical text. Through careful compilation, our team has chosen the above core manuscripts. By doing this work, not only have we created public, searchable transcriptions of important texts hitherto available only as fragile, handwritten documents, we are also able to better understand the history of the transmission of this text.
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Incentive-Vacation Queueing in Extreme Edge Computing: An Analytical Reward-Based Framework
No full textEdge Computing (EC) emerged to address the cloud's shortcomings in meeting demand and latency requirements, leading to a shift in computation closer to the end-user. Extreme Edge Computing (XEC) extends this approach by utilizing nearby user-owned computational resources to support latency-sensitive applications in a distributed manner. In this study, we introduce Reward Edge Computing (REC), a variant of XEC, where service providers recruit user devices for infrastructure support, offering rewards in return. We explore the use of Incentive-Vacation Queueing (IVQ) to manage REC and analyze both its long-term and short-term performance. Our analysis focuses on the choice of an Incentive-Vacation Function (IVF), a contractual function between workers and service providers, proposing a tunable model favoring either party. We provide closed-form expressions for long-term worker behavior under uniform workload pricing and analyze the system's overall short-term operation, including the time a worker spends in the system. REC and IVQ aim to commodify computational resources for edge services, akin to sharing economy models like Uber and Airbnb, utilizing user-owned infrastructure.Scopu
Impact of Users' Mobility on the Quality of Edge Sensing Systems
No full textEdge sensing (ES) is rising as a potential solution for remote sensing challenges, as it exploits the proliferation of smartphones, leverages their embedded sensors to collect data from users' surrounding environments and uses their processors to perform edge computing tasks. Moreover, it is characterized by its low cost and time efficiency. Tremendous efforts have been dedicated to ES systems' quality of data (QoD) and coverage to enhance its performance. Since users incentivization plays a crucial role in enhancing the system's performance, the research community concentrated on improving incentives schemes. In this paper, we evaluate the effect of users' mobility on ES systems' quality of data and coverage, and propose a users' distribution-based dynamic-incentive scheme. In particular, we use a 2-dimensional random waypoint (RWP) model to emulate the randomness of users' mobility and velocity. The proposed incentive scheme aims to eliminate the negative impact of mobility on the QoD; by considering different factors to determine users' incentives and creating users' attraction areas in the targeted cells.ACKNOWLEDGEMENT This work was supported by Qatar University Grants M-QJRC-2020-4 and QUHI-CENG-21/22-1. The statements made herein are solely the responsibility of the authors. This research is also supported by a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) under grant number RGPIN-2019-05667.Scopu
Incentive-Vacation Queueing for Edge Crowd Computing
No full textEdge computing aims to push services closer to endusers, greatly enhancing latency and scale. Yet, there's untapped potential beyond the network's last mile, on the extreme edge. Extreme edge computing (XEC) is a computing paradigm that exploits computational resources in the end-user's immediate vicinity. Edge crowd computing (ECC) is an orchestrated sharing economy model within XEC that uses idle resources on userowned devices for service provision, compensating owners. We analyze an orchestrated ECC where devices rent resources in exchange for incentives. Our incentive-vacation queueing (IVQ) model associates performance with incentive payments using vacation queueing, considering the multitenancy of devices through a server vacation dependent on incentives received. In this article, we offer a framework for analyzing any sharing economy system that can be modeled using IVQ. We discuss the relationship between incentives and vacations on performance, namely, the incentive-vacation or IVQ function. We examine two families of IVQ functions that can be adjusted to benefit either the orchestrator or the worker and introduce a performance metric for such preference. We derive analytical expressions for system performance that consider the random nature of worker devices' availability due to fluctuating incentives. The IVQ model explores commodifying user-owned resources in an ECC system, presenting a general approach for performance analysis in such environments.Scopu
Queueing Analysis of Incentive-Based Extreme Edge Service Systems
No full textIn Edge Computing, computation is pushed towards the end-user to reduce backhaul load, address nascent privacy issues, and enable a range of low latency applications. Extreme Edge Service systems (EES) are a subset of Edge Computing in which services are deployed on user-owned devices in the proximity of the end-user. In this work, we model and analyze an orchestrator-based EES in which users' devices are recruited in exchange for an incentive. We propose to model the incentives' impact on performance using Incentive-Vacation Queueing (IVQ), a vacation queueing model in which server vacations are a proxy for incentives. Moreover, we derive closed-form expressions to evaluate the performance and directly link the performance to incentives, showing the impact of each one of the system parameters.ACKNOWLEDGMENT This research is supported by the Natural Sciences and Engineering Research Council of Canada under grant number ALLRP 549919-20, by the Qatar National Research Fund through grant NPRP13S-0130-200200, and by a grant from Distributive Ltd.Scopu
Incentive-Vacation Queueing for Extreme Edge Computing Systems
No full textThe demand for cloud services is expected to exceed the capacity of the centralized cloud. This rise compelled service providers to decentralize the cloud by physically pushing service provision to the proximity of the end-users, which led to the synthesis of solutions such as Fog and Edge computing. Edge Computing seeks to deploy services in the last mile to the end-user, however there is still opportunity on the edge beyond the last mile: the user's own devices. Extreme Edge Computing (EEC) is an edge sub-paradigm that seeks to tap into the idle computational power on non-enterprise user-owned devices. In this work, we navigate some of the challenges posed by EEC that constrain the usage of resources on user-owned devices. We evaluate an orchestrator-based extreme edge system, that oversees user-owned worker devices, and it provides resources in exchange for an incentive payment. We propose the Incentive-Vacation Queueing (IVQ) model to investigate the performance of user-owned worker devices under a vacation policy that is influenced by incentives. We derive closed-form expressions for the system performance that capture the epistemic uncertainty stemming from unexpected user behavior, to show the impact of each parameter in the system performance, and to optimize it. The IVQ model provides insight into the impact of introducing incentives on the workers' performance.ACKNOWLEDGMENT This research is supported by a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) under grant number ALLRP 549919-20.Scopu
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