1,342 research outputs found

    Raamistik mobiilsete asjade veebile

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    Internet on oma arengus läbi aastate jõudnud järgmisse evolutsioonietappi - asjade internetti (ingl Internet of Things, lüh IoT). IoT ei tähista ühtainsat tehnoloogiat, see võimaldab eri seadmeil - arvutid, mobiiltelefonid, autod, kodumasinad, loomad, virtuaalsensorid, jne - omavahel üle Interneti suhelda, vajamata seejuures pidevat inimesepoolset seadistamist ja juhtimist. Mobiilseadmetest nagu näiteks nutitelefon ja tahvelarvuti on saanud meie igapäevased kaaslased ning oma mitmekülgse võimekusega on nad motiveerinud teadustegevust mobiilse IoT vallas. Nutitelefonid kätkevad endas võimekaid protsessoreid ja 3G/4G tehnoloogiatel põhinevaid internetiühendusi. Kuid kui kasutada seadmeid järjepanu täisvõimekusel, tühjeneb mobiili aku kiirelt. Doktoritöö esitleb energiasäästlikku, kergekaalulist mobiilsete veebiteenuste raamistikku anduriandmete kogumiseks, kasutades kergemaid, energiasäästlikumaid suhtlustprotokolle, mis on IoT keskkonnale sobilikumad. Doktoritöö käsitleb põhjalikult energia kokkuhoidu mobiilteenuste majutamisel. Töö käigus loodud raamistikud on kontseptsiooni tõestamiseks katsetatud mitmetes juhtumiuuringutes päris seadmetega.The Internet has evolved, over the years, from just being the Internet to become the Internet of Things (IoT), the next step in its evolution. IoT is not a single technology and it enables about everything from computers, mobile phones, cars, appliances, animals, virtual sensors, etc. that connect and interact with each other over the Internet to function free from human interaction. Mobile devices like the Smartphone and tablet PC have now become essential to everyday life and with extended capabilities have motivated research related to the mobile Internet of Things. Although, the recently developed Smartphones enjoy the high performance and high speed 3G/4G mobile Internet data transmission services, such high speed performances quickly drain the battery power of the mobile device. This thesis presents an energy efficient lightweight mobile Web service provisioning framework for mobile sensing utilizing the protocols that were designed for the constrained IoT environment. Lightweight protocols provide an energy efficient way of communication. Finally, this thesis highlights the energy conservation of the mobile Web service provisioning, the developed framework, extensively. Several case studies with the use of the proposed framework were implemented on real devices and has been thoroughly tested as a proof-of-concept.https://www.ester.ee/record=b522498

    Power Consumption Analysis, Measurement, Management, and Issues:A State-of-the-Art Review of Smartphone Battery and Energy Usage

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    The advancement and popularity of smartphones have made it an essential and all-purpose device. But lack of advancement in battery technology has held back its optimum potential. Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone. For that, a fair understanding of a smartphone's energy consumption factors is necessary for both users and device manufacturers, along with other stakeholders in the smartphone ecosystem. It is important to assess how much of the device's energy is consumed by which components and under what circumstances. This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor. The main contribution of this paper is four comprehensive literature reviews on: 1) smartphone's power consumption assessment and estimation (including power consumption analysis and modelling); 2) power consumption management for smartphones (including energy-saving methods and techniques); 3) state-of-the-art of the research and commercial developments of smartphone batteries (including alternative power sources); and 4) mitigating the hazardous issues of smartphones' batteries (with a details explanation of the issues). The research works are further subcategorized based on different research and solution approaches. A good number of recent empirical research works are considered for this comprehensive review, and each of them is succinctly analysed and discussed

    On the Deployment of Healthcare Applications over Fog Computing Infrastructure

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    Fog computing is considered as the most promising enhancement of the traditional cloud computing paradigm in order to handle potential issues introduced by the emerging Interned of Things (IoT) framework at the network edge. The heterogeneous nature, the extensive distribution and the hefty number of deployed IoT nodes will disrupt existing functional models, creating confusion. However, IoT will facilitate the rise of new applications, with automated healthcare monitoring platforms being amongst them. This paper presents the pillars of design for such applications, along with the evaluation of a working prototype that collects ECG traces from a tailor-made device and utilizes the patient's smartphone as a Fog gateway for securely sharing them to other authorized entities. This prototype will allow patients to share information to their physicians, monitor their health status independently and notify the authorities rapidly in emergency situations. Historical data will also be available for further analysis, towards identifying patterns that may improve medical diagnoses in the foreseeable future

    Multi-Criteria Decision-Making Approach for Container-based Cloud Applications: The SWITCH and ENTICE Workbenches

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    Many emerging smart applications rely on the Internet of Things (IoT) to provide solutions to time-critical problems. When building such applications, a software engineer must address multiple Non-Functional Requirements (NFRs), including requirements for fast response time, low communication latency, high throughput, high energy efficiency, low operational cost and similar. Existing modern container-based software engineering approaches promise to improve the software lifecycle; however, they fail short of tools and mechanisms for NFRs management and optimisation. Our work addresses this problem with a new decision-making approach based on a Pareto Multi-Criteria optimisation. By using different instance configurations on various geo-locations, we demonstrate the suitability of our method, which narrows the search space to only optimal instances for the deployment of the containerised microservice.This solution is included in two advanced software engineering environments, the SWITCH workbench, which includes an Interactive Development Environment (IDE) and the ENTICE Virtual Machine and container images portal. The developed approach is particularly useful when building, deploying and orchestrating IoT applications across multiple computing tiers, from Edge-Cloudlet to Fog-Cloud data centres

    Edge Intelligence : Empowering Intelligence to the Edge of Network

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    Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

    Edge Intelligence : Empowering Intelligence to the Edge of Network

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    Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

    Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

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    Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap with arXiv:1703.0352

    VirtFogSim: A parallel toolbox for dynamic energy-delay performance testing and optimization of 5G Mobile-Fog-Cloud virtualized platforms

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    It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emergingMobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is aMATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times, (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering, and (v) itsMATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared
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