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26th ieee international conference on Electronics, Circuits and systems (ICECS)

作者： Younes, Hamoud Ibrahim, Ali Rizk, Mostafa Valle, Maurizio Univ Genoa Dept Elect Elect & Telecommun Engn & Naval Archit COSMIC Lab Genoa Italy Lebanese Int Univ Dept Comp & Commun Engn Bekaa Lebanon Lebanese Univ Dept Phys & Elect Beirut Lebanon

ISBN: (纸本)9781728109961

Efficiency, real-time operation and low-power consumption are the main requirements of embedded Machine Learning implementations. this paper proposes an approach for applying Algorithmic level Approximate computing Techniques (ACTs) on two supervised machine learning algorithms. the proposed approach has been validated in two different applications: touch modality and image classification. Results demonstrate that the proposed approach improves the performance with a speedup up to 3.2x with an accuracy loss of less than 4.7%.

关键词： Approximate computing embedded Machine Learning Supervised Learning Tensorial Data time Latency

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Performance and Correctness of Mobile Cloud computing systems: Taxonomy and Open Challenges 15

Performance and Correctness of Mobile Cloud Computing System...

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15th ieee international Wireless Communications and Mobile computing conference (ieee IWCMC)

作者： Fakhfakh, Fairouz Univ Sfax Sfax Tunisia

ISBN: (纸本)9781538677476

Nowadays, mobile devices (e.g., tablet and smartphone) have become an important part of human life as they provide effective communication tools not limited by place and time. Nevertheless, they are facing many problems in terms of resource storage capabilities and communications. So, Mobile Cloud computing (MCC) has emerged as a new paradigm to overcome these limitations by integrating Cloud computing into the mobile environment. Adopting this paradigm raises several challenges, particularly with regard to the performance and the correctness. On the one hand, attaining the optimal performance in MCC is not a trivial task due to the variety of often conflicting objectives. On the other hand, because of the complexity of the studied applications, their modeling is more prone to errors on the part of the designers. In this paper, we present a background on MCC including definition, advantages and applications. We also provide a survey of the proposed approaches in the literature focusing on the performance and the verification in MCC systems. then, we show a comparative study of the existing solutions based on some criteria. Finally, we identify the main research issues and we suggest some directions for future research.

关键词： Mobile Cloud computing survey performance correctness

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embedded Partitioning real-time Operating System Based on Microkernel 22

Embedded Partitioning Real-time Operating System Based on Mi...

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22nd ieee international conference on Computational Science and Engineering (ieee CSE) / 17th ieee international conference on embedded and Ubiquitous computing (ieee EUC)

作者： Chen, Tanhong Li, Huiyong Niu, Jianwei Ren, Tao Xu, Guizhou Beihang Univ Sch Comp Sci Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China Beijing Acoinfo Technol Co Ltd Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China

ISBN: (纸本)9781728116648

the embedded partitioning operating system is active in security-related fields such as avionics and automotive embedded software. the temporal and spatial isolation of the partition is the guarantee of system security. In addition, as these areas become more demanding for applications, improving the real-time performance of partitioning operating systems has become a trend. To reach this goal, we propose a solution that leverages the microkernel architecture in the embedded partitioning operating system. the microkernel ensures the determinacy of real-time scheduling policies and system calls by simplifying kernel functionality and preserving the most basic resources. Partitions built on top of the microkernel enable low-cost communication, fast context switching and mixed task scheduling. Based on the microkernel, we developed a complete real-time partitioning operating system. the system supports multi-core processors to take full advantage of the high performance of existing embedded processors. On different hardware platforms, we conducted two types of tests about interrupt response time and latency. the results show that our proposed architecture exhibits high real-time performance.

关键词： isolation partitioning microkernel real-time embedded system multi-core

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SoC-based computing infrastructures for scientific applications and commercial services: Performance and economic evaluations

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FUTURE GENERATION COMPUTER systems-thE international JOURNAL OF ESCIENCE 2019年 96卷 11-22页

作者： D'Agostino, Daniele Quarati, Alfonso Clematis, Andrea Morganti, Lucia Corni, Elena Giansanti, Valentina Cesini, Daniele Merelli, Ivan Natl Res Council Italy Inst Appl Math & Informat Technol E Magenes Genoa Italy Italian Inst Nucl Phys CNAF Sect Bologna Italy Natl Res Council Italy Inst Biomed Technol Milan Italy

Energy consumption represents one of the most relevant issues by now in operating computing infrastructures, from traditional High Performance computing Centers to Cloud Data Centers. Low power System-on-Chip (SoC) architectures, originally developed in the context of mobile and embedded technologies, are becoming attractive also for scientific and industrial applications given their increasing computing performances, coupled with relatively low costs and power demands. In this paper, we investigate the performance of the most representative SoCs for a computational intensive N-body benchmark, a simple deep learning based application and a real-life application taken from the field of molecular biology. the goal is to assess the trade-off among time-to-solution, energy-to-solution and economical aspects for both scientific and commercial purposes they are able to achieve in comparison to traditional server-grade architectures adopted in present infrastructures. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Low power systems-on-Chip N-body benchmark Deep learning Next-Generation Sequencing Performance and economic evaluations

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Energy harvesting and smart management platform for low power IoT systems 15

Energy harvesting and smart management platform for low powe...

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15th Annual international conference on Distributed computing in Sensor systems (DCOSS)

作者： Filios, Gabriel Katsidimas, Ioannis Nikoletseas, Sotiris Tsenempis, Ioannis Univ Patras Dept Comp Engn & Informat Patras Greece Comp Technol Inst & Press Diophantus Patras Greece

ISBN: (纸本)9781728105703

Advances in Micro-Electro-Mechanical systems (MEMS) and energy scavenging technologies are now able to provide viable solutions based on different application environments. In particular, rechargeable battery-operated Wireless Sensor Networks (WSN) can now be set upon a new basis that targets both ambient energy harvesting and wireless charging technologies in different forms and scales. Hence, the assumption of finite energy budget (that may also affect the system's performance) can be over-passed by potentially unlimited energy supply. the presented platform is able to effectively power a low power IoT system with processing, sensing and wireless communication capabilities. It embeds an advanced energy management IC that allows extremely high efficiency energy harvesting, suitable for low-power and miniaturized energy generators. Unlike other systems it not only supports a variety of power supply options, but also a hybrid energy storage scheme. this paper aims to provide a description of the system's design, the embedded intelligence and the performance in terms of energy autonomy.

关键词： energy harvesting IoT smart sensing low power

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Utilizing CNNs for Object Detection with LiDAR Data for Autonomous Driving

Utilizing CNNs for Object Detection with LiDAR Data for Auto...

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international conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Vinay Ponnaganti Melody Moh Teng-Sheng Moh San Jose State University San Jose CA USA

ISBN: (数字)9781665423182

ISBN: (纸本)9781665446198

this project evaluates the feasibility of utilizing popular Convolutional Neural Networks (CNNs) to detect objects present in LiDAR (Light Detection And Ranging) data, and the resulting neural network's performance. this work aims to further existing experimentation using raw LiDAR data that is analyzed and represented in a two-dimensional frame. Using this method, hundreds of frames were generated to create a dataset that was used for neural network training and validation on an existing CNN architecture. the LiDAR dataset was used to train YOLOv3, a popular CNN model, to detect vehicles. this research aims to test a smaller version of the network, YOLOv3-tiny, to measure the change in accuracy between using YOLOv3 and YOLOv3-tiny on the LiDAR dataset. the results are then compared to the loss typically found when going from YOLOv3 to YOLOV3-tiny on camera-based images. In prior experimentation, a preprocessing method was also introduced to attempt to isolate target objects in the frame. the method will be evaluated in this paper to measure its effect on the final accuracy metric of the network. Lastly, the runtime performance of these networks will be evaluated on two embedded platforms to understand if the frame rate that the networks perform on is usable for real-world applications, based on the frame rate the sensor is capable of outputting and the inference speed of the network on the embedded platforms.

关键词： Training Laser radar Image edge detection Object detection Cameras real-time systems Hardware

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Efficient EEG Mobile Edge computing and Optimal Resource Allocation for Smart Health applications 15

Efficient EEG Mobile Edge Computing and Optimal Resource All...

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15th ieee international Wireless Communications and Mobile computing conference (ieee IWCMC)

作者： Al-Marridi, Abeer Z. Mohamed, Amr Erbad, Aiman Al-Ali, Abdulla Guizani, Mohsen Qatar Univ Dept Comp Sci & Engn Doha 2713 Qatar

ISBN: (纸本)9781538677476

In the past few years, a rapid increase in the number of patients requiring constant monitoring, which inspires researchers to develop intelligent and sustainable remote smart healthcare services. However, the transmission of big real-time health data is a challenge since the current dynamic networks are limited by different aspects such as the bandwidth, end-to-end delay, and transmission energy. Due to this, a data reduction technique should be applied to the data before being transmitted based on the resources of the network. In this paper, we integrate efficient data reduction with wireless networking transmission to enable an adaptive compression with an acceptable distortion, while reacting to the wireless network dynamics such as channel fading and user mobility. Convolutional Auto-encoder (CAE) approach was used to implement an adaptive compression/reconstruction technique with the minimum distortion. then, a resource allocation framework was implemented to minimize the transmission energy along with the distortion of the reconstructed signal while considering different network and applications constraints. A comparison between the results of the resource allocation framework considering both CAE and Discrete wavelet transforms (DWT) was also captured.

关键词： Optimization Resource optimization Convolutional Auto-encoder e-Health systems Compression

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CAPE: Continuous Access Policy Enforcement for IoT Deployments 15

CAPE: Continuous Access Policy Enforcement for IoT Deploymen...

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15th ieee international Wireless Communications and Mobile computing conference (ieee IWCMC)

作者： Alkhresheh, Ashraf Elgazzar, Khalid Hassanein, Hossam S. Queens Univ Sch Comp Kingston ON K7L 3N6 Canada Univ Ontario Inst Technol Dept Elect Comp & Software Engn Oshawa ON L1H 7K4 Canada

ISBN: (纸本)9781538677476

Advancements and convergence in IoT enabling technologies along with ubiquitous connectivity have led to the generation of new wave of smart services and applications based on real-time data access. the popularity of ubiquitous data access and accelerated adoption of these services pose significant challenges on user and data privacy. thus, controlling access to such services in highly dynamic environments with continuously changing context becomes even more challenging. the wide adoption of IoT in our everyday life in many vital domains such as healthcare and military operations requires continuous and tight access control to prevent unauthorized and unintended access. A delay in making access decisions when context changes may result in consequences that cause harm and property damage. therefore, continuity in access policy enforcement becomes a necessity in highly dynamic IoT environments for the entire access session not only at the time of request. this paper presents CAPE, a continuous access policy enforcement framework for IoT deployments. CAPE describes access control elements using predicates, and stores them as primitive facts in a K Dimensional tree data structure. Our algorithms automatically match access requests with primitive facts, generate access policies, make context-aware access decisions at run time and continuously monitor access control parameters based on which access decisions were made. Performance evaluation of CAPE demonstrates that this framework efficiently controls access in highly dynamic IoT environments.

关键词： Access control Internet of things Privacy Monitoring real-time systems Medical services"

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Introducing a Deferrable Server into AUTOSAR

Introducing a Deferrable Server into AUTOSAR

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ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Jorge Luis Martinez Garcia Ignacio Sañudo Olmedo University of Modena and Reggio Emilia Modena Italy

ISBN: (数字)9781728144030

ISBN: (纸本)9781728144047

In the automotive domain there is a renewed interest for the adoption of fixed priority real-time servers, as a way to implement more efficient reservation mechanisms than TDMAbased methods. In this paper, we take advantage of their temporal isolation to serve tasks originally scheduled in the background so that they can meet their deadlines. Hence, we present a method to implement a Deferrable Server (DS) on top of ETAS RTA-OS, a ubiquitous AUTOSAR-compliant OS, and propose a heuristic to select its parameters. We then prove the effectiveness of the parametrization by applying the technique to an industrial case study consisting of an automotive engine control system.

关键词： Task analysis Servers Automotive engineering real-time systems Standards Timing Software

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PotatoScanner - A Mobile Delay Tolerant Wireless Sensor Node for Smart Farming applications 15

PotatoScanner - A Mobile Delay Tolerant Wireless Sensor Node...

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15th Annual international conference on Distributed computing in Sensor systems (DCOSS)

作者： Gernert, Bjoern Schlichter, Jan Wolf, Lars Tech Univ Carolo Wilhelmina Braunschweig Inst Operating Syst & Comp Networks Braunschweig Germany

ISBN: (纸本)9781728105703

Smart Farming has the goal to support farmers in their various activities, i.e., achieve best results in quality and quantity of their products, with low and efficient resource usage, working in sustainable manners etc. For that, a lot of information is needed, e.g., about the current state of the monitored agricultural area. Typically this requires a large number of sensors which collect important sensor data at predefined intervals. For further evaluation such data is usually processed in a central place, for which it must first be transferred from the field to a data sink at the farm premises. Cellular networks could be used for such data transmissions, but the network deployment in rural areas is costly with less likely revenues. thus, mobile operators are not eager to roll out their networks in rural areas so that they are often not well covered. In this paper we introduce PotatoScanner, a mobile Delay Tolerant Wireless Sensor Node (DTWSN) that closes this gap and acts with the help of a field sprayer as a data mule for the collected data. In addition, PotatoScanner itself has sensors to obtain further information about the cultivated field. the paper provides an overview of the overall system and analyses the measurement data obtained in real operation over the past two years 2017 and 2018.

关键词： wireless sensor networks delay tolerant networking wireless sensor nodes smart farming precision farming field sprayer

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