FPGA-Based Web Services -- Infinite Potential or a Road to Nowhere?

Brzoza-Woch, Robert; Nawrocki, Piotr

doi:10.1109/mic.2015.23

Cited by 16 publications

(14 citation statements)

References 10 publications

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“…FPGAs would provide energy efficiency, interactivity, powerful computation abilities and reconfigurable embedded system to IoT. Moreover, compared to other computing platforms such as Raspberry Pi, FPGAs provide a better performance [97]. Although the implementation of CS-based edge computing on the FPGA for IoT applications have never been addressed in the literature, we present herein some of the efforts which can be explored to provide powerful computing platforms.…”

Section: Processing Layermentioning

confidence: 99%

Compressive Sensing-Based IoT Applications: A Review

Djelouat

Amira

Bensaali

2018

JSAN

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The Internet of Things (IoT) holds great promises to provide an edge cutting technology that enables numerous innovative services related to healthcare, manufacturing, smart cities and various human daily activities. In a typical IoT scenario, a large number of self-powered smart devices collect real-world data and communicate with each other and with the cloud through a wireless link in order to exchange information and to provide specific services. However, the high energy consumption associated with the wireless transmission limits the performance of these IoT self-powered devices in terms of computation abilities and battery lifetime. Thus, to optimize data transmission, different approaches have to be explored such as cooperative transmission, multi-hop network architectures and sophisticated compression techniques. For the latter, compressive sensing (CS) is a very attractive paradigm to be incorporated in the design of IoT platforms. CS is a novel signal acquisition and compression theory that exploits the sparsity behavior of most natural signals and IoT architectures to achieve power-efficient, real-time platforms that can grant efficient IoT applications. This paper assesses the extant literature that has aimed to incorporate CS in IoT applications. Moreover, the paper highlights emerging trends and identifies several avenues for future CS-based IoT research.

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Section: Processing Layermentioning

confidence: 99%

Compressive Sensing-Based IoT Applications: A Review

Djelouat

Amira

Bensaali

2018

JSAN

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“…For example the FPGAaccelerated smart camera described in [5] is able to run multiple configurations which can be substituted depending on a higher level adaptation policy. Enhancing an FPGA-based device with the remote reconfiguration feature costs design issues due to losing the programmable logic functionality during the reconfiguration process [6]. In that case the partial reconfiguration feature [7], [8] could be helpful, but it tends to complicate the hardware-software design flow hence it may be ineffective for time-critical project.…”

Section: Related Researchmentioning

confidence: 99%

“…Bootloader is not required during initial development, and can be added at later development stages. Remote FPGA reconfiguration, programmable logic partial reconfiguration with traditional design flow approach [5], [6], [7], [8] Complex design which includes a reconfiguration subsystem and more elaborate design flow when considering partial reconfiguration.…”

Section: Reviewed Solutionmentioning

confidence: 99%

Rapid Embedded Systems Prototyping - an effective approach to embedded systems development

Brzoza-Woch

Gurdek

Szydło

2018

Annals of Computer Science and Information Systems

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In this paper we introduce the Rapid Embedded Systems Prototyping (RESP) approach aimed at accelerating the development of novel, experimental, and proof-of-concept implementations of embedded devices based on microcontrollers and Field Programmable Gate Array (FPGA) chips. It is intended to be used in the fast-paced business environment in which an early working prototype is required. The RESP approach can be useful for remote developing and temporary monitoring of various embedded devices: primarily for resource-constrained IoT platforms, microcontroller-based sensor nodes, and customized ad hoc systems. The RESP-compliant system uses a central server and one or multiple Remote Reconfiguration and Monitoring (RRM) modules. Each RRM allows the software developers to manage reprogramming and monitoring of multiple target embedded devices. It can be applied to a device that needs to be remotely reconfigured, tested, or reprogrammed in its target environment without implementing a reliable bootloader. The RRM described in this paper has been successfully implemented and its functionality and performance have been tested.

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“…An important aspects of service adaptation are possibility of reconfiguration [34,35] and providing an efficient mechanism-a repository-that allows searching for services in a distributed environment [36]. The services found should have a Service Level Agreement (SLA) parameter that specifies the conditions for their use [37].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Service Management in Mobile Cloud Computing by Means of Supervised and Reinforcement Learning

Nawrocki

Śnieżyński

2017

J Netw Syst Manage

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Since the concept of merging the capabilities of mobile devices and cloud computing is becoming increasingly popular, an important question is how to optimally schedule services/tasks between the device and the cloud. The main objective of this article is to investigate the possibilities for using machine learning on mobile devices in order to manage the execution of services within the framework of Mobile Cloud Computing. In this study, an agent-based architecture with learning possibilities is proposed to solve this problem. Two learning strategies are considered: supervised and reinforcement learning. The solution proposed leverages, among other things, knowledge about mobile device resources, network connection possibilities and device power consumption, as a result of which a decision is made with regard to the place where the task in question is to be executed. By employing machine learning techniques, the agent working on a mobile device gains experience in determining the optimal place for the execution of a given type of task. The research conducted allowed for the verification of the solution proposed in the domain of multimedia file conversion and demonstrated its usefulness in reducing the time required for task execution. Using the experience The research presented in this paper was supported by the Polish Ministry of Science and Higher Education under AGH University of Science and Technology Grant 11.11.230.124. We thank Małgorzata Pła_ zek, Jakub Czy_ zewski and Michał Janiec for assistance with implementation and testing. Neither the entire paper nor any part of its content has been published or has been accepted for publication elsewhere. It has not been submitted to any other journal. gathered as a result of subsequent series of tests, the agent became more efficient in assigning the task of multimedia file conversion to either the mobile device or cloud computing resources.

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FPGA-Based Web Services -- Infinite Potential or a Road to Nowhere?

Cited by 16 publications

References 10 publications

Compressive Sensing-Based IoT Applications: A Review

Compressive Sensing-Based IoT Applications: A Review

Rapid Embedded Systems Prototyping - an effective approach to embedded systems development

Adaptive Service Management in Mobile Cloud Computing by Means of Supervised and Reinforcement Learning

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