15,894 research outputs found
Data Management in Industry 4.0: State of the Art and Open Challenges
Information and communication technologies are permeating all aspects of
industrial and manufacturing systems, expediting the generation of large
volumes of industrial data. This article surveys the recent literature on data
management as it applies to networked industrial environments and identifies
several open research challenges for the future. As a first step, we extract
important data properties (volume, variety, traffic, criticality) and identify
the corresponding data enabling technologies of diverse fundamental industrial
use cases, based on practical applications. Secondly, we provide a detailed
outline of recent industrial architectural designs with respect to their data
management philosophy (data presence, data coordination, data computation) and
the extent of their distributiveness. Then, we conduct a holistic survey of the
recent literature from which we derive a taxonomy of the latest advances on
industrial data enabling technologies and data centric services, spanning all
the way from the field level deep in the physical deployments, up to the cloud
and applications level. Finally, motivated by the rich conclusions of this
critical analysis, we identify interesting open challenges for future research.
The concepts presented in this article thematically cover the largest part of
the industrial automation pyramid layers. Our approach is multidisciplinary, as
the selected publications were drawn from two fields; the communications,
networking and computation field as well as the industrial, manufacturing and
automation field. The article can help the readers to deeply understand how
data management is currently applied in networked industrial environments, and
select interesting open research opportunities to pursue
Deep Echo State Network (DeepESN): A Brief Survey
The study of deep recurrent neural networks (RNNs) and, in particular, of
deep Reservoir Computing (RC) is gaining an increasing research attention in
the neural networks community. The recently introduced Deep Echo State Network
(DeepESN) model opened the way to an extremely efficient approach for designing
deep neural networks for temporal data. At the same time, the study of DeepESNs
allowed to shed light on the intrinsic properties of state dynamics developed
by hierarchical compositions of recurrent layers, i.e. on the bias of depth in
RNNs architectural design. In this paper, we summarize the advancements in the
development, analysis and applications of DeepESNs
Underwater Optical Wireless Communications, Networking, and Localization: A Survey
Underwater wireless communications can be carried out through acoustic, radio
frequency (RF), and optical waves. Compared to its bandwidth limited acoustic
and RF counterparts, underwater optical wireless communications (UOWCs) can
support higher data rates at low latency levels. However, severe aquatic
channel conditions (e.g., absorption, scattering, turbulence, etc.) pose great
challenges for UOWCs and significantly reduce the attainable communication
ranges, which necessitates efficient networking and localization solutions.
Therefore, we provide a comprehensive survey on the challenges, advances, and
prospects of underwater optical wireless networks (UOWNs) from a layer by layer
perspective which includes: 1) Potential network architectures; 2) Physical
layer issues including propagation characteristics, channel modeling, and
modulation techniques 3) Data link layer problems covering link configurations,
link budgets, performance metrics, and multiple access schemes; 4) Network
layer topics containing relaying techniques and potential routing algorithms;
5) Transport layer subjects such as connectivity, reliability, flow and
congestion control; 6) Application layer goals and state-of-the-art UOWN
applications, and 7) Localization and its impacts on UOWN layers. Finally, we
outline the open research challenges and point out the future directions for
underwater optical wireless communications, networking, and localization
research.Comment: This manuscript is submitted to IEEE Communication Surveys and
Tutorials for possible publicatio
Human experience in the natural and built environment : implications for research policy and practice
22nd IAPS conference. Edited book of abstracts. 427 pp. University of Strathclyde, Sheffield and West of Scotland Publication. ISBN: 978-0-94-764988-3
SciTech News Volume 71, No. 2 (2017)
Columns and Reports From the Editor 3
Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division 9 Aerospace Section of the Engineering Division 12 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 14
Reviews Sci-Tech Book News Reviews 16
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Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
Cloud Computing - Architecture and Applications
In the era of Internet of Things and with the explosive worldwide growth of
electronic data volume, and associated need of processing, analysis, and
storage of such humongous volume of data, it has now become mandatory to
exploit the power of massively parallel architecture for fast computation.
Cloud computing provides a cheap source of such computing framework for large
volume of data for real-time applications. It is, therefore, not surprising to
see that cloud computing has become a buzzword in the computing fraternity over
the last decade. This book presents some critical applications in cloud
frameworks along with some innovation design of algorithms and architecture for
deployment in cloud environment. It is a valuable source of knowledge for
researchers, engineers, practitioners, and graduate and doctoral students
working in the field of cloud computing. It will also be useful for faculty
members of graduate schools and universities.Comment: Edited Volume published by Intech Publishers, Croatia, June 2017. 138
pages. ISBN 978-953-51-3244-8, Print ISBN 978-953-51-3243-1. Link:
https://www.intechopen.com/books/cloud-computing-architecture-and-application
RIS-Enhanced WPCNs: Joint Radio Resource Allocation and Passive Beamforming Optimization
Wireless-powered communication and reconfigurable intelligent surface (RIS) can complement each other for increasing energy utilization and spectrum efficiency by reconfiguring the surrounding radio environment, however, which has not been sufficiently studied by the existing works. In this paper, we propose a joint radio resource and passive beamforming optimization scheme for a downlink RIS-assisted wireless-powered communication network with a harvest-then-transmit protocol to improve system energy efficiency (EE). In the considered model, the single-antenna wireless devices (WDs) harvest wireless energy from a multi-antenna dedicated power station (PS) through the RIS in the downlink and transmit their independent information to a single-antenna receiver in the uplink by a time-division-multiple-access mode. Our goal is to maximize the total EE of all WDs. To make full use of the beamforming gain provided by both the PS and the RIS, we jointly optimize the active beamforming of the PS and the passive beamforming of the RIS. To deal with the challenging non-convex optimization problem with multiple coupled variables, we first consider fixing the passive beamforming, and converting the remaining radio resource allocation problem into an equivalent convex problem which is solved by using Lagrange dual theory. Then, we fix the optimized resource allocation parameters and optimize the passive beamforming of the RIS by using a semidefinite programming method. Simulation results demonstrate that the proposed algorithm achieves higher EE compared to the conventional schemes
Multi-BD Symbiotic Radio-Aided 6G IoT Network: Energy Consumption Optimization with QoS Constraint Approach
The commensal symbiotic radio (CSR) system is proposed as a novel solution for connecting systems through green
communication networks. This system enables us to establish
secure, ubiquitous, and unlimited connectivity, which is a goal of 6G. The base station uses MIMO antennas to transmit its signal. Passive IoT devices, called symbiotic backscatter devices (SBDs), receive the signal and use it to charge their power supply. When the SBDs have data to transmit, they modulate the information onto the received ambient RF signal and send it to the symbiotic user equipment, which is a typical active device. The main purpose is to enhance energy efficiency in this network by minimizing energy consumption (EC) while ensuring the minimum required throughput for SBDs. To achieve this, we propose a new scheduling scheme called Timing-SR that optimally allocates resources to SBDs. The main optimization problem involves non-convex objective functions and constraints. To solve this, we use mathematical techniques and introduce a new approach called sequential quadratic and conic quadratic representation to relax and discipline the problem, leading to reducing its complexity and convergence time. The simulation results demonstrate that the proposed approach outperforms other outlined schemes in reducing EC
A Survey on Simulation Tools and Testbeds for Cognitive Radio Networks Study
Efficient utility of radio spectrum has been a hot topic as the wireless
communication spectrum is a precious resource. The past decade has witnessed
intensive research in spectrum sharing techniques. Most of the techniques are
based on cognitive radio networks (CRNs) because cognitive capabilities are
essential for optimizing spectrum efficiency and guaranteeing safe coexistence
in the presence of the spectrum uncertainty. However, due to the high
complexity of the problem, most research has been limited to theoretical
analysis. It is non-trivial to build a simulator that is capable of carrying
out a comprehensive experimental study. In this paper, a survey is conducted to
provide a big picture of the available simulators in CRNs research. By
illustrating their major functionalities, the insight enables researchers to
select tools that match their needs. In addition, with a better understanding
of the advantages and constraints, this survey aims at providing a guideline
for simulator designers who have been trying to meet the requirements from the
CRN research community
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