7,551 research outputs found
A Smart Modular Wireless System for Condition Monitoring Data Acquisition
Smart sensors, big data, the cloud and distributed data processing are some of the most interning changes in the way we collect, manage and treat data in recent years. These changes have not significantly influenced the common practices in condition monitoring for shipping. In part this is due to the reduced trust in data security, data ownership issues, lack of technological integration and obscurity of direct benefit. This paper presents a method of incorporating smart sensor techniques and distributed processing in data acquisition for condition monitoring to assist decision support for maintenance actions addressing these inhibitors
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Closing the loop of SIEM analysis to Secure Critical Infrastructures
Critical Infrastructure Protection is one of the main challenges of last
years. Security Information and Event Management (SIEM) systems are widely used
for coping with this challenge. However, they currently present several
limitations that have to be overcome. In this paper we propose an enhanced SIEM
system in which we have introduced novel components to i) enable multiple layer
data analysis; ii) resolve conflicts among security policies, and discover
unauthorized data paths in such a way to be able to reconfigure network
devices. Furthermore, the system is enriched by a Resilient Event Storage that
ensures integrity and unforgeability of events stored.Comment: EDCC-2014, BIG4CIP-2014, Security Information and Event Management,
Decision Support System, Hydroelectric Da
A Survey on Communication Networks for Electric System Automation
Published in Computer Networks 50 (2006) 877–897, an Elsevier journal. The definitive version of this publication is available from Science Direct. Digital Object Identifier:10.1016/j.comnet.2006.01.005In today’s competitive electric utility marketplace, reliable and real-time information become the key factor for reliable delivery of power to the end-users, profitability of the electric utility and customer satisfaction. The operational and commercial demands of electric utilities require a high-performance data communication network that supports both existing functionalities and future operational requirements. In this respect, since such a communication network constitutes the core of the electric system automation applications, the design of a cost-effective and reliable network architecture is crucial.
In this paper, the opportunities and challenges of a hybrid network architecture are discussed for electric system automation.
More specifically, Internet based Virtual Private Networks, power line communications, satellite communications and wireless communications (wireless sensor networks, WiMAX and wireless mesh networks) are described in detail. The motivation of this paper is to provide a better understanding of the hybrid network architecture that can provide heterogeneous electric system automation application requirements. In this regard, our aim is to present a structured framework for electric utilities who plan to utilize new communication technologies for automation and hence, to make the decision making process more effective and direct.This work was supported by NEETRAC under
Project #04-157
Spectrum Sensing of DVB-T2 Signals using a Low Computational Noise Power Estimation
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted ncomponent of this work in other works.Cognitive radio is a promising technology that answers the spectrum scarcity problem arising from the proliferation of wireless networks and mobile services. In this paper, spectrum sensing of digital video broadcasting-second generation terrestrial (DVB-T2) signals in AWGN, WRAN and COST207 multipath fading environment are considered. ED is known to achieve an increased performance among low computational complexity detectors, but it is susceptible to noise uncertainty. Taking into consideration the edge pilot and scattered pilot periodicity in DVB-T2 signals, a low computational noise power estimator is proposed. Analytical forms for the detector are derived. Simulation results show that with the noise power estimator, ED significantly outperforms the pilot correlation-based detectors. Simulation also show that the proposed scheme enables ED to obtain increased detection performance in multi-path fading environments. Moreover, based on this algorithm a practical sensing scheme for cognitive radio networks is proposed.Peer reviewedFinal Accepted Versio
Energy aware performance evaluation of WSNs
Distributed sensor networks have been discussed for more than 30 years, but the vision
of Wireless Sensor Networks (WSNs) has been brought into reality only by the rapid advancements
in the areas of sensor design, information technologies, and wireless networks
that have paved the way for the proliferation of WSNs. The unique characteristics of
sensor networks introduce new challenges, amongst which prolonging the sensor lifetime
is the most important. Energy-efficient solutions are required for each aspect of WSN design
to deliver the potential advantages of the WSN phenomenon, hence in both existing
and future solutions for WSNs, energy efficiency is a grand challenge. The main contribution
of this thesis is to present an approach considering the collaborative nature of WSNs
and its correlation characteristics, providing a tool which considers issues from physical
to application layer together as entities to enable the framework which facilitates the
performance evaluation of WSNs. The simulation approach considered provides a clear
separation of concerns amongst software architecture of the applications, the hardware
configuration and the WSN deployment unlike the existing tools for evaluation. The
reuse of models across projects and organizations is also promoted while realistic WSN
lifetime estimations and performance evaluations are possible in attempts of improving
performance and maximizing the lifetime of the network. In this study, simulations are
carried out with careful assumptions for various layers taking into account the real time
characteristics of WSN.
The sensitivity of WSN systems are mainly due to their fragile nature when energy
consumption is considered. The case studies presented demonstrate the importance of
various parameters considered in this study. Simulation-based studies are presented,
taking into account the realistic settings from each layer of the protocol stack. Physical
environment is considered as well. The performance of the layered protocol stack in
realistic settings reveals several important interactions between different layers. These
interactions are especially important for the design of WSNs in terms of maximizing the
lifetime of the network
Ontology-based data semantic management and application in IoT- and cloud-enabled smart homes
The application of emerging technologies of Internet of Things (IoT) and cloud computing have increasing the popularity of smart homes, along with which, large volumes of heterogeneous data have been generating by home entities. The representation, management and application of the continuously increasing amounts of heterogeneous data in the smart home data space have been critical challenges to the further development of smart home industry. To this end, a scheme for ontology-based data semantic management and application is proposed in this paper. Based on a smart home system model abstracted from the perspective of implementing users’ household operations, a general domain ontology model is designed by defining the correlative concepts, and a logical data semantic fusion model is designed accordingly. Subsequently, to achieve high-efficiency ontology data query and update in the implementation of the data semantic fusion model, a relational-database-based ontology data decomposition storage method is developed by thoroughly investigating existing storage modes, and the performance is demonstrated using a group of elaborated ontology data query and update operations. Comprehensively utilizing the stated achievements, ontology-based semantic reasoning with a specially designed semantic matching rule is studied as well in this work in an attempt to provide accurate and personalized home services, and the efficiency is demonstrated through experiments conducted on the developed testing system for user behavior reasoning
Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary).
In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena
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