Modeling Context-Aware and Agent-Ready Systems for the Outdoor Smart Lighting

Klimek, Radosław; Rogus, Grzegorz

doi:10.1007/978-3-319-07176-3_23

Cited by 12 publications

(6 citation statements)

References 18 publications

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“…Table 3 shows lighting classes for actual flow values (note that a daily flow can be updated every 15 min) and the corresponding relative power usages (For the street examined in the Smart Power Grids project, financed by the Polish National Fund for Environmental Protection and Water Management [15], it was proved that for a 24-h performance structure ME2-51%, ME3b-25%, ME4a-24%, the power usage reduction at the level of 20% is achieved). Environment state change events are reported to the lighting control system either from a telemetric layer or from other sources such as BTS-based (Base Transceiver Station) monitoring facilities [16]. Such a control system imposes on a design process the need of a prior preparation of multiple setups of a lighting installation, covering all combinations of lighting classes and ambient light levels-the additional motivation for automating a design process.…”

Section: Rangementioning

confidence: 99%

Towards Highly Energy-Efficient Roadway Lighting

Sędziwy

Kotulski

2016

Energies

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Abstract:The reports presented by consulting firms show that annual energy costs generated by 340 million streetlights are expected to reach $23.9 to $42.5 billion by 2025. Those numbers reveal a motivation behind the research aiming at optimizing outdoor lighting energy efficiency. They show that even a small unit improvement can yield large benefits due to the effect of scale. The development of solid state lighting solutions enables highly effective modernization of street lighting installations. It allows obtaining power saving not only by replacing high pressure lamps with LEDs but also by improving a design quality and by introducing a dynamic street lighting control. Both methods, however, are not feasible for industry-standard software tools due to the significant complexity related to a configuration optimization, especially for large-scale projects. The goal of this article is presenting the workaround to the complexity issue, which is based on application of graph methods. They enable optimizing lighting installations in the scale of a city by providing a scalable computational environment. The presented case study shows that, thanks to applying the proposed method, one can design a lighting system which has the energy consumption reduced by up to 70%.

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Section: Rangementioning

confidence: 99%

Towards Highly Energy-Efficient Roadway Lighting

Sędziwy

Kotulski

2016

Energies

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“…The historical behaviors are encoded into logical specifications, and can be later analyzed for satisfiability, c.f. works [9] or [7,8,10], supporting the current reasoning process and behavior recognition.…”

Section: Btsmentioning

confidence: 99%

Artificial Intelligence and Soft Computing

2015

Lecture Notes in Computer Science

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This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use.Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher.The consent of CRC Press LLC does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from CRC Press LLC for such copying. PREFACEThe book, to the best of the author's knowledge, is the first text of its kind that presents both the traditional and the modern aspects of 'AI and Soft Computing' in a clear, insightful and highly comprehensive writing style. It provides an in-depth analysis of the mathematical models and algorithms, and demonstrates their applications in real world problems of significant complexity. About the bookThe book covers 24 chapters altogether. It starts with the behavioral perspective of the 'human cognition' and covers in detail the tools and techniques required for its intelligent realization on machines. The classical chapters on search, symbolic logic, planning and machine learning have been covered in sufficient details, including the latest research in the subject. The modern aspects of soft computing have been introduced from the first principles and discussed in a semi-informal manner, so that a beginner of the subject is able to grasp it with minimal effort. Besides soft computing, the other leading aspects of current AI research covered in the book include nonmonotonic and spatio-temporal reasoning, knowledge acquisition, verification, validation and maintenance issues, realization of cognition on machines and the architecture of AI machines. The book ends with two case studies: one on 'criminal investigation' and the other on 'navigational planning of robots,' where the main emphasis is given on the realization of intelligent systems using the methodologies covered in the book.The book is unique for its diversity in contents, clarity and precision of presentation and the overall completeness of its chapters. It requires no mathematical prerequisites beyond the high school algebra and elementary differential calculus; however, a mathematical maturity is required to follow the logical concepts presented therein. An elementary background of data structure and a high level programming language like Pascal or C is helpful to understand the book. The book, thus, though meant for two semester courses of computer science, will be equally useful to readers of other engineering disciplines and psychology as well as for ...

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“…Table 1 shows how contexts from four different AmI scenarios in literature: smart home [18], smart office [19], outdoor smart lighting [20], and elderly care [21], can be expressed by the same ontology model.…”

Section: Generic Context Aware Cross-layer Frameworkmentioning

confidence: 99%

“…To illustrate the generality of the ontology model itself (not its context data), we followed a design evaluation method called scenarios [ 17 ], which constructs detailed scenarios around the artefact (in this case our ontology model) to demonstrate its utility. Table 1 shows how contexts from four different AmI scenarios in literature: smart home [ 18 ], smart office [ 19 ], outdoor smart lighting [ 20 ], and elderly care [ 21 ], can be expressed by the same ontology model.…”

Section: Generic Context Aware Cross-layer Frameworkmentioning

confidence: 99%

Ambient Intelligence Context-Based Cross-Layer Design in Wireless Sensor Networks

Liu

Seet

Al-Anbuky

2014

Sensors

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By exchanging information directly between non-adjacent protocol layers, cross-layer (CL) interaction can significantly improve and optimize network performances such as energy efficiency and delay. This is particularly important for wireless sensor networks (WSNs) where sensor devices are energy-constrained and deployed for real-time monitoring applications. Existing CL schemes mainly exploit information exchange between physical, medium access control (MAC), and routing layers, with only a handful involving application layer. For the first time, we proposed a framework for CL optimization based on user context of ambient intelligence (AmI) application and an ontology-based context modeling and reasoning mechanism. We applied the proposed framework to jointly optimize MAC and network (NET) layer protocols for WSNs. Extensive evaluations show that the resulting optimization through context awareness and CL interaction for both MAC and NET layer protocols can yield substantial improvements in terms of throughput, packet delivery, delay, and energy performances.

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Modeling Context-Aware and Agent-Ready Systems for the Outdoor Smart Lighting

Cited by 12 publications

References 18 publications

Towards Highly Energy-Efficient Roadway Lighting

Towards Highly Energy-Efficient Roadway Lighting

Artificial Intelligence and Soft Computing

Ambient Intelligence Context-Based Cross-Layer Design in Wireless Sensor Networks

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