Collaborative wireless sensor networks in industrial and business processes

Marin-Perianu, Mihai

doi:10.3990/1.9789036527460

Cited by 2 publications

(2 citation statements)

References 111 publications

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“…On the other hand, commercial interest is lagging behind. However, given a typical 10 year time lag from research to commercial deployment, adoption of WSNs by commercial applications is prudent [189].…”

Section: Discussionmentioning

confidence: 99%

Multi-channel wireless sensor networks : protocols, design and evaluation

Durmaz¹

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Pervasive systems, which are described as networked embedded systems integrated with everyday environments, are considered to have the potential to change our daily lives by creating smart surroundings and by their ubiquity, just as the Internet. In the last decade, "Wireless Sensor Networks" have appeared as one of the real-world examples of pervasive systems by combining automated sensing, embedded computing and wireless networking into tiny embedded devices.A wireless sensor network typically comprises a large number of spatially distributed, tiny, battery-operated, embedded sensor devices that are networked to cooperatively collect, process, and deliver data about a phenomenon that is of interest to the users. Traditionally, wireless sensor networks have been used for monitoring applications based on low-rate data collection with low periods of operation. Current wireless sensor networks are considered to support more complex operations ranging from target tracking to health care which require efficient and timely collection of large amounts of data. Considering the low-bandwidth, low-power operation of the radios on the sensor devices, interference and contention over the wireless medium and the energy-efficiency requirements due to the battery-operated devices, fulfilling the mentioned data-collection requirements in complex applications becomes a challenging task.This thesis focuses on the efficient delivery of large amounts of data in bandwidth-limited wireless sensor networks by making use of the multi-channel capability of the sensor radios and by using optimal routing topologies. We start with experimenting the operation of the sensor radios to characterize the behavior of multi-channel communication. We propose a set of algorithms to increase the throughput and timely delivery of the data and analyze the bounds on the data collection capacity of the wireless sensor networks. The main contributions of the thesis are listed as follows:• Contribution 1 -Characteristics, challenges and the use of multi-channel communication in wireless ad hoc networks and wireless sensor networks: We review the state of the art channel assignment protocols in wireless multi-hop networks, particularly in wireless ad hoc networks and wireless sensor networks. We classify the existing solutions according to the number of transceivers required per node and according to the dynamics of the channel assignment. Since the channel assignment methods designed for general wireless ad hoc networks may not be directly applicable to wireless sensor networks, we give brief comparisons of them and discuss the additional challenges and requirements for wireless sensor networks.• Contribution 2 -Characterization of multi-channel interference: The assumption of perfectly orthogonal, interference-free channels, which is adopted in most of the multi-channel communication studies, may fail in practice. Radio signals are not limited to their allocated frequency band, but cause interference in adjacent bands as well -how much depends on...

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Section: Discussionmentioning

confidence: 99%

Multi-channel wireless sensor networks : protocols, design and evaluation

Durmaz¹

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“…More specifically, the wireless radio transceiver used in WSNs has a low data rate (typically between 10 and 100 kbps) and low coverage (typically between 20 and 200 m) [77]. The microcontroller processing unit used in WSNs is usually associated with limited computational power (typically 8 or 16 bit CPUs at 4-8 MHz), while the storage space is in the order of 10kB random access memory (RAM) and 48kB programmable flash memory [78]. This relatively low-capability hardware greatly influences the quality of collected sensor data.…”

Section: Sensor Data Qualitymentioning

confidence: 99%

Observing the unobservable : distributed online outlier detection in wireless sensor networks

Zhang¹

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This research was conducted within the EU projects e-SENSE and SENSEI. Copyright c 2010 by Yang Zhang, Enschede, The Netherlands. All rights reserved. No part of this book 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 the prior written permission of the author.Printed by Wöhrmann Print Service. AbstractThe generation of wireless sensor networks (WSNs) makes human beings observe and reason about the physical environment better, easier, and faster. The wireless sensor nodes equipped with sensing, processing, wireless communication and actuation capabilities can be densely deployed in a wide geographical area and measure various parameters continuously from the physical world. Compared with traditional environmental sensing technologies, such densely deployed WSNs enable collection of fine-grained high spatial and temporal resolution data with less installation, maintenance, and operation costs.However, raw sensor observations often have low data quality and reliability due to both internal and external factors including low quality of cheap sensors, dynamicity of network conditions, and harshness of the deployment environment. Use of low quality sensor data in any data analysis and decision making process will not only negatively impact analysis results and decisions made but also waste huge amount of valuable and limited network resources such as energy, as many incorrect values are transmitted. Low quality sensor data also prevents WSNs to fulfill their promises in terms of reliable real-time situation-awareness, as the low quality sensor data may generate large number of false alarms.Motivated by the need to improve quality of data analysis and decision making, enhance efficiency of using WSNs resources by preventing unnecessary transmission of erroneous sensor observations, and increase effectiveness of monitoring and situation-awareness capabilities of the WSNs, in this thesis we focus on online identification of outliers whenever and wherever they occur. Outliers in WSNs are those observations that represent erroneous values (errors) or indicate particular phenomenal changes (events). Our outlier detection techniques, which are based on distributed in-network data processing, identify sensor observations that do not conform to normal behavior of sensor data without using a pre-defined threshold or triggering conditions.Our main research objective is to design and implement effective and efficient outlier detection techniques for WSNs to identify outliers in an online and disv tributed manner and distinguish between errors and events with high accuracy and low false alarm, while maintaining the communication, computation and memory complexity low. Main contributions of this thesis can be summarized as: 3. Statistical-Based outlier detection techniques for WSNs. We take two approaches in designing our outlier detection techniques. One approach originates fr...

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Collaborative wireless sensor networks in industrial and business processes

Cited by 2 publications

References 111 publications

Multi-channel wireless sensor networks : protocols, design and evaluation

Multi-channel wireless sensor networks : protocols, design and evaluation

Observing the unobservable : distributed online outlier detection in wireless sensor networks

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