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17 results for “wireless sensor network”
Wireless Sensor Network Deployments (2013-2017)
<p><strong>Wireless Sensor Network Deployments (2013-2017)</strong></p> <p>This is an open data repositiory.</p> <p>It is concerned with systematically reviewing scientific publications containing actual Wireless Sensor Network deployments in five year span from 2013 to 2017.</p> <p><strong><em>Identification</em></strong></p> <p>Articles were first searched for in SCOPUS and Web of Science databases on 2018-06-12 using these queries/settings:</p> <p>SCOPUS</p> <p>Query: KEY({sensor network} OR {sensor networks}) AND TITLE-ABS-KEY(test* OR experiment* OR deploy*) AND NOT TITLE-ABS-KEY(review) AND NOT TITLE-ABS-KEY(simulat*) AND ( LIMIT-TO ( PUBYEAR,2017 ) OR LIMIT-TO ( PUBYEAR,2016 ) OR LIMIT-TO ( PUBYEAR,2015 ) OR LIMIT-TO ( PUBYEAR,2014 ) OR LIMIT-TO ( PUBYEAR,2013 ) )</p> <p>Raw results: 11536 articles</p> <p>De-duplicated results: 11374 articles</p> <p>Contained 4814 articles not found in Web of Science</p> <p>Web Of Science</p> <p>Querry: TS = ("sensor network" OR "sensor networks") AND TS = (test* OR experiment* OR deploy*) NOT TI="review" NOT TS=simulat*</p> <p>Additional query parameters: Indexes=SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC Timespan=2013-2017</p> <p>Raw results: 10204 articles</p> <p>De-duplicated results: 10196</p> <p>Contained 3636 articles not found in SCOPUS</p> <p>Final results</p> <p>When article results were merged from both databases finally 15010 articles were identified as possible candiates. Of those 6560 were found in both databases.</p> <p><em><strong>Screening</strong></em></p> <p>Data was exported as bibtex files and imported in Mendeley software for screening.</p> <p>4910 articles were left after the screening phase</p> <p><em><strong>Eligibility check</strong></em></p> <p>Then all screened included articles were checked for eligibility and 3017 eligible articles were identified.</p> <p><em><strong>Data extraction</strong></em></p> <p>In these articles 3059 wireless sensor network deployments were identified and codified data extracted from them.</p> <p><em><strong>Timeline</strong></em></p> <p>This data analysis took total time (including validation and error checking) from 2018-06-12 till 2020-05-29, after which the data was prepared for publiching till 2020-07-02.</p>
Data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks"
<p>This upload contains the data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks", (D.O.I: <a href="https://doi.org/10.3390/jsan9010012">10.3390/jsan9010012</a>) published in the the special issue on "Localization in Wireless Sensor Networks" of the <a href="https://www.mdpi.com/journal/jsan"><em>Journal of Sensor and Actuator Networks</em></a> (ISSN 2224-2708).</p> <p>The data and code included allows to replicate the results of the article.</p>
Snow depth, air temperature, humidity, soil moisture and temperature, and solar radiation data from the basin-scale wireless-sensor network in American River Hydrologic Observatory (ARHO)
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Data for Secure communication in IP-based wireless sensor networks via a trusted gateway publication
<p>This archive file contains the raw data obtained from Contiki sensor nodes during Cooja experiments in the folders e2e, terminate, terminate_1st and plaintext.</p> <p>The archive accompagnies the IEEE ISSNIP 2015 publication titled "Secure communication in IP-based wireless sensor networks via a trusted gateway" by Floris Van den Abeele, Tom Vandewinckele, Jeroen Hoebeke, Ingrid Moerman and Piet Demeester.</p> <p><br /> Also included is the data_parser python script that converts the raw data into CSV files that are parseable by R. The script contains the definitions of the contents of the raw data files.<br /> Finally, the R scripts that use the CSV files to generate the plots from the paper are also included.</p>
On Synchronization of Wireless Acoustic Sensor Networks in the Presence of Time-varying Sampling Rate Offsets and Speaker Changes
<p>We present an open-source database for evaluation of time synchronization algorithms for wireless acoustic sensor networks . More Information and examples on how to use the database can be found on our GitHub page: <a href="https://github.com/fgnt/paderwasn">https://github.com/fgnt/paderwasn</a></p>
Available Wireless Sensor Network and Internet of Things testbed facilities: dataset
<p>In this data set, we present data collected for the purpose of carrying out a systematic review of the available Wireless Sensor Network and Internet of Things testbed facilities. The data was collected through multiple stages and in each stage the pre-defined criteria were applied. We provide a dataset describing the hardware and software aspects of Wireless Sensor Network and Internet of Things testbed facilities available in the market and scientific community. The data were gathered through an extensive systematic review process of scientific articles published between the years 2011 and 2021. The review aims to obtain good quality data for people who are actively researching the Internet of Things facilities or anyone who is interested in that field.</p>
Write-only File System for Privacy-aware Wireless Sensor Networks Evaluation Dataset
<p>Evaluation dataset for the paper <strong>"WoFS: A Write-only File System for Privacy-aware Wireless Sensor Networks"</strong> published at the <em>49th IEEE Conference on Local Computer Networks (2024)</em></p>
Reliable Many-to-Many Routing in Wireless Sensor Networks Using Ant Colony Optimisation
<p>Results files for testing of ACO protocol for many to many routing in wireless sensor networks. </p>
Instances of the problem of Designing a Multi-sink Clustered Wireless Sensor Network.
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New key management scheme lattice-based for wireless sensor networks
<p><span>The cluster structure can effectively reduce the cost of mutual authentication of sensor nodes, which is conducive to the expansion of the network, and can guarantee the security of authentication between sensor nodes even in the post-quantum era. The size of the lattice-based authentication proposed in this paper does not change much with the continuous improvement of the security level of the RSA algorithm. The size of the certificate is kept at a stable level, which is more suitable for encrypting large data at a high-security level.</span></p>
Validation of a Remote Wireless Sensor Network (WSN) Approach to the Individualized Detection of Cocaine Use in Humans
ClinicalTrials.gov study NCT02018263. IPD Sharing: Not stated. Countries: 1. Publications: 0.
New key management scheme lattice-based for wireless sensor networks
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Data from: Travelling Wave Pulse Coupled Oscillator (TWPCO) Using a Self-Organizing Scheme for Energy-efficient Wireless Sensor Networks
Recently, Pulse Coupled Oscillator (PCO)-based travelling waves have attracted substantial attention by researchers in wireless sensor network (WSN) synchronization. Because WSNs are generally artificial occurrences that mimic natural phenomena, the PCO utilizes firefly synchronization of attracting mating partners for modelling the WSN. However, given that sensor nodes are unable to receive messages while transmitting data packets (due to deafness), the PCO model may not be efficient for sensor network modelling. To overcome this limitation, the current study proposed a new scheme called the Travelling Wave Pulse Coupled Oscillator (TWPCO). For this, the study used a self-organizing scheme for energy-efficient WSNs that adopted travelling wave biologically inspired network systems based on phase locking of the PCO model to counteract deafness. From the simulation, it was found that the proposed TWPCO scheme attained a steady state after a number of cycles. It also showed superior performance compared to other mechanisms, with a reduction in the total energy consumption of 25 %. The results showed that the performance improved by 13 % in terms of data gathering. Based on the results, the proposed scheme avoids the deafness that occurs in the transmit state in WSNs and increases the data collection throughout the transmission states in WSNs.
A flexible lightweight signcryption scheme for underwater wireless sensor networks
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Security for Software-Defined Wireless Sensor Networks: Performance evaluation comparison
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Data from: Travelling Wave Pulse Coupled Oscillator (TWPCO) Using a Self-Organizing Scheme for Energy-efficient Wireless Sensor Networks
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Inverse Modeling Using a Wireless Sensor Network (WSN) for Personalized Daylight Harvesting
Smart lighting systems in low energy commercial buildings can be expensive to implement and commission. Studies have also shown that only 50% of these systems are used after installation, and those used are not operated at full capacity due to inadequate commissioning and lack of personalization. Wireless sensor networks (WSN) have great potential to enable personalized smart lighting systems for real-time model predictive control of integrated smart building systems. In this paper we present a framework for using a WSN to develop a real-time indoor lighting inverse model as a piecewise linear function of window and artificial light levels, discretized by sub-hourly sun angles. Applied on two days of daylight and ten days of artificial light data, this model was able to predict the light level at seven monitored workstations with accuracy sufficient for daylight harvesting and lighting control around fixed work surfaces. The reduced order model was also designed to be used for long term evaluation of energy and comfort performance of the predictive control algorithms. This paper describes a WSN experiment from an implementation at the Sustainability Base at NASA Ames, a living laboratory that offers opportunities to test and validate information-centric smart building control systems.
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