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Special Issue "Environmental Wireless Sensor Networks"

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A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708).

Deadline for manuscript submissions: closed (1 October 2014)

Special Issue Editors

Guest Editor
Dr. Kirk Martinez (Website)

Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
Interests: wireless sensor networks; power management; environmental monitoring
Guest Editor
Prof. Dr. Jane K. Hart

Geography and Environment University of Southampton Southampton SO17 1BJ, UK
Fax: (023) 8059 3295
Interests: glaciers and climate change; subglacial processes (experiments and sedimentology); environmental sensor networks

Special Issue Information

Dear Colleagues,

Wireless Sensor Networks design for earth science applications have led to an area of research often described as Environmental Sensor Networks. These have to tackle the often hostile environments being sensed, including volcanoes, oceans, forests, and glaciers.

This Special Issue will bring together papers that describe complete systems and their deployment, as well as key technological advances brought about by challenging conditions.

Topics of interest include, but are not limited to:

  • radio communications for deployed WSNs
  • energy management
  • energy harvesting
  • data management
  • user-centered design and interfaces
  • system management
  • novel platforms and hardware
  • autonomous behavior
  • use of 6LowPan in real-world deployments

Dr. Kirk Martinez
Prof. Dr. Jane K. Hart
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Sensor and Actuator Networks is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.


Published Papers (3 papers)

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Research

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Open AccessArticle Context-Aware Local Optimization of Sensor Network Deployment
J. Sens. Actuator Netw. 2015, 4(3), 160-188; doi:10.3390/jsan4030160
Received: 30 September 2014 / Accepted: 7 July 2015 / Published: 23 July 2015
PDF Full-text (1049 KB) | HTML Full-text | XML Full-text
Abstract
Wireless sensor networks are increasingly used for tracking and monitoring dynamic phenomena in urban and natural areas. Spatial coverage is an important issue in sensor networks in order to fulfill the needs of sensing applications. Optimization methods are widely used to efficiently [...] Read more.
Wireless sensor networks are increasingly used for tracking and monitoring dynamic phenomena in urban and natural areas. Spatial coverage is an important issue in sensor networks in order to fulfill the needs of sensing applications. Optimization methods are widely used to efficiently distribute sensor nodes in the network to achieve a desired level of coverage. Most of the existing algorithms do not consider the characteristics of the real environment in the optimization process. In this paper, we propose the integration of contextual information in optimization algorithms to improve sensor network coverage. First, we investigate the implication of contextual information in sensor networks. Then, a conceptual framework for local context-aware sensor network deployment optimization method is introduced and related algorithms are presented in detail. Finally, several experiments are carried out to evaluate the validity of the proposed method. The results obtained from these experiments show the effectiveness of our approach in different contextual situations. Full article
(This article belongs to the Special Issue Environmental Wireless Sensor Networks)
Open AccessArticle Towards Long-Term Multi-Hop WSN Deployments for Environmental Monitoring: An Experimental Network Evaluation
J. Sens. Actuator Netw. 2014, 3(4), 297-330; doi:10.3390/jsan3040297
Received: 23 September 2014 / Revised: 13 November 2014 / Accepted: 26 November 2014 / Published: 5 December 2014
Cited by 5 | PDF Full-text (1664 KB) | HTML Full-text | XML Full-text
Abstract
This paper explores the network performance and costs associated with the deployment, labor, and maintenance of a long-term outdoor multi-hop wireless sensor network (WSN) located at the Audubon Society of Western Pennsylvania (ASWP), which has been in operation for more than four [...] Read more.
This paper explores the network performance and costs associated with the deployment, labor, and maintenance of a long-term outdoor multi-hop wireless sensor network (WSN) located at the Audubon Society of Western Pennsylvania (ASWP), which has been in operation for more than four years for environmental data collection. The WSN performance is studied over selected time periods during the network deployment time, based on two different TinyOS-based WSN routing protocols: commercial XMesh and the open-source Collection Tree Protocol (CTP). Empirical results show that the network performance is improved with CTP (i.e., 79% packet reception rate, 96% packet success rate and 0.2% duplicate packets), versus using XMesh (i.e., 36% packet reception rate and 46% packet success rate, with 3%–4% duplicate packets). The deployment cost of the 52-node, 253-sensor WSN is $31,500 with an additional $600 per month in labor and maintenance resulting in a cost of $184 m−2·y−1 of sensed area. Network maintenance during the first four years of operation was performed on average every 12 days, costing approximately $187 for each field visit. Full article
(This article belongs to the Special Issue Environmental Wireless Sensor Networks)

Review

Jump to: Research

Open AccessReview On the Modeling of Solar-Powered Wireless Sensor Nodes
J. Sens. Actuator Netw. 2014, 3(3), 207-223; doi:10.3390/jsan3030207
Received: 4 June 2014 / Revised: 26 June 2014 / Accepted: 14 July 2014 / Published: 4 August 2014
Cited by 1 | PDF Full-text (7185 KB) | HTML Full-text | XML Full-text
Abstract
Solar energy harvesting allows for wireless sensor networks to be operated over extended periods of time. In order to select an appropriate harvesting architecture and dimension for its components, an effective method for the comparison of system implementations is required. System simulations [...] Read more.
Solar energy harvesting allows for wireless sensor networks to be operated over extended periods of time. In order to select an appropriate harvesting architecture and dimension for its components, an effective method for the comparison of system implementations is required. System simulations have the capability to accomplish this in an accurate and efficient manner. In this paper, we evaluate the existing work on solar energy harvesting architectures and common methods for their modeling. An analysis of the existing approaches demonstrates a mismatch between the requirement of the task to be both accurate and efficient and the proposed modeling methods, which are either accurate or efficient. As a result, we propose a data-driven modeling method based on artificial neural networks for further evaluation by the research community. Preliminary results of an initial investigation demonstrate the capability of this method to accurately capture the behavior of a solar energy harvesting architecture, while providing a time-efficient model generation procedure based on system-level data. Full article
(This article belongs to the Special Issue Environmental Wireless Sensor Networks)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Context-Aware Optimization of Sensor Network Deployment
Authors: Meysam Argany 1, Mir Abolfazl Mostafavi 1, Christian Gagné 2
Affiliations: 1 Center for Research in Geomatics, Laval University, Quebec, Canada
2 Department of Electrical Engineering and Computer Engineering, Laval University, Quebec, Canada
Abstract: Adequate coverage is an important issue in sensor networks in order to fulfill the sensing applications. For this purpose different optimization methods are widely used for deployment of sensor networks to achieve a desired level of coverage. Most of these algorithms suffer from over simplification of the sensing environment. In this paper, we study the problem of placing sensors to get optimum coverage by integrating contextual information in optimization algorithms, and introduce a local context-aware sensor network deployment optimization method. Finally we present the obtained results that show the effectiveness of the proposed approach

Type of Paper: Review
Title: On the Modeling of Solar-Powered Wireless Sensor Nodes
Authors: Sebastian Bader, Xinyu Ma and Bengt Oelmann
Affiliations: Department of Electronics Design, Mid Sweden University, Sundsvall, Sweden
Abstract: Solar energy harvesting allows for wireless sensor networks to be operated over extended periods of time. In order to select an appropriate harvesting architecture and dimension its components, an effective method for the comparison of system implementations is required. System simulations have the capability to accomplish this in an accurate and efficient manner. In this paper, we evaluate the existing work on solar energy harvesting architectures and common methods for their modeling. An analysis of the existing approaches demonstrates a mismatch between the requirement to be both accurate and efficient, and the proposed modeling methods, which are either accurate or efficient. As a result, we propose a data-driven modeling method based on artificial neural networks. Preliminary results of an initial investigation demonstrate the capability of this method to accurately capture the behavior of a solar energy harvesting architecture, while providing a time-efficient model generation procedure.

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