The evolution of the SEMAT sensor network management system

Lee, Yong Jin; Trevathan, Jarrod; Atkinson, Ian; Read, Wayne; Myers, Trina; Johnstone, Ron

doi:10.1109/issnip.2011.6146633

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…the positioning of nodes and sensors are available for the SEMAT deployments, but not for the Rowes Bay deployment. Furthermore, each deployment was initially integrated with different types of visualization and analysis tools based on individual project requirements [21].…”

Section: Proposed Sensor Network Projectsmentioning

confidence: 99%

The Integration, Analysis and Visualization of Sensor Data from Dispersed Wireless Sensor Network Systems Using the SWE Framework

Lee,

Trevathan,

Atkinson

et al. 2015

JTIT

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Wireless Sensor Networks (WSNs) have been used in numerous applications to remotely gather real-time data on important environmental parameters. There are several projects where WSNs are deployed in different locations and operate independently. Each deployment has its own models, encodings, and services for sensor data, and are integrated with different types of visualization/analysis tools based on individual project requirements. This makes it difficult to reuse these services for other WSN applications. A user/system is impeded by having to learn the models, encodings, and services of each system, and also must integrate/interoperate data from different data sources. Sensor Web Enablement (SWE) provides a set of standards (web service interfaces and data encoding/model specifications) to make sensor data publicly available on the web. This paper describes how the SWE framework can be extended to integrate disparate WSN systems and to support standardized access to sensor data. The proposed system also introduces a web-based data visualization and statistical analysis service for data stored in the Sensor Observation Service (SOS) by integrating open source technologies. A performance analysis is presented to show that the additional features have minimal impact on the system. Also some lessons learned through implementing SWE are discussed.

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Section: Proposed Sensor Network Projectsmentioning

confidence: 99%

The Integration, Analysis and Visualization of Sensor Data from Dispersed Wireless Sensor Network Systems Using the SWE Framework

Lee,

Trevathan,

Atkinson

et al. 2015

JTIT

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A Geo-Aware Location Based Taxi Carrying System

Dow

Lee

Wang

et al. 2014

2014 11th International Conference on Information Technology: New Generations

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Evolutionary techniques for sensor networks energy optimization in marine environmental monitoring

Grimaccia

Johnstone

Mussetta

et al. 2012

High-Performance Computing in Remote Sensing II

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The sustainable management of coastal and offshore ecosystems, such as for example coral reef environments, requires the collection of accurate data across various temporal and spatial scales. Accordingly, monitoring systems are seen as central tools for ecosystem-based environmental management, helping on one hand to accurately describe the water column and substrate biophysical properties, and on the other hand to correctly steer sustainability policies by providing timely and useful information to decision-makers. A robust and intelligent sensor network that can adjust and be adapted to different and changing environmental or management demands would revolutionize our capacity to wove accurately model, predict, and manage human impacts on our coastal, marine, and other similar environments. In this paper advanced evolutionary techniques are applied to optimize the design of an innovative energy harvesting device for marine applications. The authors implement an enhanced technique in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches, Particle Swarm Optimization and Genetic Algorithms. Here, this hybrid procedure is applied to a power buoy designed for marine environmental monitoring applications in order to optimize the recovered energy from sea-wave, by selecting the optimal device configuration.

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The evolution of the SEMAT sensor network management system

Cited by 4 publications

References 7 publications

The Integration, Analysis and Visualization of Sensor Data from Dispersed Wireless Sensor Network Systems Using the SWE Framework

The Integration, Analysis and Visualization of Sensor Data from Dispersed Wireless Sensor Network Systems Using the SWE Framework

A Geo-Aware Location Based Taxi Carrying System

Evolutionary techniques for sensor networks energy optimization in marine environmental monitoring

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