Studies of Flexible Wireless Sensor Networks Architectures to Support Decision-Making in Ecological Experiments
Jeferson Martin Araujo, Luis Carlos Trevelin, Pedro Correa
Last modified: 2008-09-13
Abstract
Research in sensor networks is widely regarded both as one of the most challenging and prospering area. Matching Computer Science concepts with Electrical Engineer applicability, Wireless Sensor Networks (WSNs) can be used to develop several case studies in manner to build applications which will be useful for evolving the WSNs architecture, infrastructure and its components in general.
Due to technologies advances reached by these scientific researches, the processing unities became exponentially more powerful with smaller size and lower cost, enabling the widespread use of WSNs and making this technology more popular. Wireless sensor networks are extending current capabilities and will allow researchers to conduct studies that are not feasible now. So, it is clear that application which depends on the WSNs infrastructure to build system, in several applications domains, have been emerging. Actually, there are applications ranging from habitat monitoring, biological diversity, ecosystem functioning to climate variability. These have proven to be quite a rich area for the deployment of WSNs.
For instance, in ecological monitoring real data can be used to inform the development of entire simulated ecosystems, made up of individual-based models, which can be used for long term predictions of ecological change. However, a key step in exploring the challenges of building a wireless communication platform is in understating the system requirements for the communication mechanism necessary to construct the multi-hop networks that are envisioned.
In this study, the infrastructure was based on Service Oriented Architecture (SOA) to address the set of core challenges. The focus of this work was to develop a general architecture that addresses the needs of biological researches in order to provide an architecture able to abstract the specializations inherent in almost every biological application domain. So, this work presents a flexible and modular architecture that meets the requirements of biological multi-purposes applications, allowing the quick and ease development of systems to support decision making in ecological experiments.
Due to technologies advances reached by these scientific researches, the processing unities became exponentially more powerful with smaller size and lower cost, enabling the widespread use of WSNs and making this technology more popular. Wireless sensor networks are extending current capabilities and will allow researchers to conduct studies that are not feasible now. So, it is clear that application which depends on the WSNs infrastructure to build system, in several applications domains, have been emerging. Actually, there are applications ranging from habitat monitoring, biological diversity, ecosystem functioning to climate variability. These have proven to be quite a rich area for the deployment of WSNs.
For instance, in ecological monitoring real data can be used to inform the development of entire simulated ecosystems, made up of individual-based models, which can be used for long term predictions of ecological change. However, a key step in exploring the challenges of building a wireless communication platform is in understating the system requirements for the communication mechanism necessary to construct the multi-hop networks that are envisioned.
In this study, the infrastructure was based on Service Oriented Architecture (SOA) to address the set of core challenges. The focus of this work was to develop a general architecture that addresses the needs of biological researches in order to provide an architecture able to abstract the specializations inherent in almost every biological application domain. So, this work presents a flexible and modular architecture that meets the requirements of biological multi-purposes applications, allowing the quick and ease development of systems to support decision making in ecological experiments.