Wireless Mesh Sensor Networks (WMSNs) are specialized in providing mesh capabilities through collaborative sensor nodes that not only sense the environment and forward monitoring data, but are also able to perform the necessary routing tasks to deliver the acquired information to the final destination.
In autonomic computing, an autonomic manager implements control loops in which the managed element and the environment is monitored, data is analyzed, and actions are taken if components are in an undesirable state.
Under proactive network application mode, sensor nodes continuously monitor the surrounding environment and send periodically the monitored data to the user at a constant cycle rate.
However, the emerging care environment will necessarily involve live data streams, the natural form of monitored data.
In addition, the Commission introduced IPCHEM [4] to collate monitoring data from the environment and human populations and to make these data accessible for regulation, research, and the public.
Based on the proposed framework selected case studies are presented to verify its potential and advantages when (1) interactively visualizing time-dependent settlement monitoring data in an environment-aware context and (2) generating advanced numerical simulation models to predict settlements.
The M-A model structure and its contents are designed using the XML language which gives a relatively comprehensive description of agricultural remote sensing monitoring data and the Grid environment.
Recent advances in wireless sensor networks (WSNs) technologies and their incorporation with geographic information system (GIS) technologies offer vast opportunities for development and application of environment monitoring data communication.
Simulation results demonstrate that the predicted information collection node locations by the proposed method are consistent with the majority of real ones, the hops tend to straight lines, the hops count is the least, lower repetition rate of the nodes on different hops, and the environment monitoring data can be saved and queried.
Environment monitoring data showed that concentrations of NO2 in the polluted area were significantly higher than those in the clear area.
