Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

Wireless Sensor Networks (WSNs) comprise of small nodes with sensing, computation and wireless communication capabilities. WSNs are used for various applications such as habitat monitoring, automation, agriculture, and security. This work is targeted for Weather Forecasting. Since numerous sensors are usually deployed on remote and inaccessible places, the deployment and maintenance should be made easy and scalable. Nodes in a sensor network are severely constrained by energy and computing power.

To prolong the lifetime of the sensor nodes, efficient routing protocol that could also optimize the energy consumption is critical. To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is tricky if a systemís demands are not elastic and its hardware components are not energy-proportional. We explore the use of weather forecasts to improve a systemís ability to satisfy demand by improving its predictions.

The simulation results are expected to indicate that the proposed prediction for weather forecasting would increase the lifetime of sensor networks. It is also anticipated that this could outperform than any other prediction algorithm.

Existing System:

Energy harvesting systems collect and store environmental energy to sustain continuous operation without access to external power sources. Energy harvesting has been well studied in the context of sensor networks, because they are often deployed in remote locations with no access to the power grid. Regulating energy usage is current issue since the system must choose when to satisfy its demands based on its current energy reserves and predictions of its future energy supply.

Proposed System:

We consider weather forecast, and energy data to formulate a model. We estimate our model for energy sources with and without inelastic demands. We show that using weather forecasts for predictions in sensor systems increases each systemís ability to satisfy its demands compared with existing strategies. Energy predictions derived from weather forecasts for large regions improve nearby systemsí ability to satisfy their demand over the time-scales, when compared against predictions derived from the immediate past.