Reducing Expenses of Sensor-Cloud Services for Dynamic Skyline Monitoring

In the concept of Sensor-Cloud, many sensor networks are unified and provide Sensing as a Service to users. The users can utilize provisioning virtual sensors to perform desired tasks. The expense (cost) is based on the amount and types of data requests. In this paper, unlike other existing works, we propose a cost-minimizing framework for dynamic skyline monitoring. Our approach avoids paying the cost of all attributes by iteratively requesting the most cost-effective attributes that can identify whether a tuple is in dynamic skyline. We conduct some experiments on various real datasets. The results confirm the effectiveness compared with the baseline and the random strategy.