A divide and merge method for sensor data processing on large-scale publish/subscribe systems

As sensor-networking technologies have rapidly been developed, various sensor data have become available thanks to Publish/Subscribe mechanisms. Aggregating and combining such different types of sensor data, such as the amount of rainfall and water levels of rivers, can help to develop more valuable applications. However, these processes may cause load concentrations on a particular processing node, which consequently may cause a scalability issue. To handle this issue, we consider a division strategy that is appropriate for large sensor networks as well as providing a data processing mechanism. This system enhances the application fields in ubiquitous sensing environments. In this paper, we propose a highly scalable sensor data processing mechanism on the basis of a content-based network. Our mechanism has a load distribution mechanism that dynamically divides and moves subscriptions so that our system can efficiently avoid an excessive load on a particular processing node. The performance evaluation of our proposed system shows that the load distribution mechanism works well and has high scalability.