A distributed range query framework for the internet of things

The range query referring to the Internet of Things is a tough challenge since the data information is fully distributed. In order to support efficient range query, most existing approaches focused on designing the data management mechanism, which ensures that the similar data are stored nearby within the network. However, it will introduce large extra overhead to each peer in the Internet of Things especially when the peers generate data frequently. In this paper, a distributed range query framework is proposed, which consists of three core modules, reporting and indexing module, along with a query executor. The reporting module learns the sensed data and predicts a data range in which the coming future data is likely to be. Only the abnormal data that exceeds the data range will be detected, which greatly reduces the frequency and quantity of data migration in these data management mechanism. The indexing module is responsible for collecting reported data information and establishing data index used for responding to query request. Based on the above two modules, the range query is processed by the query executor. The experimental results show that this proposal could support range query effectively and efficiently, with load balance among the peers at the same time.