A Dynamically Scalable Cloud Data Infrastructure for Sensor Networks
Tonglin Li, Kate Keahey, Ke Wang, Dongfang Zhao, Ioan Raicu · 2015
As small, specialized sensor devices become more ubiquitous, reliable, and cheap, increasingly more domain sciences are creating "instruments at large" - dynamic, often self-organizing, groups of sensors whose outputs are capable of being aggregated and correlated to support experiments organized around specific questions. This calls for an infrastructure able to collect, store, query, and process data set from sensor networks. The design and development of such infrastructure faces several challenges. The challenges reflect the need to interact with and administer the sensors remotely. The sensors may be deployed in inaccessible places and have only intermittent network connectivity due to power conservation and other factors. This requires communication protocols that can withstand unreliable networks as well as an administrative interface to sensor controller. Further, the system has to be scalable, i.e., capable of ultimately dealing with potentially large numbers of data producing sensors. It also needs to be able to organize many different data types efficiently. And finally, it also needs to scale in the number of queries and processing requests. In this work we present a set of protocols and a cloud-based data streaming infrastructure called WaggleDB that address those challenges. The system efficiently aggregates and stores data from sensor networks and enables the users to query those data sets. It address the challenges above with a scalable multi-tier architecture, which is designed in such way that each tier can be scaled by adding more independent resources provisioned on-demand in the cloud.