Concinnity takes sensor data from collection to final product via a cloudbased data repository and easy-to-use workflow system. ensors are now the dominant source of data generated worldwide, producing 1,250 billion gigabytes of data in 2010. 1 Sensor data is also high velocity (collected and processed in real time) and highly variable (collected by diverse sensor networks). Because these sensor data are interconnected, the volume of the integrated data is even larger. A sensor data platform should thus be able to support both a high volume of data and largescale applications. However, the bottleneck is not how to collect, store, and manage the resulting big sensor data, given that various technologies exist to address these elements, but how to build systems that enable us to use such data effectively.We propose a platform, Concinnity, that enables the collaborative contribution, sharing, and use of big sensor data. Concinnity takes sensor data from collection to fi nal product via a cloud-based data repository and easy-to-use workfl ow system. It supports rapid development of applications built on sen-sor data using data fusion and the integration and composition of models to form novel workfl ows. These key features enable value to be derived from sensor data effi ciently. Challenges in Sensor DataAlthough sensor data is certainly valuable to its owners for their specifi c purposes, it could also be valuable to a wider audience. Making this data widely available, however, requires revisiting key challenges in sensor system design. (We should note that in this article we focus exclusively on the informatics challenges of big sensor data, upstream of the lower-level hardware or networking considerations.) Crowdsourcing and CollaborationThe fi rst challenge in designing systems for collaborative use of large-scale sensor data relates to creating an ecosystem in which users get mutual benefi t from contributing, sharing, and using data.

This paper presents a new methodology for collaborative sensor data management known as WikiSensing. It is a novel approach that incorporates online collaboration with sensor data management. We introduce the work on this research by describing the motivation and challenges of designing and developing an online collaborative sensor data management system. This is followed by a brief survey on popular sensor data management and online collaborative systems. We then present the architecture for WikiSensing highlighting its main components and features. Several example scenarios are described to present the functionality of the system. We evaluate the approach by investigating the performance of aggregate queries and the scalability of the system.

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In large-scale machine-to-machine sensor networks, the applications such as urban air pollution monitoring require information management over widely distributed sensors under restricted power, processing, storage, and communication resources. The continual increases in size, data generating rates, and connectivity of sensor networks present significant scale and complexity challenges. Traditional schemes of information management are no longer applicable in such a scenario. Hence, an elastic resource allocation strategy is introduced which is a novel management technique based on elastic computing. With the discussion of the challenges of implementing real-time and high-performance information management in an elastic manner, an air pollution monitoring system, called EIMAP, was designed with a four-layer hierarchical structure. The core technique of EIMAP is the elastic resource provision scheduler, which models the constraint satisfaction problem by minimizing the use of resources for collecting information for a defined quality threshold. Simulation results show that the EIMAP system has high performance in resource provision and scalability. The experiment of pollution cloud dispersion tracking presents a case study of the system implementation.

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