WikiSensing: An Online Collaborative Approach for Sensor Data Management

Silva, Dilshan; Ghanem, Moustafa; Guo, Yike

doi:10.3390/s121013295

Cited by 15 publications

(7 citation statements)

References 20 publications

(21 reference statements)

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“…There are also commercial sensor data sharing platforms; representative examples are TempoDB [29], Xively [12], Wikisensing [30] and SensorCloud [14]. These platforms share some common features such as focusing on storing timeindexed sensor data and providing REST APIs to facilitate storing, retrieving, and querying time series data.…”

Section: Related Workmentioning

confidence: 99%

Cloud-Based Programmable Sensor Data Provision

Lin

Guo

Chen

2015

2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering

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As sensor data grow towards an explosion due to the popularity of Internet of Things and mobile computing, many sensor data sharing platforms are developed to support various sensor-based applications. Although these platforms are able to provide capabilities such as collecting data from sensors and sensor data provision for applications, their capabilities are normally confined in direct retrieval of sensor data with little composition such as SQL aggregation or even no composition at all. This kind of raw sensor data provision not only increases the network traffic between platforms and applications, but also put most computation burden on the client side, which poses big challenges for applications running on resource-constrained devices such as mobile phones.In this paper, we propose cloud-based programmable sensor data provision, which moves the sensor data processing logic from client applications to cloud-based services. The key technique behind this is FilterCombine, a two-step sensor programming support framework that enables developers to specify sensor processing logic in the cloud service. By moving sensor data processing logic to the cloud, we not only reduce network traffic due to data transfer and computation on the client side, we also improve code reusability in the cloud side, as many sensor data processing logic can be shared among multiple applications. We build a prototype platform of cloud-based programming sensor data provision called MiWoT, which implements the proposed FilterCombine mechanism on the cloud side. We demonstrate the feasibility of the proposed techniques through case studies.

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Section: Related Workmentioning

confidence: 99%

Cloud-Based Programmable Sensor Data Provision

Lin

Guo

Chen

2015

2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering

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“…It consists of the WikiModeling application editor, which provides an easy-to-use application design environment; the WikiModeling workflow engine, which provides a workflow execution environment based on the HierSynth platform 6 ; and the WikiSensing data store, which provides elastic capabilities for large-scale data storage and management. 7 Figure 2 shows the interaction between these three layers.…”

Section: Concinnity Platformmentioning

confidence: 99%

“…The framework includes application editor, workflow engine, and data store components that support the collaborative contribution, sharing, and use of big sensor data. estimation) of a quantity (such as pollutant concentration) based on the nearby sensor readings 7. …”

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confidence: 99%

Concinnity: A Generic Platform for Big Sensor Data Applications

Birch

Silva

et al. 2014

IEEE Cloud Comput.

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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.

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“…In this experiment, we use WikiSensing [43] and Siege benchmarking utility [44] to simulate our EIMAP system. WikiSensing is an online collaborative platform for sensor data management.…”

Section: Performance Analysismentioning

confidence: 99%

Elastic Information Management for Air Pollution Monitoring in Large-Scale M2M Sensor Networks

Guo

Silva

et al. 2013

International Journal of Distributed Sensor Networks

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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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WikiSensing: An Online Collaborative Approach for Sensor Data Management

Cited by 15 publications

References 20 publications

Cloud-Based Programmable Sensor Data Provision

Cloud-Based Programmable Sensor Data Provision

Concinnity: A Generic Platform for Big Sensor Data Applications

Elastic Information Management for Air Pollution Monitoring in Large-Scale M2M Sensor Networks

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