Towards a Quality-centric Big Data Architecture for Federated Sensor Services

Ramaswamy, Lakshmish; Lawson, Victor J.; Gogineni, Siva Venkat

doi:10.1109/bigdata.congress.2013.21

Cited by 52 publications

(18 citation statements)

References 19 publications

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“…The pervasive dimension of Big Data is known, especially when applied to the IoT and sensors. Research initiatives focused on software architecture in this context address (i) the storage dimension of the platform [10], (ii) the quality of the collected data [4] and (iii) the availability of the datasets as services [3]. The architecture presented in this paper complement these efforts, as it strengthen the hardware dimension of such an architecture.…”

Section: Related Workmentioning

confidence: 96%

An Architecture to Support the Collection of Big Data in the Internet of Things

Cecchinel

Jiménez

Mosser

et al. 2014

2014 IEEE World Congress on Services

127

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International audienceThe Internet of Things (IoT) relies on physical objects interconnected between each others, creating a mesh of devices producing information. In this context, sensors are surrounding our environment (e.g., cars, buildings, smartphones) and continuously collect data about our living environment. Thus, the IoT is a prototypical example of Big Data. The contribution of this paper is to define a software architecture supporting the collection of sensor-based data in the context of the IoT. The architecture goes from the physical dimension of sensors to the storage of data in a cloud-based system. It supports Big Data research effort as its instantiation supports a user while collecting data from the IoT for experimental or production purposes. The results are instantiated and validated on a project named SMARTCAMPUS, which aims to equip the SophiaTech campus with sensors to build innovative applications that supports end-users

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Section: Related Workmentioning

confidence: 96%

An Architecture to Support the Collection of Big Data in the Internet of Things

Cecchinel

Jiménez

Mosser

et al. 2014

2014 IEEE World Congress on Services

127

View full text Add to dashboard Cite

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“…As an example, Liu et al [58] used CouchDB for storing and analyzing log data from a Platform as a Service (PaaS). Similarly, document data stores have also been used to store sensor network data, as suggested by Ramaswamy et al [59]. Document data stores have also been chosen in scenarios in which high development productivity and low maintenance cost are essential.…”

Section: Document Storesmentioning

confidence: 99%

Data management in cloud environments: NoSQL and NewSQL data stores

et al. 2013

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Advances in Web technology and the proliferation of mobile devices and sensors connected to the Internet have resulted in immense processing and storage requirements. Cloud computing has emerged as a paradigm that promises to meet these requirements. This work focuses on the storage aspect of cloud computing, specifically on data management in cloud environments. Traditional relational databases were designed in a different hardware and software era and are facing challenges in meeting the performance and scale requirements of Big Data. NoSQL and NewSQL data stores present themselves as alternatives that can handle huge volume of data. Because of the large number and diversity of existing NoSQL and NewSQL solutions, it is difficult to comprehend the domain and even more challenging to choose an appropriate solution for a specific task. Therefore, this paper reviews NoSQL and NewSQL solutions with the objective of: (1) providing a perspective in the field, (2) providing guidance to practitioners and researchers to choose the appropriate data store, and (3) identifying challenges and opportunities in the field. Specifically, the most prominent solutions are compared focusing on data models, querying, scaling, and security related capabilities. Features driving the ability to scale read requests and write requests, or scaling data storage are investigated, in particular partitioning, replication, consistency, and concurrency control. Furthermore, use cases and scenarios in which NoSQL and NewSQL data stores have been used are discussed and the suitability of various solutions for different sets of applications is examined. Consequently, this study has identified challenges in the field, including the immense diversity and inconsistency of terminologies, limited documentation, sparse comparison and benchmarking criteria, and nonexistence of standardized query languages.

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“…First, it divides sensor nodes into clusters. Second, it decides the route for patrolling each cluster [2] [3]. The work assumes a simple data collection scheme whereby the mobile sink node divides sensor nodes into grids regardless of the sensor nodes" location, and patrols the grids by using random walk between the neighboring grids.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Routing and Data Gathering Technique for Big data in Wireless Sensor Network

S¹,

Ramesh²

2016

IOSR

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Recently, the big data emerged as a hot topic because of the tremendous growth of the information and communication technology. One of the highly anticipated key contributors of the big data in the future networks is the distributed wireless sensor networks (WSNs). Although the data generated by an individual sensor may not appear to be significant, the overall data generated across numerous sensors in the densely distributed WSNs can produce a significant portion of the big data. Energy-efficient big data gathering in the densely distributed sensor networks is, therefore, a challenging research area. One of the most effective solutions to address this challenge is to utilize the sink node's mobility to facilitate the data gathering. While this technique can reduce energy consumption of the sensor nodes, the use of mobile sink presents additional challenges such as determining the sink node's trajectory and cluster formation prior to data collection. In this paper, we propose a new mobile sink routing and data gathering method through network clustering based on modified expectation-maximization technique. In addition, we derive an optimal number of clusters to minimize the energy consumption. The effectiveness of our proposal is verified through numerical results.

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Towards a Quality-centric Big Data Architecture for Federated Sensor Services

Cited by 52 publications

References 19 publications

An Architecture to Support the Collection of Big Data in the Internet of Things

An Architecture to Support the Collection of Big Data in the Internet of Things

Data management in cloud environments: NoSQL and NewSQL data stores

An Efficient Routing and Data Gathering Technique for Big data in Wireless Sensor Network

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