Sensing as a Service and Big Data

By Arkady B. Zaslavsky, Charith Perera, D. Georgakopoulos. arXiv.org, 2013

523 citations recorded by the source corpus.

Internet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. Data streams coming from these devices will challenge the traditional approaches to data management and contribute to the emerging paradigm of big data. This paper discusses emerging Internet of Things (IoT) architecture, large scale sensor network applications, federating sensor networks, sensor data and related context capturing techniques, challenges in cloud-based management, storing, archiving and processing of sensor data.

Subfields: Computer Networks and Communications, Information Systems, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/f4af1ac435e0e31602c63bb8875b10e89229086c

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Citations in the corpus

On the Integration of Cloud Computing and Internet of Things (2014, background): “• SaaS (Sensing as a Service) [50], [56], [27], providing ubiquitous access to sensor data;”
The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems (2017, background): “As a consequence, analyses of unprecedented complexity (Parwekar 2011) are possible, and prediction algorithms can be employed at low cost, providing means to increase revenues and reduce risks (Zaslavsky et al. 2013).”
Critical analysis of Big Data challenges and analytical methods (2017, background): “Big data researchers consider value as an essential feature, as somewhere within that data, there is valuable information – extracting golden data (high-valued data), though most of the pieces of data independently may seem insigni ﬁ - cant (Zaslavsky, Perera, & Georgakopoulos, 2012).”
A Survey on Internet of Things From Industrial Market Perspective (2015, background): “IoT’s popularity is governed by both the value that it promises to create and market growth and predictions [3].”
Federated Learning in Smart City Sensing: Challenges and Opportunities (2020, unknown): “An example of this is transportation, monitoring roads, bridges and providing the collected data to more efﬁciently direct trafﬁc [24].”
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, background): “S2 aaS : MCS follows a Sensing as a Service (S2aaS) business model, which makes data collected from sensors available to cloud users [97], [104], [110].”
Human-Data Interaction: The Human Face of the Data-Driven Society (2014, background): “ysical activity to sleep patterns and diet (Fan, 2013). Similar problems arise with data that are inherent ambiguous such as those collected about communities through Internet-of-Things technologies (Zaslavsky et al., 2012). However, the potential for data visualisation to reveal aspects of the ince”
End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications (2017, background): “In order to handle this large volume of data and extract meaningful information over the entire spectrum, a scalable platform for processing, analysing and learning from big spectrum data has to be designed and implemented [3], [34].”
Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey (2018, background): “3 billion in 2010 [11], and reached $101.”
A review of Internet of Things for smart home: Challenges and solutions (2017, background): “This volume, velocity and variety (not to mention variable veracity) make the storing and analytics solution, which will generate useful insights, a very complex one (Zaslavsky et al., 2013).”
IoT based smart parking system (2016, unknown): “Data collected from various sensors is usually transmitted to more powerful nodes where its aggregation and processing can be done[18].”
Cognitive Internet of Things: A New Paradigm Beyond Connection (2014, background): “…service provisioning directly supports various services (e.g., Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), Sensing-as-a-Service (SaaS), and more broadly Everything-as-a-service (EaaS) [26]) to human/social networks, which has been investigated recently (see, e.g., [27]–[29]).”
Middleware for Internet of Things: A Survey (2016, unknown): “These solutions are highly diverse in their design approaches (e.g., event-based, database), level of programming abstractions (e.g., local or node level, global or network level), and implementation domains (e.g., WSNs, RFID, M2M, and SCADA).”
City Data Fusion: Sensor Data Fusion in the Internet of Things (2015, background): “In particular, in smart cities applications, when 50 to 100 billion devices start sensing [84], it would be essential to fuse, and reason about the data automatically and intelligently.”
Energy Management-as-a-Service Over Fog Computing Platform (2015, background): “Also, this huge amount of data produced by the IoT will be possibly managed, processed, and analyzed, as part of the managing procedure [34].”
Fog Computing for Sustainable Smart Cities (2017, methodology): “In the early days, Big Data was produced by large tech companies such as Google, Yahoo, Microsoft, and large scientiﬁc research institution such as European Organization for Nuclear Research (CERN).”
Context Aware Computing for The Internet of Things: A Survey (2013, background): “Context information plays a critical in data matching where sensors can be considered as entities in the sensing as a service model [5].”