Next-Generation Smart Environments: From System of Systems to Data Ecosystems

Curry, Edward; Sheth, Amit P.

doi:10.1109/mis.2018.033001418

Cited by 40 publications

(32 citation statements)

References 7 publications

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“…In our future work we plan to investigate: Large-scale Deployments and Reduced Cost of Operation: We will investigate the performance of the RLD in larger-scale deployments (e.g. city-wide data ecosystems [51]) and within different types of smart environments including mobility and marine. In particular, we will explore ways to improve the maintenance and operational costs of the platform within large-scale deployments.…”

Section: Discussionmentioning

confidence: 99%

A Real-time Linked Dataspace for the Internet of Things: Enabling “Pay-As-You-Go” Data Management in Smart Environments

Curry

Derguech

Hasan

et al. 2019

Future Generation Computer Systems

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Section: Discussionmentioning

confidence: 99%

A Real-time Linked Dataspace for the Internet of Things: Enabling “Pay-As-You-Go” Data Management in Smart Environments

Curry

Derguech

Hasan

et al. 2019

Future Generation Computer Systems

Self Cite

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“…Focusing on the design of data ecosystems and the relationships among their participants, [51] developed a typology for data ecosystems with the two key criteria resource control and interdependence. This typology, like typologies in general, helps to differentiate between idealized types of data ecosystems but is less assistant when classifying realworld data ecosystems [21], which is the goal of this study.…”

Section: Related Taxonomiesmentioning

confidence: 99%

“…Table 1 gives a summary of the data ecosystem-related taxonomies and systemizations described above. Characteristics Framework for data ecosystems in developing countries [51] Typology Typology of data ecosystems [52] Design characteristics Characteristics for the design of data ecosystem [36] Morphology Morphology of data ecosystems with a SDL perspective…”

Section: Related Taxonomiesmentioning

confidence: 99%

“…The second possible objective of data ecosystems is the interaction between the actors [14,66,67]. These interactions can consist of communication or the transfer and sharing of knowledge and experience [32,51,63]. The third possible purpose of data ecosystems, especially for platform-based data ecosystems, is the facilitation of transactions between the data ecosystem actors [32,68,69].…”

Section: Meta-dimension: Economicmentioning

confidence: 99%

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A Taxonomy for Data Ecosystems

Gelhaar¹,

Groß²,

Otto³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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In the increasingly interconnected business world, economic value is less and less created by one company alone but rather through the combination and enrichment of data by various actors in so-called data ecosystems. The research field around data ecosystems is, however, still in its infancy. With this study, we want to address this issue and contribute to a deeper understanding of data ecosystems. Therefore, we develop a taxonomy for data ecosystems which is grounded both theoretically through the linkage to the scientific knowledge base and empirically through the analyses of data ecosystem use cases. The resulting taxonomy consists of key dimensions and characteristics of data ecosystems and contributes to a better scientific understanding of this concept. Practitioners can use the taxonomy as an instrument to further understand, design and manage the data ecosystems their organizations are involved in.

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“…With regard to deploying driver-behavior-profiling algorithms for real-time applications, there exist multiple options, such as the use of smartphones integrated with vehicles (e.g., AGL [ 8 , 14 ]), in-vehicle dedicated embedded computers (e.g., advanced driver-assisted systems [ 35 ]), and cloud- or edge-based services in a connected car ecosystem [ 36 ]. This study broadly consider the application of driver identification algorithms on an edge server under the umbrella of mobile edge computing.…”

Section: Related Workmentioning

confidence: 99%

Lightweight Driver Behavior Identification Model with Sparse Learning on In-Vehicle CAN-BUS Sensor Data

Ullah

Kim

2020

Sensors

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This study focuses on driver-behavior identification and its application to finding embedded solutions in a connected car environment. We present a lightweight, end-to-end deep-learning framework for performing driver-behavior identification using in-vehicle controller area network (CAN-BUS) sensor data. The proposed method outperforms the state-of-the-art driver-behavior profiling models. Particularly, it exhibits significantly reduced computations (i.e., reduced numbers both of floating-point operations and parameters), more efficient memory usage (compact model size), and less inference time. The proposed architecture features depth-wise convolution, along with augmented recurrent neural networks (long short-term memory or gated recurrent unit), for time-series classification. The minimum time-step length (window size) required in the proposed method is significantly lower than that required by recent algorithms. We compared our results with compressed versions of existing models by applying efficient channel pruning on several layers of current models. Furthermore, our network can adapt to new classes using sparse-learning techniques, that is, by freezing relatively strong nodes at the fully connected layer for the existing classes and improving the weaker nodes by retraining them using data regarding the new classes. We successfully deploy the proposed method in a container environment using NVIDIA Docker in an embedded system (Xavier, TX2, and Nano) and comprehensively evaluate it with regard to numerous performance metrics.

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Next-Generation Smart Environments: From System of Systems to Data Ecosystems

Cited by 40 publications

References 7 publications

A Real-time Linked Dataspace for the Internet of Things: Enabling “Pay-As-You-Go” Data Management in Smart Environments

A Real-time Linked Dataspace for the Internet of Things: Enabling “Pay-As-You-Go” Data Management in Smart Environments

A Taxonomy for Data Ecosystems

Lightweight Driver Behavior Identification Model with Sparse Learning on In-Vehicle CAN-BUS Sensor Data

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