Special Issue: Big Data for context-aware applications and intelligent environments

Preuveneers, Davy; Ilie‐Zudor, Elisabeth

doi:10.1016/j.future.2018.11.031

Cited by 2 publications

(2 citation statements)

References 11 publications

(8 reference statements)

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“…The ideal implication of this sensingas-a-service provisioning can be characterized mainly by two prominent features: heterogeneity and redundancy. Variety of intelligent services [7] running at the edge depend on the quality of data generated from a massive amount of sensory sources of different types and deployed in abundance. This primarily aims to address two aspects: fault tolerance and coverage.…”

Section: Introductionmentioning

confidence: 99%

UniPreCIS : A data pre-processing solution for collocated services on shared IoT

Das¹,

Singh²,

Chakraborty³

2022

Preprint

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Next-generation smart city applications, attributed by the power of Internet of Things (IoT) and Cyber-Physical Systems (CPS), significantly rely on the quality of sensing data. With an exponential increase in intelligent applications for urban development and enterprises offering sensing-as-aservice these days, it is imperative to provision for a shared sensing infrastructure for better utilization of resources. However a shared sensing infrastructure that leverages low-cost sensing devices for a cost effective solution, still remains an unexplored territory. A significant research effort is still needed to make edge based data shaping solutions, more reliable, feature-rich and costeffective while addressing the associated challenges in sharing the sensing infrastructure among multiple collocated services with diverse Quality of Service (QoS) requirements. Towards this, we propose a novel edge based data pre-processing solution, named UniPreCIS that accounts for the inherent characteristics of lowcost ambient sensors and the exhibited measurement dynamics with respect to application-specific QoS. UniPreCIS aims to identify and select quality data sources by performing sensor ranking and selection followed by multimodal data pre-processing in order to meet heterogeneous application QoS and at the same time reducing the resource consumption footprint for the resource constrained network edge. As observed, the processing time and memory utilization has been reduced in the proposed approach while achieving upto 90% accuracy which is arguably significant as compared to state-of-the-art techniques for sensing. The effectiveness of UniPreCIS has been evaluated on a testbed for a specific use case of indoor occupancy estimation that proves its effectiveness.

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Section: Introductionmentioning

confidence: 99%

UniPreCIS : A data pre-processing solution for collocated services on shared IoT

Das¹,

Singh²,

Chakraborty³

2022

Preprint

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“…In light of the foregoing, it is difficult to determine the relevant context elements from both the data management and system design perspectives [15]. Accounting for the former (i.e., by identifying which visible, monitorable, and domain-dependent facts can be respectively sensed, inferred, and accumulated from heterogeneous sources in a system or system of systems) is essential to make data storage and processing more efficient, while accounting for the latter (i.e., by identifying which abstract states are the most interesting, in which different applications should adapt their behaviors at different levels) is essential to make the design process more effective.…”

Section: Introductionmentioning

confidence: 99%

CIM-CSS: A Formal Modeling Approach to Context Identification and Management for Intelligent Context-Sensitive Systems

Baddour

Sang

et al. 2019

IEEE Access

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Context modeling is often used to relate the context in which a system will operate to the entities of interest in the problem domain. It remains the case that context models are inadequate in emerging computing paradigms (e.g., smart spaces and the Internet of Things), in which the relevance of context is shaped dynamically by the changing needs of users. Formal models are required to fuse and interpret contextual information obtained from the heterogeneous sources. In this paper, we propose an integrated and formal context modeling approach for intelligent systems operating in the context-sensitive environments. We introduce a goal-driven, entity-centered identification method for determining which context elements are influential in adapting the system behavior. We then describe a four-layered framework for metamodeling the identification and management of context. First, the framework presents a formal metamodel of context. A formalization of context using the first-order logic with relational operators is then presented to specify formally the context information at different abstraction levels. The metamodel, therefore, prepares the ground for building a formal modeling language and automated support tool (https://github.com/metamodeler/ CIM-CSS/). The proposed model is then evaluated using an application scenario in the smart meeting rooms domain, and the results are analyzed qualitatively.

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Special Issue: Big Data for context-aware applications and intelligent environments

Cited by 2 publications

References 11 publications

UniPreCIS : A data pre-processing solution for collocated services on shared IoT

UniPreCIS : A data pre-processing solution for collocated services on shared IoT

CIM-CSS: A Formal Modeling Approach to Context Identification and Management for Intelligent Context-Sensitive Systems

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