Abstract
As the Internet of Things (IoT) is evolving at a fast pace, the need for contextual intelligence has become more crucial for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables interactions between IoT devices such as sensors/actuators, smartphones and connected vehicles, to name but a few. Context management platforms (CMP) are emerging as a promising solution to deliver contextual intelligence for IoT. However, the development of a generic solution that allows IoT devices and services to publish, consume, monitor, and share context is still in its infancy. In this paper, we propose, validate and explain the details of a novel mechanism called Context Query Engine (CQE), which is an integral part of a pioneering CMP called Context-as-a-Service (CoaaS). CQE is responsible for efficient execution of context queries in near real-time. We present the architecture of CQE and illuminate its workflows. We also conduct extensive experimental performance and scalability evaluation of the proposed CQE. Results of experimental evaluation convincingly demonstrate that CoaaS outperforms its competitors in executing complex context queries. Moreover, the advanced functionality of the embedded query language makes CoaaS a decent candidate for real-life deployments.
Highlights
Nowadays, advancements in hardware and software technologies have made it possible to embed sensing, computation, and communication capabilities in everyday objects, from a coffee mug to an autonomous car, and turn them into smart connected objects
We evaluated the performance of the execution of basic context queries, where each query only contains one entity type
We studied the impact of the query load on query execution performance to show how CoaaS and Orion platforms perform when the number of parallel queries increases
Summary
Advancements in hardware and software technologies have made it possible to embed sensing, computation, and communication capabilities in everyday objects, from a coffee mug to an autonomous car, and turn them into smart connected objects. A fundamental requirement of a CMP is to be able to provide support for publishing, querying, monitoring, and sharing contextual information Such a platform will manage the interaction between the sources of context and offer contextual information to context-aware IoT applications. The existing CMPs suffer from one common shortcoming, which is the lack of a generic and expressive interface that allows IoT devices, applications, and services to publish, consume, monitor, and share context data seamlessly To address this shortcoming, in our earlier research, we have proposed a high-level language for querying context [6,11,12], which called Context Definition and Query Language (CDQL). In continuation of our efforts towards operationalising and externalising context for smart IoT applications, in this paper, we propose, develop, implement, and evaluate a comprehensive and efficient mechanism for enabling execution of context queries in IoT ecosystem in near real-time.
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