Abstract
Satisfying a context consumer’s quality of context (QoC) requirements is important to context management platforms (CMPs) in order to have credibility. QoC indicates the contextual information’s quality metrics (e.g., accuracy, timeliness, completeness). The outcomes of these metrics depend on the functional and quality characteristics associated with all actors (context consumers (or) context-aware applications, CMPs, and context providers (or) IoT-data providers) in context-aware IoT environments. This survey identifies and studies such characteristics and highlights the limitations in actors’ current functionalities and QoC modelling approaches to obtain adequate QoC and improve context consumers’ quality of experience (QoE). We propose a novel concept system based on our critical analysis; this system addresses the functional limitations in existing QoC modelling approaches. Moreover, we highlight those QoC metrics affected by quality of service (QoS) metrics in CMPs. These recommendations provide CMP developers with a reference system they could incorporate, functionalities and QoS metrics to maintain in order to deliver an adequate QoC.
Highlights
In the evolving world of pervasive computing, many IoT applications are becoming context-aware by obtaining seamless access to inferred IoT data, which is known as ‘context’
We visualise and provide an overview of a system with end-to-end quality of context (QoC)-awareness through a motivating scenario involving an autonomous car; We discuss the significance of each actor and the functional adaptions required of them for enabling end-to-end QoC-awareness; We investigate the quality of experience (QoE) metrics in context management platforms (CMPs) and reflect on their effect on QoC; We investigate the quality of service (QoS) metrics related to both processing and network in CMPs; We elaborate on existing QoC-aware selection and measurement models and present their drawbacks; We present the QoC metrics that may be affected by each QoS metric related to context processing and delivery; Lastly, we present a novel concept system that addresses the identified drawbacks of
We present QoC aware-selection and QoC measurement models—in addition, we present their limitations in satisfying the QoE metrics during context acquisition
Summary
In the evolving world of pervasive computing, many IoT applications are becoming context-aware by obtaining seamless access to inferred IoT data, which is known as ‘context’. For processing each request of a CC, the CMP performs various interlinked processes, which are together known as the context lifecycle [3] These processes include context acquisition, modelling, reasoning and dissemination. In context acquisition, the CMP obtains the relevant raw data (known as low-level context) from the CPs based on the CC’s requirements. There have only been a handful of surveys related to QoC They either did not discuss or lacked an in-depth discussion on factors that may affect the QoC in context-aware IoT environments. The functional and QoS factors directly affect QoC, whereas the factor QoE has an indirect impact To elaborate on such a classification, the internal factors of a system related to its functionalities or performance affect the context lifecycle processes
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