Context Aware Systems in Internet of Things: A Review, Taxonomy, and Open Challenges
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Date
2021
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UMT, Lahore
Abstract
In the recent era of computing, Internet of Things (IoT) has evolved as a very constructive technology. Internet refers to dynamic and ever-evolving environments. It also generates contextual information which varies in terms of content, usability, quality and complexity. Day-by-day, the number of users are rapidly increasing, so that there is tremendous increase in user’s mobility and unreliable sensor availability in IoT. Hence, there is necessity to dynamically adapt their behavior at run time in the context-aware applications. In this paper, we have carried out survey of various approaches related to Context-aware systems and self-learning techniques in IoT. We have also focused on the need of different self-learning techniques to unravel the openness of IoT environment. The evolution of Internet of Things (IoT) has increased the appetite for the energy efficient wireless infrastructures. ). In terms of their traffic needs, many of the IoT gadgets are typically resource-intensive and heterogeneous. Furthermore, for the various ecological environments, these units must be made flexible. In any case, the current traffic planning and delivery cycle estimates are inadequate to meet the complex quality criteria for variable plant data IoT applications. In particular, they can not be implemented in cases where multi-hop correspondence is needed for IoT use. This paper aims to define an appropriate access name with several hops for the Wi-Fi-based IoT devices in IoT Fundamentals. In accordance with their heterogeneous traffic needs, IoT implementations are immediately addressed and are designed in accordance with the unambiguously weighted quality classes. At this stage, the IoT system understanding is provided and an improvement model is implemented, which depends on the criteria for administrative efficiency and background requirements. In addition, an energy-efficient, conscious traffic planning (EE-CATS) calculation is proposed, where a prediction strategy for subsidence specifies the mixture of the model.