# A Context-Aware Edge Computing Framework for Smart Internet of Things
## 2 Related Works
### Context-Based Data/Device Selection  
[PERSON] et al. [14] proposed a context-aware sensing and collection (GCSC) scheme for wireless sensor networks to improve data collection accuracy and decrease the data sizes transmitted to the sink. They used a combination of data spatial and temporal correlation analysis along with an event data fusion mechanism during routing. Their techniques were adapted using the context information consisting of the residual energy of nodes. [PERSON] et al. [15] proposed a context-aware approach to support users in selecting optimal configurations for assessing data quality in big data according to a specified goal, such as time, cost minimization or confidence maximization. They tested their approach on data collected from the Curitiba smart city on three scenarios, each corresponding to one goal. Their model showed relatively high precision. [PERSON] et al. [16] introduced a context-aware mobile crowd-sensing system. They collected context information from mobile phones to evaluate the quality of sensed data and estimated the reliability of the devices which are then used to select the suitable devices for the clients. A classification model was used to map the real-time context, which consists of the hardware information and user activity, to the quality of the sensed data. To address the issue of limited energy resources in sensors used to detect landslides in India, [PERSON] et al. [17] proposed a context-aware sensing system in place of the existing continuous sensing scheme. Their work mainly consists of two different data collection modules; first, context-aware data management in which the sensors' sampling rate is adjusted according to the network context. The network context concerns events spanning large areas, it includes rainfall, moisture, pore pressure, movement and more information. Second, context-aware energy management in which sensors are activated and deactivated with different sampling rates based on small sensor area context. Compared to the continuous sensing scheme, the two modules extended the lifetime of the sensing system by six times and 20 times, respectively. A cloud computing model for context-aware Internet of Things services was proposed by [PERSON] et al. [18]. Their design consists of two layers, a cloud control layer which manages cloud resource allocation, service scheduling and service profiles. In addition to a user control layer which manages end-to-end service connections and service contexts. This design allows for service context management using machine learning. [PERSON] et al. [19] proposed a healthcare monitoring architecture to overcome the notification delays. The proposed architecture comprises four modules. First, an IoT module consisting of sensors and actuators. Second, a data preprocessing module which performs data collection and storage and insures redundancy. Third, a context-aware module is responsible for aggregating the appropriate data, extracting the context and sending these the cloud. This module is deployed in two layers, namely a fog layer and a cloud layer. Finally, a decision-making module in which feature extraction and classification are performed using a back-propagation neural network in addition to an adaptive grasshopper optimization algorithm. [PERSON] et al. [20] improved upon learning systems by leveraging the available context information in the learner's environment. They proposed a three-layer architecture: (1) front-end applications which allow the learners and teachers to interact with the system by viewing and posting content; (2) learning edge devices which allow the learners and teachers to interact and retrieve contextual information from their surroundings; and (3) all the content is published to back-end servers which are used to analyze and store the content. Using the constrained application protocol (CoAP) they showed better performance than other protocols. SmartVent is a real-time context-aware indoor air quality measuring system developed by [PERSON] et al. [21].