# A Context-Aware Edge Computing Framework for Smart Internet of Things
## 1 Introduction  
The recent and rapid growth in IoT adoption in various fields such as agriculture, healthcare, smart city and industry is undeniably challenging current communication and computing infrastructures. Huge amounts of sensed raw data require timely transportation, processing and storage, which existing networks are incapable of handling. IoT sensing systems that rely on real-time data processing are becoming an essential part of IoT environments which are the most affected by the witnessed data explosion [1; 2; 3]. Typical sensing systems allow applications to access sensors either directly or through middleware. Direct access uses dedicated communication channels between processing centers and sensors. These are normally seen in early sensing systems at the small scale or in applications that require high bandwidth and time-critical data processing capabilities, such as surveillance systems using high definition cameras [4]. Despite the benefits that this type of access offers, such as efficiency and a high level of control over sensors, it requires a high technical level and effort to deploy, in addition to being costly and difficult to scale. Most IoT sensing systems, on the other hand, tend to use middleware frameworks deployed in edge devices to bring processing closer to the sensors for faster responses and serve various purposes such as data and device management [5]. This provides faster response times and helped building heterogeneous, scalable and reliable sensing systems [6]. Many middleware frameworks offer some basic data reduction techniques as an initial step before the main objective of the framework, which has helped alleviate the data explosion issue but to a limited extent. A great amount of middleware and frameworks with varyingpurposes have been surveyed in [7]. The main take is that most works offer very limited real-time analytics capabilities and very few of them attempted to utilize these analytics results to overcome data explosion problems. Systems that are capable of acquiring, understanding and recognizing their surrounding environment, in addition to performing actions according to its changes, are considered to be context-aware. With that ability, a context-aware sensing system decides what information and which service should be presented to the user. According to [PERSON] et al. [8], the context is defined as \"any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application\". Such systems are a one step closer to reducing the effects of limited sensing resources. However, their main goal is focused on understanding the context and the reactions to situational changes rather than reducing the amounts of sensed data. [PERSON] et al. [9] and [PERSON] et al. [10] have conducted comprehensive surveys on context-aware computing in IoT. Sensing in any environment unavoidably involves capturing extra noise through the sensors, which is irrelevant to the purpose of the application. The raw data can contain temporal and spatial redundancies in addition to the undesired attributes of the real world [11]. Processing all the raw sensed data is impractical and unfeasible, especially with the ever-increasing IoT growth. Preprocessing the raw data with compression and filtering is of little help when it comes to complex environments such as smart cities and smart homes. Adaptive and smart sensing schemes that take advantage of the higher processing capacity that is becoming available at the edge of the network are a necessity [4]. Selective sensing is a sensing scheme in which only the desired and important parts of the generated data are considered for processing and dissemination [12]. This concept is introduced to combat the data explosion issue facing the limited sensing resources by leveraging the currently existing infrastructure. Selective sensing is an attractive concept that is receiving some traction in the research community.