# Self-Configuring IoT Service QoS Guarantee Using QBAIoT
## 2 State of the Art
### Internet of Things
#### 2.1.4 Self-Management in the IoT Environment  
The human and operational cost for managing the IoT infrastructure is very important due to the interconnection of billions of smart devices and the heterogeneity of used technologies. In this context, the autonomic computing paradigm characterized by the self-management concept allows to cope with the increasing complexity and operational cost of the IoT infrastructure management. The importance of considering self-management capability within the IoT environment had been highlighted by different organizations such as the ITU-T in their Y.2066 document presenting the common requirements of the IoT [20]. ITU-T describes the importance of self-management within IoT environments provided by self-configuring, self-healing, self-optimizing, and self-protecting functions.  
Most of the research works integrating the Autonomic Computing paradigm are based on the IBM MAPE-K closed control loop [21] that specifies the self-management concept and describes the transition from a manual to an autonomic system. The latter is based on two main components, the autonomic manager (AM) and the managed element (ME). MEs are the controlled elements that undergo the decision made by the AM based on the knowledge base.  
MAPE-K closed control loop, illustrated by Figure 2, stands for monitor, analyze, plan, execute-knowledge [21]. Each function has a specific role in the closed control loop process. The monitor function collects information from the MEs, through the sensors interfaces. Monitored data are considered by the analyze function to predict future states of the system. Therefore, the plan function aims to schedule actions according to policy information and rules provided by the knowledge base. At the end, the execute function controls the execution of the provided plan and dispatches the recommended actions on the effectors of the MEs. The knowledge base stores the policy information and the specified rules corresponding to different scenarios that can occur within the autonomic system environment [21]. Sensors are used to communicate sensed information by the ME to the AM, whereas, effectors are used by the AM to execute orders or communicate information to the ME.  
Different research works focused on the integration of the self-management capabilities in the IoT environment. Indeed, the OpenIoT project [22], a FP7 funded European research project, specified an OpenIoT autonomic service control loop and an OpenIoT self-management framework in order to implement the autonomic computing concept in the IoT environment. The specified control loop is based on sensors and effectors interfaces as well as different control functions. The authors in [23] insist on the idea that IoT devices should not require any technical configuration. According to their vision, an IoT device should be simply plugged in, switched on, and everything else should be automatically configured. The authors used semantic data in order to describe the capabilities of the objects, and the IoT environment in general. In this research work, devices deduct their configurations by finding other devices with similar states through semantic data processing.  
Figure 2: MAPE-K closed control loop.