# Self-Configuring IoT Service QoS Guarantee Using QBAIoT
## 5 Conclusions  
Recent research works in the field of the IoT are trying to optimize the services provided while satisfying a certain service level dependent on the class of the traffic. Therefore, IoT services and in particular e-health applications include different types of sensors and connected objects that create different types of data with different QoS requirements. Consequently, a prioritized processing for highly critical real-time data is necessary. Hence, we presented a QoS based access approach to minimize the delay for this type of traffic while giving better packet delivery ratio for all traffic types. Therefore, we proposed the QBAIoT access method as an enhancement of the IEEE 802.15.4 slotted CSMA/CA mechanism. In our proposal, four QoS classes are taken into consideration in order to configure the corresponding QoS CAPs within an adapted superframe structure. The QoS based access method is deployed within the sensing layer gateways and objects of our proposed IoT architecture in order to guarantee QoS requirements specified in the iSLAs. In addition, providing the IoT environment with a self-configuring capability allows minimizing the operational expenditure and improving the performance of our QoS based access method. Providing the sensing layer with a self-configuring capability allows responding better to environment changes. Through this research work, we presented the self-configuring process for our QoS based wireless access method. This self-management capability provides the LL-GW and IoT objects with specific configurations autonomously without human intervention, in order to adapt to existing traffic changes. We evaluated our self-configuring capability and compared our proposed access method to the IEEE 802.15.4 standard and the SDA-CSMA/CA approach. The conducted simulations showed that we obtain better results while using QBAIoT: reduced delay for real-time traffic as well as a greater PDR for all QoS classes. When incrementing the number of IoT objects in each QoS CAP, QBAIoT can use additional coordinators (i.e., LL-Gw) in order to extend the capacity of the IoT environment and respect the requirements of subscribed iSLAs.  
As ongoing work, we are trying to minimize the overall energy consumption of the QBAIoT system due to the elimination of the inactive period. We are implementing an algorithm that classifies the different sensors based on different metrics. This classification allows choosing for each IoT application a set of sensors to be used while the others will be in a sleeping mode in order to minimize the energy consumption and to maximize the system lifetime.  
Conceptualization, A.K., N.M. and O.T.; Methodology, A.K., N.M. and O.T.; Validation, A.K., N.M. and O.T.; Writing--original draft, A.K., N.M. and O.T.  
Figure 22: Delay evaluation comparison using QBAIoT and SDA CSMA/CA.  
**Funding:** This research was funded by the Conseil Regional de Bourgogne Franche Comte through the \"plan d'actions regional pour l'innovation (PARI)\" and the European Union through the \"PO FEDER-FSE Bourgogne 2014/2020 programs\".  
**Conflicts of Interest:** The authors declare no conflicts of interest.