# Self-Configuring IoT Service QoS Guarantee Using QBAIoT
## 3 QoS Guarantee within Internet of Things Environment
### QoS Motivation and Challenges in IoT  
The exponential increasing number of IoT connected objects leads to a greater amount of data created in the IoT environment. In addition, the generated data has different and even contradictory characteristics that we have to cope with by providing QoS guarantee within the IoT environment, which is a challenging task. QoS models such as IntServ and DiffServ are difficult to be used within the IoT environment due to the constraints in terms of memory and computing capacity of IoT objects but also due to the large scale of the IoT and the random deployment of objects. Therefore, new and well-adapted QoS models must be proposed to provide IoT services performance guarantees [24]. Various international organizations consider that QoS integration in IoT environments is very important and necessary. The ITU-T describes the importance of integrating QoS into the IoT environment through their Y.2066 document [21]. They focus on the importance of service priority in order to satisfy specific requirements of different IoT services during congestion periods that are frequent due to the huge amount of generated data in the IoT. Indeed, during congestion periods, IoT traffic performance will be affected and especially the performance of QoS constrained data streams. In order to avoid performance degradation when delivering critical data, an effective and optimized management of the available resources is necessary to guarantee a certain service level in the IoT environment. This service level guarantee is possible thanks to the specification of QoS mechanisms in different layers of the IoT architecture in order to satisfy different QoS requirements and parameters such as delay, jitter, bandwidth, and packet loss ratio for various traffic types [25]. Furthermore, these QoS mechanisms must take into consideration different components of the IoT environment (sensing, gateway, network, and cloud) in order to achieve an end-to-end service level guarantee for IoT services. Different research works developed approaches to integrate the QoS in different layers of the IoT architecture. OpenIoT [22], a FP7 funded European project, had presented different QoS parameters for the IoT environment. Each parameter has its own set of metrics: energy consumption associated to the lifetime of the sensor layer, data volume produced by the sensors, trustworthiness correlated to the quality of sensor, etc. Traditional metrics had been also used by OpenIoT such as the jitter, the delay, etc. All these metrics are used to define the service level expected by the client and to specify the requirements of each IoT service. In addition, this European research project proposed a QoS manager that keeps tracking of the defined metrics for guaranteeing the service level. Furthermore,different research works proposed the integration of QoS mechanisms within the lower layer of the IoT architecture. F-Interop Platform [26] is a H2020 European funded research project that focuses on developing online interoperability and performance test tools for the IoT environment. Remote online testing software was proposed to measure the QoS, the scalability and the energy in such environment. The collection of statistics in F-Interop testing tools can be ensured by using a passive or an active method thanks to software defined networking (SDN) and network functions virtualization (NFV) techniques. QUASIMODO [27], a French national research project, proposed different methods and algorithms for providing an end-to-end self-adaptive QoS support for real-time applications and energy efficiency in wireless sensor networks (WSN) and IoT systems. Different issues are studied for guaranteeing the QoS. Thus, QUASIMODO presented a routing protocol for multi-hop routing under multi-constraints (energy, link reliability, delay). It is based on the operator calculus algebra and it was successfully implemented on Contiki/TelosB platform. Furthermore, several academic research projects focused on the guarantee of service level in the IoT environment.