# A Lightweight Slice-Based Quality of Service Manager for IoT
## 2 Related Work  
Focusing on the specific QoS proposals using the SDN and OvS capabilities, a recent work [16] proposed a QoS framework for network slicing in 5G networks using SDN, NFV and OvS. The experimental setup includes well-known tools such as ONOSand Mininet. However, it is not feasible for the IoT domain due to complexity. [PERSON] et al. [17] proposed the implementation of traffic policing based on priority-based packet discarding using OpenFlow 1.0. Additionally, this work chose to use a set of Raspberry Pis in order to create an SDN switch considering the Open vSwitch (OvS) capabilities for testing purposes. Moreover, our work focuses on IoT considering a well-known lightweight Ryu controller instead of POXand applying other QoS models, not only priority queuing. Furthermore, [PERSON] et al. [18] presented dynamic QoS mechanisms for flows in an OpenFlow-based network, focusing their results on prioritisation and bandwidth guarantees and working for both classless and classful queueing disciplines, not considering Differentiated Services Code Point (DSCP) traffic marking. [PERSON] realised that the use of OpenFlow on different network switches creates a variety of testing results, which must be considered when implementing QoS concepts in the network by SDN application developers. [PERSON] et al. [19] went into detail on a proposed architecture for QoS and Quality of Experience (QoE) mapping and application adjustment in the SDN context, taking advantage of the Ryu controller and OvS-based OpenFlow switches to do so. This work's objective was to implement and design an application capable of perceiving the users' QoE in order to redirect that information for the Internet Service Provider (ISP). With that valuable information, it is possible to adjust the network rules, providing a higher overall QoS. However, the only considered rate-limiting approach is the OpenFlow Meter Table [20], discarding OvS capabilities in the QoS rules' implementation. [PERSON] and [PERSON] [21] addressed both major QoS models, not taking into consideration the IoT real world in the testing methodology. This work used the Ryu controller Representational State Transfer (REST) Application Programming Interface (API) to be able to apply QoS rules in the OpenFlow OvS-based switches. Additionally, the majority of tests use Mininet [22] in order to emulate the virtualised network, not reviewing the impact multiple clients might have on the tested network. This work did not address the concept of network slicing. Regarding network slicing applied in the IoT domain, the IoT slicing method proposed by [PERSON] et al. [23] is a technique for processing heterogeneous temperature data collected by an IoT network in a smart building. The solution combines complex networks and clusters to reduce algorithm input errors and improve the monitoring and control. This work differs from ours as IoT slicing is not the network slicing concept that is applicable to SDN as in our proposal. An approach for network slicing provisioning for IoT based on flyweight Network Functions (fNF) was proposed by [PERSON] et al. [24] in order to support QoS requirements to extend 5G networks. fNFs are network functions whose deployment incurs no virtualization overhead. Our work is quite close to their since they claimed an IoT I4.0 use case using the network slice. However, the work resorted to fNF and use cases considering the 5G concept offering no implementation and real-world evaluation. [PERSON] et al. [14] proposed a slice management solution considering QoS with a focus on reducing the wireless interference among slices. The approach considers routing based on the prioritization of interference and admission control. Still, the focus of this work was not IoT, and the validations were performed over NS2 simulations. [PERSON] et al. [25] proposed an inter-operator network slice framework that is able to coordinate and access spectrum resources in both licensed and unlicensed bands. The evaluation was carried out using the LTE and WiFi networks.