## 1 Introduction
For example, many audio and video sensors apply constant bit rate (CBR) or average bitrate (ABR) encoding. CBR ensures that the output traffic rate is fixed, while ABR ensures that the output traffic achieves a predictable long-term average bitrate. The sensors may choose a bitrate depending on the time or the environment conditions. As the traffic sources have a limited number of mean bitrate options, the IoT networks set up with such sensors exhibit a limited number of mean traffic matrices, which can be determined before running the network. To prevent congestion and increase the QoS in IoT networks like the surveillance networks that exhibit a limited number of traffic matrices, we propose an SDN-based traffic engineering framework that uses a different methodology for estimating the traffic matrix. Instead of the traditional way calculating a traffic matrix analytically, we propose identifying the latest traffic matrix from a list of possible traffic matrices that the IoT network may exhibit. We use only the utilization statistics of a limited set of edge links, which can be easily received via SDN. Our method has a number of advantages. First of all, an identification may be simpler with less bandwidth and processing requirements than calculating a traffic matrix. Moreover, the identification is a direct solution. If the identification is correct, it gives the exact mean traffic matrix directly. On the other hand, the traditional matrix calculation methods usually do not give the exact solution because of the noise and the variations in ABR traffic, so a calculated traffic matrix may deviate from all possible traffic matrices that the IoT network may exhibit. To the best of our knowledge, ours is the first study that tries to identify the traffic matrix from a list of possible traffic matrices. For the identification, we investigated the feasibility of applying a brain-inspired Bayesian attractor model (BAM), which is an ML algorithm that models the decision making process of the brain [8]. Previously, we had applied a BAM for virtual network reconfiguration of optical networks and showed that it can decrease the number of virtual network reconfigurations to find a virtual network suitable for the current traffic situation [9]. In this work, we applied a BAM for identifying the current traffic pattern by checking the utilization statistics of edge links. The reasons for choosing BAM are  
* Unlike the neural network proposals in the literature, BAM does not require training. * Unlike the proposals that require partial measurements of traffic matrix or flows, BAM does not need prior measurements
* BAM is an online algorithm. * BAM may open the way towards more autonomous networks by adding a more human-like artificial intelligence to the network. As for the identification time, there may be algorithms that can do identification in fewer steps than BAM, but the experiments in this paper show that BAM can identify the traffic patterns in a reasonable time fast enough for traffic engineering purposes. When a new traffic pattern is identified, our framework optimizes the network by applying a network configuration that is pre-calculated for the identified traffic pattern. Moreover, our framework supports network slicing, which can greatly improve the QoS and security in heterogeneous IoT networks by applying slice-specific traffic engineering and policies [10,11]. We implemented our framework as an SDN application and evaluated it on an IoT testbed. The experiments reveal that our SDN framework can correctly identify a changing traffic pattern by BAM and increase the QoS and the energy efficiency by applying an optimized configuration employing network slicing and traffic engineering. The paper is organized as follows. Section 2 presents the related works in the literature. Section 3 presents the Bayesian attractor model. Section 4 explains the architecture of our framework. Section 5 introduces the testbed. Section 6 explains the experiment scenario and presents the experiment results. Section 7 concludes the paper.