## 3 Proposed Framework
### Network Congestion Prediction  
Figure 3 depicts the structure of the proposed ITE with a haptic-enabled bilateral teleoperation system scenario in line with the IEEE P1918.1 Tactile Internet reference architecture. The human operator at the master side uses the haptic device to control the remote objects at the salve domain over the communication network and received haptic feedback. The core network comprises SDN-based technology that enables the separation of the control and data plane and realizes Tactile applications. The SDN employs the OpenFlow protocol to communicate between the SDN switch and controller. The solid line indicated the transmission link weights in ms and the yellow dotted lines indicate the SDN control link with SDN-enabled switches. As we discussed in the previous section haptic-enabled services demand high QoS and QoE. As the packet size, and sampling rate for haptic traffic increase the network system induces congestion problems. To avoid congestion problems Active Queue Management (AQM) techniques are used that avoid the buffers overflow by dropping or marking the before buffer overflow [29; 30]. In the literature, a lot of studies on AQM schemes have been suggested. The process of marking the data packets instead of dropping them when the network encounters incipient congestion is called ECN. The ECN process helps to reduce packet loss and latency. The data including ECN-marked packets help to understand the nature of the application and the level of congestion in the network. In our proposed ITE we utilized the ECN mechanism and take benefit of the Explicit Congestion Experienced (ECE) flag to collect the ECE-marked packets during haptic traffic transmission among the master and the slave. The ECE-marked packets are input into the LSTM model to forecast the level of congestion in the network. LSTM is a special form of recurrent neural network with memory blocks that make it efficient to learn long-term dependencies. The LSTM model comprises a cell, an input, an output, and a forget gate. LSTM mitigate the vanishing-gradient problem of recurrent neural network. In [31], the authors proposed intelligent active queue management using ECN, where they, utilized the neural network to estimate the congestion and improve the AQM algorithms based on predicted congestion. We borrowed the congestion prediction concept from the work present in [31] to predict the congestion on the rest of the path in real-time. The LSTM model was considered with three hidden layers and the following formula is used to calculate the number of neurons in each layer, it is defined as follows:  
\[N_{n}=(N_{in}+\sqrt{N})/L, \tag{1}\]  
where the \(N_{in}\) is the number of inputs to the LSTM layer, \(N\) refers to the number of samples and \(L\) is the number of hidden layers. Around 30 neurons are selected for each hidden layer in the LSTM model. The model is trained on 6000 samples at 100 ms intervals for ten minutes period. The proposed ITE deploys the trained model at the edge, which is also known as the tactile support engine in the IEEE P1918.1 Tactile Internet reference architecture. At each iteration, the model predicts the congestion level in the network and inputs the Q-learning algorithm to select the appropriate action to provide the required QoS/QoE. The work in [32] motivates us to integrate the advantages of the RL algorithm and LSTM-based prediction method in resource management. The proposed utilizes LSTM to predict user mobility and an actor-critic algorithm to allocate the network resources in serval slices of the network.