## 5 Simulations Results and Discussion  
This section aims to assess the effectiveness of the suggested DRL approach for simultaneous computation offloading along with resource allocation in IoT devices, as it was designed to minimize energy consumption in E-RAN. Furthermore, the greedy algorithm was also evaluated as a precursor for computing resource allocation through numerical upshots. For the simulation platform, TensorFlow 1.11.0 and Python 3.6 were utilized, with an 8 CPU core i5 @ 1.6 GHz along with Intel UHD graphics 620. The system under evaluation assumes a maximum transmission power of 23 dBm for each piece of user equipment (UE) and a deployment area of 400 \(\times\) 400 m, and the system performance was assessed using 10 E-APs and 10 IoT devices. It was assumed that the path loss model is given by \(128+37*\log 10(d)\)[40], where d is the distance between nodes. Additionally, the noise power was \(-174\) dBm and the system bandwidth was set to 20 MHz. The QoS demand for both edge and cloud mode was regarded as min. Our computational model utilizes a fully connected deep neural network (DNN) architecture. The (DNN) consists of four discrete layers, namely, two hidden layers, an input layer, and a single output layer. This design allows the network to process and transform input data through these layers to generate the desired output. The first hidden layer consists of 64 neurons, while the second hidden layer has 32 neurons. The ReLU is used as the activation function in the hidden layers. All the necessary parameters of the simulation are listed in Table 2.  
Figure 2: DRL-based learning process of individual agent. The proposed scheme was evaluated against the following baseline schemes:  
1. Random scheme: the random algorithm involves making choices or decisions without considering any specific criteria;
2. Fixed scheme: The fixed algorithm follows pre-determined rules or fixed strategies for decision-making. It was designed based on pre-defined guidelines and does not adapt to changes in the environment or task requirements;
3. Q-learning scheme: The Q-learning allows dynamic cloud-edge selection. The system learns via interactions, updating the Q-table to estimate rewards for actions in different states;
4. DRL-based computation offloading and resource allocation scheme (DRL-CORA) [36]: The DRL-CORA scheme employs a DQN scheme that enables the system to choose between cloud and edge computing modes dynamically. Through iterative interactions with the environment, the system refines its neural network's Q-values to approximate anticipated rewards associated with diverse actions across varying states. Figure 3 illustrates the correlation between the variation in offloading energy consumption and the number of tasks in different modes. The jointly offloading mode dominated the performance gain since the local, edge, and cloud modes execute the tasks simultaneously. It can be observed from Figure 3 that, when compared to C-RAN and edge mode, the jointly offloading mode minimized the energy consumption by approximately 39% and 42%, respectively. Moreover, the jointly offloading mode converged when the number of tasks was 90, with much less energy consumption than the other modes. Due to the controlled resources of E-RAN nodes, the energy was linearly increased at the edge. C-RAN mode also showed poor performance because of long-distance and constrained fronthaul. Figure 4 illustrates the comparison between the average energy consumption of our suggested scheme and the benchmark scheme, as it relates to the varying number of computation tasks. However, it is evident from Figure 4 that, as the number of computation tasks increased, the energy consumption of all the schemes also increased. In addition, the proposed strategy takes into account the advantages of local, edge, and cloud computing in order to mitigate energy consumption. Additionally, it is worth noting that the proposed technique had the lowest energy use across all computational activities.