# Optimal User Association Strategy for Large-Scale IoT Sensor Networks with Mobility on Cloud RANs
## 4 Evaluation  
### Network Configuration  
For the purpose of evaluation and comparison, we configured the network as follows. The area of our interest is 1000 m by 1000 m. There are 15 RRHs and 50 devices installed by default, and all of them are randomly located using the uniform distribution. The number of IoT devices established for evaluation is from 50 to 250. The transmission range of each homogeneous RRH is 300 m. Devices on the network keep moving using the random way-point model. The maximum speed of each IoT device, \(v\), is chosen between 1 and 100 m/s for each simulation. Then, for each time slot, an IoT device moves at the speed of Uniform(0,\(v\)) m/s. We specify the specific value of \(v\) before introducing the evaluation results. Each time slot is 1 s, and each simulation runs for 1000 s. We set \(c\) to \(|\mathcal{I}|\) meaning each RRHcan handle or process \(|\mathcal{I}|\) number of data packets per a time slot. Within the current configuration, it is considered to be large enough not to cause any buffer overflow to any of RRHs. The background traffic \(b_{r}\) is randomly chosen using the uniform distribution from the set \(\{1,2,3,\cdots,\lfloor\frac{c}{2}\rfloor\}\). The data rate at each IoT device, \(l_{i}\), is randomly chosen from the set \(\{1,2,3\}\). Both \(b_{r}\) for all \(r\) and \(l_{i}\) for all \(i\) remain constant throughout each simulation. The network parameters are summarized in Table 3. The initial layout of the network with 50 IoT devices is depicted in Figure 3.  
We used MATLAB [28] to implement the discrete time simulator. To formulate and solve each optimization problem, we used CVX [29] to model the proposed optimization problem formulations and Gurobi [30] as an optimization problem solver. A desktop PC with Intel(r) CoreTM i7-7700 CPU @ 3.60 GHz and 16 GB memory was used for evaluation without graphics processing units (GPU) support. To perform the comparison, we implemented the following two conventional association schemes:  
* Shorted-Distance-First association scheme (SDF): Each IoT device associates with the closest RRH. It is an approximation of the highest-channel-gain-first association scheme.
* Random Association scheme (RND): Each IoT device randomly chooses an RRH to associate with.  
It should be noted that neither SDF nor RND allows any association between RRH \(r\) and IoT device \(i\) if \(i\) is outside the coverage of \(r\). The time complexity of SDF is \(O(|\mathcal{R}|\cdot|\mathcal{I}|)\), while that of RND is either \(O(|\mathcal{R}|\cdot|\mathcal{I}|)\) or \(O(|\mathcal{R}|)\) depending on implementation.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(|\mathcal{R}|\) & Number of RRHs & 15 \\ \(|\mathcal{I}|\) & Number of IoT devices & Between 50 and 250, inclusive \\ \(l_{i}\) & Traffic rate from IoT devices & Randomly chosen from \(\{1,2,3\}\) (pkts/slot) \\ \(c\) & RRH processing capacity & \(|\mathcal{I}|\) (data packets per time slot) \\ \(b_{r}\) & RRH background traffic & Randomly chosen from \(\{1,2,3,\cdots,\lfloor\frac{c}{2}\rfloor\}\) (pkts/slot) \\ \(v\) & Velocity upperbound & Between 1 and 100, inclusive (m/s) \\ \(\alpha\) & Weight used in ALGO-2/3 and P. (3) & 0.99, 0.50, 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Network parameters.  
Figure 3: The initial layout of the network with 15 RRHs and 50 IoT devices. Dotted lines indicate the transmission ranges of RRHs and the empty rectangles indicate the initial positions of the IoT devices.