# NB-IoT for D2D-Enhanced Content Uploading with Social Trustworthiness in 5G Systems
## 6 Performance Evaluation  
In this section we provide the output of an extensive simulation campaign finalized to demonstrate the robustness of the proposed solution to the presence of malicious nodes. The presented results are obtained using a built-in simulator in Matlab already used in previous works [9; 10]. The proposed solution, hereafter named _trust-based_, is compared to an alternative _basic_ approach that does not take into account any trustworthiness for the involved users and is unable to detect the malicious behavior. As discussed earlier (see Section 4), we consider two different types of malicious nodes, i.e., (1) _type A_, where users forward corrupted packets (for instance) to perform an attack to security, and (2) _type B_, where users drop the packets to exploit the benefits given by multi-hop D2D connections without forwarding any content further in the chain. The reference scenario is composed by a single LTE-A cell with a 500-m radius and 10-MHz bandwidth (i.e., 50 RBs available) where 20 UE devices are uniformly distributed. As for the NB-IoT, we use the \"in band\" where 6 RBs (for a total number of 288 tones) are allocated for the transmissions among the selected gateways and eNodeB. The main simulation parameters are listed in Table 2. The content size for all the nodes is set to 50 MB and radio resources used on a D2D transmission are limited to the so-called \"virtual resources\" allocated by the eNodeB to the involved pairs of UE (see Section 4 for more details). The performance parameters we focus on for the system-level performance are: (1) _data loss_; (2) _average data uploading time gain_; and (3) _average energy consumption gain_. \begin{table}
\begin{tabular}{l l c c} \hline \hline
**Relationship** & **Description** & \multicolumn{2}{c}{**Social player**} & **Recommendation** \\  & **Reliability \((spr_{i,j})\)** & **Reliability \((rr_{i,j})\)** \\ \hline Ownership object relationship (OOR) & Objects owned by the same person & 1 & 0.9 \\ Co-location object relationship (C-LOR) & Objects sharing personal experiences & 0.8 & 0.6 \\ Co-work object relationship (C-WOR) & Objects sharing public experiences & 0.7 & 0.5 \\ Social object relationship (SOR) & Objects in contact for owner’s relations & 0.6 & 0.5 \\ Parental object relationship (FOR) & Objects with production relations & 0.5 & 0.4 \\ No relationship & & 0.1 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Player and recommendation reliability values associated to the social relationship between devices. In particular, the latter two parameters represent the gain achieved by the cooperative upload a pure cellular upload solution where each user uploads directly the content to the network infrastructure by using standard LTE unicast transmissions. The first analysis we discuss is the impact that the two classes of malicious nodes have on the uploading time and the energy consumption. In particular, we consider three different distributions of malicious nodes in the system, namely: (1) prevalence of type A (i.e., 75-25%) malicious node; (2) equal number of type A and type B malicious nodes (i.e., 50-50%); and (3) prevalence of type B malicious nodes (i.e., 25-75%). As we can observe from Figure 2, the proposed trust-based solution always performs better compared to the basic strategy (we consider here the sample case with FT = 0.5). In particular, when there is a prevalence of type A malicious nodes in the system we obtain lower benefits in terms of uploading time and energy consumption. The motivation behind this is that the energy consumed for a UE when receiving corrupted packets is added to the energy required to upload the content with a unicast link to the eNodeB. In the presence of type B malicious nodes (i.e., dropping packets), instead, the only energy consumption for the UE is due to the unicast uplink transmission from the UE to the eNodeB.