# Research on Secure Community Opportunity Network Based on Trust Model
## 4 Performance Evaluation
As illustrated in Figure 12, the average transmission delay for the Epdic and BubbleRap algorithms initially decreases and subsequently increases with the rising number of malicious nodes. This trend is attributable to the flooding forwarding strategy employed by both algorithms, which engenders significant network redundancy. Such redundancy congests the network, whereas a judicious amount of malicious packet loss can alleviate transmission pressure, thus reducing the average transmission delay. However, an excess  
\[overhead=\frac{message_{rel}-message_{del}}{message_{del}} \tag{14}\]1 _Deliveryratio_ and malicious nodes. sive presence of malicious nodes leads to the potential discard of messages en route to the destination node, resulting in an augmented average transmission delay. Conversely, as the prevalence of malicious nodes escalates, the Spray algorithm experiences a notable increase in delay due to malicious message dropping. Meanwhile, the SCTM algorithm maintains the lowest average transmission delay, benefitting from the division of secure communities. The transmission efficiency of the Direct algorithm, predicated on the probabilistic encounters of nodes, exhibits minimal susceptibility to malicious nodes. Figure 13 delineates that the Direct and Spray algorithms, which adopt a restricted copy forwarding strategy, show negligible impact on routing overhead from the presence of malicious nodes. In contrast, due to malicious packet loss, the Epdic and BubbleRap algorithms necessitate the successful transmission of over 100 messages to the destination node, causing routing overhead to escalate with an increase in malicious nodes. The SCTM algorithm, which segments communities based on dynamic security thresholds, experiences a lowering of trust thresholds and an expansion of the node's local community in the presence of malicious nodes. Nonetheless, by considering node centrality in the selection of the next hop relay node, the SCTM algorithm manages to ensure message delivery to the destination with fewer transmissions, thereby maintaining low routing overhead despite the challenges posed by malicious nodes. **Discussion:** Table 3 presents a performance evaluation of various algorithms following a six-hour simulation in a scenario with 1000 nodes within the opportunistic network and a node cache size of 30 MB. The Secure Community Trust Model (SCTM) algorithm outperforms others in terms of both transmission success rate and average transmission delay. This superiority is attributed to its strategy of limiting message copies and its preference for selecting nodes with closer connections to the destination node for message forwarding, which leads to a marginally higher routing overhead compared to the Direct and Spray algorithms. Notably, the SCTM algorithm enhances the transmission success rate by 125% over the Epdic algorithm, while cutting routing overhead by 77%. In comparison with the Spray algorithm, it also reduces the average transmission delay by 37%, marking a substantial improvement over its counterparts. Moreover, the SCTM algorithm orchestrates  
\[overhead=\frac{message_{rel}-message_{del}}{message_{del}} \tag{14}\]2 _Latency_avg_ and malicious nodes. secure community divisions, prioritizing nodes within the security community for relay node selection and taking into account factors such as node centrality, remaining cache, and energy levels. This approach ensures the selection of safer and more efficient nodes for relay purposes. Simulation findings corroborate the SCTM algorithm's capability to adeptly identify and exclude malicious nodes from the network, effectively counteracting malicious threats and sustaining algorithmic stability.