# A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability
## 6 Our Approach: Proposed Model
### Layer 1: FT-OLSR Protocol  
The Fuzzy-Logic-based Trusted Optimized Link State Routing (FT-OLSR), a detection method designed to identify blackhole nodes, was introduced in [33]. This technique involves examining various communication links to identify nodes that drop HELLO and TC messages [34]. Once a blackhole node is pinpointed, any messages received from it are disregarded and not processed. Consequently, these nodes are disqualified. The FT-OLSR protocol operates on a fuzzy logic framework [35]. Given the context of cybersecurity, precise trust computation is crucial. Therefore, we opted for a fuzzy logic approach to assess trust levels. Fuzzy logic offers distinct advantages in this context:  
* It allows for more precise trust computation, enhancing our ability to assess trust levels accurately. * By leveraging fuzzy logic, we can consider nuanced factors that might not fit well into traditional binary trust models, thus providing a more comprehensive evaluation of trustworthiness. The development of a reliable scheme for detecting and isolating misbehaving OppNets necessitates the achievement of several key objectives [35; 36]:  
**Distributed Management:** Trust evaluation of nodes must occur in a decentralized manner, meaning that trust values should be calculated and evaluated at the individual node level, ensuring mutual assessment among nodes. **Reliability and Availability Requirements:** Each node should maintain a trust table to evaluate neighboring nodes, enabling the selection of optimal nodes for seamless data transfer. **Security and Privacy:** Enhanced information exchange in the network raises concerns about user vulnerability and potential exploitation by attackers. Without cooperation among OppNet components, attackers can target various nodes repeatedly. Protocols must incorporate trust requirements to mitigate these security risks. **Scalability:** Nodes face challenges in management and limited bandwidth. In this resource-constrained environment, it is advisable to adopt a simpler method with a certain level of security, rather than a more complex approach that demands intricate mechanisms and consumes additional resources. Prioritizing simplicity ensures a practical balance between security and resource conservation. To achieve our objectives, we isolated malicious nodes detected by FT-OLSR. By enhancing cooperation among components in a dynamic environment with limited resources, we streamlined the process, eliminating the need for intricate calculations. The proposed system, illustrated in Figure 1, can be segmented into components tailored to the specific requirements of OppNets. **Trust Value Computation**  
In the FT-OLSR protocol, the trustworthiness assessment of neighboring nodes relies on the analysis of exchanged control messages, namely HELLO and TC. If a node's trust value equals or exceeds the predefined trust threshold, it qualifies as a potential candidate for transmitting packets. To accommodate the newly introduced modules, modifications will be made to the neighbor list. This enhanced list will not only contain neighbor IDs but also include the count of messages received from each neighbor and their respective trust levels. These parameters will be dynamically updated each time HELLO or TC messages are received. Our proposed detection system operates on fuzzy logic, comprising three core modules: the Extraction of Fuzzy-Based Parameters, the Fuzzy Inference Module, and the Fuzzy Decision Module. This approach allows for a nuanced evaluation of trust values,  
Figure 1: Proposed FT-OLSR scheme. enabling accurate identification of suitable MPR candidates and enhancing the overall reliability of the FT-OLSR protocol. **Consensus Algorithm (Proof of Trust)**  
In the context of our study, the trust value assumes a pivotal role in identifying nodes to be isolated and subsequently incorporated into blocks. Given the decentralized nature of OppNets and the absence of centralized management, the consensus process must occur at the individual node level.