## 2 Related Works
### Integration of Trust and Reputation Models into Network Protocols  
Mesh networks represent a recent development for IoT devices, and there is not much work on the integration of reputation models inside the mesh protocol. Some authors worked on the problem of self-sovereign identity management on Internet of Things mesh networks [16]. They used LoRaWAN (Low-Range Wide Area Network) as an example and applied Sovrin's decentralised identifiers and verifiable credentials in combination with Schnorr signatures for secure communications. They focused on simplex and broadcast connections. In detail, they also discussed an ESP32-based implementation using SX1276/SX1278 LoRa chips and adaptations made to the Imic- and MbedTLS-based software stack, and practically evaluated performance aspects (overhead, time-on-air impact, and power consumption). The research work discussed in this paragraph is slightly different from our work because the authors used LoRaWAN as an example and applied Sovrin's decentralised identifiers and verifiable credentials in combination with Schnorr signatures for secure communications. On the other hand, the authors have tested their solution for self-sovereign identity management in a real test-bed, which represents our objective for future research. In [17], the authors study the problem of intrusion prevention techniques in the context of WMNs (wireless mesh networks); indeed, in this case, it is especially challenging and requires particular design considerations. To address this problem, they propose a particular anomaly detection scheme (RADAR) to detect anomalous mesh nodes in WMNs. First of all, they provide a first definition of reputation to quantify the mesh node's behaviour/status and, therefore, to define fine-grained performance metrics. In this way, they define a robust baseline for leveraging and measuring the derivation between normal and anomalous behaviour of mesh nodes. They also propose a cooperative anomaly detection scheme (based on the measurement of reputation) based on the full exploration of the spatio-temporal properties of mesh nodes' behaviour. They provide an implementation based on a reactive routing protocol, aimed at detecting malicious mesh nodes which intentionally violate normal routing mechanisms. Finally, they provide a few simulation results to validate their approach in terms of detection accuracy, false positive rate, computational overhead, and scalability. The interesting part of the protocol developed by the authors is represented, in our opinion, by the reactive routing protocol, which represents an effective countermeasure to isolate anomalous nodes of the mesh network. On the other hand, our research work is different because we introduce a very simple reputation protocol as a payload of the Meshtastic protocol. In [18], the authors take into account the model of the hybrid wireless mesh network, which has the advantage of a flexible infrastructure but is vulnerable to attacks. They state that traditional reputation schemes are effective in addressing security, but they are not suitable for direct application to hybrid wireless mesh networks, because node cooperation and hierarchical construction are not taken into account. Therefore, they propose a dynamic hierarchical reputation evaluation scheme (DHRES) to provide security for hybrid wireless mesh networks: the scheme includes a virtual cluster structure to introduce the reputation relation, including the related nodes' roles and functions. The reputation evaluation mechanism is based on the correlation between nodes, and each node's reputation is updated according to the different roles of the mesh nodes. They provide a few simulation results to compare their approach with traditional reputation models: the results highlight that the proposed scheme can accurately reflect the security status of nodes, particularly of malicious nodes.