Mesh networking and UAV (Unmanned Aerial Vehicle) swarm flight communication share several commonalities, particularly in how they handle connectivity and data transfer: 

 

Dynamic Topology: Both systems often operate in environments where the network topology can change dynamically. In mesh networks, nodes can join or leave the network, and in UAV swarms, drones can move in and out of range. 

 

Self-Healing: Mesh networks are designed to automatically reroute data if a node fails or a connection is lost. Similarly, UAV swarms use mesh networking to maintain communication even if some drones drop out or move out of range. 

 

Redundancy: Both systems use redundancy to ensure reliable communication. In mesh networks, multiple paths can be used to send data, while in UAV swarms, multiple drones can relay information to ensure it reaches its destination. 

 

Decentralization: Mesh networks are decentralized, meaning there is no single point of failure. UAV swarms also benefit from decentralized communication, allowing them to operate independently and collaboratively without relying on a central control point. 

 

Scalability: Both mesh networks and UAV swarms can scale to accommodate more nodes or drones, respectively, without significant degradation in performance. 

 

These commonalities make mesh networking an ideal solution for UAV swarm communication, ensuring robust and reliable connectivity even in challenging environments. 

Similarly, distributed hash tables, cachepoints arranged in a ring and consensus algorithms also play a  part in the communications between drones. 

Cachepoints are used with consistent hashing. They are arranged along the circle depicting the key range and cache objects corresponding to the range. Virtual nodes can join and leave the network without impacting the operation of the ring. 

Data is partitioned and replicated using consistent hashing to achieve scale and availability. Consistency is facilitated by object versioning. Replicas are maintained during updates based on a quorum like technique. 

In a distributed environment, the best way to detect failures and determine memberships is with the help of gossip protocol. When an existing node leaves the network, it may not respond to the gossip protocol so the neighbors become aware.  The neighbors update the membership changes and copy data asynchronously. 

Some systems utilize a state machine replication such as Paxos that combines transaction logging  for consensus with write-ahead logging for data recovery. If the state machines are replicated, they are fully Byzantine tolerant.