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.
References:
2.
https://github.com/ravibeta/local-llm/blob/main/README.md
3.
https://github.com/raja0034/azureml-examples
4.
https://fluffy-space-fiesta-w469xq5xr4vh597v.github.dev/
5.
https://github.com/raja0034/openaidemo/blob/main/copilot.py
6.
https://vimeo.com/886277740/6386d542c6?share=copy
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