# Dynamic Storage Optimization for Communication between AI Agents
## 2 Use Case  
To illustrate the role of the ontology, consider a use case where the ontology is designed to facilitate communication between AI agents in a smart city environment. In a smart city, multiple AI agents are responsible for managing different aspects of traffic. These include traffic signal controllers, autonomous vehicles, public transportation systems, and emergency response units. Efficient communication between embedded AI agents is critical for optimizing traffic flow, reducing congestion, and responding to incidents.  
The ontology example is as follows:  
**Traffic Signal Controller AI**:  
* Ontology Concept: TrafficSignalStatus.
* Fields: SignalID (string), Status (string: \"Red\", \"Yellow\", \"Green\"), Timestamp (datetime).  
Description: The traffic signal controller AI updates the status of each traffic signal in real time. These data are encoded and transmitted using the TrafficSignalStatus concept defined in the ontology.  
**Autonomous Vehicle AI**:  
* Ontology Concept: VehicleStatus.
* Fields: VehicleID (string), Location (GPS coordinates), Speed (numeric), Destination (GPS coordinates).
* Description: Autonomous vehicles periodically share their status, including their current location and speed. This information is structured according to the VehicleStatus concept.  
**Public Transportation System AI**:  
* Ontology Concept: BusArrivalEstimate.
* Fields: BusID (string), RouteID (string), EstimatedArrivalTime (datetime).
* Description: The public transportation system provides real-time updates on bus arrival times, allowing other agents to adjust traffic management strategies accordingly.  
**Emergency Response Unit AI**:  
* Ontology Concept: EmergencyEvent.
* Fields: EventID (string), Location (GPS coordinates), Severity (string: \"Low\", \"Medium\", \"High\"), Description (string).
* Description: In case of an incident, the emergency response unit broadcasts information about the event, including the location and severity. Other agents, such as traffic signal controllers and autonomous vehicles, prioritize emergency response routes.  
An example of a step-by-step communication flow is as follows:  
1. The Traffic Signal Controller AI detects a change in the signal status at a major intersection and updates the TrafficSignalStatus concept.
2. Autonomous vehicles approaching the intersection receive the updated TrafficSignalStatus, adjust their speed, and plan their routes accordingly.
3. A bus on the public transportation system calculates its updated arrival time due to the traffic signal change and updates the BusArrivalEstimate concept.
4. An emergency occurs nearby, and the Emergency Response Unit AI broadcasts an EmergencyEvent concept. The Traffic Signal Controller AI and Autonomous Vehicle AI reprioritize routes to clear the way for emergency vehicles.
5. The data exchanged among these AI agents are stored and retrieved from a decentralized storage system (e.g., IPFS, Tendermint Cosmos, or Hyperledger Fabric), ensuring fast and reliable communication.  
By utilizing the multi-layered ontology framework, the communication model ensures that only the necessary layers are used for machine-to-machine communications, reducing latency and improving efficiency. For example, in the scenario described above, core layers such as structural, connection, and encoding are used, while optional layers like naming and usage are omitted, enhancing read and write speeds.