Edge computing solutions are designed to handle intermittent connectivity and data synchronization by ensuring that they can operate both online and offline, and are able to synchronize data when connectivity is restored.
Intermittent connectivity can occur when devices in a distributed network are brought up and taken down frequently, resulting in disruptions in communication. To address this issue, edge computing solutions usually incorporate offline capabilities. This means that devices, sensors, and other components can store and process data locally even when there is no network connection. For instance, edge nodes can cache data from remote sensors while waiting for network connectivity to be restored.
To ensure data synchronization when connectivity is re-established, edge computing solutions use different techniques such as data buffering, delta updates, and conflict resolution.
Data buffering involves storing data that is generated during an offline period and then forwarding it to the central cloud-based system when the network is restored. However, buffering has its limitations as the memory capacity of edge nodes can be limited, and some data may need to be prioritized.
Delta update tracks changes to data that have occurred since the last synchronization and only updates those changes. This saves bandwidth and storage capacity on the edge device, resulting in a quicker turnaround.
Conflict resolution involves reconciling differences between actions and data inputs in the network when synchronization resumes. For instance, if two edge nodes generate different data that needs to be synchronized with the same central cloud-based system, the conflict resolution mechanism decides which data to keep and which to discard.
A good example of how edge computing handles intermittent connectivity and data synchronization is the German railway company Deutsche Bahn’s use of edge computing to monitor its train engine data. The system collects data from thousands of sensors installed in train engines, and when a train is in an area without a network connection, it saves data locally on the edge mechanism. When the train enters an area with a network connection, the edge mechanism syncs the data with the central cloud-based system. The data is then used to analyze the health of the train engines and plan maintenance schedules, resulting in improved efficiency and reduced downtime.