Using Edge Computing is particularly beneficial when real-time responses are required. In critical applications such as autonomous vehicles, drones, or connected healthcare systems, low latency is crucial for safe and efficient operation. By processing data locally instead of sending them to the cloud, response times are drastically reduced. Additionally, in scenarios where internet connectivity is limited or intermittent, Edge Computing allows operations to continue uninterrupted. Therefore, in any situation where speed and availability are essential, Edge Computing becomes the preferred computing architecture.

Another reason to use Edge Computing is its efficiency in managing large volumes of data. In industrial and IoT (Internet of Things) environments, the massive amount of data constantly generated requires efficient processing and filtering. Running these tasks at the edge before sending data to the cloud can significantly reduce bandwidth usage and associated costs. Only data that require long-term analysis, large-scale storage, or are less time-sensitive—such as aggregating data from multiple devices for historical analysis—are sent to the cloud. This also reduces the load on central servers, allowing for more efficient processing and better management of large-scale data. In this way, Edge Computing provides an efficient and cost-effective solution.

Edge Computing is also advantageous in terms of security and privacy. Processing data locally reduces the need to transmit sensitive information over the network, minimising the risk of interception and cyberattacks. This is especially important in sectors such as healthcare, banking, critical infrastructure management, or defence, where data privacy and/or criticality are extremely important. Furthermore, having direct control over data at the edge allows organisations to implement stricter and more customised security measures. However, this decentralisation can also present challenges, as it requires implementing and maintaining security measures across multiple devices and locations.

Despite its numerous advantages, Edge Computing also has some drawbacks. One of the main challenges is the complexity of managing and maintaining a distributed network of edge devices. This can require significant investment in infrastructure and staff training to ensure that all devices operate optimally and securely. Additionally, reliance on local hardware can limit scalability compared to the cloud, where resources can be easily adjusted according to demand. There is also the risk of inconsistencies

and lack of synchronisation between devices, which can complicate data integrity and application consistency.

Taking into account the advantages and disadvantages, Edge Computing should be considered when latency, bandwidth efficiency, security, and privacy are critical factors. Its ability to process data in real time and reduce dependence on internet connectivity makes it ideal for applications in industrial and IoT environments, among others. However, it is important to evaluate the potential downsides, such as management complexity and implementation costs. By weighing these factors, organisations can make informed decisions about when and how to implement Edge Computing to maximise its benefits and minimise challenges. In this way, the full potential of edge computing can be harnessed to solve specific problems and improve operational efficiency.