Edge Computing vs Cloud IoT Quiz Quiz

Explore the fundamental differences and advantages between edge computing and cloud IoT with this engaging quiz designed to deepen your understanding of distributed processing, data management, and network architecture in modern IoT solutions.

1. Processing Location Comparison

   Which of the following best describes where primary data processing occurs in an edge computing IoT setup, such as a factory sensor network analyzing production defects in real-time?

   1. Transferred via manual data logs
   2. In a remote data warehouse
   3. Near the data source, on local devices
   4. At a centralized global server

   Explanation: Edge computing processes data close to where it is generated, such as on sensors or local gateways, reducing latency and bandwidth usage. Processing at a centralized global server is typical of traditional cloud IoT but not edge setups. Data warehouses are for storage and later analysis, not immediate processing. Manual data logs are neither real-time nor automated, making them unsuitable for edge computing in IoT.

2. Latency Implications in IoT

   Why is edge computing preferred over traditional cloud IoT for applications like self-driving cars requiring instant response to changing road conditions?

   1. Because edge computing significantly reduces latency
   2. Because edge computing increases internet traffic
   3. Because edge computing uses more power than the cloud
   4. Because edge computing eliminates the need for any servers

   Explanation: Edge computing enables faster decision-making by processing data locally, which is critical for time-sensitive tasks like autonomous navigation. Increasing internet traffic is a disadvantage and not a benefit. Power usage can vary, but lower latency is the key reason for preferring edge over cloud. Eliminating all servers is incorrect because edge devices often still coordinate with central systems.

3. Bandwidth Usage Differences

   How does the deployment of edge computing in an IoT healthcare scenario, such as wearable devices, affect network bandwidth requirements compared to cloud-only IoT?

   1. It reduces bandwidth usage by filtering and aggregating data locally
   2. It increases bandwidth usage due to constant cloud uploads
   3. It blocks internet access entirely for IoT devices
   4. It has no impact on bandwidth; both are the same

   Explanation: Edge computing helps decrease bandwidth demands by processing, filtering, or summarizing data before sending only necessary information to the cloud. Unlike edge, cloud-only solutions involve frequent, large uploads, which increase bandwidth usage. Saying there is no difference ignores the benefits of edge filtering. IoT devices do not block internet access; they optimize its use.

4. Data Privacy and Security Considerations

   Which key benefit does edge computing offer for sensitive data, such as surveillance video streams, when compared to centralized cloud IoT solutions?

   1. Centralized backups are all stored offsite
   2. Encrypted Wi-Fi is always required by default
   3. All devices are isolated from each other
   4. Local processing limits exposure of raw data

   Explanation: By handling data locally, edge computing keeps sensitive information, like live video streams, closer to its source and reduces exposure risks during transmission to the cloud. Centralized offsite backups are unrelated to edge-specific privacy benefits. While encrypted Wi-Fi adds security, it is not exclusive to edge solutions. Device isolation may occur for security but is not a guaranteed aspect of edge computing.

5. Scalability in Edge vs Cloud IoT

   What is a primary scalability challenge unique to edge computing in large distributed IoT systems, such as smart city deployments?

   1. Running all processing via hand-written logs
   2. Overloading a single central data center
   3. Automatic scalability with no human intervention
   4. Managing and updating many dispersed devices

   Explanation: Edge computing places intelligence across many devices, making it challenging to maintain, update, and secure them at scale. Centralized cloud systems risk overloading a single center, but edge computing distributes workload. Hand-written logs are outdated and not relevant to modern scalability. While some automation exists, edge networks require ongoing human oversight to address device management.