IoT Edge Computing

Correct Answer: Data preprocessing at the edge

The best answer for reducing network bandwidth usage and improving response times in an IoT edge computing environment is 'Data preprocessing at the edge.'

Edge computing allows data to be processed closer to the source, which significantly reduces the amount of data that needs to be sent to the cloud or central servers. This approach has several benefits:

1. Reduced network bandwidth: By processing data locally, only relevant or summarized information is transmitted, reducing overall network traffic.
2. Improved response times: Processing data at the edge minimizes latency, allowing for faster decision-making and real-time responses.
3. Enhanced privacy and security: Sensitive data can be processed locally, reducing the risk of data breaches during transmission.

The other options are less suitable for this scenario:

'Continuous data streaming to the cloud' would increase network bandwidth usage and potentially introduce latency, contrary to the goal.

'Real-time data synchronization with central servers' would also increase network traffic and may not be necessary for all data points in an IoT environment.

'Extensive data logging for later analysis' does not address the immediate need for reduced bandwidth and improved response times, as it focuses on storing data for future use rather than real-time processing.

Correct Answer: Data compression

Data compression is the best answer for enhancing data privacy and reducing bandwidth consumption in IoT edge computing. Data compression techniques allow IoT devices to process and store data locally, reducing the amount of information that needs to be transmitted to central servers. This approach has several benefits: 1. Enhanced privacy: By processing data at the edge, sensitive information can be filtered or anonymized before transmission, reducing the risk of data breaches. 2. Reduced bandwidth consumption: Compressed data requires less bandwidth to transmit, making it more efficient for IoT networks with limited connectivity. 3. Lower latency: Processing data locally allows for faster decision-making and response times. 4. Improved scalability: Edge computing with data compression can handle a larger number of IoT devices more effectively. The other options are incorrect: Data replication across multiple cloud servers does not address privacy concerns or reduce bandwidth consumption. It actually increases data transmission and storage requirements. Continuous data streaming to central servers would increase bandwidth consumption and potentially expose more data to privacy risks. Real-time data broadcasting to all network nodes would significantly increase network traffic and bandwidth usage, contradicting the goal of reducing bandwidth consumption.

Correct Answer: Data aggregation

Data aggregation combines and filters data generated by IoT devices at the edge. By reducing the amount of transmitted data, it minimizes network bandwidth usage and ensures that only relevant information is sent to the cloud.