IoT Data Management and Analytics

Correct Answer: Use outlier detection methods on incoming data

Using outlier detection methods on the incoming data from the soil moisture sensors is the best approach. This allows identifying anomalous readings while still leveraging the data from the affected sensor. Outlier detection algorithms can flag unusual values that deviate significantly from the expected range or pattern. This enables the system to handle the anomalies gracefully without completely discarding potentially useful information. Decreasing the sampling rate would reduce data granularity and may cause important variations to be missed. Completely ignoring the data from the affected sensor would result in loss of coverage for that specific area of the field. Replacing all sensors is an extreme and unnecessary step when only one sensor is exhibiting anomalies, as it would incur additional costs and effort.

Correct Answer: Implement a Data Processing Pipeline

A Data Processing Pipeline is best for handling data from various sources and transforming it before analysis. It provides scalability and can manage the diverse data easily while maintaining efficiency.

Correct Answer: Implement Data Aggregation techniques

Implementing Data Aggregation techniques is the best solution to handle the massive amount of incoming IoT data that is causing the analytics system to slow down in the smart city project. Data Aggregation involves combining and summarizing the data from multiple sources before it reaches the analytics system, reducing the overall volume of data to be processed. This approach allows the system to efficiently analyze the most relevant and important information without being overwhelmed by the raw data from every single IoT device. Restricting the number of IoT devices or reducing the data collection frequency would limit the potential insights and benefits of the smart city project. Upgrading the servers for the analytics system may help but does not address the fundamental issue of efficiently handling the large influx of data.