Question

Design a process architecture for an environmental monitoring system that collects data from a set of air quality sensors situated around a city. There are 5000 sensors organized into 100 neighborhoods. Each sensor must be interrogated four times per second. When more than \(30 \%\) of the sensors in a particular neighborhood indicate that the air quality is below an acceptable level, local warning lights are activated. All sensors return the readings to a central computer, which generates reports every 15 minutes on the air quality in the city.

Short answer

Create a distributed architecture with local neighborhood processing to manage data and activate alerts, then central servers for comprehensive reporting.

Step-by-step solution

Data Collection Requirements

First, identify the data collection needs. Each of the 5000 sensors needs to send data 4 times per second. This requires a robust system capable of handling high-frequency data sampling from a large sensor network.

Sensors Organization

Organize the sensors into the 100 predefined neighborhoods. This means each neighborhood consists of 50 sensors. Clarifying this organization helps in both data handling and in understanding how the alert system should be implemented.

Local Processing and Alerts

Design a local processing unit for each neighborhood. This unit should collect data from the 50 sensors and calculate the percentage of sensors reporting poor air quality. If more than 30% indicate poor quality, activate the neighborhood's warning systems.

Data Transfer to Central System

Ensure that data from each neighborhood is continually transmitted to a central computing server. Data pipelines must be capable of aggregating large amounts of data from all 100 neighborhoods in real time.

Report Generation

The central computing server should analyze the collected data and generate reports every 15 minutes. These reports will give a comprehensive overview of air quality across the entire city, potentially influencing public health responses.

System Architecture

Develop a distributed system architecture. Local processing reduces data load and network traffic, while central computing ensures comprehensive analysis and reporting. This balances workload and increases system efficiency and responsiveness.

Key concepts

Data Collection

In an environmental monitoring system, data collection is a critical process that involves gathering information from a set of sensory devices. For our scenario, where you have 5000 air quality sensors distributed across a city, the system is designed to collect data from each sensor four times per second. This results in a high volume of data that needs to be managed effectively to ensure accuracy and timely processing.

Key considerations for data collection include:

• Establishing a reliable connection to each sensor to maintain continuous data flow.
• Ensuring each data packet contains time-stamps and location identifiers for proper tracking and analysis.
• Implementing error-checking mechanisms to validate data integrity.

These considerations help in ensuring that accurate and meaningful data is collected for subsequent processing stages.

Sensor Network

The sensor network forms the backbone of the monitoring system. It comprises 5000 sensors organized into 100 neighborhoods, with each neighborhood containing 50 sensors. This structured organization aids in both localized monitoring and controlling data loads.

The architecture of the sensor network should ensure:

• Robust placement of sensors to cover all necessary areas without blind spots.
• Efficient data transmission pathways to prevent bottlenecks.
• Scalability to accommodate future additions or changes in sensor locations.

With such a carefully planned sensor network, the system can function optimally, collecting accurate real-time data and enabling effective environmental control measures.

Local Processing Unit

A local processing unit (LPU) is an essential component for each neighborhood within the monitoring system. It serves as a mini-computer that deals with data from the 50 sensors within its vicinity. The main role of an LPU is to analyze incoming data streams, compute local statistics, and manage alert activations if necessary.

Functions of the LPU include:

• Evaluating the data and determining if over 30% of sensors report poor air quality.
• Triggering warning lights within the neighborhood when thresholds are breached.
• Minimizing the data sent to the central system by processing some data locally.

This distributed approach lightens the central system's processing burden and increases system responsiveness, especially during critical environmental changes.

Central Data System

The central data system acts as the processing core of the environmental monitoring setup. It aggregates all the data coming from the various local processing units throughout the city. Once the data is collected, the central system compiles and analyzes it to provide an overarching view of the city's air quality.

Important functions of the central system include:

• Receiving continuous data streams from the 100 neighborhoods.
• Processing aggregated data to detect city-wide air quality patterns.
• Generating reports every 15 minutes to inform city health officials and the public.

With its centralized approach, the system can coordinate city-wide responses to air quality issues, aiding in public health planning and emergency response efforts.

Distributed System Architecture

The distributed system architecture is crucial in striking a balance between localized data management and central processing. This approach divides tasks between local processing units and the central system to maximize efficiency and minimize latency.

Key features of this architecture include:

• Reducing data traffic on the central network by processing locally what can be managed within each neighborhood.
• Allowing for scalability, which means the system can grow and adapt over time without overwhelming any single component.
• Ensuring redundancy and increased fault tolerance as the distributed nature allows some units to compensate for others in case of failures.

By adopting distributed system architecture, the monitoring system achieves a high-performance level capable of real-time processing while maintaining flexibility and reliability.