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Edge and Fog GatewayConfiguration Guide

Configuration Guide

Table of Contents

  1. Introduction
  2. System Architecture
  3. Configuration Structure
  4. Available Tasks
  5. Modbus Client Configuration
  6. Configuration Examples
  7. Dependency Management
  8. Best Practices

Introduction

The Sensoworks Fog Gateway is a modular edge-computing data processing system that allows you to create data acquisition, processing, and transmission pipelines through declarative JSON configurations.

Key Features

  • Modular architecture: composable and reusable tasks
  • Configurable pipelines: defined through JSON files
  • Queue management: asynchronous communication between tasks
  • Checkpointing: automatic recovery after interruptions
  • Multi-instance: horizontal scaling of tasks
  • Multiple protocols: MQTT, Modbus, HTTP, FTP, Sparkplug B

System Architecture

Main Components

┌─────────────────────────────────────────────────────┐
│              MainSensoworksFogGateway               │
│                                                     │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────┐   │
│  │ConfigManager │  │EngineManager │  │StatusMgr │   │
│  └──────────────┘  └──────────────┘  └──────────┘   │
└─────────────────────────────────────────────────────┘
                           │
                           ▼
              ┌────────────────────────┐
              │   Workflow Engine      │
              │   (per flow)           │
              └────────────────────────┘
                           │
        ┌──────────────────┼──────────────────┐
        ▼                  ▼                  ▼
   ┌────────┐        ┌────────┐        ┌────────┐
   │ Task 1 │───────▶│ Task 2 │───────▶│ Task 3 │
   │ Input  │ Queue  │ Process│ Queue  │ Output │
   └────────┘        └────────┘        └────────┘

Execution Flow

  1. Configuration: loading JSON files from the config/ folder
  2. Initialization: queue creation and task instantiation
  3. Execution: each task operates in a separate thread
  4. Communication: data passing through queues
  5. Checkpointing: periodic state saving

Configuration Structure

Basic Schema

{
  "id": "unique_flow_identifier",
  "name": "Descriptive Flow Name",
  "description": "Detailed workflow description",
  "version": "1.0",
  "task_configs": [
    {
      "task_id": "task_identifier",
      "type": "task_type",
      "instances": 1,
      "dependencies": ["previous_task"],
      "params": {
        // Task-specific parameters
      }
    }
  ]
}

Main Fields

Flow Level

FieldTypeRequiredDescription
idstringYesUnique workflow identifier
namestringYesReadable workflow name
descriptionstringNoWorkflow description
versionstringNoWorkflow version
task_configsarrayYesArray of task configurations

Task Level

FieldTypeRequiredDescription
task_idstringYesUnique task identifier in the flow
typestringYesTask type (see Available Tasks)
instancesintegerNoNumber of parallel instances (default: 1)
dependenciesarrayNoList of task_id dependencies
paramsobjectYesTask-specific parameters

Available Tasks

Data Input Tasks

1. modbus_client

Description: Periodic reading of Modbus TCP/IP registers

Parameters:

{
  "host": "192.168.1.100",
  "port": 502,
  "slave_id": 1,
  "register_offset": 0,
  "polling_sec": 10,
  "input_mapping": {
    "temperature": {
      "address": [0, 1],
      "register_type": "HOLDING_REGISTER",
      "data_type": "FLOAT32",
      "decimals": 0
    },
    "pressure": {
      "address": [2, 3],
      "register_type": "INPUT_REGISTER",
      "data_type": "FLOAT32",
      "decimals": 2
    }
  }
}

Detailed Parameters:

ParameterTypeDefaultDescription
hoststring”localhost”Modbus server IP address
portinteger502Modbus TCP port
slave_idinteger1Modbus slave ID
register_offsetinteger0Register address offset
polling_secinteger10Polling interval in seconds
input_mappingobject-Variable mapping to read

Input Mapping - Variable Configuration:

Each variable in input_mapping requires:

  • address: Array of register addresses (e.g., [0, 1] for FLOAT32)
  • register_type: Register type
    • HOLDING_REGISTER: Read/write registers
    • INPUT_REGISTER: Read-only registers
    • DISCRETE_INPUT: Discrete inputs (bool)
    • COIL: Coils (bool)
  • data_type: Data type to read
    • INT16, UINT16: 16-bit integer (1 register)
    • INT32, UINT32: 32-bit integer (2 registers)
    • INT64, UINT64: 64-bit integer (4 registers)
    • FLOAT32: 32-bit float (2 registers)
    • FLOAT64: 64-bit float (4 registers)
    • STRING: String (N registers)
  • decimals: (optional) For INT* types only, divides value by 10^decimals

Output Format:

{
  "data": {
    "temperature": 25.5,
    "pressure": 1013.25
  },
  "metadata": {
    "timestamp": 1703340000000
  }
}

Complete Modbus Example:

{
  "task_id": "plc_reader",
  "type": "modbus_client",
  "instances": 1,
  "params": {
    "host": "10.0.1.50",
    "port": 502,
    "slave_id": 1,
    "register_offset": 0,
    "polling_sec": 5,
    "input_mapping": {
      "tank_level": {
        "address": [0, 1],
        "register_type": "HOLDING_REGISTER",
        "data_type": "FLOAT32",
        "decimals": 0
      },
      "flow_rate": {
        "address": [2, 3],
        "register_type": "HOLDING_REGISTER",
        "data_type": "FLOAT32",
        "decimals": 2
      },
      "valve_status": {
        "address": [10],
        "register_type": "COIL",
        "data_type": "UINT16",
        "decimals": 0
      },
      "alarm_code": {
        "address": [20],
        "register_type": "INPUT_REGISTER",
        "data_type": "UINT16",
        "decimals": 0
      }
    }
  }
}

2. mqtt_client_subscriber

Description: MQTT topic subscription with TLS support

Parameters:

{
  "broker_url": "mqtt.example.com",
  "broker_port": 1883,
  "mqtt_topics": ["sensors/#", "devices/+/data"],
  "qos": 1,
  "keep_alive": 60,
  "use_tls": false,
  "username": "user",
  "password": "pass",
  "client_id": "fog_gateway_01",
  "tls_params": {
    "ca_certs": "/path/to/ca.crt",
    "certfile": "/path/to/client.crt",
    "keyfile": "/path/to/client.key"
  }
}

Parameters:

ParameterTypeDefaultDescription
broker_urlstring”localhost”MQTT broker URL
broker_portinteger1883Broker port (1883 or 8883 for TLS)
mqtt_topicsarray[”#“]List of topics to subscribe to
qosinteger1Quality of Service (0, 1, 2)
keep_aliveinteger60Keep-alive in seconds
use_tlsbooleanfalseEnable TLS/SSL
usernamestringnullAuthentication username
passwordstringnullAuthentication password
client_idstringtask_idMQTT client ID
tls_paramsobject{}TLS parameters (if use_tls=true)

Output Format:

{
  "data": {
    // MQTT message payload (parsed JSON or raw)
  },
  "metadata": {
    "topic": "sensors/temperature/01",
    "qos": 1,
    "retain": false,
    "timestamp": 1703340000000
  }
}

3. mqtt_sparkplug_subscriber

Description: MQTT subscription with Sparkplug B Protobuf decoding

Parameters:

{
  "broker_host": "sparkplug.example.com",
  "broker_port": 1883,
  "username": "spb_user",
  "password": "spb_pass",
  "client_id": "fog_spb_gateway",
  "keep_alive": 60,
  "clean_session": true,
  "topics": [
    "spBv1.0/group_id/NBIRTH/edge_node/+",
    "spBv1.0/group_id/NDATA/edge_node/+",
    "spBv1.0/group_id/DDATA/edge_node/+"
  ],
  "qos": 1,
  "sparkplug_enabled": true,
  "protobuf_enabled": true,
  "output_format": "json",
  "include_metadata": true
}

Output Format:

{
  "data": {
    "timestamp": 1703340000000,
    "metrics": [
      {
        "name": "Temperature",
        "datatype": 9,
        "float_value": 25.5
      }
    ],
    "seq": 123
  },
  "metadata": {
    "topic": "spBv1.0/group/DDATA/node/device",
    "sparkplug_type": "DDATA"
  }
}

4. csv_reader

Description: CSV file reading with checkpoint and type conversion support

Parameters:

{
  "delimiter": ",",
  "header": 0,
  "headers_and_types": {
    "Timestamp": "type:datetime|format:%d/%m/%y %H:%M:%S|function:sec|rename:timestamp",
    "Temperature": "type:float|rename:temp",
    "DeviceID": "type:int|rename:device_id"
  },
  "csv_basepath": "/data/stage/",
  "processing_dir": "/data/processing/",
  "processed_dir": "/data/processed/",
  "sleep_duration": 5,
  "chunksize": 1000,
  "data_at_row": 0,
  "decimal_delimeter": "."
}

5. ftp_downloader

Description: Periodic file download from FTP server

Parameters:

{
  "host": "ftp.example.com",
  "username": "ftpuser",
  "password": "ftppass",
  "remote_dir": "/data/incoming",
  "output_dir": "/data/stage/",
  "sleep_duration": 300,
  "file_extensions": [".csv", ".json"],
  "recursive": false
}

6. json_reader

Description: JSON file reading from filesystem

Parameters:

{
  "json_basepath": "/data/json/",
  "processing_dir": "/data/processing/",
  "processed_dir": "/data/processed/",
  "sleep_duration": 5
}

7. random_number_generator

Description: Random data generator for testing

Parameters:

{
  "min_value": 0,
  "max_value": 100,
  "interval_sec": 10,
  "num_values": 5
}

Processing Tasks

1. json_to_payload_converter

Description: JSON structure conversion and mapping

Parameters:

{
  "auto_generate_timestamp": true,
  "mapping": {
    "timestamp": "ts",
    "value": "reading.value",
    "device_id": "device.id",
    "sensor_type": "metadata.sensor_type"
  }
}

How it works:

  • Maps fields from one JSON structure to another
  • Supports nested paths with dot notation (e.g., reading.value)
  • auto_generate_timestamp: generates UTC timestamp in milliseconds if not present

Input Example:

{
  "data": {
    "value": 25.5,
    "device_id": "sensor_01",
    "sensor_type": "temperature"
  }
}

Output Example:

{
  "data": {
    "ts": 1703340000000,
    "reading": {
      "value": 25.5
    },
    "device": {
      "id": "sensor_01"
    },
    "metadata": {
      "sensor_type": "temperature"
    }
  }
}

2. json_remapper

Description: Complete remapping of nested JSON structures

Parameters:

{
  "mapping": {
    "old.path.value": "new.path.value",
    "sensor.temp": "readings.temperature"
  },
  "default_value": null
}

3. json_filter

Description: JSON message filtering based on conditions

Parameters:

{
  "filter_key": "sensor_type",
  "filter_values": ["temperature", "humidity"],
  "filter_mode": "include"
}

4. timeslot_splitter

Description: Data splitting into time windows

Parameters:

{
  "timeslot_minutes": 5,
  "time_column": "timestamp"
}

5. timeslot_aggregator

Description: Data aggregation by time windows

Parameters:

{
  "timeslot_minutes": 15,
  "time_column": "timestamp",
  "aggregation_functions": {
    "temperature": "avg",
    "pressure": "max",
    "counter": "sum"
  }
}

6. column_data_converter

Description: Data type conversion for specific columns

Parameters:

{
  "conversions": {
    "temperature": "float",
    "timestamp": "datetime",
    "device_id": "string"
  }
}

Data Output Tasks

1. http_sender

Description: HTTP/HTTPS data sending with compression and batching

Parameters:

{
  "endpoint": "https://api.example.com/ingest",
  "api_key": "your_api_key_here",
  "compress": false,
  "batch_size": 1,
  "streaming": true
}

Parameters:

ParameterTypeDefaultDescription
endpointstring-API endpoint URL
api_keystringnullAPI key for Bearer authentication
compressbooleanfalseEnable gzip compression
batch_sizeinteger1Number of messages per batch
streamingbooleanfalseStreaming mode (send immediately)

2. s3_sender

Description: File upload to Amazon S3

Parameters:

{
  "bucket_name": "my-iot-data",
  "aws_access_key_id": "AKIAXXXXX",
  "aws_secret_access_key": "secret",
  "region_name": "eu-west-1",
  "base_s3_path": "processed/"
}

3. csv_writer

Description: Data writing in CSV format

Parameters:

{
  "output_path": "/data/output/",
  "filename_pattern": "data_{timestamp}.csv",
  "delimiter": ",",
  "header": true
}

4. json_writer

Description: Data writing in JSON format

Parameters:

{
  "output_path": "/data/output/",
  "filename_pattern": "data_{timestamp}.json",
  "pretty_print": true
}

5. stdout_writer

Description: Console output for debugging

Parameters:

{
  "format_string": "Received: {data[value]} from {data[device_id]}",
  "print_interval": 0
}

6. raw_writer

Description: Raw binary file writing

Parameters:

{
  "output_path": "/data/raw/",
  "filename_pattern": "raw_{timestamp}.bin"
}

Modbus Client Configuration

Detailed Guide

The modbus_client task is one of the most used for data acquisition from PLCs and industrial devices.

Modbus Registers

Register Types

TypeFunction CodeAccessDescription
DISCRETE_INPUT02RDiscrete inputs (boolean)
COIL01, 05, 15R/WCoils (boolean)
INPUT_REGISTER04RInput registers (16-bit)
HOLDING_REGISTER03, 06, 16R/WHolding registers (16-bit)

Data Types

TypeRegistersBytesRange
UINT16120 - 65535
INT1612-32768 - 32767
UINT32240 - 4294967295
INT3224-2147483648 - 2147483647
FLOAT3224IEEE 754 single
FLOAT6448IEEE 754 double

Practical Example: Plant Monitoring

{
  "task_id": "industrial_plc_monitor",
  "type": "modbus_client",
  "instances": 1,
  "params": {
    "host": "192.168.100.10",
    "port": 502,
    "slave_id": 1,
    "register_offset": 0,
    "polling_sec": 2,
    "input_mapping": {
      "temperature_reactor_1": {
        "address": [100, 101],
        "register_type": "HOLDING_REGISTER",
        "data_type": "FLOAT32",
        "decimals": 0
      },
      "pressure_inlet": {
        "address": [102, 103],
        "register_type": "HOLDING_REGISTER",
        "data_type": "FLOAT32",
        "decimals": 1
      },
      "flow_rate": {
        "address": [104],
        "register_type": "INPUT_REGISTER",
        "data_type": "INT16",
        "decimals": 2
      },
      "pump_running": {
        "address": [200],
        "register_type": "COIL",
        "data_type": "UINT16",
        "decimals": 0
      },
      "alarm_status": {
        "address": [300],
        "register_type": "DISCRETE_INPUT",
        "data_type": "UINT16",
        "decimals": 0
      }
    }
  }
}

Modbus Troubleshooting

Common Errors

  1. Connection Timeout

    • Check network connectivity
    • Verify firewall and ports
    • Confirm correct IP and port

  2. Invalid Register Address

    • Check register offset
    • Review PLC documentation
    • Some PLCs use 1-based addressing

  3. Byte Order Issues

    • Multi-register data may require swapping
    • Verify device endianness

Configuration Examples

Example 1: MQTT → HTTP Pipeline

Scenario: Receiving data from MQTT sensors, transformation, and cloud sending

{
  "id": "mqtt_to_cloud",
  "name": "MQTT Sensors to Cloud API",
  "description": "MQTT sensor data acquisition and cloud API sending",
  "version": "1.0",
  "task_configs": [
    {
      "task_id": "mqtt_subscriber",
      "type": "mqtt_client_subscriber",
      "instances": 1,
      "params": {
        "broker_url": "mqtt.local",
        "broker_port": 1883,
        "mqtt_topics": ["sensors/temperature/#", "sensors/humidity/#"],
        "qos": 1,
        "keep_alive": 60,
        "client_id": "fog_gateway_sensors"
      }
    },
    {
      "task_id": "data_transformer",
      "type": "json_to_payload_converter",
      "instances": 2,
      "dependencies": ["mqtt_subscriber"],
      "params": {
        "auto_generate_timestamp": true,
        "mapping": {
          "timestamp": "ts",
          "value": "sensor.reading",
          "device_id": "device.id",
          "type": "sensor.type"
        }
      }
    },
    {
      "task_id": "cloud_sender",
      "type": "http_sender",
      "instances": 1,
      "dependencies": ["data_transformer"],
      "params": {
        "endpoint": "https://api.cloud.com/v1/ingest",
        "api_key": "sk_prod_xxxxxxxxxxxxx",
        "compress": true,
        "batch_size": 10,
        "streaming": false
      }
    }
  ]
}

Example 2: Modbus → CSV with Aggregation

Scenario: Modbus PLC reading, 15-minute aggregation, CSV saving

{
  "id": "modbus_aggregation_pipeline",
  "name": "Modbus PLC Data Aggregation",
  "description": "PLC data acquisition, time aggregation and CSV saving",
  "version": "1.0",
  "task_configs": [
    {
      "task_id": "plc_reader",
      "type": "modbus_client",
      "instances": 1,
      "params": {
        "host": "192.168.1.100",
        "port": 502,
        "slave_id": 1,
        "polling_sec": 5,
        "input_mapping": {
          "tank_level": {
            "address": [0, 1],
            "register_type": "HOLDING_REGISTER",
            "data_type": "FLOAT32",
            "decimals": 1
          },
          "flow_rate": {
            "address": [2, 3],
            "register_type": "HOLDING_REGISTER",
            "data_type": "FLOAT32",
            "decimals": 2
          },
          "pump_status": {
            "address": [10],
            "register_type": "COIL",
            "data_type": "UINT16",
            "decimals": 0
          }
        }
      }
    },
    {
      "task_id": "payload_converter",
      "type": "json_to_payload_converter",
      "instances": 1,
      "dependencies": ["plc_reader"],
      "params": {
        "auto_generate_timestamp": true,
        "mapping": {
          "timestamp": "ts",
          "tank_level": "measurements.tank_level",
          "flow_rate": "measurements.flow_rate",
          "pump_status": "status.pump"
        }
      }
    },
    {
      "task_id": "time_aggregator",
      "type": "timeslot_aggregator",
      "instances": 1,
      "dependencies": ["payload_converter"],
      "params": {
        "timeslot_minutes": 15,
        "time_column": "ts",
        "aggregation_functions": {
          "measurements.tank_level": "avg",
          "measurements.flow_rate": "avg",
          "status.pump": "max"
        }
      }
    },
    {
      "task_id": "csv_output",
      "type": "csv_writer",
      "instances": 1,
      "dependencies": ["time_aggregator"],
      "params": {
        "output_path": "/data/aggregated/",
        "filename_pattern": "plc_data_{date}.csv",
        "delimiter": ",",
        "header": true
      }
    }
  ]
}

Example 3: FTP → Processing → S3

Scenario: CSV file download from FTP, processing, S3 upload

{
  "id": "ftp_to_s3_pipeline",
  "name": "FTP to S3 Data Pipeline",
  "description": "FTP file download, processing and S3 upload",
  "version": "1.0",
  "task_configs": [
    {
      "task_id": "ftp_download",
      "type": "ftp_downloader",
      "instances": 1,
      "params": {
        "host": "ftp.datasource.com",
        "username": "datauser",
        "password": "datapass",
        "remote_dir": "/exports/daily",
        "output_dir": "/data/ftp_stage/",
        "sleep_duration": 3600,
        "file_extensions": [".csv"],
        "recursive": false
      }
    },
    {
      "task_id": "csv_reader",
      "type": "csv_reader",
      "instances": 1,
      "dependencies": ["ftp_download"],
      "params": {
        "delimiter": ";",
        "header": 0,
        "headers_and_types": {
          "DateTime": "type:datetime|format:%Y-%m-%d %H:%M:%S|function:sec|rename:timestamp",
          "Value": "type:float|rename:value",
          "Quality": "type:int|rename:quality"
        },
        "processing_dir": "/data/processing/",
        "processed_dir": "/data/processed/",
        "chunksize": 5000
      }
    },
    {
      "task_id": "data_filter",
      "type": "json_filter",
      "instances": 1,
      "dependencies": ["csv_reader"],
      "params": {
        "filter_key": "quality",
        "filter_values": [0, 192],
        "filter_mode": "include"
      }
    },
    {
      "task_id": "json_output",
      "type": "json_writer",
      "instances": 1,
      "dependencies": ["data_filter"],
      "params": {
        "output_path": "/data/processed_json/",
        "filename_pattern": "processed_{timestamp}.json",
        "pretty_print": false
      }
    },
    {
      "task_id": "s3_upload",
      "type": "s3_sender",
      "instances": 1,
      "dependencies": ["json_output"],
      "params": {
        "bucket_name": "company-iot-data",
        "aws_access_key_id": "AKIAIOSFODNN7EXAMPLE",
        "aws_secret_access_key": "wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY",
        "region_name": "eu-west-1",
        "base_s3_path": "processed/sensors/"
      }
    }
  ]
}

Example 4: Sparkplug B → Multi-Stage Processing

Scenario: Sparkplug B data reception, metric extraction, multiple outputs

{
  "id": "sparkplug_processing",
  "name": "Sparkplug B Multi-Output Pipeline",
  "description": "Sparkplug B acquisition with multiple outputs",
  "version": "1.0",
  "task_configs": [
    {
      "task_id": "sparkplug_subscriber",
      "type": "mqtt_sparkplug_subscriber",
      "instances": 1,
      "params": {
        "broker_host": "sparkplug.broker.com",
        "broker_port": 1883,
        "username": "spb_user",
        "password": "spb_pass",
        "topics": [
          "spBv1.0/factory/NDATA/line_01/+",
          "spBv1.0/factory/DDATA/line_01/+"
        ],
        "qos": 1,
        "sparkplug_enabled": true,
        "output_format": "json"
      }
    },
    {
      "task_id": "metrics_extractor",
      "type": "json_to_payload_converter",
      "instances": 2,
      "dependencies": ["sparkplug_subscriber"],
      "params": {
        "auto_generate_timestamp": false,
        "mapping": {
          "timestamp": "payload.timestamp",
          "seq": "payload.seq",
          "device": "mqtt.device_id",
          "temperature": "metrics.Temperature.value",
          "pressure": "metrics.Pressure.value",
          "vibration": "metrics.Vibration.value"
        }
      }
    },
    {
      "task_id": "console_debug",
      "type": "stdout_writer",
      "instances": 1,
      "dependencies": ["metrics_extractor"],
      "params": {
        "format_string": "[{data[timestamp]}] Device: {data[device]} | Temp: {data[temperature]}°C | Pressure: {data[pressure]} bar",
        "print_interval": 0
      }
    },
    {
      "task_id": "cloud_api",
      "type": "http_sender",
      "instances": 1,
      "dependencies": ["metrics_extractor"],
      "params": {
        "endpoint": "https://api.monitoring.com/metrics",
        "api_key": "prod_key_xxxxxxxxx",
        "compress": true,
        "batch_size": 50,
        "streaming": false
      }
    },
    {
      "task_id": "local_backup",
      "type": "json_writer",
      "instances": 1,
      "dependencies": ["metrics_extractor"],
      "params": {
        "output_path": "/data/backup/sparkplug/",
        "filename_pattern": "spb_{date}_{time}.json",
        "pretty_print": false
      }
    }
  ]
}

Dependency Management

How Dependencies Work

Dependencies define the data flow between tasks:

{
  "task_id": "task_B",
  "dependencies": ["task_A"],
  "params": { ... }
}
  • Tasks without dependencies: input tasks, start the flow
  • Tasks with dependencies: receive data through input_queue
  • Multiple dependencies: supported, data is merged

Dependency Graph

Task A (no deps) ──────┐
                       ├──▶ Task C (deps: A, B) ──▶ Task D (deps: C)
Task B (no deps) ──────┘

Communication Queues

The workflow engine automatically creates queues with naming:

{source_task_id}_to_{destination_task_id}

Example:

  • Queue task_A_to_task_C
  • Queue task_B_to_task_C
  • Queue task_C_to_task_D

Multiple Instances

For horizontal scaling:

{
  "task_id": "processor",
  "type": "json_to_payload_converter",
  "instances": 4,
  "dependencies": ["reader"],
  "params": { ... }
}

Creates 4 parallel instances:

  • processor_0
  • processor_1
  • processor_2
  • processor_3

All share the same input_queue (parallel consumers).

Best Practices

1. Naming Convention

{
  "id": "project_environment_purpose",
  "task_id": "function_description"
}

Examples:

  • Flow ID: factory_prod_plc_monitor
  • Task ID: modbus_line1_reader, mqtt_cloud_sender

2. Logging and Debugging

Use stdout_writer for intermediate debugging:

{
  "task_id": "debug_output",
  "type": "stdout_writer",
  "dependencies": ["transformer"],
  "params": {
    "format_string": "DEBUG: {data}",
    "print_interval": 1
  }
}

3. Error Handling

  • Use batch_size=1 for critical sends
  • Implement checkpointing for long tasks
  • Monitor logs in /logs/

4. Performance

  • Polling: don’t go below 1 second
  • Batch size: increase to reduce HTTP overhead
  • Instances: max 2-4 for CPU-intensive tasks
  • Queue size: default 100, increase only if needed

5. Security

  • Credentials: use environment variables or secret manager
  • TLS: always enable for external connections
  • API Keys: rotate periodically

6. Testing

Test pipeline with simulated data:

{
  "task_id": "test_generator",
  "type": "random_number_generator",
  "params": {
    "min_value": 0,
    "max_value": 100,
    "interval_sec": 1
  }
}

7. Versioning

Always keep the version field updated:

{
  "version": "1.2.3",
  "description": "v1.2.3 - Added temperature filtering"
}

File and Directory Management

Environment Variables

APP_URL=http://127.0.0.1:5000
CONFIGURATIONS_PATH=config/
STATUS_PATH=status/status.json
QUEUE_SIZE=100

Standard Folder Structure

/data/
├── stage/          # Incoming files
├── processing/     # Files being processed
├── processed/      # Completed files
├── temp/           # Temporary files
└── to_process/     # Files to process

Checkpoints

Checkpoints are saved in:

/checkpoints/{flow_id}/{task_id}.json

Format:

{
  "file_path": "/data/processing/data.csv",
  "row_number": 15234,
  "timestamp": 1703340000000
}

Troubleshooting

Problem: Task doesn’t start

Cause: Missing required parameters Solution: Check logs in logs/{flow_id}.json

Problem: Queue full

Cause: Consumer too slow Solution:

  • Increase instances for consumer task
  • Increase QUEUE_SIZE (env variable)
  • Reduce batch_size if used

Problem: Modbus timeout

Cause: Polling too frequent Solution: Increase polling_sec to 5-10 seconds

Problem: MQTT disconnections

Cause: Keep-alive too low Solution: Increase keep_alive to 60-120 seconds

References

Log Files

  • Application: sensoworks_fog_gateway_logger.ini
  • Flow logs: /logs/{flow_id}.json
  • Status: /status/status.json

Protocol Documentation

Repository

git clone https://github.com/sensoworks/sensoworks-fog-gateway.git
cd sensoworks-fog-gateway
pip install -r requirements.txt
python sensoworks_fog_gateway.py
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