To quote my last article, "Working with IoT data is a layered process, not unlike a parfait." But whereas last time we worked with an Onion Omega 2 to ingest GPS data, we'll use a Raspberry Pi Zero. Let's run through the equipment you need and how to make it happen.

I recommend the BU-353-S4 USB GPS. It works well with Raspberry Pis and is very affordable. Connecting this to a RPIWZ, I can run this on a small battery and bring this everywhere for location tracking. Put it on your delivery truck, chasis, train, plane, drone, robot, and more. I'll track myself so I can own the data.

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What Do These GPS Fields Mean?

• EPS = Error Estimate in Meter/Second

• EPX = Estimated Longitude Error in Meters

• EPV = Estimated Vertical Error in Meters

• EPT = Estimated Timestamp Error

• Speed = Speed!!!

• Climb = Climb (Positive) or Sink (Negative) rate in meters per second of upwards or downwards movement.

• Track = Course over ground in degrees from True North

• Mode = NMEA mode; values are 0 - NA, 1 - No Fix, 2D and 3D.

My point is, "If you already have an estimate of the error, do something about it!"

Python Walkthrough

First, install the utilities you need for GPS and Python. We also install NTP to get as accurate time as possible.

sudo apt-get install gpsd gpsd-clients python-gps ntp

For testing to make sure that everything works, try two of these GPS utilities. Make sure you have the USB plugged in — you will need a RPIZero adapter to convert from little USB to normal size. I then connect a small USB hub to connect the GPS unit as well as sometimes mouse and keyboard. Get one of these, you will need it. You will also need an adapter from little to full size HDMI. You only really need the mouse, keyboard, and monitor while you are doing the first setup up Wi-Fi. Once that's set up, just SSH into your device and forget it. You'll want to keep an eye on these (we'll get to them shortly):

• cgps

• gpsmon

• gpxlogger dumps XML data in GPX format.

gpspipe -l -o test.json -p -w -n 10

Without -o, it goes to STDOUT/STIN.

These will work from the command line and give you a readout. It will take a few seconds or maybe a minute the first time to calibrate. If you don't get any numbers, stick your GPS on a window or put it outside.

If you have to manually run the GPS demon:

gpsd -n -D 2 /dev/ttyUSB0

I found some code to read the GPS sensor over USB with Python. From there, I modified the code for a slower refresh and no UI, as I want to just send this data to Apache NiFi over MQTT using Eclipse Paho MQTT client.

One enhancement I have considered is an offline mode to save all the data as a buffer and then on reconnect mass send the rest. Also you could search for other WiFi signals and try to use open and free ones. You probably want to then add SQL, encryption and some other controls. Or you could install and use Apache MiniFi Java or C++ agent on the Zero.

#! /usr/bin/python
# Based on
# Written by Dan Mandle http://dan.mandle.me September 2012
# License: GPL 2.0

import os
from gps import *
from time import *
import time
import threading
import json
import paho.mqtt.client as paho

gpsd = None 

class GpsPoller(threading.Thread):
  def __init__(self):
    threading.Thread.__init__(self)
    global gpsd #bring it in scope
    gpsd = gps(mode=WATCH_ENABLE) #starting the stream of info
    self.current_value = None
    self.running = True #setting the thread running to true

  def run(self):
    global gpsd
    while gpsp.running:
      gpsd.next() #this will continue to loop and grab EACH set of gpsd info to clear the buffer

if __name__ == '__main__':
  gpsp = GpsPoller() # create the thread
  try:
    gpsp.start() # start it up
    while True:
      if gpsd.fix.latitude > 0:
        row = [ { 'latitude': str(gpsd.fix.latitude),
         'longitude': str(gpsd.fix.longitude),
         'utc': str(gpsd.utc),
         'time':   str(gpsd.fix.time),
         'altitude': str(gpsd.fix.altitude),
         'eps': str(gpsd.fix.eps),
         'epx': str(gpsd.fix.epx),
         'epv': str(gpsd.fix.epv),
         'ept': str(gpsd.fix.ept),
         'speed': str(gpsd.fix.speed),
         'climb': str(gpsd.fix.climb),
         'track': str(gpsd.fix.track),
         'mode': str(gpsd.fix.mode)} ]

        json_string = json.dumps(row)
        client = paho.Client()
        client.username_pw_set("jrfcwrim","UhBGemEoqf0D")
        client.connect("m13.cloudmqtt.com", 14162, 60)
        client.publish("rpiwzgps", payload=json_string, qos=0, retain=True)

        time.sleep(60)

  except (KeyboardInterrupt, SystemExit): #when you press ctrl+c
    gpsp.running = False
    gpsp.join() # wait for the thread to finish what it's doing

Example JSON Data

{
"track": "0.0",
"speed": "0.0",
"utc": "2017-05-01T23:49:46.000Z",
"epx": "8.938",
"epv": "29.794",
"altitude": "40.742",
"eps": "23.66",
"longitude": "-74.529216408",
"mode": "3",
"time": "2017-05-01T23:49:46.000Z",
"latitude": "40.268141521",
"climb": "0.0",
"ept": "0.005"
}

Ingest Any Data, Anywhere, Anytime, and from Any Dimension in Time and Space

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1. ConsumeMQTT

2. InferAvroSchema

3. ConvertJSONtoAvro

4. MergeContent

5. ConvertAvroToORC

6. PutHDFS

Visualize Whirled Peas

We turn this raw data into Hive tables and then visualize into pretty tables and charts with Apache Zeppelin. You could also report with any ODBC and JDBC reporting tool like Tableau or PowerBI.

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Store It Where?

su hdfs 
hdfs dfs -mkdir -p /rpwz/gps 
hdfs dfs -chmod -R 777 /rpwz/gps

If you store it, you can query it!

Why Do I Love Apache NiFi? Let Me Count the Ways....

Instead of hand-rolling some Hive DDL, NiFi will automagically generate all the DDL I need based on an inferred AVRO Schema (soon using Schema Registry lookup!). So you can easily drop in a file, convert to ORC, save to HDFS, generate an external Hive table and query it in seconds. All with no coding. Very easy to wire this to send a message to a front end via Web Sockets, JMS, AJAX, etc. So we can drop a file in S3 or HDFS, convert to ORC for mega fast LLAP queries and tell a front-end what the table is and it could query it.

Source code: https://community.hortonworks.com/repos/101680/rpi-zero-wireless-nifi-mqtt-gps.html?shortDescriptionMaxLength=140

Quick tip: Utilize Apache NiFi's scheduler to limit the number of calls you make to third-party services, a single NiFi instance can easily overwhelm most free tiers of services. I made 122 calls to Weather Underground in a few seconds. So set those times! For instance, for weather, once every 15-30 minutes or even 1 hour is good.

Note: GPS information can also be read from drones, cars, phones, and lots of custom sensors in IIoT devices.