Iot + Data Science + Big Data = Big Opportunities in the Global South

The combination of iot / data engineering / data science could  be a way to solve a number of common problems found in the global south. Many of those issues are recurrent and span over multiple domain knowledge. However the same sequence: absorbing data, saving data, exploring data could be the beginning to many solutions of those issues.

WHAT ???

Let’s start with an example. Would that be Guatemala, Nigeria, Vietnam or many countries in the global south, one common problem arise during rainy seasons also called monsoon. Heavy render some patch of savana or jungle roads  totally unusable. The location of those bottlenecks are variable through the rainy season and from year to year.  The state of those roadblocks would depend on the amount of rain water, type of terrain, traffic etc… In any case, it renders the road impossible to use. The consequences are environmental, logistical, health wise among other. Indeed, delivery trucks flip over and often spill their content. Food delivery trucks are stuck for long period of time and as a result food goes bad. Sick people can’t reach hospitals for emergencies etc…

Truck stuck in mud in forest road. How recurrent is this mud bottleneck ? Where is it located ? [Photo 25980795 © Paop | Dreamstime.com]

On the other hand  those patches of flooded roads stay an hindrance but a necessity. On the other hand those forests are too large, too numerous and too massive. It would be widely unecological, expensive to pave them all.  However, a first step to any type of solution would be to first locate those road bottlenecks. This is where the combinaison of Iot, big data and data science could come in handy.

HOW ???

If we equip let’s say 5 vehicles driving the same road with sensors. Each sensor measures 4 variables:  gps location andacceleration on three axes. It is not far fetched to forsee that  sensor data coming from 5 different vehicules  traveling through the same  patch of heavy mud will display some commonalites. Exploring the data transmitted by the  accelerometer sensor will show  identical variations.  In effect vibrations will  increase because as vehicules drive through a rough patch or no variations at all as the vehicule is stuck in the mud. For those 5 vehicle the GPS location will be close. independament of the time.  It would make sense that by exploring the data corelations between those 4 variables data can we can get  the exact location of difficult travel patch. That would be the exploratory  phase over one single rainy season. Over Many seasons it is clear that the data will show some patterns as far as the location, the length of the roadblock etc.. It  becomes the evident to take remedial measures.

OK… And THEN WHAT ???

Let’s take another example. At the moment lot of the personal transportation in many countries of the Global South  is done through  private entreprise and not through public transportation. Those transport means can be individual taxis or motorbike. However little analysis is used. Data analysis would show: what are the patterns on where to pick up customer. What are the best time, is there any seasonality on the routes taken etc…  Again, it is would not be difficult to gather this information at the end of the day. Some Iot devices such as presence sensors would feed the data. And at the end of the day those data can be uploaded in an HDFS platform for analytics to find all the insights.

Motor Taxi Jam in an Asian country. How much traffic insight can be gathered by all those motorbikes ? [Photo 45645931 / Road © Hoxuanhuong | Dreamstime.com]

If we look at differnt Global South issues through the perspective of Sensor – Data Engineering – Data analysis, how many seemingly insurmountable issues can be solved ??  Transportation, food industry, health …????  Naturally, in any case, the context would have to be first understood.