Consumer Tech

Technology and products affecting everyday life

Drone Commerce, Part 2: Global Internet Access

In Part 1 of this series, I looked at Amazon’s use of drones for same-day delivery. In this post, I will examine Google’s proposed use of drones for ubiquitous Internet access and near-Earth monitoring from the point of view of someone who has built things that fly, the software that controls them and large-scale Internet platforms.

The Drones of Titan

The drones created by Titan (now Google) Aerospace are quite different from the quadcopters you can buy online or the military UAVs featured so prominently in the news since 9-11. They are high-endurance drones intended to stay continuously aloft at 65,000’ (20 km) for 3 to 5 years. Running on solar-rechargeable batteries, they are designed to function as in-atmosphere satellites, providing communications (like COMSATs) or sensor-based observation (like weather and surveillance satellites).

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Packets of energy, not goods

Amazon’s is exploring use of drones to delivery physical goods. This brings on a host of complex aeronautic and air traffic challenges: the ability to carry payload while staying small enough to navigate inside cities; efficiently taking off and landing several times per day in the midst of wind gusts and other weather conditions; and the need to avoid trees, birds, power lines, buildings and host of other obstacles. Google’s drones avoid all of these challenges:

  • Flying at 65,000’ places them above all weather events and a majority of atmospheric turbulence. It also places them above birds, buildings, mountains and even commercial airline traffic
  • Staying aloft for years (or even just a few months) eliminates exposure to the highest-risk operation any non-military aircraft can do: takeoff or land. It also reduces equipment replacement costs and virtually eliminates re-fueling costs.
  • By transmitting and receiving photons (light and other electromagnetic waves) the drones do not need to be engineered to carry high payloads. They also do not need to be engineered for repeated loading and unloading of packages.

These changes significantly reduce operational risk and cost. From an engineer’s point of view, the technology is a great fit to its intended function. However…

Is this just and engineer’s fantasy?

Yes, the Google Drones appear to be great candidates for in-atmosphere satellites. However, keeping hundreds or thousands of drones aloft is a pricey enterprise with complexity akin to that of operating a mid-sized airport. Aren’t there technologies already available that already meet the needs these drones are intended to satisfy? Let’s look at the two commonly considered alternatives to help answer this:

Cellular (GSM/GPRS/3G/LTE/4G):

Cellular technology already exists in many, many parts of the world (even 95% of the people in Africa who live in areas with electrical power, live within coverage of cell towers). At first examination, using drones to give coverage to everyone outside cell tower coverage seems to be a display of “First World Hi-Tech Hubris”. If these drones were just intended to provide Internet (as Facebook was exploring), I would agree 100%.

However these drones can have cameras and other sensors to provide monitoring of the environment, climate change, and natural disasters that cell towers cannot. Given the benefits already provided by using Google Earth data for analysis of climate, population, infrastructure and more, one can easily see the doors that opened by feeding camera and sensor data from these drones to developers and researchers via Google’s Maps APIs (including weather and traffic layers and ‘satellite’ views).

Finally as these drones are powered by sunlight, they would continue to function and provide monitoring and Internet access even if a natural disaster took at power grids and energy pipelines for an area.

Satellite:

horizon-1One could easily argue that satellites (between Iridium, SPOT, INMARSAT, COMSAT, and all those government programs I cannot mention) cover all the gaps cellular technology misses. At 65,000’ of altitude, these drones would only be able to cover a 300-mile radius: satellites (depending on orbital parameters) can cover up to 160x this coverage area.

However, satellites are expensive (as we have learned with the disappearance of flight MH370), satellite is expensive (about $0.14-$0.18 per small 1-Kilobyte message). The reason for this high-cost is two-fold: the high-cost of launching a satellite and the distance they are above the earth (it takes over 1500x the power to transmit a signal to an Iridium satellite than it does to transmit a signal to a drone overhead at 65,000’).

This opens to door to communication with a whole new class of technologies, ones far less expensive than satphones. This includes everything from low-cost mobile phones to OLPC (One Laptop Per Child) laptops to sensors used to track endangered species and protect them against poaching.

This distance factor goes beyond power consumption to image resolution (Ground Sample Distance or GSD). Quite simply, a drone at 65,000’ can get photos with 6x the resolution of satellite in Low Earth Orbit (LEO) and 40x the resolution of satellites like SPOT.

A great addition, but not the only answer

The Google Drone concept is not a one-size-fits-all answer. It would take thousands of drones to cover the Earth, a very costly operation. While providing more coverage than cell towers, they would often be farther away and more costly to operate. While providing better bandwidth and GSD than satellite, they would have less coverage area. As such the answer, like all things in Internet access (and sensor technology) is a blended combination of fixed-line Internet, multiple terrestrial wireless technologies (from ZigBee to 4G), satellites and drones.

This begs an important question…

One question that has plagued me from the day I first saw Facebook’s interest in Titan was why communications companies like Vodafone (which is rather well known for its 21-country mobile SIM network) were not interested in companies like Titan. Overall, using drones for ubiquitous Internet would appear to be a much better strategic fit to a company that already charges customers for Internet access. Perhaps Google can make more money from higher-resolution image and sensor data than it would initially appear. Or perhaps these drones could serve as a potential grid network that could bypass carriers if the Net Neutrality wars go in a bad direction (just like Netflix is exploring with its peer-to-peer research).

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Only time will tell.

Who Won Sochi? Wrangling Olympic Medal Count Data

I have always been a big fan of the Olympics (albeit I like the Summer Games better given my interest in Track & Field, Fencing and Soccer). However, something that has always bothered me is concept of the Medal Count. For years I have seen countries listed as “winning” because their medal count was higher—even though several countries “below” it often had many more Gold medals. Shouldn’t a Gold medal count for more than a Silver (and much more than a Bronze)? What would you rather have as an athlete: three Gold medals or four Bronzes?

Evidently, I am not the only one debating this point. Googling “value of olympic medals for rank count” yielded a range of debates on the first page alone (Bleacher Report, USA Today, The New Republic, the Washington Post and even Bicycling.com). Wikipedia even has an entry on this debate.

This year, however, I noticed that throughout the games that Google’s medal count stats page (Google “olympic medal count”) was not ranking countries by absolute medal count. For quite a while Norway and Germany were on top—even when they did not have the highest total number of medals—because they had more Gold medals than anyone else. Clearly Google was using a different weighting than “all medals are alike.” Not a surprise given their background in data.

Winter_Games_CoverartI started to wonder what type of weighting they were using. In 1984 (when the Olympics were in Los Angeles) a bunch of gaming companies came out with various Olympic games. Konami’s standup arcade game Track & Field was widely popular (and highly abusive to trackballs). The game I used to play the most (thanks to hacking it) was Epyx’s Summer (and Winter) Games. This game had the “real” challenge of figuring out a “who won the Olympics” as it was a head-to-head multi-player game (someone had to win). It used the 5:3:1 Medal Weighting Model to determine this: each Gold medal was worth 5 points, each Silver 3 points, each Bronze 1. I wondered if Google was using this model, so I decided to wrangle the data and find out.

Data processing

I used Google’s Sochi Olympic Medal Count as my source of data as this had counts and Google ranks of winners (I go this via their Russian site so I could get final results, there were 26 countries who won any Olympic Medal).

Of course, by the end of the Olympics it was a bit less interesting as Russia had both the most medals and the highest rank. However, I still wanted to figure out their weighting as a curious exercise. I built a model that calculated ranks for various Medal Weighting Model (MWM) approaches and calculated the absolute value of all Rank Error deltas from Google’s ranking. I both computed both the sum of these errors (Total Rank Error or TRE) and highlighted any non-zero error, enabling me to quickly see any errors in various MWM weightings.

Trying out a few random models

The first model I tried was the “Bob Costas Model” where every medal is the same (1:1:1). This was a clearly different than Google’s as it a TRE of 72. I then tried the Epyx 5:3:1 model… no dice: this one had a TRE of 35 (better than Bob, but not great). I tried a few other mathematical series:

  • Fibonacci: 0,1,1 (TRE=50); 1,1,2 (TRE=42); and 1,2,3 (TRE=43)
  • Fibonacci Prime (TRE=54)
  • Abundant Numbers (TRE=54)
  • Prime Numbers: (TRE=42)
  • Lucas Numbers (TRE=28)
  • Geometric Sequence (TRE=23)
  • Weird Numbers (TRE=2)
  • Happy Numbers (TRE=39)

I then tried logical sequences such as the lowest ratios where a Silver is worth more than a Bronze, and Gold is worth more than both (TRE=31). Still not luck

Getting more systematic

I decided to get more systematic and begin to visualize the TRE based on different MWM weights. I decided to keep Whole Number weights as I was operating under the general principal that each Medal has N points and that points (true in most sports—but not in things like Diving, Figure Skating and Gymnastics—nevertheless, I wanted to keep things simple).

I first looked at Gold Weight influence, WGOLD:1:1 where I varied WGOLD from 1 upwards. This clearly showed a rapid decay in TRE that flattened out at 2 with Gold was worth 13x that of a single Silver or Bronze medal:

Rapid decay in TRE as Gold medals gain higher weighting
Rapid decay in TRE as Gold medals gain higher weighting

This reinforced that Gold was King, but that Silver was better than Bronze by some value (not surprising). I then kept WGOLD at 13 and started to reduce WBRONZE. I found an interesting result: as soon as I made Bronze worth any value smaller than Silver (even ε = 0.001), I got Zero TRE (a complete match to Google’s Rank). However, I could not image a scoring system of 13:1:<1 (or 13:1:0.99). It was just too geeky. As such I tried a different approaches, all with Whole Number ratios of Gold:Silver:Bronze. The lowest ratios I found with Zero TREs were the following:

  • Gold=21, Silver=2, Bronze=1
  • Gold=29, Silver=3, Bronze=1
  • Gold=40, Silver=4, Bronze=1
  • Gold=45, Silver=5, Bronze=1

TRE never went to zero when Bronze was given Zero weight. Of these models, 40:4:1 had the most symmetry (10:1 to 4:1), so used that is my approximated Google Olympic Rank MDW (it did have zero TRE for all medal winners).

So who won?

I figured I would look at the Top Five Ranked Countries over various models:

Demonstration of how easy it is to add a Grading Curve to the rankings. The higher the TRE the more underweighted winning Gold medals (i.e., truly winning events) is. The country in bold is the one that benefits most from the Grading Curve
Demonstration of how easy it is to add a Grading Curve to the rankings. The higher the TRE the more underweighted winning Gold medals (i.e., truly winning events) is. The country in bold is the one that benefits most from the Grading Curve

Obviously, Russia is the all around winner as they won the most medals and the most Golds and the most Silvers. (Making this exercise a bit less interesting than it was about a week ago). However, it will be fun to apply this in 2016.

And at least Mr Putin is happy.