BitCluster Brings A New Way To Snoop Through BitCoin Transactions

July 23, 2016 by Mike Szczys 8 Comments

Mining the wealth of information in the BitCoin blockchain is nothing new, but BitCluster goes a long way to make sense of the information you’ll find there. The tool was released by Mathieu Lavoie and David Decary-Hetu, PH.D. on Friday following their talk at HOPE XI.

I greatly enjoyed sitting in on the talk which began with some BitCoin basics. The cryptocurrency uses user generated “wallets” which are essentially addresses that identify transactions. Each is established using key pairs and there are roughly 146 million of these wallets in existence now

If you’re a thrifty person you might think you can get one wallet and use it for years. That might be true of the sweaty alligator-skin nightmare you’ve had in your back pocket for a decade now. It’s not true when it comes to digital bits — they’re cheap (some would say free). People who don’t generate a new wallet for every transaction weaken their BitCoin anonymity and this weakness is the core of BitCluster’s approach.

Every time you transfer BitCoin (BTC) you send the network the address of the transaction when you acquired the BTCs and sign it with your key to validate the data. If you reuse the same wallet address on subsequent transactions — maybe because you didn’t spend all of the wallet’s coins in one transaction or you overpaid and have the change routed back to your wallet. The uniqueness of that signed address can be tracked across those multiple transactions. This alone won’t dox you, but does allow a clever piece of software to build a database of nodes by associating transactions together.

Mathieu’s description of first attempts at mapping the blockchain were amusing. The demonstration showed a Python script called from the command line which started off analyzing a little more than a block a second but by the fourth or fifth blocks hit the process had slowed to a standstill that would never progress. This reminds me of some of the puzzles from Project Euler.

After a rabbit hole of optimizations the problem has been solved. All you need to recreate the work is a pair of machines (one for Python one for mondoDB) with the fastest processors you can afford, a 500 GB SSD, 32 GB of RAM (but would be 64 better), Python 64-bit, and at least a week of time. The good news is that you don’t have to recreate this. The 200GB database is available for download through a torrent and the code to navigate it is up on GitHub. Like I said, this type of blockchain sleuthing isn’t new but a powerful open source tool like this is.

Both Ransomware and illicit markets can be observed using this technique. Successful, yet not-so-cautious ransomers sometimes use the same BitCoin address for all payments. For example, research into a 2014 data sample turned up a ransomware instance that pulled in $611k (averaging $10k per day but actually pulling in most of the money during one three-week period). If you’re paying attention you know using the same wallet address is a bad move and this ransomware was eventually shut down.

Illicit markets like Silk Road are another application for BitCluster. Prior research methods relied on mining comments left by customers to estimate revenue. Imagine if you had to guess at how well Amazon was doing reading customer reviews and hoping they mentioned the price? The ability to observe BTC payment nodes is a much more powerful method.

A good illicit market won’t use just one wallet address. But to protect customers they use escrow address and these do get reused making cluster analysis possible. Silk Road was doing about $800k per month in revenue at its height. The bulk of purchases were for less than $500 with only a tiny percentage above $1000. But those large purchases were likely to be drug purchases of a kilo or more. That small sliver of total transactions actually added up to about a third of the total revenue.

It’s fascinating to peer into transactions in this manner. And the good news is that there’s plenty of interesting stuff just waiting to be discovered. After all, the blockchain is a historical record so the data isn’t going anywhere. BitCluster is intriguing and worth playing with. Currently you can search for a BTC address and see total BTC in and out, then sift through income and expense sorted by date, amount, etc. But the tool can be truly great with more development. On the top of the wishlist are automated database updates, labeling of nodes (so you can search “Silk Road” instead of a numerical address), visual graphs of flows, and a hosted version of the query tool (but computing power becomes prohibitive.)

Hacklet 117 – NFC Projects

July 23, 2016 by Adam Fabio 5 Comments

Near Field Communication (NFC) is something we take for granted these days. Nearly all smartphones have it. We even have NFC interfaces for all our favorite development boards. NFC’s history goes back all the way to 1997, when an early version was used in Star Wars special edition toys. Radio Frequency Identification (RFID), which NFC builds on, goes back even further. The patent citation trail leads all the way back to 1983 in a patent awarded to [Charles Walton]. NFC is much more than RFID though. The idea of two way communication between devices opens up tons of possibilities for projects and hacks. This week on the Hacklet we’re checking out some of the best NFC projects on Hackaday.io!

We start with [Patrick] and Ctrl-O. Somewhere in the hackerspace bible there is a clause that states “Thou shalt build an electronic access control system”. In [Patrick’s] case, a door lock became a complex membership subscription management database. Members who have paid can use an NFC tag to gain access to the hackerspace. The system consists of a Raspberry Pi with an NFC interface. A relay allows the Pi to control the door lock. The Pi can be manually configured through a web interface. It connects to Paypal to verify that each user’s membership has actually been paid. Of course a project like this is never done. The last we heard from [Patrick], he was planning future upgrades such as startup company memberships with multiple people.

Next up is [Pierre Charlier] and KeyDuino. KeyDuino is an Arduino compatible board with all the NFC hardware baked right in. The board is based upon the Arduino Leonardo, with an ATmega32u4 processor. [Pierre] must be on to something, because the KeyDuino had a successful Kickstarter back in 2015. It’s also open source hardware, so you can build your own whenever you want. The real gem is checking out [Pierre’s] other projects. He’s documented all his KeyDuino example projects right on Hackaday.io. These include an NFC Controlled infinity mirror coffee table, a locking wooden gift box, and NFC controlled car door locks, just to name a few.

Next we have [John McLear] with 2016 NFC Ring. [John] jumped into wearable technology with one of the toughest form factors imaginable – a ring. Between the tiny amount of space and the lack of batteries, you might think there isn’t much you can do with a ring. Undaunted, [John] managed to fit two NXP NFC chips and their antennas inside a standard ring. This is the upgraded 2016 version of the ring. [John] was nice enough to supply several hundred of the earlier models to hackers at the Hackaday Supercon back in 2015. [John’s] rings would be hard for the average hacker to reproduce. [Sean Hodgins] comes to the rescue here with his own project, DIY NFC Bentwood Ring.

Finally, we have [CaptMcAllister] with RFID air pressure sensor. As the name implies, this sensor measures air pressure. It could be in open air, a tire, or even a football used by the New England Patriots. Sure, cars all have Tire Pressure Monitoring Systems (TPMS) sensors which do something similar. [CaptMcAllister’s] design has one important difference – it has no batteries. The heart of the system is a Texas Instruments RF430FRL15X, a device with the NFC radio and a low power MSP430 microcontroller in one chip. The system is energy harvesting, being powered by an external reader. As you can imagine, tuning the antenna was critical to this design. You can read all about it in [CaptMcAllister’s] 24 project logs.

If you want to see more NFC projects and hacks, check out our new near field communication projects list. See a project I might have missed? Don’t be shy, just drop me a message on Hackaday.io. That’s it for this week’s Hacklet, As always, see you next week. Same hack time, same hack channel, bringing you the best of Hackaday.io!

Semisolid Lithium Ion Batteries Promise Better Cars, Solar

July 23, 2016 by Al Williams 28 Comments

Lithium-ion batteries make possible smaller and lighter electronics. Unfortunately, they are also costly to produce. In a conventional lithium-ion battery, many thin layers create the finished product much like filo dough in baklava. A startup company called 24M thinks they have the answer to making less expensive lithium-ion batteries: a semisolid electrode made by mixing powders and liquid to form an electrolyte goo.

Not only will the batteries be cheaper and faster to create, but the cost of the factory will be less. Currently, 24M has a pilot manufacturing line, but by 2020 they expect to scale to produce batteries that cost less than $100 per kilowatt hour (today’s costs are about $200 to $250 for conventional batteries). Under $100, the batteries become competitive with the cost of internal combustion engines, according to the article.

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Manipulators Get A 1000x FPGA-based Speed Bump

July 23, 2016 by Sonya Vasquez 35 Comments

For humans, moving our arms and hands onto an object to pick it up is pretty easy; but for manipulators, it’s a different story. Once we’ve found the object we want our robot to pick up, we still need to plan a path from our robot hand to the object all the while lugging the remaining limbs along for the ride without snagging them on any incoming obstacles. The space of all possible joint configurations is called the “joint configuration space.” Planning a collision-free path through them is called path planning, and it’s a tricky one to solve quickly in the world of robotics.

These days, roboticists have nailed out a few algorithms, but executing them takes 100s of milliseconds to compute. The result? Robots spend most of their time “thinking” about moving, rather than executing the actual move.

Robots have been lurching along pretty slowly for a while until recently when researchers at Duke University [PDF] pushed much of the computation to hardware on an FPGA. The result? Path planning in hardware with a 6-degree-of-freedom arm takes under a millisecond to compute!

It’s worth asking: why is this problem so hard? How did hardware make it faster? There’s a few layers here, but it’s worth investigating the big ones. Planning a path from point A to point B usually happens probabilistically (randomly iterating to the finishing point), and if there exists a path, the algorithm will find it. The issue, however, arises when we need to lug our remaining limbs through the space to reach that object. This feature is called the swept volume, and it’s the entire shape that our ‘bot limbs envelope while getting from A to B. This is not just a collision-free path for the hand, but for the entire set of joints.

swept_volume — Image Credit: Robot Motion Planning on a Chip

Encoding a map on a computer is done by discretizing the space into a sufficient resolution of 3D voxels. If a voxel is occupied by an obstacle, it gets one state. If it’s not occupied, it gets another. To compute whether or not a path is OK, a set of voxels that represent the swept volume needs to be compared against the voxels that represent the environment. Here’s where the FPGA kicks in with the speed bump. With the hardware implementation, voxel occupation is encoded in bits, and the entire volume calculation is done in parallel. Nifty to have custom hardware for this, right?

We applaud the folks at Duke University for getting this up-and-running, and we can’t wait to see custom “robot path-planning chips” hit the market some day. For now, though, if you’d like to sink your teeth into seeing how FPGAs can parallelize conventional algorithms, check out our linear-time sorting feature from a few months back.

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Liddiard Omnidirectional Wheels

July 23, 2016 by Jenny List 46 Comments

Omnidirectional wheels are one of the hardy perennials of the world of invention. There seems to be something about the prospect of effortless parallel parking that sets the creative juices of backyard inventors flowing, and the result over the years have been a succession of impressively engineered ways to move a car sideways.

The latest one to come our way is courtesy of Canadian inventor [William Liddiard], and it is worthy of a second look because it does not come with some of the mechanical complexity associated with other omnidirectional wheel designs. [Liddiard]’s design uses a one-piece tyre in the form of a flexible torus with a set of rollers inside it which sits on a wheel fitted with a set of motorised rollers around its circumference. The entire tyre can be rotated round its toroidal axis, resulting in a tread which can move sideways with respect to the wheel.

The entire process is demonstrated in a video which is shown below the break, and the small Toyota used as a demonstration vehicle can move sideways and spin with ease. We would be wary of using these wheels on a road car until they can be demonstrated to match a traditional tyre in terms of sideways stability when they are not in their omnidirectional mode, but we can instantly see that they would be a significant help to operators of industrial machines such as forklifts in confined spaces.

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Bluetooth And Arduino Vaporizer Upends Stoner Stereotypes

July 23, 2016 by Elliot Williams 63 Comments

Back in the day, stoners were content to sit around, toke on a joint, mellow out, and listen to the Grateful Dead or something. Nowadays, they practically need a degree in electrical engineering just to get high. [Beiherhund] sent us his VapeBox build. Like so many projects on Hackaday, we’re not going to make one ourselves, but we appreciate a well-done project.

First off, there’s a home-built induction heater. A 30A current sensor and switch-mode power supply regulate the amount of juice going to the coil that surrounds the heating chamber. [Beiherhund] discovered that brass doesn’t have enough internal resistance to heat up in an induction heater, so he built a stainless steel insert into the chamber. Optimal temperature is monitored from outside the chamber by a MLX90614 IR thermometer.

Fans, controlled by PWM, keep the box cool. Lights, an LCD, an HC-05 Bluetooth unit, and everything else are all tied to the obligatory Arduino that serves as the brains. A cell-phone application lets [Beiherhund] control all the functions remotely. (We’re guessing, just because he could.) It’s wrapped up in a nice acrylic case. The video, embedded below, starts with real details at 4:28.

Before you loyal Hackaday commenteers get on your high horses (tee-hee!) bear in mind that smoking dope is legal in a number of states in the USA, and that Hackaday has an international readership. We don’t encourage drug abuse or soldering in shorts and flip-flops.

https://www.youtube.com/watch?v=2xSBCHC3Vhs&t=4m28s

Hackaday Prize Entry: Piezo Gait Analysis

July 22, 2016 by Brian Benchoff 7 Comments

Go into a fancy drug store, and you might just find one of the most amazing sales demonstrations you’ll ever see. Step right up, take your shoes off, and place your feet onto the magical Dr. Scholl’s machine, and you’ll get a customized readout of how your feet touch the ground. As an added bonus, you’ll also get a recommendation for a shoe insert that will make your feet feel better and your shoes fit better.

There is, of course, one problem with this setup. You don’t stand on a footprint measuring device all day. A better solution to the problem of measuring how your feet hit the ground is doing it while you walk. That’s where [chiprobot]’s Alli-Gait-Or Analysis comes in. It’s that Dr. Scholl’s machine tucked into the sole of a shoe. It can be worn while you walk, and it can tell you exactly how your feet work.

[chiprobot]’s robotic shoes consist of a 3D printed insert that holds eighteen piezo transducers per shoe. These are connected to ADCs, which feed into a microcontroller which sends the data out to a computer. That’s simple enough, but making sense of the data is the real problem.

To turn this data into something that could be used for selecting orthotics or simply finding a better shoe, [chiprobot] is plugging this data into Blender and creating some very cool visualizations. It’s good enough to get some serious data off a shoe, and since this Alli-Gait-Or is wearable, the data is much more valid than a machine sitting in a drug store.