Why Does It Take So Long to Connect to a WiFi Access Point?

If you're using a pre-shared key, the password is verified during the 4-way handshake. The thing is, if your password is wrong, then the Message Authentication Code (MAC) of the messages your are sending is wrong. The AP will simply drop frames with a wrong MAC, and will not respond to them. The problem is that as a client you do not know whether the AP is not responding because (1) the MAC was wrong, and hence the password was wrong; or (2) the message did not arrive at the AP (or you did not receive the response of the AP).

tl;dr: can't tell the difference between dropped messages due to wrong authentication check (i.e. wrong password), or dropped messages due to bad connection.

Depends on the operating system. For example on an Android phone it will usually say 'authentication problem'

I'm a little more bothered by the glibness of moving from 'we have a good prediction/regression algorithm' to 'we can causally control and optimize connections using it'. When they claim in the abstract that

> Based on the measurement analysis, we develop a machine learning based AP selection strategy that can significantly improve WiFi connection set-up performance, against the conventional strategy purely based on signal strength, by reducing the connection set-up failures from 33% to 3.6% and reducing 80% time costs of the connection set-up processes by more than 10 times.

They don't actually demonstrate this and don't know that it does improve WiFi connection performance, because they don't benchmark it on any real-world devices. It's pure extrapolation from the algorithm's predictive performance on the dataset. (And there are some suspicious inputs to the algorithm like time of day.) When they write on pg9 about where they are pulling these numbers from:

> To evaluate, we first divide our connection log dataset into two parts, each subset contains 50% of the overall data. ...This fresh dataset ensures that we can accurately evaluate the performance of our algorithm if we deploy our algorithm in the wild, where many of the APs will not be seen by the mobile devices before.

They're just wrong. Splitting like this only ensures good out-of-sample performance when drawing from the same distribution, but when you use the algorithm to make choices, the distribution is different. Correlation!=causation; it's no more guaranteed to help than data mining hospital records and finding that antibiotics apparently are killing patients and so hospitals should stop using them.

In general, any opinions about where we are on the hype curve for machine learning (as a stand-alone concept separate from traditional statistical inference)?

https://en.wikipedia.org/wiki/Hype_cycle

If the object is only to improve performance, a black-box machine-learning model should outperform a hand-tuned algorithm. The generalisation (out-of-sample) performance will depend on how representative the training and evaluation sets are, but this is also true for an algorihtm with human-selected features.

And in the process walking all over various security best practices?

And was there not a similar security snafu involving iPhones broadcasting past ssids every time they tried to connect to a access point?

> [Abstract] "we develop a machine learning based AP selection strategy that can significantly improve WiFi connection set-up performance, against the conventional strategy purely based on signal strength, by reducing the connection set-up failures from 33% to 3.6% and reducing 80% time costs of the connection set-up processes by more than 10 times."

> [Conclusion] "The correlation analysis finds that though the signal strength is important, knowing the AP model and mobile device model has great help to predict the connection set-up time cost. To the best of our knowledge, we are the first to add AP model and mobile device model as features which greatly increases the accuracy to predict the connection set-up time cost."

_____

(NOTE: Please do not use block quote formatting, it's unreadable on mobile.)

The paper says that the biggest delay is in the "scan" phase, just getting a list of all the available APs in the area. This would be the same problem addressed by Apple's "try to associate to all known SSIDs on powerup" approach.

Maybe I'm missing something, but their actual machine learning model seems to address a different problem:

  The final features we choose to train: the connection
  time cost includes hour of day, RSSI, mobile device
  model, AP model, Encrypted

Given this, I they're basically giving lower priority to known incompatible device & AP pairs based on their BSS IDs.

Not sure I like the approach that much, seems an AP running a recent OpenWRT and very reliable would be penalized for having buggy factory firmware.

This might explain what I've seen, which is that whenever I go home for the weekend and join my parent's wifi it somehow screws up the internet for everyone else in the house.

Naive question...why does that mess with the network? Your router has the correct MAC <-> IP mapping, and iPhone kid is the only one losing out.

It should be noted that many routers (my Netgear not being an exception) reserve private IP's for saved MAC addresses, which makes this a non-issue. Every device on my LAN has a static private IP (I haven't used my full address space yet, so curious what will happen when I do).

Aren't there TTLs on those leases? Without have read up on it now, I would assume that if the lease is given with a 24h day the device is in its right to assume it can re-use that IP address in the 24 hours to come?

Since you work in WiFi, I was wondering if you could comment the linked chart of SNR to Modulation rates [0]. Obviously the exactly values are going to depend on hardware to some degree.

[0]https://dl.dropboxusercontent.com/u/8644251/Revolution%20Wi-...

Probably not at home, but that's not when I want to connect faster. I use the Fonera network when I'm in the street, and there's often three or four APs near me, so this could be helpful. Another case is campus wifi, when I was in college. In both of these cases, I want to connect to a specific SSID, but not to a specific AP.

That article is crap because it never says why connections are slow. Lots of words and no substance.

There are many cases where one wants to use wifi while out and about without "leeching". In my city alone, we have areas with paid networks, tit-for-tat networks (Fonera) and public networks (as in paid by taxes).

It's a problem I deal with daily as my personal notebook takes 5-10 seconds to connect to my home network, annoying when I expect near instant connectivity to a known network.

Only the paper authors can fix their PDF, I guess.