1 00:00:00,250 --> 00:00:03,882 In this data point, frank notices a unique data collection 2 00:00:03,946 --> 00:00:07,390 device while on holiday in Hilton Head, South Carolina. 3 00:00:08,210 --> 00:00:11,722 Well, hello, LinkedIn, YouTube, Facebook, Twitch, 4 00:00:11,866 --> 00:00:15,550 and Twitter or X or whatever it's called 5 00:00:15,620 --> 00:00:19,440 this week. My name is Frank Lavinia. And I am 6 00:00:20,290 --> 00:00:23,600 Juan. I'm on vacation in 7 00:00:23,970 --> 00:00:27,558 Hilton Head, South Carolina. And one of the things 8 00:00:27,724 --> 00:00:29,430 that happened was hurricane 9 00:00:31,930 --> 00:00:35,686 ran through here. But fortunately, by the time IoT got to us, it was 10 00:00:35,708 --> 00:00:39,226 pretty weak. We didn't lose power. There are a lot of down trees and stuff 11 00:00:39,248 --> 00:00:41,980 like that, but one of the things 12 00:00:43,230 --> 00:00:47,034 we were watching, the Weather Channel, apparently Florida got hit really bad, I think 13 00:00:47,072 --> 00:00:50,774 big Bend, Florida. My thoughts and prayers 14 00:00:50,822 --> 00:00:54,526 go out to them. It looks like it was pretty badly hit. Could have been 15 00:00:54,548 --> 00:00:58,126 worse, I guess. But still, a category three hurricane is 16 00:00:58,228 --> 00:01:00,080 nothing to play games with. 17 00:01:01,810 --> 00:01:04,980 But the thing I wanted to talk about here, yes, 18 00:01:06,230 --> 00:01:09,986 maybe it was a category four, you're right. My production assistant here, 19 00:01:10,168 --> 00:01:12,340 who has been helping me test out the system, 20 00:01:14,310 --> 00:01:16,580 I'll explain what I'm testing out in a 21 00:01:17,510 --> 00:01:21,202 so actually, a couple of interesting 22 00:01:21,256 --> 00:01:24,822 bikes just went by. One of the great things about 23 00:01:24,876 --> 00:01:28,578 Hildehead Island, aside from IoT being on the sea and all that, is that there's 24 00:01:28,594 --> 00:01:32,186 a number of bike trails through here, although the sign calls them 25 00:01:32,208 --> 00:01:35,642 leisure trails because they're not strictly for bikes. People run on them, people 26 00:01:35,696 --> 00:01:39,466 walk on them, people take their scooters on them, 27 00:01:39,488 --> 00:01:42,238 et cetera, et cetera. So one of the things I noticed actually a couple of 28 00:01:42,244 --> 00:01:45,614 days ago, and this proves that I'm always thinking about data, but I guess you 29 00:01:45,652 --> 00:01:49,418 already knew that. Is there's 30 00:01:49,434 --> 00:01:53,090 something here I saw in two different places, and I think it's interesting, 31 00:01:53,160 --> 00:01:56,994 I've seen some variant of these along highways throughout my 32 00:01:57,032 --> 00:01:58,980 life, but it's called Metro Count. 33 00:02:00,550 --> 00:02:02,420 And from what I can tell, 34 00:02:04,470 --> 00:02:07,766 it's bolted to the tree for one. Right. So I did a 35 00:02:07,788 --> 00:02:11,414 quick actually, the URL is right down there, metrocount.com. 36 00:02:11,532 --> 00:02:15,366 Not a commercial for Metrocount. I did a quick look 37 00:02:15,388 --> 00:02:19,098 at their website. Apparently what they do is that they have these sensors in the 38 00:02:19,104 --> 00:02:22,906 ground, and if you can see them, hopefully you 39 00:02:22,928 --> 00:02:25,420 can see them. And 40 00:02:27,230 --> 00:02:30,380 what this does let's see what the other end looks like. 41 00:02:34,370 --> 00:02:37,834 These are two separate, according to the website, pneumatic 42 00:02:37,882 --> 00:02:40,990 tubes that are placed about this far apart. 43 00:02:42,690 --> 00:02:46,366 Sorry. And here goes a bike 44 00:02:46,398 --> 00:02:50,178 now, kind of see, and for those of you listening to 45 00:02:50,184 --> 00:02:53,346 this on the podcast, I will be sure to include links and stuff and 46 00:02:53,368 --> 00:02:55,860 pictures. But 47 00:02:57,110 --> 00:03:00,642 I've seen this in a couple of places here within this particular resort, 48 00:03:00,786 --> 00:03:04,534 and I can only guess that they're trying to figure out 49 00:03:04,572 --> 00:03:07,986 how much use people are getting on the bike trails. I don't know 50 00:03:08,028 --> 00:03:11,514 why, but it is probably going to be an interesting data point. They have these 51 00:03:11,552 --> 00:03:14,842 in I've seen at least two places here, these Metro Count 52 00:03:14,896 --> 00:03:18,746 systems. And I looked at their 53 00:03:18,768 --> 00:03:22,574 website briefly, and apparently they 54 00:03:22,612 --> 00:03:25,802 can tell between pedestrians, 55 00:03:25,866 --> 00:03:29,178 bikes and vehicles like cars. 56 00:03:29,354 --> 00:03:32,766 Cars and bikes, I think are pretty easy to figure out. Pedestrians, I 57 00:03:32,788 --> 00:03:36,318 suppose if one gets hit and the other one doesn't, 58 00:03:36,494 --> 00:03:40,306 they might do that. Plus there's also the timing incident of it. And 59 00:03:40,328 --> 00:03:43,220 there's actually a pretty lengthy section there. 60 00:03:43,910 --> 00:03:46,790 The data analytics. The data, they do analytics or they provide 61 00:03:46,860 --> 00:03:50,438 analytics and presumably an AI model of some sort. 62 00:03:50,524 --> 00:03:53,000 They can tell you what type of vehicle it is. So 63 00:03:54,330 --> 00:03:58,166 my junior engineer here 64 00:03:58,348 --> 00:04:02,060 was running back and forth in this scooter hoping to see would we know, 65 00:04:02,670 --> 00:04:05,146 right, that's what you were doing, you were trying to see if it registered the 66 00:04:05,168 --> 00:04:08,406 scooter. So we don't have access to the data that it's 67 00:04:08,438 --> 00:04:12,006 producing, but we can infer that it could 68 00:04:12,048 --> 00:04:15,854 probably tell based on the timing and the 69 00:04:15,892 --> 00:04:19,662 weight. It could definitely tell between bikes. It might even be able to tell between 70 00:04:19,716 --> 00:04:23,466 kids bikes and adult bikes. Obviously, vehicles 71 00:04:23,498 --> 00:04:27,058 are going to be much heavier and the timing of it, they can probably, 72 00:04:27,144 --> 00:04:30,706 based on the distance infer the speed, the 73 00:04:30,728 --> 00:04:34,226 distance apart. Although since it's not really fixed, you can, 74 00:04:34,408 --> 00:04:38,198 I guess, mess with the wiring and kind of mess that up. I 75 00:04:38,204 --> 00:04:41,926 don't know. But I just 76 00:04:41,948 --> 00:04:44,760 find it interesting that they are collecting this type of data 77 00:04:47,370 --> 00:04:50,790 on the island and I didn't get to shows you that data is everywhere. 78 00:04:50,870 --> 00:04:54,220 Right? So just a fascinating look 79 00:04:54,670 --> 00:04:58,362 kind of at the box, one last look at the box. And 80 00:04:58,416 --> 00:05:02,266 if anyone within the sound of my voice works for Metro Count, I'll speak 81 00:05:02,288 --> 00:05:04,870 for Andy here. Usually I don't like speaking for Andy, but I would love to 82 00:05:04,880 --> 00:05:08,494 have you, I'm sure Andy would too love to have you on the show and 83 00:05:08,532 --> 00:05:12,174 kind of talk about how this is used and how this works. Obviously nothing 84 00:05:12,292 --> 00:05:16,080 proprietary, but I would imagine what this is doing is this is 85 00:05:16,870 --> 00:05:20,340 let's call it what is, right? It's an edge device, right? And it's probably 86 00:05:20,710 --> 00:05:24,050 I don't see an antenna, but that doesn't mean anything anymore. 87 00:05:26,090 --> 00:05:29,558 And I left my radio wave detection thing 88 00:05:29,724 --> 00:05:33,526 at home, which I wanted to 89 00:05:33,548 --> 00:05:36,200 bring it, but the missus wasn't really into that. 90 00:05:37,610 --> 00:05:40,860 These are the conversations that engineer families have. 91 00:05:41,630 --> 00:05:45,418 But I think it's interesting. I'd love to know kind of like so 92 00:05:45,504 --> 00:05:49,258 I'm assuming that these are some kind of robotic tubes based on what the website 93 00:05:49,344 --> 00:05:52,750 described. And there's some kind of sensor in there, in here 94 00:05:52,900 --> 00:05:56,718 that will register probably both timestamp and 95 00:05:56,724 --> 00:06:00,446 the amount of pressure and weight, potentially. And I lost. There he 96 00:06:00,468 --> 00:06:04,174 is. Probably that's how they do it. He's jumping over 97 00:06:04,212 --> 00:06:07,554 it. So if you don't touch any of those, it doesn't register you. 98 00:06:07,752 --> 00:06:11,266 So I guess a hoverboard, a proper hoverboard like from Back to the Future would 99 00:06:11,288 --> 00:06:15,138 not register. Although if 100 00:06:15,304 --> 00:06:18,740 the antigravity pad would do that. So these are the types of 101 00:06:19,510 --> 00:06:23,046 we're going into nerd territory here. You're going to put a leaf on it. You 102 00:06:23,068 --> 00:06:26,726 think a leaf will register? Probably not. I don't know. We'll find out. 103 00:06:26,748 --> 00:06:29,770 Someone's going to be looking at this data and being like, what the heck? 104 00:06:30,670 --> 00:06:34,170 There's a leaf on this. Assuming it's that sensitive. 105 00:06:35,790 --> 00:06:39,158 What I'm assuming is happening here, I'm kind of reverse engineering this on the fly, 106 00:06:39,254 --> 00:06:42,862 is that whatever weight gets pushed on there moves some 107 00:06:42,916 --> 00:06:46,766 bit of air through the system. That device there 108 00:06:46,868 --> 00:06:50,670 will register it, presumably to timestamp, too. There's probably 109 00:06:50,740 --> 00:06:54,020 at least two, I would imagine, right? One for each one. And 110 00:06:54,710 --> 00:06:58,546 I suppose one measures speed, and the 111 00:06:58,568 --> 00:07:02,020 other one measures weight. And you have timing. You can kind of figure out 112 00:07:03,110 --> 00:07:06,946 you can assume the weight. You can infer the weight and get 113 00:07:06,968 --> 00:07:10,040 the weight, but you can infer the speed based on how that's going. 114 00:07:10,490 --> 00:07:14,326 So I don't know. I just think it's interesting. It's kind of sad that I'm 115 00:07:14,348 --> 00:07:18,006 on vacation, and I'm still thinking about data, but I 116 00:07:18,028 --> 00:07:21,818 didn't choose data life. Data life chose me. So from 117 00:07:21,984 --> 00:07:25,786 sunny Hilton Head Island. No, 118 00:07:25,808 --> 00:07:29,100 no, it's good on the vacation. It's just kind of funny that I'm thinking about 119 00:07:31,630 --> 00:07:35,054 data and stuff like that. But as I said, I didn't choose a data 120 00:07:35,092 --> 00:07:38,878 life. The data life chose me. And the little one wants to 121 00:07:38,884 --> 00:07:42,586 go back to the beach. I can't say IoT I blame him. So from Sunny 122 00:07:42,778 --> 00:07:45,818 we're going to go back to the beach. I promise. This is like the Dunkin 123 00:07:45,834 --> 00:07:49,666 Donuts episode all over again. All right, just 124 00:07:49,688 --> 00:07:52,866 just 1 second. I'm going to close this out. So thanks for watching, and thanks 125 00:07:52,888 --> 00:07:56,626 for listening, because I'm going to make this a data driven episode, too. But I 126 00:07:56,648 --> 00:08:00,498 just think it's cool, and I just think it's interesting that I'm curious 127 00:08:00,594 --> 00:08:04,278 how the community is going to use this data. Are they trying to justify the 128 00:08:04,284 --> 00:08:08,118 maintenance, the upkeep on these things, and they're just trying to get 129 00:08:08,124 --> 00:08:11,670 raw data on how this is used? How many people do it? I do wonder, 130 00:08:11,740 --> 00:08:15,446 since E Scooters are technically not allowed, are they going to 131 00:08:15,468 --> 00:08:19,254 use this to kind of figure out Escooters? I'm not saying 132 00:08:19,292 --> 00:08:22,606 I know anyone in my family that has an E Scooter with us. I'm not 133 00:08:22,628 --> 00:08:25,406 saying that. I'm just saying let's put it out there. I wonder if they're trying 134 00:08:25,428 --> 00:08:28,720 to detect hoverboards and things like that with this system. 135 00:08:30,450 --> 00:08:34,142 So with that, I'm going to end this 136 00:08:34,196 --> 00:08:37,840 and signing off from Sunny Hill Head, South Carolina, and 137 00:08:39,010 --> 00:08:39,980 you have a good day.