Giter Site home page Giter Site logo

helium_analysis_tools's Introduction

Helium Analysis Tools

This repo contains generic tools for analyzing PoC activity. These are provided for information only, there is no guarantee of accuracy.

Installation

To install simply run

git clone https://github.com/Carniverous19/helium_analysis_tools.git

At time of writing only Python3.6+ is required but you may want to install numpy and matplotlib for additional tools that may be added.

Tools

A lot of these tools require a cache of hotspots from Helium's hotspot API.
This cache will be created automatically if not present in the helium_analysis_tools folder called hotspots.json. The file is not updated on each run so may get stale.
A warning message will be printed if it is >48 hours old and you may get runtime errors if a hotspot appears in challenge activity that is not in the cache. To refresh the cache run

python3 utils.py -x refresh_hotspots

A list of tools and brief description is provided below

analyze_hotspot.py

This file provides useful reports on an individual hotspot's PoC activity. Note this code allows you to specify either hotspot name (with dashes) or hotspot address. It is suggested to always use the hotspot address as there is no guarantee that a hotspot has a unique name (there are already 3 conflicts among ~8,500 hotspots) If there is a hotspot naming conflict only the last hotspot with that name returned will be considered.

see

python3 analyze_hotspot.py -h

for more details on arguments.

poc_summary

This report gives a summary of recent hotspot PoC activity.

to run the poc_summary report run:

python3 analyze_hotspot.py -x poc_summary --address {hotspot address}

First this report lists average blocks between PoC targeting. This should be around every 120 blocks (chain variable dependent), but emprical data shows realistic numbers of around 150-190 blocks are still healthy. It also shows how often your hotspot submits poc receipts from challenge it creates. These are used to determine eligibility to be targeted. Your hotspot must submit a receipt every 300 blocks (chain variable dependent) to be eligible for PoC.

analyzing 500 challenges from block 525444-485167 over 25 days, 2 hrs
PoC Summary Report for: name-name-name

PoC Eligibility:
successfully targeted   83 times in 40277 blocks (every 485 blocks)
        longest untargeted stretch: 2407 blocks
challenger receipt txn  139 times in 40277 blocks (every 290 blocks)
        longest stretch without challenger receipt: 1245 blocks
        hotspot was untargetable for: 8241 blocks (20.5% of blocks)

In this example we can see some unhealthy behavior. The hotspot is only successfully targeted (meaning a poc_receipt_v1 transaction was submitted) every 485 blocks which is significantly less frequent than the chain variable and emprical data. Also there are large periods of time where this hotspot was ineligible to be targeted for PoC due to long stretches of blocks without poc receipts as challenger.

Secondly, a table summarizing multihop PoC for the selected hotspot is presented:

PoC Hop Summary
Hop | planned | tested (%) | passed (%) |
-----------------------------------------
  1 |     63  |  63 (100%) |  61 ( 97%) |
  2 |    250  | 194 ( 78%) |  97 ( 39%) |
  3 |    166  |  57 ( 34%) |  25 ( 15%) |
  4 |    133  |  16 ( 12%) |   4 (  3%) |
  5 |    116  |   4 (  3%) |   3 (  3%) |

The planned column lists the number of times your hotspot was planned to be challenged per hop position if all previous hops completely successfully. The tested column lists the number of times your hotspot was actually reached meaning the previous hop was successfull. The passed column lists the number of times your hotspot successfully completed the challenge. Note the passed percentage is out of planned, not tested.

poc_v10

Next is poc_v10 which looks at recent challenge activity for the selected hotspot and reports the total number of witness and PoC hops that violate PoCv10 requirements. For information on PoC v10 requirements see the blog post Blockchain PoC v10.

to run the poc_v10 report run:

python3 analyze_hotspot.py -x poc_v10 --address {hotspot address}

There are two tables in this report. The first output table gives a summary overview with an example shown below:

analyzed 202 challenges from 516276-513758
PoC v10 failures for name-name-name
SUMMARY
Category                   | Total | bad RSSI (%) | bad SNR (%) |
-----------------------------------------------------------------
Witnesses to hotspot >300m |   154 |    0 (  0%)  |   13 (  8%) |
Hotspot witnessing  >300m  |    66 |    0 (  0%)  |    0 (  0%) |
Hotspot PoC receipts       |    24 |    0 (  0%)  |    0 (  0%) |

As indicated in the first row, witnesses within 300m meters are ignored as these shouldnt (arent?) be rewarded anyway. The column "bad RSSI" means the signal violated Free Space Path Loss estimation.
The column "bad SNR" means the RSSI was too low for the given SNR. High SNR but low RSSI means noise is very very low which is suspect.

A second table gives more detail breaking down results by hotspot. This doesnt differentiate between types of interaction (witness or PoC hop) but helps to identify if failures are isolated to a few neighbors or across all neighbors (meaning its likely a problem with your hotspot).
Note: Although I say "bad neighbor" that doesnt mean the hotspot listed is actually gaming, it could be false positives, bad signal between two hotspots due to environmental factors or the reference hotspot that is not at asserted location.

An example table is shown below:

BY "BAD" NEIGHBOR
Neighboring Hotspot           | owner | dist km | heading |  bad RSSI (%)  |  bad SNR (%)   |
------------------------------+-------+---------+---------+----------------+----------------|
colossal-aquamarine-okapi     | same  |   4.0   | 280  W  |  30/ 30 (100%) |   0/ 30 (  0%) |
keen-navy-seagull             | same  |   4.3   | 285  W  |  25/ 48 ( 52%) |   0/ 48 (  0%) |
dizzy-magenta-haddock         | same  |   3.5   | 285  W  |  73/ 73 (100%) |   0/ 73 (  0%) |
faithful-tiger-crab           | to5cr |  19.2   | 145 SE  |   0/ 24 (  0%) |   4/ 24 ( 17%) |
quaint-mulberry-raccoon       | same  |   4.3   | 295 NW  |  40/ 40 (100%) |   0/ 40 (  0%) |
harsh-spruce-fox              | 49CDa |   3.8   | 285  W  |   7/ 60 ( 12%) |   0/ 60 (  0%) |
cuddly-scarlet-walrus         | 7xMGF |   7.7   | 130 SE  |   0/ 25 (  0%) |   1/ 25 (  4%) |
strong-tin-hare               | yjksQ | 186.6   | 140 SE  |   2/  2 (100%) |   0/  2 (  0%) |

The second column "owner" indicates whether the hotspot has the same owner as the reference hotspot or gives the last 5 digits of the base58 encoded owner field. The additional columns in the table are distance between hotspots, heading (degrees and compass) from reference hotspot and the breakdown of RSSI or SNR failures.

poc_reliability

Second is a report poc_reliability which analyzes the reliability of the specified hotpsot receiving PoCs from its neighbors as well as transmitting to neighbors. With the current PoC reward system transmitting to the following hotspot is less important since transmissions are rewarded as long as any hotspot receives the transmission (witness or next hop). If your hotspot cannot reliably receive challenges from its neighbors it is losing out on rewards.

Note: This does not take into account receives that fail PoCv10 thresholds. "recv" means the signal was received, not that the receipt contains valid rssi/snr.

To run the poc_reliability report run:

python3 analyze_hotspot.py -x poc_reliability --address {hotspot address}

An example output table is:

analyzing 500 challenges from block 516256-498146 over 11 days, 11 hrs
PoC hops from: name-name-name
to receiving hotspot           | owner | dist km | heading | recv/ttl | recv % |
--------------------------------------------------------------------------------

boxy-green-copperhead          | wuMEh |    8.3  |  145 SE |   2/  4  |    50% |
acidic-lime-manatee            | dwZfH |    8.8  |  145 SE |   3/  6  |    50% |
joyous-grape-chicken           | wuMEh |    6.7  |  120 SE |   2/  2  |   100% |
merry-sage-baboon              | same  |    8.7  |  135 SE |   0/  4  |     0% |
cuddly-scarlet-walrus          | 7xMGF |    7.7  |  130 SE |   2/  4  |    50% |
flaky-magenta-pigeon           | GcK64 |   13.6  |  125 SE |   1/  6  |    17% |
bouncy-neon-cottonmouth        | same  |    3.2  |  290  W |   2/  2  |   100% |
other ( 7)                     |       |  2.3-33 |   N/A   |   5/  7  |    71% |
                                                           ---------------------
                                                     TOTAL |  17/  35 |    49% |



PoC hops to: name-name-name
from transmitting hotspot      | owner | dist km | heading | recv/ttl | recv % |
--------------------------------------------------------------------------------
rapid-lemon-aardvark           | xHq7t |   22.3  |  150 SE |   2/  9  |    22% |
fast-aqua-jellyfish            | pNiHj |    7.2  |  130 SE |   0/  2  |     0% |
keen-navy-seagull              | same  |    4.3  |  285  W |  13/ 16  |    81% |
merry-sage-baboon              | same  |    8.7  |  135 SE |   0/  3  |     0% |
boxy-green-copperhead          | wuMEh |    8.3  |  145 SE |   3/  3  |   100% |
joyous-grape-chicken           | wuMEh |    6.7  |  120 SE |   2/  5  |    40% |
energetic-eggshell-hippo       | B2gu4 |    3.1  |  285  W |   2/  3  |    67% |
bright-brick-alligator         | 3oHpr |   10.7  |  145 SE |   0/  3  |     0% |
other ( 7)                     |       |  2.6-30 |   N/A   |   3/  7  |    43% |
                                                           ---------------------
                                                     TOTAL |  25/  51 |    49% |

These tables count the total number of times the hotspot failed to receive an RF transmission (meaning previous hop passed) as well as the number of times the target hotspot transmitted and the following hop failed to receive. For hotspots with lots of interactions, any neighbor with a total of 1 interaction is pooled together into an "other" category. The second column "owner" indicates whether the hotspot has the same owner as the reference hotspot or gives the last 5 digits of the base58 encoded owner field. The remaining columns give the distance between the two hotspots in km, the heading from the reference hotspot to neighbor in degrees and compass heading as well as the total interaction counts and pass percentage.

helium_analysis_tools's People

Contributors

carniverous19 avatar jamiew avatar maco2035 avatar philltran avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.