根据位置预测路由器的信号强度 [英] Predicting signal strength of a router based on location

问题描述

我正在研究数据，并尝试使用有监督的学习对其进行预测。

问题陈述：让我们考虑下面给出的路由器数据的可能值。



特点

--------

- 名称（独特）

- 品牌：思科，华为，Netgear

- 位置：卧室（B），厨房（K），大厅（H）

- IP地址：独特

- MacAddress：唯一

- 序列号：唯一

- 固件版本：变化如1.0.0,2.0.1,3.1.0等

- 状态：跑步，发现，重启



输出：

-------

力量：强/弱：1/0



管理'强度'输出的规则如下：

- 品牌==思科==>强度==在所有位置都很强。 B + H + K

- 品牌==华为==>强度==仅在大厅和厨房强。 H + K

- 品牌== Netgear ==>强度==仅在厨房强。 K



我们认为品牌和位置仅用于预测信号强度。



样本列车数据
=================

|品牌|位置|实力|

----- ----------------------

|思科|卧室|强|

|华为|卧室|弱|

| Netgear |大厅|弱|

|思科|厨房|强|

|华为|大厅|强|

| Netgear |厨房|强|



样本测试数据

=================

|品牌|位置|优势|

---------------------------

|思科|大厅|强|

|华为|厨房|强|

| Netgear |卧室|弱|



问题：

1-使用机器学习或机器学习解决这个问题陈述是否有点过分？

2-用于解决此类问题的算法/架构是什么？是否正常的神经网络或CNN更适合这个问题考虑未来的扩展？

3-我们可以包含任何其他功能以获得更好的准确性吗？

4.多少钱数据足以开始？





请分享您的建议。



提前致谢!!!



我的尝试：



问题是关于问题分析及其验证。

尝试使用SGD并且似乎有效。

解决方案

训练数据没有与测试数据（位置）的关系;没有有意义的预测是可能的。



训练数据的大小也太小，不能认真对待。

< blockquote> Praveen，RF传播工具应用程序已经创建，针对建筑材料中的多路径进行了优化，精确到8位小数。专业地使用这些工具来创建基于无线路由器放置，天线选择，墙壁，地板，天花板建筑材料等的建筑物内部蜂窝和WiFi信号水平模型，达到最佳程度。完整的设计模型，执行设备安装，然后通过步测空间验证。结果通常在2到3 dB的预测范围内。



您在各种无线路由器上收集数据结果的方法，并且不使用精确的信号电平读数，导致无法控制测量数据的无效尝试的无穷无尽的陷阱，多径环境。对于多个相同的测量，您不会得到两次相同的答案！您没有说明您使用的是2.4GHz还是5GHz频段，但天线的位置需要完全相同。在2.4GHz，1.2英寸的差异完全改变了数据。在5 GHz时，0.5到0.6英寸的重定位差异是多径环境中完全不同的测试设置。此外，人体是一大袋盐水，它吸收RF信号能量和多径，影响测试测量结果。



不要被信号电平误导，请查看路由器的空中连接速率>笔记本电脑客户正在使您需要传递数据并仅查看数据框。使用无线网卡和Ethereal或其他一些无线设备。 AirMagnet很好而且价格昂贵。



如果你不了解射频传播，你应该先停止这项工作并研究传播特性。

I am working on data and trying to make a prediction on it using supervised learning.
Problem Statement: Let's consider we have data of routers as given below with probable values.

Features
--------
- Name (Unique)
- Brand : Cisco, Huawei, Netgear
- Location: Bedroom(B), Kitchen(K), Hall(H)
- IP address : Unique
- MacAddress : Unique
- Serial Number : Unique
- Firmware version: Varying like 1.0.0, 2.0.1, 3.1.0 etc
- state: running, discovering, rebooting

Output:
-------
Strength: Strong/Weak: 1/0

Rules governing 'Strength' output is given below.
- Brand == Cisco ==> Strength == Strong in all locations. B + H + K
- Brand == Huawei ==> Strength == Strong in Hall and kitchen only. H + K
- Brand == Netgear ==> Strength == Strong in kitchen only. K

We consider Brand and Location only for predicting Signal Strength.

Sample Train Data
=================
|Brand |Location|Strength|
---------------------------
|Cisco |Bedroom | Strong |
|Huawei |Bedroom | Weak |
|Netgear|Hall | Weak |
|Cisco |Kitchen | Strong |
|Huawei |Hall | Strong |
|Netgear|Kitchen | Strong |

Sample Test Data
=================
|Brand |Location|Strength|
---------------------------
|Cisco |Hall | Strong |
|Huawei |Kitchen | Strong |
|Netgear|Bedroom | Weak |

Questions:
1- Can this problem statement solved using machine learning or machine learning is an overkill?
2- What algorithm/architecture to be used to solve such a problem? Is normal neural network enough or CNN is more appropriate for this problem consider scaling in future?
3- Can we include any other feature for better accuracy?
4. How much data is enough to start with?


Kindly share your suggestions.

Thanks in advance!!!

What I have tried:

Question is about problem analysis and its validation.
Tried with SGD and it seems to work.

解决方案

The training data has no relation to the "test data" (location); no meaningful "prediction" is possible.

The size of the training data is also "too small" to be taken seriously.

Praveen, RF Propagation tool apps have been created, refined for multipath in building materials to 8 decimal places. The tools are used, professionally, to create in-building Cellular and WiFi signal level models based on wireless router placement, antenna selection, wall, floor, ceiling construction materials, etc. to the finest degree. With a design model complete, equipment installation is performed and then verified by walk testing the space. Results are usually within 2 to 3 dB of predictions.

Your approach to collecting data results on various wireless routers and without using a refined signal level readout leads to an endless quagmire of a futile attempt to correlate measurement data in an uncontrolled, multipath environment. You will not get the same answer twice for multiple identical measurements! You did not state whether you are using 2.4GHz or 5GHz bands, however the placement of your antenna(s) need to be exactly identical. At 2.4GHz, 1.2 inch difference changes the data entirely. At 5 GHz, 0.5 to 0.6 inch relocation difference is an entirely different test setup in a multipath environment. Also, the human body is a large bag of saline which absorbs RF signal energy and multipath which affect the test measurement results.

Rather than be mislead by signal level, look at the over the air connect rate the the router > laptop client are using. You need to be passing data and look at the data frames only. Use wireless card and Ethereal or some other over the air equipment. AirMagnet is good and expensive.

If you don't understand RF propagation, you should stop this effort and study propagation characteristics first.

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