Lightingbeam Blog

Main body TBA, a page about the new Beelink EQ-12, Proxmox, Frigate and Coral TPU to work with Home assistant and Wyze camera for opject detection.
Still actively working on the set-up. Need to figure out the NVR with already working Synology Surveillance Station.

A place holder for my new gear: Unifi U6-Mesh, why I decided to ditch my U6-LR, and my new Sonos in wired mode with new stand, new HomePod Mini and Apple TV and Threads.

Steve Gibson from podcast Security Now (Check out the awesome podcast from Twit!) said the ALL IoT devices should be on a dedicated VLAN (Virtual LAN). The reason is that we have so many IoT and smart home devices in our homes these days, and most of them have some kind of open access to the WAN. What’s more, these devices most likely have the firmware baked into the hardware when the devices were manufactured. If not, the older devices which are still working perfectly might be out of support and missing critical security updates.

Choice of equipment

So I decided to invest a little more and have separate VLANs in my home network. After all, it does not make sense to have all my main devices extra secure while leaving my home network wide open.

Sadly, my recently-upgraded Asus AX-3000 does not support VLAN and LAG(Link Aggregation 802.3ad). It’s a fantastic router/Wifi access point, and it could provide IoT isolation if you want to leverage the guest network. But I do want to have the Home Assistant to control all smart home devices, so a guest Wifi with all my IoT devices not able to see each other is unacceptable.

So, I decided to do some shopping.

I looked at three solutions. Unifi, TP-link Omada, and PF Sense and I chose Unifi eventually.

This post is migrated from pandaespresso.me

Original post:

For the part 2 of my Meetupstreaming analytics, I want to use Spark Streaming to analyze the data. I’ve used all my AWS Educate credits, so I have to terminate the EC2 instance and Kinesis stream I’m using everytime I finished my work. The more painful thing is to fire up a new instance the next time and configure them again.

So to try out Spark Streaming, I decided to use IT Automation to provision and config the streaming pipeline for me. The environment config I’m aiming at is exactly the same as the Meetup Streaming analysis project I did before.

This post is migrated from pandaespresso.me

In this post, we use Amazon Kinesis Streams to collect and store streaming data. We then use Amazon Kinesis Analytics to process and analyze the streaming data continuously. Finally, we use Amazon Kinesis Firehose to export the anomalies data to Amazon Elasticsearch Service (Amazon ES). We then build a simple dashboard in the open source tool Kibana to visualize the result.

Nowadays, streaming data is seen and used everywhere—from social networks, to mobile and web applications, IoT devices, instrumentation in data centers, and many other sources. As the speed and volume of this type of data increases, the need to perform data analysis in real time with machine learning algorithms and extract a deeper understanding from the data becomes ever more important.