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Introduction
Honeybees live in swarms of tens of thousands, gathering nectar. In this process, they carry pollen from one flowering plant to another, pollinating them.
” Close to 75 percent of the world’s crops producing fruits and seeds for human use depend, at least in part, on pollinators[1]. ”
As well as being one of nature’s key pollinators, bees transform nectar into honey. With the help of beekeepers, like David Gerber from Switzerland, this delicious honey is made available for global consumption.
Figure 1: David Gerber’s IoT enabled beehives (Neuchatel, Switzerland)
Bees live in hives. These hives are often located in remote locations, like forests or high mountain pastures. These remote locations make monitoring the health of bees challenging. However, by creating connected solutions using cloud-based services, such as AWS IoT Core and AWS Lambda, beekeepers can implement near real-time monitoring tools to track health parameters for a bee hive. AWS IoT Core is a fully managed cloud service, that lets you connect Internet of Things (IoT) devices and route their messages to AWS without managing infrastructure. AWS Lambda is a serverless compute service allowing you to deploy code without provisioning or deploying servers. In this blog post, we walk through an IoT architecture and provide a hands-on example of how to create and test your own serverless anomaly detector to improve your operations.
Prerequisites
For this walk through, you should have the following prerequisites:
The hands-on example is written in Java and the CDK infrastructure code is written in Typescript. It’s not required to have deep knowledge in either to deploy and run the example. This solution can run entirely within the AWS Free Tier for one or even several executions. Clean-up instructions are provided at the end of this post.
Gaining insights into hive health
We gain insights by measuring and sending IoT events. Choosing what to measure about a hive is important. The right metric allows us to gain insights into the lives of the bees. In Figure 2, we can see the variation of a hive’s weight as the days go by. At first glance, the data appears quite chaotic. However, a closer glance reveals a wealth of information.
Figure 2 : Weight of hive over two weeks
From Figure 2, we can chart a hive’s major events over 24 hours.
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