1. Weighting Factors
1.1. Pleasure, Desire, Fun, Lust
1.2. Impact
1.2.1. For BeeLivingSensor
1.2.2. General
1.2.2.1. Biodiversity
1.2.2.2. etc
1.3. Future
1.3.1. Job
1.3.2. Science
1.3.3. University studies
2. Possible Topics
2.1. Users
2.1.1. Bring new users
2.1.1.1. Decrease entry barrier
2.1.1.2. What brings in more users
2.1.2. Keep old users
2.1.2.1. User friendliness and motivation
2.1.2.2. Visualisation
2.2. Bees / Data
2.2.1. New data sources
2.2.1.1. What other data can we take from bees
2.2.2. How to predict diversity
2.2.3. Edge devices
2.2.3.1. Find data that we can extract without uploading videos
2.2.4. Resolution
2.2.5. Video Streaming ?
2.2.5.1. Problem is solved
2.2.5.2. Calculation
2.3. Cloud Theme
2.3.1. Automate REST API testing
2.3.2. Exposing of API / Open data
2.3.3. Running without intervention
2.3.3.1. Automated scaling
2.3.3.1.1. Computer Power
2.3.3.1.2. Future Demand
2.3.3.2. Automated deployment
2.3.3.3. GraphQL
2.3.4. Resillience
2.3.4.1. Security vulnerabilities
2.3.4.1.1. Platform overload
2.3.4.1.2. Exposed ports
2.3.4.1.3. Code injection
2.3.4.2. Restoration from node/pod failure
2.3.4.3. Data redundancy RAID
2.3.4.4. High availability of Apps
2.3.4.5. Alerting
2.3.4.6. Decoupling of services
2.3.5. Maintainability
2.3.5.1. Documentation
2.3.5.2. Monitoring
2.3.5.3. Centralized logging
2.3.5.4. Cloud topology generator
3. Final Goal BLS
3.1. Learning from the honeybees
3.1.1. For this we have to understand their system.
3.2. OS Data Plattform for Data in relation with bees
3.2.1. Data storing
3.2.2. Data Aggregation
3.2.3. Visualisation
3.2.4. Sharing
3.3. Distribute many non invasive devises and and make data accesible and comparalble
3.4. Simple visual Edge Devises for recording and analysing.
3.4.1. Qualcomm
3.4.1.1. Bringing AI at the Edge to smart cameras on the IoT
3.4.1.2. New Digital Camera Technology | Camera Processor Chips | Qualcomm
3.4.1.3. Qualcomm Vision Intelligence 300 Platform | Custom AI Processor for 4K IP & Security Cameras | Qualcomm
3.5. Qestion we could answer Applications
3.5.1. BeePollenTracking
3.5.1.1. Floral Biodiversity
3.5.1.1.1. Dronepicture
3.5.1.1.2. Floral
3.5.1.2. Honeybee resillience
3.5.1.3. Amount of pollen in correlation with nectar intake
3.5.2. Sound
3.5.2.1. swarming
3.5.3. Bee Traffic
3.5.3.1. How many bees are leaving and entering the hive
3.5.3.1.1. Comparing Microclimate location differences in relation with hive placement and hive construction (isolation, etc
3.5.4. Bee Tracking
3.5.4.1. Behavior of bees
3.5.4.1.1. how do they protect the entrance
3.5.4.1.2. How do they fight wasps