• UAV - Unmanned aerial vehicle
• WSN - Wireless sensor network
• TSP - Travelling salesman problem
• DEEC - Distributed energy-efficient clustering algorithm

• Cluster analysis
• Convex hull algorithms
• Gift wrapping algorithm (Jarvis march)
• Greedy algorithm

• K-means
• K-means++
• Forel

• Brute-force
• Convex hull insertion
• Nearest neighbour
• Spiral route
• FPPWR

• Initial Centroid Selection Method for an Enhanced K-means Clustering Algorithm
• Centroid Initialization Methods for k-means Clustering
• Data Clustering with K-Means++ Using C#
• An energy-efficient distributed clustering algorithm for heterogeneous WSNs
• Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks
• Energy-efficient clustering algorithm based on game theory for wireless sensor networks
• Ameliored Threshold Distributed Energy Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks

We have a field with positioned sensor nodes.

1. Detect sensors position.
2. Create sensor network map.
3. Clustering of nodes (according to specified parameters).
4. Build optimal route for UAV station (according to specified parameters).
5. Collect data from nodes.

1. Create a node generator.
2. Create a node map.
3. Node clustering. Algorithm comparison.
   • k-means++
   • forel
4. Route calculation. Algorithm comparison. With mathematical calculation:
   • Brute-force
   • Nearest neighbour
   • Convex hull insertion (by angle of rotation)
   • Spiral route
   • FPPWR
5. Process of data collection.
   1. DEEC algorithm.
   2. Data transmission types.
      • Direct transmittion.
      • Point-to-point.
   3. Create an energy model of the system.
   4. Create statistic.