About "Clusterization_algorithms" project

Abbreviations, terms and articles links

Abbreviations

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

Terms

General

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

Clustering algorithms

• K-means
• K-means++
• Forel

Route search algorithms

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

References to used articles

• 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

Project goals and objectives

Physical model of system operation

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.

Project tasks

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.