IoT Sensor Network Energy Optimization Dataset - 500 Simulation Scenarios

Dataset Purpose

This dataset addresses one of the most critical challenges in IoT deployments: energy efficiency in resource-constrained sensor networks. It provides comprehensive performance comparisons between traditional routing protocols and advanced ICSHSO (Improved Cuckoo Search-based Harmony Search Optimization) techniques.

Dataset Structure

Each of the 500 simulation instances represents a unique network configuration with varying parameters: network scale (50 to 200 sensor nodes), deployment area (100×100m to 500×500m), initial energy (0.5J to 2.0J per node), transmission rates (10 to 50 packets per second), and optimization methods (Traditional vs ICSHSO-based routing).

Performance Metrics (13 Columns)

Network configuration parameters include Simulation_ID, Nodes, Area_m2, Initial_Energy_J, Transmission_Rate_pps, and Optimization_Technique. Performance outcomes include Network_Lifetime_Improvement_% (8.9% - 45.2%), Energy_Consumption_Reduction_% (6.5% - 38.7%), PDR_% (78.4% - 95.3%), End_to_End_Delay_ms (12.5ms - 28.3ms), Residual_Energy_Utilization_% (65.1% - 87.2%), Reduction_in_Redundant_Transmissions_% (15.2% - 42.1%), and binary Optimization_Effective target (0 or 1).

Key Findings

The dataset reveals up to 45% improvement in network lifetime, nearly 40% reduction in energy consumption, PDR consistently above 95% for optimized networks, and 50% reduction in end-to-end delay compared to traditional routing. Perfect for ML classification, regression, clustering, and feature importance analysis.