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IoT-Power-Consumption-WSN-Optimization

This project explores the design and analysis of an IoT-based parking occupancy detection system using an ESP32 microcontroller, an ultrasonic sensor, and esp_now Wi-Fi communication. The study also evaluates energy consumption patterns across different operating states (transmission, deep sleep, and sensor read), and investigates sink node optimization in a Wireless Sensor Network (WSN) to maximize system lifetime. Project Overview

  1. Parking Occupancy Node
  • Implemented in Wokwi using ESP32 + HC-SR04 ultrasonic sensor.
  • Detects whether a parking space is free or occupied.
  • Sends messages via esp_now protocol ("Free" or "Occupied").
  • Utilizes deep sleep to reduce power consumption.
  1. Energy Consumption Estimation
  • Analyzed real power consumption data from three scenarios: Transmission Power (low/high states, idle state) Deep Sleep Cycles (Wi-Fi on/off, sleep mode) Sensor Reading (active vs. idle phases)
  • Data processed using Python (CSV parsing, timestamp handling, histograms).
  • Calculated average power, energy per cycle, and battery lifetime.
  • Battery capacity used: 16,128 J.
  1. Optimization & Improvements
  • Proposed adaptive sensing to reduce redundant transmissions.
  • Suggested batch data transmission for efficiency.
  • Highlighted potential gains in battery life and system reliability.
  1. Wireless Sensor Network (WSN) Sink Optimization
  • Evaluated system lifetime based on sensor-to-sink distances.
  • Found optimal sink position ≈ [6.9 , 7.6].
  • Discussed trade-offs: Fixed sink → simpler, lower cost, but reduced lifetime. Mobile sink → extended lifetime, balanced energy use, but higher complexity.

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