IoT and AI for Waste Collection Optimization

Module: Sustainable Tech | Difficulty: Premium

Fill Level Prediction

where is population density factor, is weather factor, and is noise.

Collection Efficiency

Comparison

Python Implementation

import numpy as np

class SmartWasteManager:
    def __init__(self, n_bins):
        self.n_bins = n_bins
        self.fill_levels = np.zeros(n_bins)

    def predict_fill_level(self, current, days_ahead, collection_day):
        daily_fill_rate = np.random.uniform(0.05, 0.15, self.n_bins)
        predicted = current + daily_fill_rate * days_ahead
        return np.clip(predicted, 0, 1.0)

    def optimize_collection_route(self, fill_levels, threshold=0.7, truck_capacity=20):
        needs_collection = np.where(fill_levels > threshold)[0]
        if len(needs_collection) == 0:
            return []
        route = [0]
        remaining = list(needs_collection)
        current_load = 0
        while remaining:
            last = route[-1]
            nearest = min(remaining, key=lambda x: abs(x - last))
            if current_load + fill_levels[nearest] * 10 <= truck_capacity:
                route.append(nearest)
                current_load += fill_levels[nearest] * 10
                remaining.remove(nearest)
            else:
                break
        route.append(0)
        return route

    def dynamic_pricing(self, fill_level, base_price=0.10):
        if fill_level > 0.9:
            return base_price * 0.5
        elif fill_level < 0.3:
            return base_price * 1.5
        return base_price

Research Insight: IoT-based smart waste collection reduces truck routes by 30-40% and fuel consumption by 25-35%.