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Energy-Efficient IoT Sensor Networks for Smart Cities

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Energy-Efficient IoT Sensor Networks for Smart Cities

  • Use Case: Smart cities rely on IoT sensor networks (air quality, traffic, lighting, waste management, water supply) to improve citizen services and optimize urban infrastructure. However, sensors consume significant energy, leading to higher costs and reduced device lifetime. This project focuses on designing energy-efficient IoT sensor networks that optimize data transmission, reduce power consumption, and still ensure real-time insights for city management.

Objective

  • Minimize energy consumption in large-scale IoT sensor networks.

    Extend sensor lifetime while maintaining real-time data accuracy.

    Leverage Google Cloud AI and Edge computing for adaptive sensing, anomaly detection, and efficient workload distribution.

    Support sustainability goals in smart cities by optimizing power usage.

Project Description

  • The project implements an energy-aware IoT sensor data pipeline integrated with Google Cloud services. Edge AI modules filter and preprocess raw sensor data before transmission, reducing unnecessary communication overhead.

    The cloud platform hosts predictive analytics and anomaly detection models that optimize data scheduling, so sensors transmit only when necessary.

    Dynamic workload balancing ensures that critical urban services (traffic, healthcare, public safety) get prioritized low-latency data, while non-critical data is aggregated and sent periodically to conserve energy.

    This hybrid Edge–Cloud system improves QoS (Quality of Service), reduces energy footprint, and ensures scalable sensor deployment across smart cities.

Key Technologies & Google Cloud Platform Services

  • GCP Service Purpose in Project
    Cloud IoT Core Securely connects, manages, and ingests data from IoT sensors deployed across the city.
    Edge TPU / Coral Devices Runs lightweight ML inference at the sensor/edge level to filter unnecessary data and reduce transmission energy.
    Pub/Sub Provides scalable, low-latency messaging for IoT event streaming between sensors and cloud services.
    Dataflow Real-time and batch processing of sensor data streams for aggregation, filtering, and anomaly detection.
    BigQuery Stores large-scale IoT data for historical analysis, optimization of sensor scheduling, and urban planning insights.
    Vertex AI Trains and deploys predictive ML models (energy-efficient scheduling, fault prediction, anomaly detection).
    Cloud Functions Serverless automation for event-driven actions (e.g., triggering alerts when energy consumption exceeds thresholds).
    Cloud Monitoring + Operations Suite Monitors sensor performance, network efficiency, and energy usage patterns across the smart city.
    Cloud Storage Stores sensor logs, raw telemetry, and processed insights for long-term archival and auditing.