Sensepod: AIoT-Based Testbed for Sensing as a Service in Smart Homes

Sensepod: AIoT-Based Testbed for Sensing as a Service (SaaS) in Smart Homes

Sensepod, an AIoT-based testbed designed to promote Sensing as a Service (SaaS) for ambient living, is engineered to support the continuous collection and preprocessing of multimodal sensory data within a smart home environment. This platform is specifically tailored to meet the needs of ambient intelligence applications and offers a dynamic, flexible approach to data collection, allowing researchers to tailor the acquisition process to their specific objectives.

Funding Source: SEED Grant, IIT JODHPUR

Key Features of Sensepod

Continuous and Multimodal Data Collection

Sensepod is designed to collect real-time sensory data from multiple sources within a smart home. The platform supports the integration of various smart sensors that monitor environmental conditions, occupancy, and user interactions.

Application-Centric Approach

Sensepod enables applications such as:

Fire Alarm Systems – Monitoring temperature, smoke, and air quality for fire detection.
Human Occupancy Monitoring – Detecting user presence and movements in different home environments.
Environmental Sensing – Tracking indoor air quality, humidity, and lighting conditions.

Human-Centric Data Processing

Many smart home applications are user-driven, requiring systems to understand human behaviors and patterns. Sensepod is designed to capture human activities and interactions, making it valuable for research in behavior analysis, smart healthcare, and home automation.

Sensepod Sensor Nodes

Sensepod consists of various sensor nodes strategically placed throughout the smart home to ensure comprehensive data collection:

Room Nodes (Ceiling-Mounted Sensors):
Installed on the ceiling of bedrooms and living rooms, these nodes measure:
- Temperature – Monitors room temperature to detect environmental changes.
- Humidity – Tracks moisture levels to assess comfort and air quality.
- CO₂ Levels – Measures carbon dioxide concentration for air quality assessment.
- Light Intensity – Detects ambient lighting conditions to optimize smart lighting solutions.
- MM-Wave Sensors – Determines occupancy levels by detecting human presence and movement.
- Noise Levels – Monitors sound levels for ambient noise analysis and activity recognition.
Washroom Nodes (Water Level Monitoring):
Installed on water tanks to monitor the water level.
Window Nodes (Motion Detection for Windows):
Placed on windows to detect window movements.
Chair Nodes (Occupancy Detection for Seating Areas):
Installed on chairs, beds, and sofas to detect whether they are occupied.

Sensepod Gallery