Mālama SensorsLive Demo
Simulated Data Center Monitoring

Real-Time Sensor Dashboard

This demo simulates what Mālama dMRV sensor integration looks like inside a data center. Every reading is blockchain-verified, creating trusted, objective environmental data that enables dynamic optimization.

PUE 1.15
340.5kW
Total Power
● normal
23.4°C
Avg Temperature
17.2L/min
Water Flow
-12% vs avg
132g/hr
CO₂ Rate
84%
GPU Utilization
41%
CPU Utilization

Power Consumption

Real-time facility power draw (kW)

Live
07:48:3707:48:5207:49:0707:49:2207:49:3707:49:5207:50:0707:50:2207:50:3707:50:57319330341352363

Temperature & Water

Ambient temp (°C) and cooling water flow (L/min)

Temp
Water
07:48:3707:48:5207:49:0707:49:2207:49:3707:49:5207:50:0707:50:2207:50:3707:50:5706121824

Facility Zones

Real-time monitoring across 6 data center zones

68 racks monitored
A1GPU Cluster Alpha
Temp
23.1°C
Power
148 kW
Racks
24
A2GPU Cluster Beta
Temp
24.8°C
Power
112 kW
Racks
18
B1Storage Array
Temp
21.2°C
Power
42 kW
Racks
12
B2Network Core
Temp
20.8°C
Power
28 kW
Racks
6
C1Cooling Plant
Temp
18.4°C
Power
38 kW
Racks
0
C2Inference Edge
Temp
26.1°C
Power
67 kW
Racks
8

Blockchain Verification

Cardano mainnet — immutable audit trail

07:50:57Energy Reading0x3dc2e3ab...3dc2#12847293
Verified on-chainCardano L1

Efficiency Scores

AI-computed optimization ratings

87
Energy
92
Cooling
74
Utilization
AI Optimization Insight

Shifting 18% of inference workloads from Zone C2 to Zone A2 during off-peak hours could reduce total facility power by ~14 kW and lower PUE from 1.12 to 1.08.

24-Hour Distribution

Power consumption and water usage by hour

Power
Water
00:0003:0006:0009:0012:0015:0018:0021:00090180270360

Cumulative Savings from Sensor-Driven Optimization

Simulated YTD
12,847
kWh
Energy Saved
5,139
kg CO₂
Carbon Avoided
8,421
liters
Water Conserved

How Mālama dMRV Works

01

Deploy Sensors

IoT sensors installed at rack, row, and facility level measure power, temperature, humidity, water flow, and air quality in real time.

02

Verify on Blockchain

Every reading is cryptographically signed and recorded to Cardano mainnet, creating a tamper-proof, immutable audit trail.

03

AI Analysis

Machine learning models analyze sensor data to identify inefficiencies, predict failures, and recommend real-time optimizations.

04

Dynamic Optimization

Automated workload shifting, cooling adjustments, and resource allocation based on verified sensor data — reducing impact dynamically.

From Estimation to Truth

Move Beyond Estimates.
Measure What Matters.

Current AI energy reporting relies on software estimates, rules of thumb, and self-reported data. Mālama dMRV sensors provide hardware-verified, blockchain-anchored measurements that create a trusted foundation for environmental accountability and dynamic optimization.

Hardware-level energy measurement at rack and facility scale
Blockchain-verified data — tamper-proof and auditable
AI-driven optimization reducing energy, carbon, and water in real time
Standardized reporting aligned with emerging regulatory frameworks

This is a simulated demonstration of Mālama dMRV sensor capabilities. All sensor readings, blockchain hashes, and optimization metrics shown are generated for illustrative purposes. Actual deployment metrics will vary by facility.

Mālama Sensors Demo v1.0