1. Low-Cost Adaptive Monitoring
Techniques for the Internet of Things
The AdaM Framework

2. The Challenges…
Challenge 1 - Taming data volume and data velocity with limited
processing and network capabilities
Challenge 2 - IoT devices are usually battery-powered which means
intense processing leads to less battery-life
Data Volume Network Bandwidth Battery Life
The Problems…

3. Research Question
Can we reduce the volume of IoT
generated data while at the same time
reduce on device processing to preserve
battery life?What if:
• A framework could predict metric stream evolution and dynamically
adapt the rate at which metrics are collected…
• Filter out metric values when consecutive values do not differ and
adapt to ensure accuracy defined by users is always met…
𝑠𝑖
𝑠𝑖+1
𝑇𝑖+1
Metric Stream 𝑀′ dynamically sampledMetric Stream 𝑀 with 𝑇 = 1𝑠

4. The AdaM Framework
Raspberry Pi
Arduino
Beacons
Processing
Unit
Dissemination
Unit
Adaptive
Sampling
Knowledge
Base
AdaM IoT Device
metrics
adjust filter
range
Adaptive
Filtering
A
P
I
Activity Trackers
Sensing
Unit
adjust
sampling rate
filtered
metrics
• Reduces on device processing, energy consumption and allocated bandwidth
• Reduces volume and velocity of data generated in streaming networks
• Achieves a balance between efficiency and accuracy
• AdaM dynamically adapts the monitoring intensity of IoT devices
based on metric stream evolution and variability
AdaM: an Adaptive Monitoring Framework for Sampling and Filtering on IoT Devices. Trihinas, D.; Pallis, G.; and Dikaiakos, M. D. In 2015
IEEE International Conference on Big Data, (IEEE BigData 2015), pages 717--726, 2015.

5. AdaM in action!
Steps Heartrate
Calories
94%
accuracy!
96%
accuracy!
91%
accuracy!

6. AdaM in action!
6x less processing! 4x less network traffic!
4x less energy usage!
+ AdaM
3 - 5 days
7 - 8 days