Low-Cost Adaptive Monitoring Techniques for the Internet of Things

The AdaM Framework

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…

Research QuestionCan 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+1𝑇 𝑖+1

Metric Stream dynamically sampledMetric Stream with

The AdaM Framework

Raspberry Pi

Arduino

Beacons

ProcessingUnit

DisseminationUnit

AdaptiveSampling

Knowledge Base

AdaM

IoT Devicemetrics

adjust filter range

AdaptiveFiltering

API

Activity Trackers

SensingUnit

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.

AdaM in action!

Steps Heartrate

Calories

94% accuracy!

96% accuracy!

91% accuracy!

AdaM in action!

6x less processing! 4x less network traffic!

4x less energy usage!

+ AdaM

3 - 5 days

7 - 8 days