Low-Cost Approximate and Adaptive Monitoring Techniques for the Internet of Things

3/13/2017 1Demetris Trihinas 1
Low-Cost Approximate & Adaptive Monitoring
Techniques for the Internet of Things
Demetris Trihinas
trihinas@cs.ucy.ac.cy
PhD Candidate
Department of Computer Science
University of Cyprus

Cloud Computing
Big Data Systems and Analytics
Internet Computing and IoT
Online Social Networks
Activity Areas
Current EU-Funded
Projects
http://linc.ucy.ac.cy/

The Internet of Things
The physical world is now becoming an information system
Exchanging continuous data streams with other network-
enabled devices, systems and humans…
Physical (battery-powered) and network-enabled devices
with smart processing capabilities…

Why is IoT Just Now Becoming a Reality?
Nano Bio-Sensors and
Printed Electronics
Communication
Protocols
Data-Mining
Learning Algorithms
- Distributed closed neighbors
- Outlier/event detection
- Pattern matching
- Stream processing
- Learning algorithms
- ….
“The Emerging Internet of Things Market Perspective: A Survey”, Perera et al., IEEE Trans. Computing, 2015
“Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions”, J. Gubbi and R. Buyya, FGCS, 2013
The Building Blocks of the Internet of
Things

IoT in Agriculture
Precision Farming
Detect crop condition and spread
nutrients to parts of field with need
Cattle as a Service
Detect optimum breading
period, monitor health and
dairy protection
“Connected Cattle: Wearables are Changing the Dairy Industry”, Kathy Pretz, IEEE Magazine: the Institute, May 2016.

IoT in Medicine and Health Tracking
Precision Medicine
Pill-shaped cameras traversing
human body
“Fitbit data led doctors to shock a patient’s heart”, Steven Dent, Engadget, Apr 2016.
Wearables
Biosignal and activity tracking

IoT in Intelligent Transportation Systems
Smart Traffic Flow
In Toronto travel time
was reduced by 26%
Smart Parking
In California carbon emissions
were reduced by 730 tons per
15 block radius
“Can smart traffic lights ease Toronto’s road congestion?”, Joseph Hall, Toronto Star, Jun 2014.

The “Big Data” in IoT
[IDC, Jun 2014]
[Cisco, Apr 2011]
IoT Monitoring Data
2% of digital universe in
2012
Projections for >12% in
2020
[Gartner, Mar 2015]
IP Traffic 1.6ZB in 2018, 57% from IoT Devices
[Cisco, Apr 2014]

Big Data and the Cloud

IoT and Cloud Computing
Driving Cloud Adoption for IoT Developers
- Simplified resource management
- low capital expenses to get started
[IoT Developer Survey, Eclipse Foundation, 2016]
67% of production-ready
IoT services reside in the
cloud
[State of IoT, Embarcadero, 2016]

So…is Cloud Monitoring Really
Ready for the Internet of Things?

Monitoring Topologies
Scalability and Single points of failure
Monitoring servers closer to the root are overloaded
with relay costs preventing scalability
“JCatascopia: Monitoring Elastically Adaptive Applications in the Cloud”, D. Trihinas, G. Pallis and M. D. Dikaiakos, IEEE/ACM CCGrid 2014

Monitoring as a Service (MaaS)
• Eases management of the monitoring infrastructure
• Service providers enable multi-tenant and scalable
monitoring over the internet
• Pay-as-you-use business model (although usually hourly)
Monitoring libraries
for metric collection from
popular languages and
frameworks
REST API to
insert/extract
monitoring data
Shared data warehouse with
services to query, process and
view analytic insights
custom app-specific metrics
managed by cloud provider

Augmenting IoT with the Cloud
“
app
The Harsh Reality
Network latencies, bandwidth limitations,
pricing (in/out cloud traffic is billed),
constant location awareness
Datacenter
Interesting Articles
“The cloud is not enough: Saving IoT from the cloud”, B. Zhang et al., Usenix HotCloud 2015
“Taking the internet to the next physical level”, V. Cerf and M. Senges, IEEE Computer, 2016
The IoT Developer Perspective

The Cost of Monitoring
Based on AWS pricing scheme (May 2015)
for a 3-tier (video) streaming application
$0.12/GB
network traffic
“Monitoring Elastically Adaptive Multi-Cloud Services”, D. Trihinas, G. Pallis and M.D. Dikaiakos, IEEE TCC 2016

P2P Monitoring
• Monitored elements form Gossip Overlay Network to propagate monitoring
info -> ease coordination and query computation
• Gossip denotes periodic and pairwise propagation of the current state between
monitored peers
“Monitoring Elastically Adaptive Multi-Cloud Services”, D. Trihinas, G. Pallis and M.D. Dikaiakos, IEEE TCC 2016
“Self managing monitoring for highly elastic large scale cloud deployments”, S. Ward et al., ACM DIDC 2014

Edge Computing
Init
Sense
Act
Sleep
Receive
Decide
Transmit
A term coined to reflect data processing and decision-making on
“smart” devices that sit at the edge of IoT networks
Init
Sense
Transmit
Sleep
“The promise of edge computing”, W. Shi and S. Dustdar, IEEE Computer, 2016
“The swarm at the edge of the cloud”, E. Lee et al., IEEE Design & Test, Jun 2014
Simple
Sensing
Edge
Computing
<10ms
Big Data
Traffic
>100ms
ProcessingNo Processing

Challenge 1 – Taming data volume and data velocity with limited
processing and network capabilities in the IoT/Edge realm
Challenge 2 - IoT devices are usually battery-powered which
means intense processing leads to less battery-life
Processing and data dissemination are
the main energy drains in embedded
and mobile devices
Interesting Article
“Practical data prediction for real-world wireless sensor networks”, U. Raza and T. Palpanas, IEEE TKDE, 2015

Challenge 3 – Security and data privacy
October 2016, DDOS Attack

Formulating the Challenges as
Research Questions

Preliminaries
• A monitoring stream 𝑀 = {𝑑𝑖}𝑖=0
𝑛
published by a monitoring source is a large
stochastic sequence of i.i.d datapoints, denoted as 𝑑𝑖 , where 𝑖 = 0, 1, … , 𝑛
and 𝑛 → ∞
• A datapoint 𝑑𝑖 is a tuple 𝑚𝑖𝑑, 𝑡𝑖, 𝑣𝑖 described by a unique identifier for the
monitoring source 𝑚𝑖𝑑, a timestamp 𝑡𝑖 and a value 𝑣𝑖
• A datapoint may have a set of other attributes (e.g., location) although for
brevity we will omit other attributes without loss of generality
𝑑𝑖
𝑑𝑖+1
Monitoring Stream M

Periodic Sampling
• The process of triggering the collection mechanism of a monitored source
every 𝑇 time units such that the 𝑖 𝑡ℎ datapoint is collected at time 𝑡𝑖 = 𝑖 ∙ 𝑇
𝑑𝑖
𝑑𝑖+1
Metric Stream M sampled every T = 1s
Compute resources and energy are wasted while generating large data volumes at a high velocity
Metric Stream M sampled every T = 10s
Sudden events and significant insights are missed

More processing
(e.g., peak detection
for hr monitoring) Driving Peripherals
(e.g., accelerometer,
leds)
“Energy-harvesting wearables for activity-aware services”, S. Khalifa et al., IEEE Internet Computing, 2015
Emulated behavior of wearable device on Raspberry Pi with ARM processor
(1 core 32MHz, 128MB RAM)
Periodic Sampling with Physical
Sensing
OS Monitoring

Periodic Sampling in an Energy Perspective
Lithium battery (35mAh) State of Charge (SoC) projection for a wearable
device with step, calorie and heartrate monitoring
Interesting Article
“A universal state-of-charge algorithm for batteries”, B. Xiao et al., ACM DAC, 2010

Periodic Dissemination
Monitoring
Source
Receiving
Entity
• The process of triggering the network controller of a monitored source every
𝐷 time units such that the 𝑖 𝑡ℎ
datapoint is disseminated at time 𝑡𝑖 = 𝑖 ∙ 𝐷 to
interested receiving entities
No data loss
• Aggregation-based policy dissemination:
• Withhold triggering dissemination until a window of 𝑲 datapoints is collected
• Withhold dissemination for fixed period of time and disseminate all datapoints
collected until then

Periodic Dissemination Monitoring Cost
𝜇 𝑠 + 𝛽𝑠 ∙ χ + εs
per message cost
per χ byte cost
datapoint d (t,v)
-> message compression
Interesting Article
“Real-Time Data Analytics in Sensor Networks”, T. Palpanas, ACM Sensors, 2013
energy for state transitions
Example Values (Mica2)
𝜇 𝑠 = .645 mJ
εs = .331 mJ
𝛽𝑠 = .0144 mJ/byte

Low-Cost Approximate
and Adaptive Monitoring

Preliminaries
• Approximate Techniques?
• Decision mechanisms based on estimation models capturing and predicting
monitoring stream runtime evolution within certain accuracy guarantees
• Adaptive?
• Adapt monitoring source properties based on the predicaments of the
estimation models
• Low-Cost?
• The costs (time, resources) of applying adaptive monitoring techniques are
(much) less than leaving the monitoring process as is
• We are interested in techniques running in-place and inexpensively

Adaptive Sampling
• Dynamically adapt the sampling period 𝑇𝑖 based on some estimation model
𝜌(𝑀), containing runtime information of the monitoring stream evolution
• How large of an adjustment is required, “ideally”, depends on some evaluation
(distance) metric denoting the ability of the model to (correctly) estimate and
follow metric stream evolution
𝑑𝑖
𝑑𝑖+1
𝑇𝑖+1
Metric Stream 𝑀′ dynamically sampledMetric Stream 𝑀 with 𝑇 = 1𝑠

Adaptive Sampling
𝑑𝑖
𝑑𝑖+1
𝑇𝑖+1
Metric Stream 𝑀′ dynamically sampledMetric Stream 𝑀 with 𝑇 = 1𝑠
Find the max 𝑇 ∈ [𝑇 𝑚𝑖𝑛, 𝑇 𝑚𝑎𝑥] to collect 𝑑𝑖+1 based on an estimation of
the monitoring stream evolution 𝜌(𝑀), such that 𝑀′ differs from 𝑀 less
than a user-defined imprecision value 𝛾 (𝑑𝑖𝑠𝑡 < 𝛾)

Adaptive Dissemination
• Dynamically adapt dissemination rate by applying approximation
techniques to sensed datapoints to reduce communication overhead
• By suppressing consecutive datapoints from dissemination with
“little” change in their metric values
• How much is change is considered as “little” depends on:
• The approximation technique
• Accuracy guarantees given by the user and denoting the probability
(confidence δ) with which sensed datapoints are approximated

Adaptive Dissemination (Model-Based)
• Monitoring source maintains runtime estimation model 𝜌(𝑀) capturing monitoring
stream evolution and variability
• At the 𝑖 𝑡ℎ
time interval instead of metric values, the model is disseminated
• Receiving entities predict the IoT device state from given model assuming subsequent
k-datapoints can be approximated di+k|i = f(𝜌 𝑀 , 𝑑𝑖) within the given guarantees
Metric Stream 𝑀 Approximated Metric Stream 𝑀′

Adaptive Dissemination (Model-Based)
• Monitoring source withholds further dissemination interacting with receiver
only when shifts in monitoring stream value distribution render model as
inconsistent with the actual IoT device state
• If model parameterization is inconsistent, at this point, it must be updated
decision criteria?
decision
function?
Metric Stream 𝑀 Approximated Metric Stream 𝑀′

Metric Filtering
• A form of adaptive dissemination: suppress latest datapoint(s)
dissemination if their values have not “changed” since last reported
• Metric stream suppression but… no model -> no forecasting
• Fixed filter ranges assume monitoring stream value distribution will not
change in in the future (no guarantees any values will be filtered at all)
if (curValue ∈ [prevValue – R, prevValue + R ])
filter(curValue)
Interesting Article
“Monitoring, aggregation and filtering for efficient management of virtual networks”, S. Clayman, IEEE NOMS, 2011

Adaptive Filtering
• Dynamically adjust the filter range 𝑅 based on the current variability of the
monitoring stream, denoted as 𝑞(𝑀)
Metric Stream 𝑀′ with adaptive RangeMetric Stream 𝑀
𝑑𝑖
𝑑𝑖+1
𝑅𝑖+1
𝑅𝑖
• Find the max filter range 𝑅𝑖+1 ∈ [𝑅 𝑚𝑖𝑛, 𝑅 𝑚𝑎𝑥] for 𝑑𝑖+1 such that 𝑀′ differs
from 𝑀 less than a user-defined imprecision value 𝛾 based on the variability
of the metric stream 𝑞(𝑀)

AdaM

The Adaptive Monitoring Framework
μProcessor
μOS
Memory
Low-Cost Approximate Stream Estimation
Adaptive
Sampling
Adaptive
Filtering
S
E
N
S
I
N
G
U
N
I
T
metric
updates
A
P
I
updated
periodicity
estimation
confidence
Adaptive
Dissemination
model updates &
buffer content
when shift
detected
updated filter range
stream variability
filtered metric stream
AdaM
N
E
T
W
O
R
K
U
N
I
T
Model
Base
stream
evolution
and online
stats
Datapoint
Buffer
IoT Device
metric
stream
compressed
metric
stream

Low-Cost Approximate Estimation Model
• Three (3) phase approach to approximate monitoring stream
evolution and variability by exploiting knowledge (hidden) in
the monitoring stream
Step 1
Estimate Monitoring
Stream Evolution
and label datapoints
as “(un)expected”
Step 2
Detect if Gradual
Trends Exist in
Monitoring Stream
Evolution
Step 3
Test if Seasonality
Enrichment is
Beneficial to Stream
Evolution

Phase 1: Update runtime monitoring stream evolution 𝜌 Μ
• Probabilistic Exponential Weighted Moving Average (PEWMA) to estimate
𝑣𝑖+1 from 𝜇𝑖 and the standard deviation 𝜎𝑖+1:
𝑣𝑖+1 = 𝜇𝑖 = 𝑎 ∙ 𝜇𝑖−1 + 1 − 𝑎 𝑣𝑖
• Datapoints labelled as “expected” if estimation lands in prediction intervals
determined from user confidence guarantees or “unexpected” otherwise
• “Unexpected” datapoints are stored in local buffer for dissemination
Looks like an exponential moving average, right?
But weighting 𝑎 = 𝛼 1 − 𝛽P𝑖 is probabilistically applied!

EWMA after spikes
overestimates
subsequent values
EWMA slow to
acknowledge spikes
With probabilistic reasoning each datapoint will contribute to the
estimation process depending on it’s p-value
Why use a Probabilistic EWMA?

AdaM: Adaptive Sampling and Filtering
“AdaM: Adaptive Monitoring Framework for Sampling and Filtering on IoT Devices”, D. Trihinas, G. Pallis and M.D. Dikaiakos, IEEE BigData 2015
“[Low-Cost Adaptive Monitoring Techniques for the Internet of Things”, D. Trihinas, G. Pallis and M.D. Dikaiakos, IEEE TSC 2016
μProcessor
μOS
Memory
Adaptive
Sampling
Adaptive
Filtering
S
E
N
S
I
N
G
U
N
I
T
metric
updates
A
P
I
updated
periodicity
estimation
confidence
Adaptive
Dissemination
model updates &
buffer content
when shift
detected
stream variability
AdaM
N
E
T
W
O
R
K
U
N
I
T
Model
Base
stream
evolution
and online
stats
Datapoint
Buffer
IoT Device
metric
stream
compressed
metric
stream

Adaptive Sampling
• After updating estimation model, we compute current confidence (𝑐𝑖 ≤ 1)
of approach based on the actual and estimated standard deviation
• Next, we update sampling period 𝑇𝑖+1 based on the current confidence
and the user-defined imprecision γ (e.g. 10% tolerance to errors)
𝑐𝑖 = 1 −
| 𝜎𝑖 − 𝜎𝑖|
𝜎𝑖
… the more “confident” the algorithm is, the larger the
outputted sampling period 𝑇𝑖+1 can be…
λ is an
aggressiveness
multiplier
(default λ=1)

Adaptive Filtering
• Compute current variability of the metric stream using moving Fano
Factor 𝐹𝑖 which is an indicator of the stream value dispersion
• We then compare 𝜎𝑒𝑟𝑟 to the user-provided maximum tolerable
imprecision guarantees 𝛾 in attempt to widen filter range
𝐹𝑖 =
𝜎𝑖
2
𝜇𝑖
…a low 𝐹𝑖 (due to 𝜎𝑖) indicates a currently in-dispersed data
stream which means low variability in the metric stream…

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

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

Memory Trace
Java Sorting Benchmark
CPU Trace
Carnegie Mellon RainMon Project
Disk I/O Trace
Carnegie Mellon RainMon Project
TCP Port Monitoring Trace
Cyber Defence SANS Tech Institute
Step Trace
Fitbit Charge HR Wearable
Heartrate Trace
Fitbit Charge HR Wearable
More datasets!

Balance between efficiency and accuracy!
Energy Consumption
CPU Cycles Network Traffic
Error (MAPE)
FAST [L. Fan et al., SIGMOD 2014] L-SIP [E Gaura et al., IEEE Trans. on Sensors, 2013]

Dublin Smart City Intelligent Transportation Service (Dublin ITS)
.
.
.
.
.
.
1000 Buses* with GPS tracking
sending updates to ITS with 16
params (e.g. busID, location,
current route delay)
*Real data from Jan. 2014
Apache Kafka queuing
service on x-large VM
(16VCPU, 16GB RAM,
100GB Disk)
Apache Spark cluster with 5 workers on large VMs
(8VCPU, 8GB RAM, 40GB Disk)
Alerts ITS operators when more than 10 buses in a
Dublin city area, per 5min window, are reporting delays
over 1 standard deviation from their weekly average
AdaM… integrated in big data streaming service!

Spark total delay (processing + scheduling) for T=1, 5, 10 intervals and
AdaM with max imprecision γ = 0.15

AdaM achieves a >85% accuracy in major Dublin areas

AdaM: Adaptive Dissemination
“ADMin: Adaptive Monitoring Dissemination for the Internet of Things”, D. Trihinas, G. Pallis and M.D. Dikaiakos, IEEE INFOCOM, 2017
μProcessor
μOS
Memory
Adaptive
Sampling
Adaptive
Filtering
S
E
N
S
I
N
G
U
N
I
T
metric
updates
A
P
I
updated
periodicity
estimation
confidence
Adaptive
Dissemination
model updates &
buffer content
when shift
detected
stream variability
AdaM
N
E
T
W
O
R
K
U
N
I
T
Model
Base
stream
evolution
and online
stats
Datapoint
Buffer
IoT Device
metric
stream
compressed
metric
stream

AdaM: Adaptive Dissemination
“ADMin: Adaptive Monitoring Dissemination for the Internet of Things”, D. Trihinas, G. Pallis and M.D. Dikaiakos, IEEE INFOCOM, 2017
Network
Unit
ADMin
Seasonality
Enrichment
Shift
Detection
A
P
I
Model
Base
Approximate
Stream
Estimation
Remote seasonal
periodicity
update
test if enrichment
is beneficial?
metric
updates
update
stream
evolution
and trend
seasonal
enrichment of
stream evolution
compressed
metric stream
Seasonal Periodicity
Detection
The ADMin
plugin for AdaM
Don’t disseminate
datapoint values...
Instead disseminate
model updates!

Step 2: Detect over time gradual trends in monitoring stream to reduce
“lagging” effects in monitoring stream evolution estimation
𝑣𝑖+𝑘|𝑖 𝜇𝑖 + 𝑘 𝑋𝑖
Upward
trend
Downward
trend
• Holt’s Trend Method used to bring moving average to
appropriate value base
• Improve forecasting from 1-step ahead (moving
average) predictions to k-datapoint values

Step 3: Test if seasonality enrichment is beneficial to estimation process
and update low-cost approximate model accordingly
seasonal
Seasonal with
exponential trend
Seasonal with
damped trend
• Tendency of the metric stream to exhibit behavior that
repeats itself every L periods (e.g., hourly)
• Seasonal effects highly evident in IoT data (e.g., human
biosignals, environmental data)
PV Panel Production Air Temperature

• Holt Winter’s Method used to estimate seasonal contribution
• Forecasting k-subsequent datapoints with trend and seasonality
• However… perfect seasonal behavior is rarely observed in real-life
systems PV Panel Production
𝑣𝑖+𝑘|𝑖 𝜇𝑖 + 𝑘 𝑋𝑖 + 𝑆𝑖
Considering day before hourly
average (Si-L) will lead to
overestimation

• Online testing (T-Test) to determine if seasonal contribution beneficial
to estimation or not
• Detecting optimal seasonality cycle (L) is an open research challenge
especially when different cycles exist in monitoring stream
• Approximate runtime seasonal periodicity detection
• ComCube Framework (Matsubara et al., WWW, 2016): lightweight tensor-based
and parameter-free framework for near-optimal seasonal periodicity detection
𝑣𝑖+𝑘|𝑖 𝜇𝑖 + 𝑘 𝑋𝑖 + 𝑆𝑖
“Non-linear mining of competing local activities ”, Y. Matsubara, Y. Sakurai and C. Faloutsos, WWW 2016

Detecting Shifts in a Monitoring Stream
• Cumulative Sum (CUSUM) to detect shifts in monitoring stream value
distribution which render estimation model as inconsistent
ts
prior shift
after shift
𝑐𝑖 = ln
𝑃(𝑀𝑖, 𝜇′′
)
𝑃 𝑀𝑖, 𝜇′
𝜇𝑖
′′
= 𝜇𝑖
′
+ 𝜀
where ε magnitude
of change
1. log-likelihood ratio 2. Detecting shifts in mean
However, 𝜀 not known beforehand
but… estimation model model provides us with an approximate 𝜺

3. Online CUSUM
4. Decision Function
Challenge 1: Linear time… Can 𝒕𝒊 be used
instead of 𝒕 𝒔?
ts ti
ti may greatly from ts differ in
cases of gradual trends
𝑖𝑓 𝐺𝑖,{𝑙𝑜𝑤,ℎ𝑖𝑔ℎ} > ℎ → 𝑠ℎ𝑖𝑓𝑡 𝑑𝑒𝑡𝑒𝑐𝑡𝑒𝑑
h is measured
in standard
deviation units
5. Actual Shift Time
the time the
CUSUM detects
the shifts

ts ti
Trend and seasonality knowledge provide model with greater accuracy ( 𝜀 → 𝜀) and to
adapt to unexpected, abrupt and and volatile changes is monitoring stream
Challenge 2: CUSUM threshold h static and
sensitive when stream variability is low
(𝜎 → 0 ) thus triggering… false alarms
Adapt CUSUM sensitivity by adapting
h after dissemination triggered and
restrict h with hmin

ADMin Evaluation
• ADMin in 3 configurations
• No seasonality enrichment (ADMin)
• Static seasonality enrichment - previous day hourly average (ADMin_S1)
• Dynamic seasonality enrichment - ComCube integration (ADMin_S2)
• Under comparison frameworks
• LANCE [Werner et al., ACM SenSys 2011]
• G-SIP [Gaura et al., IEEE Trans. on Sensors 2013]
• ADWIN [Bifet et al., SIAM 2010]
All three framework parameters configured to output best results

Photovoltaic Panel Current (IDC) Production
2 Weeks of data collected every 1 second
Data reduction: 87% -- Accuracy: 93%
Weather Station Air Temperature (oC)
2 Weeks of data collected every 1 second
Wearable Human Heartrate (bpm)
2 Weeks of data collected every 1 minute
ADMin in Action!

Shift Detection Evaluation
ground truth
(offline PELT
algorithm)
false
alarms
correctly
detected
shifts
Air Temperature Dataset Wearable Dataset
Shift Detection Delay

Energy Consumption
Data Volume Reduction
1O
interval
aggregation
ADWIN large energy consumption due to
complexity and absence of data reduction
scheme
LANCE large energy consumption due to enabling
dissemination x2 more than ADMin (false alarms)
and downloading each time all available datapoints
G-SIP large energy consumption due to shift
detection sensitivity (only EWMA based) which
enables false alarm disseminations
Overhead Evaluation

Facebook DB Cluster Adaptive Monitoring
“Inside the Social Network’s (Datacenter) Network”, A. Roy et al., ACM SIGCOMM, 2015

• Edge-mining on IoT devices is both resource and energy
intensive
• Big data streaming engines struggle to cope as the volume
and velocity of IoT-generated data keep increasing
• Adapts the monitoring intensity based on current metric
evolution and variability
• Reduces processing, network traffic and energy
consumption on IoT devices and the IoT network
• Achieves a balance between efficiency and accuracy
The AdaM Framework
Conclusion

http://linc.ucy.ac.cy/AdaM

Low-Cost Approximate & Adaptive Monitoring
Techniques for the Internet of Things
Demetris Trihinas
trihinas@cs.ucy.ac.cy
PhD Candidate
Department of Computer Science
University of Cyprus

Low-Cost Approximate and Adaptive Monitoring Techniques for the Internet of Things

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