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

2/16/21 1
Demetris Trihinas
trihinas.d@unic.ac.cy
1
Seminar Talk @ UPitt | Feb 2021
Department of
Computer Science
Low-Cost Approximate and Adaptive
Monitoring Techniques
Demetris Trihinas
Department of Computer Science
University of Nicosia

2/16/21 2
Demetris Trihinas
2
Department of
Computer Science
Full-Time Faculty Member
University of Nicosia
“Designing and developing scalable and self-adaptive tools for data
management, exploration and visualization”
@dtrihinas
http://dtrihinas.info
https://ailab.unic.ac.cy/
https://www.slideshare.net/DemetrisTrihinas
@AilabUnic

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Demetris Trihinas
4
Department of
Computer Science
The Internet of Things
The physical world is now an information system
Exchanging continuous data streams with other network-
enabled devices, systems and humans…
Internet-enabled (battery-powered) physical devices
with smart processing capabilities…
Intelligent Transportation Services
Health and Activity Tracking Precision Farming Smart Metering and Power Grids

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Demetris Trihinas
5
Department of
Computer Science
The Big Data in IoT
[HP, Oct 2016]
IoT monitoring data accounted for 2% of digital universe in 2012,
with projections that by 2020 it will rise above 12%
[Cisco VNI, 2016]
3+ million car telemetry updates per second
Data explosion outpacing the technology
[2016]
500+ million active monitoring streams at all times
A single self-driving vehicle will produce 4GB per day
…equal to 2666 internet users
[Uber Engineering, 2017]
[IDC, 2016]

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Demetris Trihinas
6
Department of
Computer Science
The Harsh Reality
Network latencies, bandwidth limitations,
pricing (in/out cloud traffic is billed),
constant location awareness
Augmenting IoT and the Cloud
The Developer Perspective
Monitoring-as-a-Service eases monitoring infrastructure management by enabling scalable
and multi-tenant monitoring over the internet via a pay-as-you-use business model

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Demetris Trihinas
7
Department of
Computer Science
Edge Computing
A term coined to reflect computation and decision-making at the
logical extremes of the network, ideally “in place” on the data source
Shorter response times
Simple
Sensing
Edge
Computing
Less network pressure

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Demetris Trihinas
8
Department of
Computer Science
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 processing and dissemination
are the main energy drains in
embedded and mobile devices
Edge Computing Challenges

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Demetris Trihinas
9
Department of
Computer Science
Motivation
“if a degree of inaccuracy can be tolerated, low-cost
approximate and adaptive monitoring techniques that
are capable of dynamically adjusting the data collection
and dissemination rate, can effectively tackle real-time
data processing and energy-efficiency on the edge of
monitored networks”

2/16/21 10
Demetris Trihinas
10
Department of
Computer Science
Presentation Outline
• Background and Motivation
• Problem Statement
• Related Work
• AdaM: the Adaptive Monitoring Framework
• ADMin: a Plugin for Model-Based Adaptive Dissemination
• Talk Conclusion

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11
Department of
Computer Science
Problem Statement
Low-Cost Approximate and Adaptive Monitoring

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Demetris Trihinas
12
Department of
Computer Science
Preliminaries
• A monitoring stream ! = {$%}%'(
)
published by a data source is a large
stochastic sequence of i.i.d datapoints, denoted as $% , where * = 0, 1, … , /
and / → ∞.
• A datapoint $% is a tuple 2%3, 4%, 5% described by a unique identifier for the
monitoring source 2%3, a timestamp 4% and a value 5%
• A datapoint may have a set of other attributes (e.g., location) although for
brevity we will omit other attributes without loss of generality
$%
$%67
Monitoring Stream M

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Demetris Trihinas
13
Department of
Computer Science
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.

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Demetris Trihinas
14
Department of
Computer Science
Periodic Sampling
The process of triggering the collection mechanism of a monitored source every !
time units such that the "#$ datapoint is collected at time %& = " ( !
)&
)&*+
Metric Stream M sampled every T = 1s
Compute resources and energy are wasted while generating large data volumes at a high velocity
Sudden events and significant insights are missed
Metric Stream M sampled every T = 10s

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Demetris Trihinas
15
Department of
Computer Science
Adaptive Sampling
• Dynamically adjust the periodicity !" based on some estimation model #(%),
capturing 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.
'"
'"()
!"()
Metric Stream %′ dynamically sampled
Metric Stream % with ! = 1-
Find max ! ∈ [!0"1, !034] to collect '"() based on estimation of metric stream evolution
#(%), such that %′ differs from % less than user-defined imprecision value 6 ('7-8 < 6)

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16
Department of
Computer Science
Periodic Dissemination
Monitoring
Source
Receiving
Entity
The process of triggering the network controller of a data source every ! time
units such that the "#$ datapoint is disseminated at time %& = " ( ! to interested
receiving entities.
No data loss
Energy consumption for data dissemination:
)#*# = +, + ., ( χ + ε1
per message cost
+, >> .,
per byte cost
., ≅ 0.01 ( +,
energy
for state transitions

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Demetris Trihinas
17
Department of
Computer Science
Adaptive Dissemination
• Dynamically adapt dissemination rate by applying approximation
techniques to sensed datapoints to reduce comm. Overhead.
• Achieve 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.

2/16/21 18
Demetris Trihinas
18
Department of
Computer Science
Model-Based Adaptive Dissemination
• Monitoring source maintains runtime estimation model !(#) capturing monitoring
stream evolution and variability.
• At the %&' time interval instead of metric values, the model is disseminated.
• Receiving entities predict source state from the given model assuming subsequent k-
datapoints can be approximated d)*+|) = f(! # , 01) within given accuracy guarantees.
Metric Stream # Approximated Metric Stream #′

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

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Department of
Computer Science
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)

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Department of
Computer Science
Adaptive Filtering
• Dynamically adjust the filter range ! based on the current variability of the
monitoring stream, denoted as "($)
Metric Stream $′ with adaptive Range
Metric Stream $
'(
'()*
!()*
!(
• Find the max filter range !()* ∈ [!-(., !-01] for '()* such that $3 differs
from $ less than a user-defined imprecision value 4 based on the variability
of the metric stream "($)

2/16/21 22
Demetris Trihinas
22
Department of
Computer Science
Related Work

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Demetris Trihinas
23
Department of
Computer Science
Approximate and Adaptive Monitoring
• Wide applicability in various domains such as:
• Data stream processing
• Distributed system and network monitoring
• Signal processing
• WSNs
• Biosignal and Biomedical
reporting (wearables!!!)
• Differential privacy preserving

2/16/21 24
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Department of
Computer Science
Limitations of the State-of-the-Art
• Require excessive profiling to configure optimal framework parameters
[Deligiannakis, ACM SIGMOD 2011], [Roy, ACM SIGCOMM, 2015], [Jorg, Middleware 2017]
• Present large runtime footprints reducing benefits of adaptiveness in the
end [Silberstein, ACM SIGMOD 2006], [Kim, IEEE IoT, 2017], [Meng, IEEE Trans. Computers 2013]
• Fail to acknowledge abrupt and transient shifts in metric stream evolution
[Werner, ACM SenSys 2011], [Gaura, IEEE Trans. Sensors 2013], [Fan, ACM CIKM 2013], [Du, IEEE
BigData 2015]
• Offline approaches or require coordination from server-side components
[Rastogi, ACM SIGMOD 2009], [Olston, ACM SIGMOD 2010], [Bailis, ACM SIGMOD 2017]

2/16/21 25
Demetris Trihinas
25
Department of
Computer Science
AdaM: the Adaptive Monitoring
Framework
“AdaM: Adaptive Monitoring Framework for Sampling and Filtering on IoT Devices”.
D. Trihinas, G. Pallis and M.D. Dikaiakos, 2015 IEEE International Conference on Big
Data (IEEE BigData 2015), May 201Santa Clara, CA, USA
“Low-Cost Adaptive Monitoring Techniques for the Internet of Things”. D. Trihinas, G.
Pallis and M.D. Dikaiakos, IEEE Transactions on Services Computing, 2018.

2/16/21 26
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26
Department of
Computer Science
The Adaptive Monitoring Framework
μProcessor
μOS
Memory
Adaptive
Filtering
metric
updates
A
P
I
updated
periodicity
compressed
metric stream
updated
filter range
filtered
metric stream
AdaM
S
E
N
S
I
N
G
U
N
I
T
estimation
confidence
Low-Cost Approximate
Stream Estimation
Adaptive
Sampling
stream
variability
stream
evolution online
stats
Model
Base
N
E
T
W
O
R
K
U
N
I
T
metric
stream
Developed as a software library offering low-cost approximate and
adaptive monitoring for software agents and IoT devices

2/16/21 27
Demetris Trihinas
27
Department of
Computer Science
Step 1: Update runtime monitoring stream evolution ! Μ
• Probabilistic Exponential Weighted Moving Average (PEWMA) to estimate
distance between consecutive datapoints #
$%&' with $% = )% − )%+'
#
$%&' = ,% = -
. / ,%+' + 1 − -
. $%
• 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 -
. = 2 1 − 3P% is probabilistically applied!
AdaM Estimation Process

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Demetris Trihinas
28
Department of
Computer Science
• Timely boost PEWMA to appropriate value base
• Metric stream evolution and variability updated with
only previous value knowledge
Step 2: Detect over time monotonic phases of growth or decay to reduce
time “lagging” effects in monitoring stream evolution estimation
Upward
trend
Downward
trend
!" = $
% & (!"()+ +"()) + 1 − $
% /"

2/16/21 29
Demetris Trihinas
29
Department of
Computer Science
EWMA after spikes overestimates
subsequent values EWMA slow to acknowledge spikes
AdaM estimation process
• Probabilistic reasoning -> No overestimation after “bursty” intervals
• Detecting gradual trends -> No “lagging” effects

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Demetris Trihinas
30
Department of
Computer Science
AdaM Adaptive Sampling
• After updating estimation model, we compute the current estimation
confidence ("# ≤ 1) based on actual and estimated standard deviation
• Next, we update sampling period '#() based on the current confidence
and the user-defined imprecision γ (e.g. 10% tolerance to errors)
"# = 1 −
| -
.# − .#|
.#
… the more “confident” the algorithm is, the larger the
outputted periodicity '#() can be…
λ is an
aggressiveness
multiplier
(default λ=1)

2/16/21 31
Demetris Trihinas
31
Department of
Computer Science
AdaM Adaptive Filtering
• Update variability of the metric stream using moving Fano Factor !"
which is an indicator of the stream value dispersion
• We then compare #$%% to the user-provided maximum tolerable
imprecision guarantees & in attempt to widen filter range
!" =
#"
(
)"
…a low !" (due to #") indicates a currently in-dispersed data
stream which means low variability in the metric stream…

2/16/21 32
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32
Department of
Computer Science
AdaM Evaluation
• Compare accuracy and on-device overheads
• Real-world and publically available datasets
• Testbeds: cloud apps, internet security services, wearables, cloud
monitoring, intelligent transportation service
• Under-evaluation frameworks
• L-SIP [Gaura et al., IEEE Trans. on Sensors 2013]
• i-EWMA [Trihinas et al., IEEE/ACM CCGrid 2014]
• FAST [Fan et al., ACM CIKM 2013, IEEE TKDE 2014]

2/16/21 33
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33
Department of
Computer Science
Memory Trace
Java Sorting Benchmark
CPU Trace
Carnegie Mellon RainMon Project
Disk I/O Trace
Carnegie Mellon RainMon Project
TCP Port Trace
Cyber Defence SANS Tech Institute
Step Trace
Fitbit Charge Wearable
Heartrate Trace
Fitbit Charge Wearable
Real-World and Publically Available Datasets

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Department of
Computer Science
Energy Consumption
Network Traffic
Error (MAPE)
Accuracy and On-Device Performance Evaluation
AdaM achieves a balance between efficiency and accuracy, reducing data volume by at least 74%,
energy consumption by 71% while always maintaining accuracy above 89%
CPU Cycles

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Department of
Computer Science
in Action!
Steps Heartrate Calories
94%
accuracy!
96%
accuracy!
91%
accuracy!
6x less processing! 4x less network traffic!
4x less energy usage!
+
3 - 5 days 7 - 8 days

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Demetris Trihinas
36
Department of
Computer Science
Intelligent Transportation Service
Dublin Smart
City ITS
1000 Buses with GPS
tracking sending
updates to ITS
Alerts when >10 buses in city area, per
5min window, are reporting delays
over 1 SD from their weekly average
With the ITS achieves 8x data volume reduction while still detecting 92% of
all reported incidents
Apache Spark cluster with 5 workers
on large VMs (8VCPU, 8GB RAM)

2/16/21 37
Demetris Trihinas
37
Department of
Computer Science
ADMin: a Plugin for Model-Based
Adaptive Dissemination
“ADMin: Adaptive Monitoring Dissemination for the Internet of Things”.
D. Trihinas, G. Pallis and M.D. Dikaiakos, 2017 IEEE Conference on Computer
Communications (IEEE INFOCOM 2017), Atlanta, GA, USA

2/16/21 38
Demetris Trihinas
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Department of
Computer Science
The Adaptive Monitoring Framework
μProcessor
μOS
Memory
Adaptive
Filtering
metric
updates
A
P
I
updated
periodicity compressed
metric stream
updated
filter range
filtered
metric stream
AdaM
S
E
N
S
I
N
G
U
N
I
T
estimation
confidence
Low-Cost Approximate
Stream Estimation
Adaptive
Sampling
stream
variability
stream
evolution
online
stats
Model
Base
change
detected
N
E
T
W
O
R
K
U
N
I
T
Model-Based
Dissemination
Datapoint
Buffer
ADMin
metric
stream

2/16/21 39
Demetris Trihinas
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Department of
Computer Science
The ADMin plugin for Model-Based Dissemination
“Instead of sending datapoints, we model the metric stream so that values are inferred
from the model and only trigger an update when changes in the stream render the
model as inconsistent”

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Department of
Computer Science
Test if seasonality enrichment is beneficial to estimation process and
update estimation 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
ADMin Seasonality Enrichment

2/16/21 41
Demetris Trihinas
41
Department of
Computer Science
• Holt Winter’s Method used to estimate seasonal contribution
• However… perfect seasonal behavior is rarely observed in real-life
PV Panel
Production
Considering the day before
hourly average (Si-L) will lead
to overestimation
• Online testing to determine if seasonal contribution beneficial
• Forecasting k-subsequent datapoints with trend and seasonality
!
"#$%|# ← (# + * +# + ,#

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Demetris Trihinas
42
Department of
Computer Science
ADMin Runtime Shift Detection
• Cumulative Sum (CUSUM) test to detect shifts in metric stream value
probability distribution which render estimation model as inconsistent
ts
prior shift
after shift
!" = ln
&((", *++)
& (", *+
*"
++
= *"
+
+ .
where ε magnitude
of change
Log-Likelihood Ratio
However, . not known beforehand
but… estimation model provides us with an approximate /
0
12 3",{567,8"98} > ℎ → >ℎ12? @A?A!?A@
h is measured in
standard
deviation units
& (", *+
& (", *++
Decision Function
Trend and seasonality provide model greater
accuracy ( ̂
. → .) and to adapt to unexpected,
abrupt and and volatile changes is metric stream

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43
Department of
Computer Science
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 under-comparison framework parameters configured to output best results
* “Non-linear mining of competing local activities ”, Y. Matsubara, Y. Sakurai and C. Faloutsos, WWW 2016

2/16/21 44
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Department of
Computer Science
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!

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45
Department of
Computer Science
• Comparison of number of monitoring stream disseminations triggered for shifts that
actually occurred (true positives) and number of false alarms (false positives)
• Ground truth pre-determined offline by PELT algorithm [Killick, 2012]
Shift Detection Accuracy Evaluation
ground truth
false
alarms
correctly
detected
shifts
Air Temperature Dataset Wearable Dataset
ADMin features high accuracy (>90%) and low false alarm ratio (<10%) which is drastically reduced
when incorporating seasonality knowledge by at least 47% compared to the other approaches

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Demetris Trihinas
46
Department of
Computer Science
Shift Detection Delay Evaluation
• Shift detection delay is the difference in time to when a shift is detected by a
technique compared to the actual time of occurrence
ADMin outperforms other
techniques by at least 29%
When incorporating trend and seasonality knowledge even for datasets with irregular seasonal
behavior ADMin reduces shift detection time by at least 67% compared to the other techniques

2/16/21 48
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48
Department of
Computer Science
Conclusion
• Monitoring at the “edge” implies significant costs
• AdaM: the Adaptive Monitoring Framework
• Low-cost estimation process featuring probabilistic learning to capture metric stream
runtime evolution, trend and variability
• Dynamically adjusts metric periodicity and filtering based on estimation model
“confidence” which rewards correct estimations
• ADMin: model-based dissemination lowers communication overhead in IoT networks
when monitoring data featuring seasonal patterns and trends
• Results show AdaM and ADMin achieve a balance between efficiency and
accuracy, reducing data volume by 74%, energy consumption by 71%, shift
detection delays by 61%, while maintaining accuracy above 89%

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Department of
Computer Science
AdaM Applicability
• Raspberry Pi micro-controllers
• Linux-based environments
• Android Wear devices
• Cloud monitoring tools with adaptive interfaces (e.g.,
JCatascopia, pyCatascopia, Netdata plugin)
• Ported to R for offloading temporal graph processing
• Python for efficient data stream processing with Apache Flink

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Department of
Computer Science
http://linc.ucy.ac.cy/AdaM

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Department of
Computer Science
ailab.unic.ac.cy
• Virtual/F2F Summer and Fall Internships
• Interesting Projects
• Fogify: Scalable Emulator for Geo-Distributed Data-Intensive Apps Deployed in Fog Realms

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Department of
Computer Science
Backup Slides

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Department of
Computer Science
More processing
(e.g., peak detection
for hr monitoring) Driving Peripherals
(e.g., accelerometer,
leds)
Interesting Articles
“Energy-harvesting wearables for activity-aware services”, S. Khalifa et al., IEEE Internet Computing, 2015
“Taking the internet to the next physical level”, V. Cerf and M. Senges, IEEE Computer, 2016
Emulated behavior of wearable device on Raspberry Pi with ARM processor
(1 core 32MHz, 128MB RAM)
Periodic Sampling with Physical
Sensing
OS Monitoring

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Department of
Computer Science
AdaM γ-Parameter Evaluation
For a wide range of γ-parameter values we compute AdaM’s
MAPE per trace
Even for extreme imprecision values (>0.3) AdaM can still take correct
decisions signifying the importance of the confidence metric

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Department of
Computer Science
AdaM Trend ξ-Parameter Evaluation
Trend estimation can shed 2-5% error when computing the metric stream
evolution for a wide range of configuration values

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Demetris Trihinas
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Department of
Computer Science
• 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
!
"#$%|# ← (# + * +# + ,#
“Non-linear mining of competing local activities ”, Y. Matsubara, Y. Sakurai and C. Faloutsos, WWW 2016

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Department of
Computer Science
ADMin Runtime Shift Detection
ts ti
Trend and seasonality provide model with greater accuracy
( ̂
" → ") 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
($ → 0 ) thus triggering… false alarms
Adapt CUSUM sensitivity by adapting
h after dissemination triggered and
restrict h with hmin
Challenge 1: Getting '( requires linear time…
Can )* be used instead of )+?

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Department of
Computer Science
Data Volume Reduction
ADMin reduces data volume by at least 60% while maintaining accuracy always above 86%
With seasonality knowledge data volume is reduced by at least 71% while accuracy is
always above 91%
Overhead Evaluation
What do users see!
Mean Absolute Percentage Error

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

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

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