3/9/17 1Demetris Trihinas 1
Low-Cost Approximate & Adaptive Monitoring Techniques for the Internet of Things
Demetris [email protected]
PhD Candidate Department of Computer Science
University of Cyprus
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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/
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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…
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Why is IoT Just Now Becoming a Reality?Nano Bio-Sensors and
Printed ElectronicsCommunication
ProtocolsData-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
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IoT in AgriculturePrecision Farming
Detect crop condition and spread nutrients to parts of field with need
Cattle as a ServiceDetect optimum breading
period, monitor health and dairy protection
“Connected Cattle: Wearables are Changing the Dairy Industry”, Kathy Pretz, IEEE Magazine: the Institute, May 2016.
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IoT in Medicine and Health Tracking
Precision MedicinePill-shaped cameras traversing
human body
“Fitbit data led doctors to shock a patient’s heart”, Steven Dent, Engadget, Apr 2016.
WearablesBiosignal and activity tracking
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IoT in Intelligent Transportation SystemsSmart Traffic Flow
In Toronto travel time was reduced by 26%
Smart ParkingIn 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.
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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]
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Big Data and the Cloud
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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]
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So…is Cloud Monitoring Really Ready for the Internet of Things?
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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
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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 metricsmanaged by cloud provider
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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
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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
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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
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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
Interesting Articles“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
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Augmenting IoT with the CloudChallenge 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 devicesInteresting Article
“Practical data prediction for real-world wireless sensor networks”, U. Raza and T. Palpanas, IEEE TKDE, 2015
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Augmenting IoT with the Cloud
“Taking the internet to the next physical level”, V. Cerf and M. Senges, IEEE Computer, 2016
Challenge 3 – Security and data privacy
October 2016, DDOS Attack
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Formulating the Challenges as Research Questions
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Preliminaries• A monitoring stream published by a monitoring source is a large stochastic
sequence of i.i.d datapoints, denoted as , where and
• A datapoint is a tuple described by a unique identifier for the monitoring
source , a timestamp and a value
• 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
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Periodic Sampling• The process of triggering the collection mechanism of a monitored source
every time units such that the datapoint is collected at time
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
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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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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
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Periodic Dissemination
Monitoring Source
Receiving Entity
• The process of triggering the network controller of a monitored source every
time units such that the datapoint is disseminated at time to interested
receiving entities
No data loss
• Aggregation-based policy dissemination:
• Withhold triggering dissemination until a window of datapoints is collected
• Withhold dissemination for fixed period of time and disseminate all datapoints collected until
then
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Periodic Dissemination Monitoring Cost
𝜇𝑠+𝛽𝑠∙ χ +ε s
per message costper 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)
mJ
mJ mJ/byte
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Low-Cost Approximate and Adaptive Monitoring
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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
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Adaptive Sampling• Dynamically adapt the sampling period based on some estimation model ,
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
𝑑𝑖𝑑𝑖+1
𝑇 𝑖+1
Metric Stream dynamically sampledMetric Stream with
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Adaptive Sampling
𝑑𝑖𝑑𝑖+1
𝑇 𝑖+1
Metric Stream dynamically sampledMetric Stream with
Find the max to collect based on an estimation of the monitoring stream
evolution , such that differs from less than a user-defined imprecision
value ()
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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
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Adaptive Dissemination (Model-Based)
• 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 the IoT device state from given model assuming
subsequent k-datapoints can be approximated within the given guarantees
Metric Stream Approximated Metric Stream
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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 Approximated Metric Stream
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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
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Adaptive Filtering• Dynamically adjust the filter range based on the current variability of the
monitoring stream, denoted as Metric Stream with adaptive RangeMetric Stream
𝑑𝑖𝑑𝑖+1
𝑅𝑖+1𝑅𝑖
• Find the max filter range for such that differs from less than a user-defined
imprecision value based on the variability of the metric stream
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AdaM
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The Adaptive Monitoring Framework
μProcessor
μOS
Memory
Low-Cost Approximate Stream Estimation
AdaptiveSampling
AdaptiveFiltering
SENSING
UNIT
metricupdates
API
updated periodicityestimation confidence
AdaptiveDissemination
model updates & buffer content when
shift detected
updated filter rangestream variability
filtered metric stream
AdaMNETWORK
UNIT
ModelBase
stream evolution and online stats
Datapoint Buffer
IoT Device
metric stream
compressedmetric stream
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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 1Estimate Monitoring
Stream Evolution and label datapoints
as “(un)expected”
Step 2Detect if Gradual
Trends Exist in Monitoring Stream
Evolution
Step 3Test if Seasonality
Enrichment is Beneficial to Stream
Evolution
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Low-Cost Approximate Estimation Model
Phase 1: Update runtime monitoring stream evolution
• Probabilistic Exponential Weighted Moving Average (PEWMA) to estimate
from and the standard deviation :
• 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 is probabilistically applied!
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Low-Cost Approximate Estimation Model
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?
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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
Low-Cost Approximate Stream Estimation
AdaptiveSampling
AdaptiveFiltering
SENSING
UNIT
metricupdates
API
updated periodicityestimation confidence
AdaptiveDissemination
model updates & buffer content when
shift detected
updated filter rangestream variability
filtered metric stream
AdaMNETWORK
UNIT
ModelBase
stream evolution and online stats
Datapoint Buffer
IoT Device
metric stream
compressedmetric stream
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Adaptive Sampling• After updating estimation model, we compute current confidence of
approach based on the 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)
𝑐 𝑖=1− ¿ �̂� 𝑖−𝜎 𝑖∨¿𝜎 𝑖
¿
… the more “confident” the algorithm is, the larger the
outputted sampling period can be…
λ is an aggressiveness
multiplier (default λ=1)
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Adaptive Filtering• Compute current 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
𝐹 𝑖=𝜎 𝑖2
𝜇𝑖
…a low (due to ) indicates a currently in-dispersed data
stream which means low variability in the metric stream…
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AdaM in action!Steps Heartrate
Calories
94% accuracy!
96% accuracy!
91% accuracy!
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6x less processing! 4x less network traffic!
4x less energy usage!
+ AdaM
3 - 5 days
7 - 8 days
AdaM in action!
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Memory TraceJava Sorting Benchmark
CPU TraceCarnegie Mellon RainMon Project
Disk I/O TraceCarnegie Mellon RainMon Project
TCP Port Monitoring TraceCyber Defence SANS Tech Institute
Step TraceFitbit Charge HR Wearable
Heartrate TraceFitbit Charge HR Wearable
More datasets!
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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]
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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!
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Spark total delay (processing + scheduling) for T=1, 5, 10 intervals and
AdaM with max imprecision γ = 0.15
AdaM… integrated in big data streaming service!
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AdaM achieves a >85% accuracy in major Dublin areas
AdaM… integrated in big data streaming service!
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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
Low-Cost Approximate Stream Estimation
AdaptiveSampling
AdaptiveFiltering
SENSING
UNIT
metricupdates
API
updated periodicityestimation confidence
AdaptiveDissemination
model updates & buffer content when
shift detected
updated filter rangestream variability
filtered metric stream
AdaMNETWORK
UNIT
ModelBase
stream evolution and online stats
Datapoint Buffer
IoT Device
metric stream
compressedmetric stream
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AdaM: Adaptive Dissemination
“ADMin: Adaptive Monitoring Dissemination for the Internet of Things”, D. Trihinas, G. Pallis and M.D. Dikaiakos, IEEE INFOCOM, 2017
NetworkUnit
ADMin
Seasonality Enrichment
Shift Detection
API
ModelBase
Approximate Stream
Estimation
Remote seasonal periodicity
updatetest 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!
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Low-Cost Approximate Estimation Model
Step 2: Detect over time gradual trends in monitoring stream to reduce
“lagging” effects in monitoring stream evolution estimation
�̂� 𝑖+𝑘∨𝑖❑←𝜇𝑖+𝑘 𝑋 𝑖
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
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Low-Cost Approximate Estimation Model
Step 3: Test if seasonality enrichment is beneficial to estimation process
and update low-cost approximate model accordinglyseasonal
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
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Low-Cost Approximate Estimation Model
• 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
Considering day before hourly average (Si-L) will lead to overestimation
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Low-Cost Approximate Estimation Model
• 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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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 shift𝑐 𝑖=ln
𝑃 (𝑀𝑖 ,𝜇′ ′ )𝑃 (𝑀𝑖 ,𝜇
′ )
where ε magnitude of change
1. log-likelihood ratio 2. Detecting shifts in mean
However, not known beforehand but… estimation model model provides us with an approximate
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Detecting Shifts in a Monitoring Stream3. Online CUSUM
4. Decision Function
Challenge 1: Linear time… Can be used
instead of ?
ts ti
ti may greatly from ts differ in
cases of gradual trends
𝑖𝑓 𝐺𝑖 ,{𝑙𝑜𝑤 ,h h𝑖𝑔 }>h❑→h𝑠 𝑖𝑓𝑡 𝑑𝑒𝑡𝑒𝑐𝑡𝑒𝑑
h is measured in standard
deviation units5. Actual Shift Time
the time the CUSUM detects
the shifts
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Detecting Shifts in a Monitoring Stream
ts ti
Trend and seasonality knowledge 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
( thus triggering… false alarms
Adapt CUSUM sensitivity by adapting
h after dissemination triggered and
restrict h with hmin
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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
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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
Data reduction: 85% -- Accuracy: 92%
Wearable Human Heartrate (bpm)
2 Weeks of data collected every 1 minute
Data reduction: 80% -- Accuracy: 90%
ADMin in Action!
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Shift Detection Evaluation
ground truth(offline PELT algorithm)
false alarms
correctly detected
shifts
Air Temperature Dataset Wearable Dataset
Shift Detection Delay
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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
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Facebook DB Cluster Adaptive Monitoring
“Inside the Social Network’s (Datacenter) Network”, A. Roy et al., ACM SIGCOMM, 2015
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• 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
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Low-Cost Approximate & Adaptive Monitoring Techniques for the Internet of Things
Demetris [email protected]
PhD Candidate Department of Computer Science
University of Cyprus
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