rt = β ∗ (vt − vt−1)/δt + (1 − β) ∗ rt−1. (2) where 0 < α,β < 1 are data and trend smoothing factor xt is the most recent data value δt is the time difference between 2 ...
Collaborative Edge Mining
Kriti Bhargava @ WD’16
Kriti Bhargava, Stepan Ivanov TSSG, WIT {kbhargava,sivanov}@tssg.org
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Outline
1. Motivation 2. State-of-the-art 3. Edge Mining 4. Collaborative Edge Mining 5. Case Study 6. Evaluation 7. Conclusions and Future Work
Kriti Bhargava @ WD’16
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Motivation
1. Computational and storage capabilities 2. Latency sensitive applications 3. Limitations of the current approaches
Kriti Bhargava @ WD’16
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State-of-the-art Sensor analytics approaches: 1. Data fusion – Improve data quality – Application specific algorithms
2. Edge Mining – Improve energy efficiency – Isolated analysis
3. Artificial Neural Networks (ANN) – Perform prediction, classification and clustering tasks – Resource-intensive network learning
Kriti Bhargava @ WD’16
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Edge Mining 1. Reduce network traffic 2. Spanish Inquisition Protocol (SIP) – Linear SIP (L-SIP) State (st ) as smoothed value (vt ) and rate of change (rt ) double Exponentially Weighted Moving Average vt = α ∗ xt + (1 − α) ∗ (vt−1 + rt−1 ∗ δt)
(1)
rt = β ∗ (vt − vt−1 )/δt + (1 − β) ∗ rt−1
(2)
where 0 < α, β < 1 are data and trend smoothing factor xt is the most recent data value δt is the time difference between 2 consecutive observations. – ClassAct – Bare Necessities (BN)
3. Isolated analysis
Kriti Bhargava @ WD’16
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Collaborative Edge Mining using L-SIP
1. Addresses limitations of the current approaches 2. Latency sensitive applications 3. Based on EM 4. Apache Storm-like framework – – – – –
Parallel and distributed processing Master node - logic and approximation model Processor nodes Raw data → Intermediate state → Application relevant state Packet transmissions based on ε or theartbeat
Kriti Bhargava @ WD’16
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Case Study - Heat Stress prediction 1. Temperature Humidity Index (THI) THI = 1.8 ∗ Ta − (1 − RH) ∗ (Ta − 14.3) + 32
(3)
where Ta is the measured ambient temperature in (◦ C) RH is the relative humidity as a fraction of the unit. 2. Threshold value: 68 3. Our WSN: – Cowputing: Cow collars as the master and mobile sink node – Static temperature sensors – Static humidity sensors
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Algorithm 1 Phase 1 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12:
procedure : t ← current time At static humidity node humt ← obtain vector of sensor readings estimate new state for humidity - dEWMA filtering vH,t ← αH ∗ humt + (1 − αH ) ∗ (vH,t−1 + rH,t−1 ∗ δt) rH,t ← βH ∗ (vH,t − vH,t−1 )/δt + (1 − βH ) ∗ rH,t−1 At static temperature node tmpt ← obtain vector of sensor readings estimate new state for temperature - dEWMA filtering vT ,t ← αT ∗ tmpt + (1 − αT ) ∗ (vT ,t−1 + rT ,t−1 ∗ δt) rT ,t ← βT ∗ (vT ,t − vT ,t−1 )/δt + (1 − βT ) ∗ rT ,t−1
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Algorithm 2 Phase 2 1: procedure : 2: if request for new THI state estimate received or 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17:
temperature or humidity event occurred, then Obtain humidity state (vH,t , rH,t ) and calculate thit ← 1.8 ∗ vT ,t − (1 − vH,t )(vT ,t − 14.3) + 32 estimate new state for THI - dEWMA filtering vTHI ,t ← αTHI ∗ thit + (1 − αTHI ) ∗ (vTHI ,t−1 + rTHI ,t−1 ∗ δt) rTHI ,t ← βTHI ∗ (vTHI ,t − vTHI ,t−1 )/δt + (1 − βTHI ) ∗ rTHI ,t−1 predict sink value using linear extrapolation 1 t − tsink THIsink,t ← THIsink,tsink 0 1 if eventful (|vsink,t − vTHI ,t | > εTHI ) or t − tsink ≥ theartbeat then a. Transmit((vTHI ,t , rTHI ,t ), n, t) b. n ← n + 1 (increment sequence number) c. when acknowledgement received i. THIsink,t ← sTHI ,t ii. tsink ← t iii. theartbeat reinitialized
Kriti Bhargava @ WD’16
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State diagram
Figure 1 : Collaborative edge mining model for THI state estimation at a temperature sensor
Kriti Bhargava @ WD’16
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Evaluation
1. Based on two metrics – Packet reduction (%) – Mean error (minutes) – Dependent on threshold values (εT , εH , εTHI and theartbeat )
2. Input parameters – α set as 0.94 → Best fit approach – β set using expectation values – theartbeat set as 60 minutes
3. Small threshold values =⇒ latency-sensitive applications – Allowed error: 3%
Kriti Bhargava @ WD’16
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Results - Packet Reduction theartbeat = 60 Packet reduction vs Humidity threshold
Packet reduction vs THI threshold
Packet reduction (%) 94 96
90 Packet reduction (%) 80
CEM with eT = 0.01 CEM with eT = 0.06
= 0.24
EM with eT
= 0.01
EM with eT
= 0.06
CEM with eH = 0.06 and eT = 0.01 CEM with eH = 0.48 and eT = 0.12
60
= 0.06
EM with eH
60
EM with eH
0.05 0.10 0.15 0.20 Temperature threshold (minutes)
90
CEM with eH = 0.24
92
70
CEM with eH = 0.06
70
Packet reduction (%) 80
90
98
100
100
Packet reduction vs Temperature threshold
0.2
0.4 0.6 0.8 Humidity threshold (%RH)
(a) Fixed eH &
(b) Fixed eT &
εTHI = 0.04
εTHI = 0.04
Kriti Bhargava @ WD’16
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0.05
0.10 0.15 THI threshold
0.20
(c) Fixed eT & eH
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Results - Mean error theartbeat = 60
Mean error (minutes) 10 15
Mean error (minutes) 10 15
20
Mean error vs THI threshold
20
Mean error vs Humidity threshold
CEM with eH = 0.06
CEM with eT = 0.01
CEM with eH = 0.24
CEM with eT = 0.06
EM with eH
= 0.06
EM with eT
= 0.01
EM with eH
= 0.24
EM with eT
= 0.06
CEM with eH = 0.06 and eT = 0.01 CEM with eH = 0.48 and eT = 0.12
5
5
5
Mean error (minutes) 10 15
20
Mean error vs Temperature threshold
0.05 0.10 0.15 0.20 Temperature threshold (minutes)
0.2
0.4 0.6 0.8 Humidity threshold (%RH)
(d) Fixed eH &
(e) Fixed eT &
εTHI = 0.04
εTHI = 0.04
Kriti Bhargava @ WD’16
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0.05
0.10 0.15 THI threshold
0.20
(f) Fixed eT & eH
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Results - Performance across theartbeat
Mean error vs Heartbeat
70
100
Packet reduction vs Heartbeat
60 Mean error (minutes) 30 40 50
CEM with eH=0.48, eT=0.12, eTHI=0.15
CEM with eH=0.06, eT=0.01, eTHI=0.04
EM with eH
=0.06, eT=0.01
EM with eH
=0.48, eT=0.12
20
50
Packet reduction (%) 60 70 80
90
CEM with eH=0.06, eT=0.01, eTHI=0.04
=0.06, eT=0.01
EM with eH
=0.48, eT=0.12
10
EM with eH
0
30
40
CEM with eH=0.48, eT=0.12, eTHI=0.15
20
40
60 80 Heartbeat (minutes)
100
120
(g) PR at fixed eT, eH and εTHI
Kriti Bhargava @ WD’16
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60 80 Heartbeat (minutes)
100
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(h) ME at fixed eT, eH and εTHI
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Conclusions and Future Work
1. Improved network intelligence 2. Improved Resource utilization 3. Timeliness of event detection 4. Improved energy efficiency 5. Generic approach 6. Balance trade-off between packet reduction and mean error 1. Implementation on sensor devices 2. Alternative approaches for sensor analytics
Kriti Bhargava @ WD’16
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References 1. E.I. Gaura, J. Brusey, M. Allen, R. Wilkins, D. Goldsmith, R. Rednic, “Edge Mining the Internet of Things,” IEEE Sensors Journal, vol. 13, no .10, pp. 3816-3825, Oct. 2013. 2. S. Ivanov, K. Bhargava, W. Donnelly, “Precision Farming: Sensor Analytics,” IEEE Intelligent systems, vol.30, no.4, pp.76-80, July-Aug. 2015. 3. V. Gantner, P. Miji´c, K. Kuterovac, D. Soli´c, Ranko Gantner, “Temperature - humidity index values and their significance on the daily production of dairy cattle,” Daily production of dairy cattle, Mljekarstvo vol. 61, pp. 56-63, 2011. 4. D. Goldsmith, J. Brusey, “The spanish inquisition protocol: Model based transmission reduction for wireless sensor networks,” Proceedings of IEEE Sensors 2010, pp. 2043-2048, Nov. 2010.
Kriti Bhargava @ WD’16
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Kriti Bhargava @ WD’16
Thank You Any Questions?
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