This talk was presented for the SoftwareCampus Alumni e.V. on 07.12.2020. For more Information about the program check https://softwarecampus-alumni.de/ and https://softwarecampus.de/ Abstract: The Internet of Things (IoT) consists of billions of devices which form a cloud of network-connected sensor nodes. These sensor nodes supply a vast number of data streams with massive amounts of sensor data. Real-time sensor data enables diverse applications including traffic-aware navigation, machine monitoring, and home automation. In this talk, we will dive into recent research which optimizes real-time data gathering and data analysis in the IoT. The talk will provide an overview of available techniques which can be deployed on sensor nodes, intermediate network nodes, and central analysis systems. We will look into the state-of-the-art in practice and research and make you aware of important tradeoffs in real-time IoT data analysis. CV: Jonas Traub is a postdoctoral researcher at the Database Systems and Information Management group at TU Berlin. His main research interests include stream processing, sensor data analysis, and data acquisition techniques. In his PhD, he studied efficient data gathering, processing, and transmission in the IoT. His research shows that one can save up to 87% in sensor reads and data transfers by applying smart data reduction techniques on sensor nodes. He further introduced a demand-based control layer which optimizes the data acquisition from thousands of sensors. With his Scotty-framework, he contributed a general aggregation technique for streaming systems which outperforms alternative solutions by an order of magnitude in throughput. His work received a Best Paper Award at the 22nd International Conference on Extending Database Technology (EDBT). Prior to his work at TU Berlin, he studied at KTH Stockholm and DHBW Stuttgart and worked several years at IBM in Germany and the USA. Jonas is an alumnus of Software Campus where he worked with SAP as industry partner.

1. Efficient Data Stream Processing
in the Internet of Things
Dr. Jonas Traub | 07.12.2020
1

2. Objective of the Talk
• Overview of recent research which optimizes real-time data gathering and data
analysis in the IoT.
• Overview of available techniques which can be deployed on sensor nodes,
intermediate network nodes, and central analysis systems.
• Provide a teaser for a broad topic, offering pointers to interesting resources.
• Speaker‘s Background: Databases and Stream Processing Research Community
2

3. High-Level Overview
A stream processing pipeline is a series of concurrently running operators.
3

4. High-Level Overview
A stream processing pipeline is a series of concurrently running operators.
3

5. High-Level Overview
A stream processing pipeline is a series of concurrently running operators.
Data Gathering
3

6. High-Level Overview
A stream processing pipeline is a series of concurrently running operators.
Data Gathering
53
3

7. High-Level Overview
A stream processing pipeline is a series of concurrently running operators.
Data Gathering Data Processing
53
3

8. High-Level Overview
A stream processing pipeline is a series of concurrently running operators.
Data Gathering Data Processing
8
3

9. High-Level Overview
A stream processing pipeline is a series of concurrently running operators.
Data Gathering Data Processing Data Transmission
8
3

10. Demand-Oblivious Processing
s1
s2
s3
s4
s5
s6
…
sN-1
sN
Sensor
Nodes
4

11. Demand-Oblivious Processing
s1
s2
s3
s4
s5
s6
…
sN-1
sN
Sensor
Nodes
4

12. Demand-Oblivious Processing
s1
s2
s3
s4
s5
s6
…
sN-1
sN
Sensor
Nodes
Stream
Analysis
System
4

13. Demand-Oblivious Processing
s1
s2
s3
s4
s5
s6
…
sN-1
sN
Sensor
Nodes
Stream
Analysis
System
Front-End
Applications
4

14. Demand-Oblivious Processing
s1
s2
s3
s4
s5
s6
…
sN-1
sN
Sensor
Nodes
Stream
Analysis
System
Front-End
Applications
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Definitions:
4

15. Demand-Oblivious Processing
s1
s2
s3
s4
s5
s6
…
sN-1
sN
Sensor
Nodes
Stream
Analysis
System
Front-End
Applications
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
4

16. Demand-Oblivious ProcessingProblems
s1
s2
s3
s4
s5
s6
…
sN-1
sN
Sensor
Nodes
Stream
Analysis
System
Front-End
Applications
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
4

17. Demand-Oblivious ProcessingProblems
Combined
Stream
Bandwidth
s1
s2
s3
s4
s5
s6
…
sN-1
sN
Sensor
Nodes
Stream
Analysis
System
Front-End
Applications
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
4

18. Demand-Oblivious ProcessingProblems
Parallel
Network
Connections
Combined
Stream
Bandwidth
s1
s2
s3
s4
s5
s6
…
sN-1
sN
Sensor
Nodes
Stream
Analysis
System
Front-End
Applications
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
4

19. Demand-Oblivious ProcessingProblems
Expensive
Cluster
Scale-Out
Parallel
Network
Connections
Combined
Stream
Bandwidth
s1
s2
s3
s4
s5
s6
…
sN-1
sN
Sensor
Nodes
Stream
Analysis
System
Front-End
Applications
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
4

20. Demand-Oblivious ProcessingProblems
Expensive
Cluster
Scale-Out
Parallel
Network
Connections
Combined
Stream
Bandwidth
Front-End
Overload
s1
s2
s3
s4
s5
s6
…
sN-1
sN
Sensor
Nodes
Stream
Analysis
System
Front-End
Applications
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
4

21. Demand-Oblivious ProcessingProblems
Expensive
Cluster
Scale-Out
Parallel
Network
Connections
Combined
Stream
Bandwidth
Front-End
Overload
s1
s2
s3
s4
s5
s6
…
sN-1
sN
Sensor
Nodes
Stream
Analysis
System
Front-End
Applications
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
Demand-oblivious
Stream Processing Pipelines
do not suite the IoT.
4

22. Demand-Based Processing
Stream
Analysis
System
Front-End
Applications
s2
s5
…
sN
Sensor
Nodes
s1
s3
s4
s6
sN-1
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
Demand-based:
Utilize requirement specifications of
data consumers to save resources.
5

23. Demand-Based Processing
Stream
Analysis
System
Front-End
Applications
s2
s5
…
sN
Sensor
Nodes
s1
s3
s4
s6
sN-1
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
Demand-based:
Utilize requirement specifications of
data consumers to save resources.
5

24. Demand-Based Processing
Stream
Analysis
System
Front-End
Applications
s2
s5
…
sN
Sensor
Nodes
s1
s3
s4
s6
sN-1
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
Demand-based:
Utilize requirement specifications of
data consumers to save resources.
5

25. Demand-Based Processing
Stream
Analysis
System
Front-End
Applications
s2
s5
…
sN
Sensor
Nodes
s1
s3
s4
s6
sN-1
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
Demand-based:
Utilize requirement specifications of
data consumers to save resources.
5

26. Demand-Based Processing
Stream
Analysis
System
Front-End
Applications
s2
s5
…
sN
Sensor
Nodes
s1
s3
s4
s6
sN-1
Sensor
Control
System
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
Demand-based:
Utilize requirement specifications of
data consumers to save resources.
5

27. Demand-Based Processing
Stream
Analysis
System
Front-End
Applications
s2
s5
…
sN
Sensor
Nodes
s1
s3
s4
s6
sN-1
Sensor
Control
System
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
Demand-based:
Utilize requirement specifications of
data consumers to save resources.
5

28. Demand-Based Processing
Stream
Analysis
System
Front-End
Applications
s2
s5
…
sN
Sensor
Nodes
s1
s3
s4
s6
sN-1
Sensor
Control
System
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
Demand-based:
Utilize requirement specifications of
data consumers to save resources.
5

29. Demand-Based Processing
Stream
Analysis
System
Front-End
Applications
s2
s5
…
sN
Sensor
Nodes
s1
s3
s4
s6
sN-1
Sensor
Control
System
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
Demand-based:
Utilize requirement specifications of
data consumers to save resources.
5

30. Demand-Based Processing
Stream
Analysis
System
Front-End
Applications
s2
s5
sN
Sensor
Nodes
s1 s3
s4 s6
sN-1
Sensor
Control
System
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
Demand-based:
Utilize requirement specifications of
data consumers to save resources.
5

31. Demand-Based Processing
Stream
Analysis
System
Front-End
Applications
s2
s5
sN
Sensor
Nodes
s1 s3
s4 s6
sN-1
Sensor
Control
System
Data Demand:
Minimum number of data points
which allows for providing a
desired functionality.
Demand-oblivious:
Not considering the data
demand of data consumers.
Definitions:
Demand-based:
Utilize requirement specifications of
data consumers to save resources.
Solution
We enable demand-based optimizations
by introducing control interfaces for expressing data demands.
5

32. End-To-End Pipeline
6

33. Optimized On-Demand
Data Streaming from Sensor Nodes
7

34. Optimized On-Demand
Data Streaming from Sensor Nodes
Optimized On-Demand Data Streaming from Sensor Nodes
Jonas Traub, Sebastian Breß, Tilmann Rabl, Asterios Katsifodimos, and Volker Markl.
ACM Symposium on Cloud Computing 2017 (SoCC '17)
Research Paper:
7

35. Sensor Read SchedulingSensor-Read Scheduling
8

36. User-Defined Sampling Functions
Read time tolerance
Desired read time
Penalty function
Sensor Read SchedulingUser-Defined Sampling Function
9

37. Sensor Read Fusion
Read time tolerance
Desired read time
Penalty function
Sensor Read SchedulingSensor-Read Fusion
10

38. Sensor Read Fusion
Read time tolerance
Desired read time
Penalty function
Sensor Read SchedulingSensor-Read Fusion
11

39. Local Filtering
Read time tolerance
Desired read time
Penalty function
Sensor Read SchedulingLocal Filtering
12

40. Local Filtering
Read time tolerance
Desired read time
Penalty function
Our scheduler minimizes the number of sensor reads and data transmissions
based on the joint data demand of all data consumers.
Sensor Read SchedulingLocal Filtering
12

41. Adaptive Sampling and Filtering
13

42. Adaptive Sampling and Filtering
A Survey of Adaptive Sampling and Filtering Algorithms for the Internet of Things
Dimitrios Giouroukis, Alexander Dadiani, Jonas Traub, Steffen Zeuch, Volker Markl.
DEBS'20: 14th ACM International Conference on Distributed and Event-Based Systems
Survey Paper:
13

43. Adaptive Sampling
14

44. Adaptive Sampling
Decide WHEN and HOW OFTEN to read a value from a sensor.
14

45. Adaptive Filtering
15

46. Adaptive Filtering
Decide WHICH values are worth to be transmitted from a sensor.
15

47. Classes of Adaptive Algorithms
See full talk at: https://www.youtube.com/watch?v=He_UmDEgPug
16

48. Scalable Data Acquisition from Sensors
17

49. Scalable Data Acquisition from Sensors
17

50. Scalable Data Acquisition from Sensors
SENSE: Scalable Data Acquisition from Distributed Sensors with Guaranteed
Time Coherence. Jonas Traub, Julius Hülsmann, Tim Stullich, Sebastian Breß,
Tilmann Rabl, and Volker Markl. (arXiv:1912.04648)
Research Paper:
17

51. Architecture: Central Join Topology
s1
s2
s3
s4
…
sN
Sensor
Nodes
(t3,v3) (t,v1,v2, …, vN)
Central
Join
Central Stream Join
18

52. Architecture: Central Join Topology
s1
s2
s3
s4
…
sN
Sensor
Nodes
(t3,v3) (t,v1,v2, …, vN)
Central
Join
Central Stream Joins do not scale to thousands of input streams.
Central Stream Join
18

53. s1 s2 s3 s4
Architecture: Sensing Loop Topology
s1
s2
s3
s4
Sensor
Nodes
Sensing Loop Topology
19

54. s1 s2 s3 s4
Architecture: Sensing Loop Topology
s1
s2 s3 s4
Sensing Loop Topology
19

55. s1 s2 s3 s4
Architecture: Sensing Loop Topology
s1
s2 s3 s4Loop
Node
(t)
Sensing Loop Topology
19

56. s1 s2 s3 s4
Architecture: Sensing Loop Topology
s1
s2 s3 s4
(t,v1)
Loop
Node
(t)
Sensing Loop Topology
19

57. s1 s2 s3 s4
Architecture: Sensing Loop Topology
s1
s2 s3 s4
(t,v1,v2)(t,v1) (t,v1,v2,v3) (t,v1,v2,v3,v4)
Loop
Node
(t)
Sensing Loop Topology
19

58. s1 s2 s3 s4
Architecture: Sensing Loop Topology
s1
s2 s3 s4
(t,v1,v2)(t,v1) (t,v1,v2,v3) (t,v1,v2,v3,v4)(t)
Loop
Node
Sensing Loop Topology
19

59. s1 s2 s3 s4
Architecture: Sensing Loop Topology
s1
s2 s3 s4
(t,v1,v2)(t,v1) (t,v1,v2,v3) (t,v1,v2,v3,v4)(t)
Loop
Node
s5 s6 s7 s8
(t,v5,v6)(t,v5) (t,v5,v6,v7) (t,v5,v6,v7,v8)(t)
Sensing Loop Topology
19

60. s1 s2 s3 s4
Architecture: Sensing Loop Topology
s1
s2 s3 s4
(t,v1,v2)(t,v1) (t,v1,v2,v3) (t,v1,v2,v3,v4)(t)
Loop
Node
s5 s6 s7 s8
(t,v5,v6)(t,v5) (t,v5,v6,v7) (t,v5,v6,v7,v8)(t)
Sensing Loop Topology
19

61. s1 s2 s3 s4
Architecture: Sensing Loop Topology
s1
s2 s3 s4
(t,v1,v2)(t,v1) (t,v1,v2,v3) (t,v1,v2,v3,v4)(t)
Loop
Node
s5 s6 s7 s8
(t,v5,v6)(t,v5) (t,v5,v6,v7) (t,v5,v6,v7,v8)(t)
We dynamically combine sensing loops and central joins to solve scalability challenges.
Sensing Loop Topology
19

62. Architecture: Sensor Node InternalsSensor Node Internals
20

63. Important Keywords
• Demand-Based Data Gathering instead of Demand Oblivious Data Gathering
• Adaptive Sampling & Adaptive Filtering
• Adaptive Rates
• Adaptive Compression
• Model-based Filtering
• Adaptive Thesholds
• Scalable Data Acquisition with Loop Topologies
• Data Transfer Optimization
21

64. Ongoing Project: NebulaStream
NebulaStream is the first general purpose, end-to-end data management system for the IoT.
22

65. Ongoing Project: NebulaStream
NebulaStream is the first general purpose, end-to-end data management system for the IoT.
Research Paper:
The NebulaStream Platform: Data and Application Management for the Internet of Things.
Steffen Zeuch, Ankit Chaudhary,BonaventuraDel Monte, Haralampos Gavriilidis, Dimitrios Giouroukis, Philipp M. Grulich,Sebastian Bress, Jonas Traub, Volker Markl
Conference on Innovative Data Systems Research (CIDR ’20) - http://cidrdb.org/cidr2020/papers/p7-zeuch-cidr20.pdf
Web: https://www.nebula.stream/
Of course, we are hiring ;)
23

66. Efficient Data Stream Processing
in the Internet of Things
Dr. Jonas Traub | 07.12.2020