This slide deck was presented by Julius Hülsmann at VLDB 2020. It presents the contents of the following paper: Julius Hülsmann, Jonas Traub, Volker Markl. Demand-based Sensor Data Gathering with Multi-Query Optimization. PVLDB, 13(12): 2801-2804, 2020. DOI: https://doi.org/10.14778/3415478.3415479 You may also check the video recording at https://www.youtube.com/watch?v=ctpj-o3b4B4 Paper Abstract: In the Internet of Things (IoT), billions of sensors provide data streams to applications. The data are predominately acquired from devices with constrained computational capabilities, often serving multiple queries simultaneously. Sensor nodes, are typically oblivious to the specific needs of applications. The potential requirements of diverse applications force them to push data at a higher rate than required by a specific, currently running application. That is suboptimal due to 1. constraints in the network bandwidth, 2. expenses for transmissions, and 3. limited computational power. However, decreasing data gathering frequency may reduce the applications' accuracy. In this paper, we demonstrate a technique for minimizing the number of network transmissions while maintaining the desired accuracy. The presented algorithm for read- and transmission-sharing among queries goes hand-in-hand with state-of-the-art machine learning techniques for adaptive sampling. We 1. implement the technique and deploy it on a sensor node, 2. replay sensor-data from two real-world scenarios, 3. provide an interface for submitting custom queries, and 4. present an interactive dashboard. Here, visitors observe live statistics on the read- and transmission savings achieved in real-world use-cases. The dashboard also visualizes optimizations currently performed by the read scheduling procedure and hence conveys real-time insights and a deep understanding of the presented algorithm.

1. Demand-based Sensor Data
Gathering with Multi-Query
Optimization
VLDB 2020 Demonstration Track

2. Demand-oblivious Data Acquisition
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
2
Billions of sensor nodes provide data-
streams to Stream Processing Engines

3. Demand-oblivious Data Acquisition
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
3
Real-time
insights

4. Demand-oblivious Data Acquisition
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
4
Expensive
system
scale-out
Bottleneck

5. Optimized On-Demand Data
Streaming from Sensor NodesJonas Traub, Sebastian Breß, Tilmann Rabl, Asterios Katsifodimos, Volker Markl
Proceedings of the 2017 Symposium on Cloud Computing. 2017
1. Data demand specification
and read-time suggestion
2. Multi-query read-scheduling
and read fusion
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
5

6. Demonstration Setup
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
6

7. Demand-based Data Acquisition: Demand
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
7
Traffic Surveillance System
Area Monitoring
…

8. Demand-based Data Acquisition: Demand
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
8
Traffic Surveillance System
Area Monitoring
…
Sample At least once a second.
Sample Once every 500 meters

9. Demand-based Data Acquisition: Sharing Pot.
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
9
Traffic Surveillance System
Area Monitoring
…
Large deviations are bad if traveling at high speed.
Deviations up to two seconds are fine.

10. User-defined Sampling Function
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
10

11. User-defined Sampling Function
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
11

12. User-defined Sampling Function
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
12

13. User-defined Sampling Function
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
13

14. Multi-Query Read-Scheduler: Input
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
14
time

15. Multi-Query Read-Scheduler: Optimal Fragment
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
15
time

16. Multi-Query Read-Scheduler: Fused read
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
16
time

17. Live Demonstration
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
17
https://youtu.be/ctpj-o3b4B4?t=265

18. Conclusion
• We present:
• demand-based sensor gathering
• multi-query read- and transmission sharing techniques
• reduction in sensor reads and –transmissions
Julius Hülsmann, Jonas Traub, Volker Markl : Demand-based
Sensor Data Gathering with Multi-Query Optimization.
VLDB 2020
18
Demo Paper Hülsmann, Julius, et al. "Demand-based Sensor Data Gathering
with Multi-Query Optimization”
Proceedings of the VLDB Endowment 13. 2020
Full Paper Traub, Jonas, et al. "Optimized on-demand data streaming from
sensor nodes."
Proceedings of the 2017 Symposium on Cloud Computing. 2017