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  4. Geometric monitoring in action: a systems perspective for the internet of things
 
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Geometric monitoring in action: a systems perspective for the internet of things

Publikationstyp
Conference Paper
Date Issued
2018-10
Sprache
English
Author(s)
Stylianopoulos, Charalampos
Almgren, Magnus  
Chalmers University of Technology  
Landsiedel, Olaf  
Papatriantafilou, Marina  
TORE-URI
https://hdl.handle.net/11420/53894
Volume
2018
Start Page
433
End Page
436
Article Number
8638079
Citation
Proceedings - 43rd IEEE Conference on Local Computer Networks, LCN 2018: 8638079, 433-436
Contribution to Conference
43rd Annual IEEE Conference on Local Computer Networks, LCN Workshops 2018  
Publisher DOI
10.1109/LCN.2018.8638079
Scopus ID
2-s2.0-85062847242
Publisher
IEEE
ISBN
978-1-5386-4413-3
978-1-5386-4414-0
978-1-5386-4412-6
Applications for IoT often continuously monitor sensor values and react if the network-wide aggregate exceeds a threshold. Previous work on Geometric monitoring (GM) has promised a several-fold reduction in communication but been limited to analytic or high-level simulation results. In this paper, we build and evaluate a full system design for GM on resource-constrained devices. In particular, we provide an algorithmic implementation for commodity IoT hardware and a detailed study regarding duty cycle reduction and energy savings. Our results, both from full-system simulations and a publicly available testbed, show that GM indeed provides several-fold energy savings in communication. We see up to 3x and 11x reduction in duty-cycle when monitoring the variance and average temperature of a real-world data set, but the results fall short compared to the reduction in communication (4.3x and 44x, respectively). Hence, we investigate the energy overhead imposed by the network stack and the communication pattern of the algorithm and summarize our findings. These insights may enable the design of protocols that will unlock more of the potential of GM and similar algorithms for IoT deployments.
Subjects
geometric monitoring | IoT | lifetime improvement
DDC Class
600: Technology
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