research-article

Multimodal Complex Event Processing on Mobile Devices

Authors:

Pablo Graubner,

Christoph Thelen,

Michael Körber,

Guido Salvaneschi,

Bernhard Seeger,

Bernd FreislebenAuthors Info & Claims

DEBS '18: Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems

Pages 112 - 123

https://doi.org/10.1145/3210284.3210289

Published: 25 June 2018 Publication History

Abstract

Mobile devices are increasingly being used in edge and fog computing environments to process contextual data collected by sensors. Although complex event processing (CEP) is a suitable approach for realizing context-aware services on mobile devices in these environments, existing mobile CEP engines do not leverage the full potential of modern mobile hardware/software architectures. In this paper, we present multimodal CEP, a novel approach to process streams of events on-device in user space (user mode), in the operating system (kernel mode), on the Wi-Fi chip (Wi-Fi mode), and/or on a sensor hub (hub mode), providing significant improvements in terms of power consumption and throughput. Multimodal CEP automatically breaks up CEP queries and selects the most adequate execution mode for the involved CEP operators. Filter, aggregation, and correlation operators can be expressed in a high-level language without requiring system-level domain-specific knowledge. Multimodal CEP enables developers to efficiently detect user activities, collect environmental conditions, or interpret operating system and network events. Furthermore, it facilitates novel context-aware services, demonstrated by a use case for gathering and analyzing mobility data by Wi-Fi probe request tracking.

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Cited By

Kuhrt MGlombiewski NKörber MMorgen ABrandenstein DSeeger B(2025)Efficient Event Processing on Modern HardwareScalable Data Management for Future Hardware10.1007/978-3-031-74097-8_3(65-89)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-3-031-74097-8_3
Ren HAnicic DRunkler T(2022)Towards Semantic Management of On-Device Applications in Industrial IoTACM Transactions on Internet Technology10.1145/351082022:4(1-30)Online publication date: 14-Nov-2022
https://dl.acm.org/doi/10.1145/3510820
Dimitriadis IMavroudopoulos IKyrama SToliopoulos TGounaris AVakali ABillis ABamidis P(2022)Scalable real-time health data sensing and analysis enabling collaborative care deliverySocial Network Analysis and Mining10.1007/s13278-022-00891-y12:1Online publication date: 20-Jun-2022
https://doi.org/10.1007/s13278-022-00891-y
Show More Cited By

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Multimodal Complex Event Processing on Mobile Devices

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cover image ACM Conferences

DEBS '18: Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems

June 2018

289 pages

ISBN:9781450357821

DOI:10.1145/3210284

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 June 2018

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DEBS '18

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DEBS '18: The 12th ACM International Conference on Distributed and Event-based Systems

June 25 - 29, 2018

Hamilton, New Zealand

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DEBS '18 Paper Acceptance Rate 12 of 31 submissions, 39%;

Overall Acceptance Rate 145 of 583 submissions, 25%

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Cited By

Kuhrt MGlombiewski NKörber MMorgen ABrandenstein DSeeger B(2025)Efficient Event Processing on Modern HardwareScalable Data Management for Future Hardware10.1007/978-3-031-74097-8_3(65-89)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-3-031-74097-8_3
Ren HAnicic DRunkler T(2022)Towards Semantic Management of On-Device Applications in Industrial IoTACM Transactions on Internet Technology10.1145/351082022:4(1-30)Online publication date: 14-Nov-2022
https://dl.acm.org/doi/10.1145/3510820
Dimitriadis IMavroudopoulos IKyrama SToliopoulos TGounaris AVakali ABillis ABamidis P(2022)Scalable real-time health data sensing and analysis enabling collaborative care deliverySocial Network Analysis and Mining10.1007/s13278-022-00891-y12:1Online publication date: 20-Jun-2022
https://doi.org/10.1007/s13278-022-00891-y
Ren HAnicic DRunkler TMargara AValle E(2021)The synergy of complex event processing and tiny machine learning in industrial IoTProceedings of the 15th ACM International Conference on Distributed and Event-based Systems10.1145/3465480.3466928(126-135)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1145/3465480.3466928
Zimmerle COliveira WGama KCastor FMachado ISouza RMaciel RSant'Anna C(2019)Reactive-based Complex Event ProcessingProceedings of the XXXIII Brazilian Symposium on Software Engineering10.1145/3350768.3352492(84-93)Online publication date: 23-Sep-2019
https://dl.acm.org/doi/10.1145/3350768.3352492
Russo G(2019)Self-Adaptive Data Stream Processing in Geo-Distributed Computing EnvironmentsProceedings of the 13th ACM International Conference on Distributed and Event-based Systems10.1145/3328905.3332304(276-279)Online publication date: 24-Jun-2019
https://dl.acm.org/doi/10.1145/3328905.3332304
Russo GCardellini VPresti F(2019)Reinforcement Learning Based Policies for Elastic Stream Processing on Heterogeneous ResourcesProceedings of the 13th ACM International Conference on Distributed and Event-based Systems10.1145/3328905.3329506(31-42)Online publication date: 24-Jun-2019
https://dl.acm.org/doi/10.1145/3328905.3329506
Das RMakkes MUta AWang LBal H(2019)Aves: A Decision Engine for Energy-efficient Stream Analytics across Low-power Devices2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9005607(441-448)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9005607

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