A novel energy optimization approach for artificial intelligence-enabled massive internet of things

Ali Hassan Sodhro, Mohammad S. Obaidat, Sandeep Pirbhulal, Gul Hassan Sodhro, Noman Zahid, Abhimanyu Rawat

Forskningsoutput: KonferensbidragArbetsdokument (paper)Peer review

26 Citeringar (Scopus)

Sammanfattning

Emerging trends in Internet of things (IoT) has caught the attention of every domain e.g., industrial, business, and healthcare etc. Sensor-embedded IoT devices are the key drivers for collecting large amount of data. Managing these large datasets is one of the critical challenges to be tackled. Continuous huge information collection through sensorenabled devices is known as the massive IoT (mIoT). Thus, there is a need of self-adaptive artificial intelligence (AI)based strategies to effectively cluster, examine and interpret the entire entities in the system. With increased data volumes and power hungry natured IoT devices it is a dire need to manage their power wisely. To fairly allot the power levels to the tiny portable devices it is important to integrate mIoT with AI-based techniques. To remedy these issues this paper proposes a novel cross-layer based energy optimization algorithm (CEOA) in mIoT system by examining the detailed features and data patterns. Experimental analysis reveals that proposed CEOA outperforms its competing counterpart i.e., Baseline in terms of efficient power management and monitoring.

OriginalspråkEngelska
DOI
StatusPublicerad - 2019-juli
Externt publiceradJa
Evenemang22nd International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2019 - Berlin, Tyskland
Varaktighet: 2019-juli-222019-juli-24

Konferens

Konferens22nd International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2019
Land/TerritoriumTyskland
OrtBerlin
Period19-07-2219-07-24

Nationell ämneskategori

  • Data- och informationsvetenskap (102)

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