Knowledge of How to Communicate in the Internet of Things with Finite Resources and Heterogeneity
A.Kalaivani
, R.Arthi , A.Muruganandham , A.Shanmuga Priya
Internet of things, machine learning, learning.
For a seamless deployment of the Internet of Things (IoT), there is a need for self-organizing solutions to overcome key IoT challenges that include data processing, resource management, coexistence with existing wireless networks, and improved IoT-wide event detection. One of the most promising solutions to address these challenges is via the use of innovative learning frameworks that will enable the IoT devices to operate autonomously in a dynamic environment. However, developing learning mechanisms for the IoT requires coping with unique IoT properties in terms of resource constraints, heterogeneity, and strict quality-of-service requirements. In this paper, a number of emerging learning frameworks suitable for IoT applications are presented. In particular, the advantages, limitations, IoT applications, and key results pertaining to machine learning, sequential learning, and reinforcement learning are studied. For each type of learning, the computational complexity, required information, and learning performance are discussed. Then, to handle the heterogeneity of the IoT, a new framework based on the powerful tools of cognitive hierarchy theory is introduced. This framework is shown to ef_ciently capture the different IoT device types and varying levels of available resources among the IoT devices. In particular, the different resource capabilities of IoT devices are mapped to different levels of rationality in cognitive hierarchy theory, thus enabling the IoT devices to use different learning frameworks depending on their available resources. Finally, key results on the use of cognitive hierarchy theory in the IoT are presented.
"Knowledge of How to Communicate in the Internet of Things with Finite Resources and Heterogeneity", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.2, Issue 7, page no.402 - 410, July-2017, Available :https://ijsdr.org/papers/IJSDR1707067.pdf
Volume 2
Issue 7,
July-2017
Pages : 402 - 410
Paper Reg. ID: IJSDR_170690
Published Paper Id: IJSDR1707067
Downloads: 000347186
Research Area: Engineering
Country: -, -, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
