A survey on low-power wide area networks for IoT applications View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-26

AUTHORS

Mncedisi Bembe, Adnan Abu-Mahfouz, Moshe Masonta, Tembisa Ngqondi

ABSTRACT

We are on the entry of the exponential advancement of the internet-of-things (IoT) due to the quick development of internet-connected smart-objects. As the number of connected smart-objects increase, IoT will continue to advance by providing connectivity and interactions between the physical and the cyber world. This connectivity is characterized by low throughput, delay sensitivity, small and wide coverage, low power consumption, low device, etc. Which explains the emergence of low power wide area network (LPWAN). LPWAN technologies are an alternative promising connectivity solutions for Internet of Things. However, the lack of an overall LPWAN knowledge that present a comprehensive analysis of LPWAN technologies is presently constraining the achievement of the modern IoT vision. In this paper, we begin with a detailed analysis of the conventional high power long-range network technologies that considers IoT applications and requirements. We further point out the need for dedicated low power wide area technologies in IoT systems. In addition, we analyse the technical specification based on the PHY and MAC layers of the technologies that are already deployed, or likely to be deployed. The focus is to incorporate both standard and proprietary technologies in our study. Furthermore, we present the modelling techniques and performance metrics that are adopted in LPWAN networks analysis. Finally, challenges and open problems are presented. The main contribution of this study is that it provides an enhanced summary of the current state-of-the-art of LPWAN suitable to meet the requirements of IoT, while uniquely providing LPWAN’s modelling techniques, performance metrics and their associated enablers. More... »

PAGES

1-26

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11235-019-00557-9

DOI

http://dx.doi.org/10.1007/s11235-019-00557-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113008701


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