Data-Driven and GIS-Based Coverage Estimation in a Heterogeneous Propagation Environment

Rathod, Nihesh; Subramanian, Renu; Sundaresan, Rajesh

doi:10.1109/glocom.2018.8647473

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…This might have been due to some shadowing although we noticed no visible blockage at the physical location. This was consistently seen even in our second round of measurements made after the submission of our conference paper [1].…”

Section: Cross-validationsupporting

confidence: 70%

“…The logistic-like regression part takes into account the communication theoretic model of transmission over a Rayleigh fading channel or a Rician fading channel. 1 Together, they exploit the knowledge that the antenna-gain parameters are common across the measurements in the different propagation environments. The outcome is a more unbiased estimate of the received signal strength than the one that relies only on measured RSSI on the correctly received packets.…”

Section: Introductionmentioning

confidence: 99%

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Coverage Estimation in Outdoor Heterogeneous Propagation Environments

Rathod

Subramanian²,

Sundaresan

2020

IEEE Access

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This paper is on a coverage estimation procedure for the deployment of outdoor Internet of Things (IoT). In the first part of the paper, a data-driven coverage estimation technique is proposed. The estimation technique combines multiple machine-learning-based regression ideas. The proposed technique achieves two purposes. The first purpose is to reduce the bias in the estimated received signal strength arising from estimations performed only on the successfully received packets. The second purpose is to exploit commonality of physical parameters, e.g. antenna-gain, in measurements that are made across multiple propagation environments. It also provides the correct link function for performing a nonlinear regression in our communication systems context. In the second part of the paper, a method to use readily available geographic information system (GIS) data (for classifying geographic areas into various propagation environments) followed by an algorithm for estimating received signal strength (which is motivated by the first part of the paper) is proposed. Together they enable quick and automated estimation of coverage in outdoor environments. It is anticipated that these will lead to faster and more efficient deployment of outdoor Internet of Things. INDEX TERMS Coverage, geographic information system (GIS), heterogeneous propagation environment, Internet of Things (IoT) deployment. NIHESH RATHOD received the B.E. degree in electronics and communication from Dharmsinh Desai University, Nadiad, in 2012, and the M.E. degree in telecommunication from the Indian Institute of Science, Bengaluru, in 2015, where he is currently pursuing the Ph.D. degree in communication and networks. Since 2015, he has been a Cisco Research Scholar with the Department of Electrical Communication Engineering, Indian Institute of Science. His interest includes the areas in design and implementation of the Internet of Things (IoT). RENU SUBRAMANIAN received the B.Tech. degree in electrical and electronics from the Rajiv

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Section: Cross-validationsupporting

confidence: 70%

Section: Introductionmentioning

confidence: 99%