Modeling and Analysis of Wireless Sensor Networks With/Without Energy Harvesting Using Ginibre Point Processes

In this paper, we analyze the performance of wireless sensor networks using stochastic geometry. In practical networks, since nodes in the networks are not independently placed, there exists a correlation among the locations of the nodes. In order to capture the effect of the correlation, we model t...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2017-06, Vol.16 (6), p.3700-3713
Main Authors: Han-Bae Kong, Ping Wang, Niyato, Dusit, Yu Cheng
Format: Article
Language:English
Subjects:
Cellular networks
Computer simulation
Correlation
Distribution functions
Energy consumption
Energy harvesting
Energy sources
Ginibre point process
Logic gates
Modelling
Power control
Probability theory
Radio frequency
Remote sensors
repulsive point process
Sensors
Spatial distribution
stochastic geometry
Uplink
wireless energy harvesting
Wireless sensor networks
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Summary:In this paper, we analyze the performance of wireless sensor networks using stochastic geometry. In practical networks, since nodes in the networks are not independently placed, there exists a correlation among the locations of the nodes. In order to capture the effect of the correlation, we model the spatial distribution of the nodes as α-Ginibre point processes (GPPs), which reflect the repulsion. It is assumed that each sensor node is associated with the closest gateway and employs a fractional channel inversion power control, which adjusts transmit power based on the contact distance. We first identify the characteristics of the contact distance and transmit power, and then investigate the outage performance of the networks using the derived characteristics. We also examine energy harvesting networks where each sensor harvests energy from radio frequency signals radiated by energy sources and transmits data to its serving gateway when the harvested energy is enough to conduct the fractional channel inversion power control. Since the α-GPP contains the Poisson point process (PPP) as a particular case, our analysis can be interpreted as a generalization of previous works on the networks modeled by PPPs. The accuracy of our analysis is validated through simulation results.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2017.2686848