Wireless Energy Harvesting Sensor Networks: Boolean–Poisson Modeling and Analysis

Flint, Ian; Kong, Han-Bae; Privault, Nicolas; Wang, Ping; Niyato, Dusit

doi:10.1109/twc.2017.2739723

Cited by 10 publications

(9 citation statements)

References 33 publications

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“…, z n ∈ R d appearing in Theorem 1 as n nodes in a wireless network, the theorem provides in computable form the probability that the nodes with indices in Θ 1 are covered by the spherical germ-grain model while the nodes in Θ 2 are not. Such quantities have been shown to be of interest in the study of sensor networks, see [7].…”

Section: Resultsmentioning

confidence: 99%

“…The above quantity appears naturally when the spherical germ-grain model is used as a model for wireless networks, as it represents the probability that the nodes in Θ 1 are covered by a network of Radio Frequency (RF) sources while the ones in Θ 2 are not. For example, one of the main objectives of [7] is the explicit computation of such quantities in the case of the Boolean model in order to estimate the average throughput in wireless energy harvesting sensor networks, see for example § V therein.…”

Section: Introductionmentioning

confidence: 99%

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Computation of Coverage Probabilities in a Spherical Germ-Grain Model

Flint

Privault

2019

Methodol Comput Appl Probab

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computation of Coverage Probabilities in a Spherical Germ-Grain Model

Flint

Privault

2019

Methodol Comput Appl Probab

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“…Flint et al [21] studied the Boolean method combined with Poisson modeling for energy harvesting in wireless sensor networks. There are some assumptions in their work, including senor nodes are distributed in the form of Poisson distribution and have a sensing range in the form of a disc.…”

Section: Related Workmentioning

confidence: 99%

Average Energy Analysis in Wireless Sensor Networks using Multitier Architecture

Singh¹

2019

IJPE

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The energy of sensor nodes plays a vital role in wireless sensor networks for different purposes such as sensing events, communicating among sensor nodes, and transmitting information from one node to another node. The average energy of the network is referred to as the ratio of the total energy of all sensor nodes in the network to the number of nodes. In this paper, multitier architecture is proposed for calculating the average energy and throughput of the network in terms of the number of packets reached at the base station (BS). The proposed approach has been compared with two existing approaches, the low energy adaptive clustering hierarchy and stable election protocol, in terms of average energy and throughput of the network. This paper presents the average energy of each node in the network in both 2D and 3D views for better interpretation of results. The proposed approach is 19.79% better in terms of average energy compared with the stable election protocol. The proposed approach is further compared with the low energy adaptive clustering hierarchy protocol and is found to be 34.20% better.

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“…Paper [6] proposed a RF-based energy harvesting technology to power the sensor nodes, and it designed a novel compact folded dipole rectenna to improve the energy harvesting capability. Paper [7] analyzed the wireless radio frequency energy harvesting network based on the Boolean-Poisson model when the sensor nodes collect energy simultaneously.…”

Section: Introductionmentioning

confidence: 99%