On Detection of Critical Events in a Finite Forest using Randomly Deployed Wireless Sensors

Pandey, Kaushlendra; Gupta, Abhishek

doi:10.23919/wiopt47501.2019.9144113

Cited by 5 publications

(2 citation statements)

References 14 publications

(13 reference statements)

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“…Similarly, a rectangle of width r + r d − x and height 2 min(r, r d ) will completely cover the intersecting area hence acts as an upper bound. For detailed discussion over the bounds readers are advised to refer [10], [13].…”

Section: Appendix Amentioning

confidence: 99%

On the Coverage Performance of Boolean-Poisson Cluster Models for Wireless Sensor Networks

Pandey¹,

Gupta²

2020

Preprint

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In this paper, we consider wireless sensor networks (WSNs) with sensor nodes exhibiting clustering in their deployment. We model the coverage region of such WSNs by Boolean Poisson cluster models (BPCM) where sensors nodes' location is according to a Poisson cluster process (PCP) and each sensor has an independent sensing range around it. We consider two variants of PCP, in particular Matérn and Thomas cluster process to form Boolean Matérn and Thomas cluster models. We first derive the capacity functional of these models. Using the derived expressions, we compute the sensing probability of an event and compare it with sensing probability of a WSN modeled by a Boolean Poisson model where sensors are deployed according to a Poisson point process. We also derive the power required for each cluster to collect data from all of its sensors for the three considered WSNs. We show that a BPCM WSN has less power requirement in comparison to the Boolean Poisson WSN, but it suffers from lower coverage, leading to a trade-off between per-cluster power requirement and the sensing performance.A cluster process with desired clustering may provide better coverage while maintaining low power requirements. I. INTRODUCTIONIn WSNs, sensors are deployed over a region such as forest or wetlands, to form a wireless network and exchange mutual data to sense an event. WSN may have a central hub to facilitate the joint detection. There are two essential aspects of WSNs. The first aspect is the coverage aspect i.e. to maximize the region covered by sensors, termed the coverage or sensing region.

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Section: Appendix Amentioning

confidence: 99%

On the Coverage Performance of Boolean-Poisson Cluster Models for Wireless Sensor Networks

Pandey¹,

Gupta²

2020

Preprint

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“…Energy harvesting is the process of collecting low-level ambient energy and converting it into electrical energy to be used as a power source for miniaturized autonomous devices. Examples of this can be seen in structural health monitoring, smart packaging solutions, communication systems, transportation, air and aerospace vehicles, structural biology, robotics, microelectromechanical systems (MEMS) devices, sensor networks, wearable electronics, agriculture, forest fire detection, or various Internet of Things (IoT) components [1,2,3,4,5,6,7,8,9]. Examples of successfully demonstrated possible applications are tire pressure monitoring systems, resulting in autonomous devices powered by the motion of the vehicle [10], or the measurement of river pollution via autonomous sensor nodes powered by the river flow itself [11].…”

Section: Introductionmentioning

confidence: 99%

Kinetic Energy Harvesting for Wearable Medical Sensors

Gljušćić

Zelenika

Blažević

et al. 2019

Sensors

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The process of collecting low-level kinetic energy, which is present in all moving systems, by using energy harvesting principles, is of particular interest in wearable technology, especially in ultra-low power devices for medical applications. In fact, the replacement of batteries with innovative piezoelectric energy harvesting devices can result in mass and size reduction, favoring the miniaturization of wearable devices, as well as drastically increasing their autonomy. The aim of this work is to assess the power requirements of wearable sensors for medical applications, and address the intrinsic problem of piezoelectric kinetic energy harvesting devices that can be used to power them; namely, the narrow area of optimal operation around the eigenfrequencies of a specific device. This is achieved by using complex numerical models comprising modal, harmonic and transient analyses. In order to overcome the random nature of excitations generated by human motion, novel excitation modalities are investigated with the goal of increasing the specific power outputs. A solution embracing an optimized harvester geometry and relying on an excitation mechanism suitable for wearable medical sensors is hence proposed. The electrical circuitry required for efficient energy management is considered as well.

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Stochastic Geometry for Sensing Environmental Processes with a known Spatio-Temporal Profile

Gupta

Pandey

Dhillon

2020

2020 54th Asilomar Conference on Signals, Systems, and Computers

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On Detection of Critical Events in a Finite Forest using Randomly Deployed Wireless Sensors

Cited by 5 publications

References 14 publications

On the Coverage Performance of Boolean-Poisson Cluster Models for Wireless Sensor Networks

On the Coverage Performance of Boolean-Poisson Cluster Models for Wireless Sensor Networks

Kinetic Energy Harvesting for Wearable Medical Sensors

Stochastic Geometry for Sensing Environmental Processes with a known Spatio-Temporal Profile

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