Identification of trespasser from the signatures of buried single mode fiber optic sensor cable

Suman, Preetam; Gupta, Pallavi; Kassey, Philip B.; Saxena, Neera; Choudhary, Yogesh Kumar; Singh, Vrijendra; Radhakrishna, M.

doi:10.1109/indicon.2015.7443850

Cited by 8 publications

(10 citation statements)

References 9 publications

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“…[18] proposed an interesting solution, which is also profitable, wherein authors employ bees to deter elephants from damaging the vegetation and trees in Africa. A few notable past works [1,[4][5][6][7][8][9][10][11][12][13] have tackled the issues of animal behavior monitoring and proposed technical solutions, mainly warning systems.…”

Section: Related Workmentioning

confidence: 99%

“…The reason is that reflectors and whistles lack the spatial and temporal precision of association between warning signal and approaching vehicles. Suman used fiber sensors-buried in the ground (within 2 m depth)-to identify the intruding animals/humans based on the characteristic signature of the intruder (animal/human) that walks over the ground where the fiber sensor is buried [1]. A smart fault detection system [6] was proposed to detect breakages in electric fences, which keeps wild elephants away from humans in order to protect both humans and animals in Sri Lanka.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, wild animals often stray into human habitats, cross roads or rails, and sometimes results into deaths of humans as well as animals due to various reasons, including non-intentional, e.g., animal-vehicle collision. Apart from Asia and Africa, where animal-human conflict is common [1], according to a statistic [2] in Germany alone 194,410 animals were involved in animal-vehicle collision in the years 2015-2016. Even though the technological advancements in the computing and networking are today utilized to build smart cities, rails, etc., these systems have not been considered for animal-human cohabitation [3].…”

Section: Introductionmentioning

confidence: 99%

“…could be added to the smart hot zones near the roads/rails or adjoining areas (to human habitation) to detect the movement of animals and warn humans/vehicles in advance for a likelihood of animals forage. Although there are a few notable works that studied some aspects of the animal-human cohabitation problem [1,[4][5][6][7][8][9][10][11][12][13]], a warning system that could intelligently process data at various layers of networking architecture-from Internet-of-Things (IoT) devices, e.g., sensors, to fog-to-cloud-has not been integrated into the system. The processing of raw data near sensors can be helpful in some scenarios, where the energy consumption is an issue due to limited source of energy on the end devices.…”

Section: Introductionmentioning

confidence: 99%

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Improving Animal-Human Cohabitation with Machine Learning in Fiber-Wireless Networks

Carpio

Jukan

2018

JSAN

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In this paper, we investigate an animal-human cohabitation problem with the help of machine learning and fiber-wireless (FiWi) access networks integrating cloud and edge (fog) computing. We propose an early warning system which detects wild animals near the road/rail with the help of wireless sensor networks and alerts passing vehicles of possible animal crossing. Additionally, we show that animals’ detection at the earliest and the related processing, if possible, at sensors would reduce the energy consumption of edge devices and the end-to-end delay in notifying vehicles, as compared to the scenarios where raw sensed data needs to be transferred up the base stations or the cloud. At the same time, machine learning helps in classification of captured images at edge devices, and in predicting different time-varying traffic profiles— distinguished by latency and bandwidth requirements—at base stations, including animal appearance events at sensors, and allocating bandwidth in FiWi access networks accordingly. We compare three scenarios of processing data at sensor nodes, base stations and a hybrid case of processing sensed data at either sensors or at base stations, and showed that dynamic allocation of bandwidth in FiWi access networks and processing data at its origin lead to lowering the congestion of network traffic at base stations and reducing the average end-to-end delay.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving Animal-Human Cohabitation with Machine Learning in Fiber-Wireless Networks

Carpio

Jukan

2018

JSAN

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show abstract

“…However, the data processing at constrained end devices, e.g., Raspberry Pi, with limited compute and storage capability could result in more false alarms, longer processing time, and the temporal precision of association between warning signal and approaching vehicles. [1] used fiber sensors-buried in the ground (within 2 m depth)-to identify the intruding animals/humans based on the characteristic signature of the intruder (animal/human) that walks over the ground where the fiber sensor is buried. A smart fault detection system [6] was proposed to detect breakages in electric fences, which keeps wild elephants away from humans in order to protect both humans and animals in Sri Lanka.…”

Section: Introductionmentioning

confidence: 99%

Improving Animal-Human Cohabitation withMachine Learning in Fiber-Wireless Networks

Carpio¹,

Jukan²

2018

Preprint

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In this paper, we investigate an animal-human cohabitation problem with the help of machine learning and fiber-wireless (FiWi) access networks integrating cloud and edge (fog) computing. We propose an early warning system which detects wild animals nearby road/rail with the help of wireless sensor networks and alerts passing vehicles of possible animal crossing. Additionally, we show that animals' detection at the earliest and the related processing, if possible, at sensors would reduce the energy consumption of edge devices and the end-to-end delay in notifying vehicles, as compared to the scenarios where raw sensed data needs to be transferred up the base stations or the cloud. At the same time, machine learning helps in classification of captured images at edge devices, and in predicting different time-varying traffic profiles-- distinguished by latency and bandwidth requirements-- at base stations, including animal appearance events at sensors, and allocating bandwidth in FiWi access networks accordingly. We compare three scenarios of processing data at sensor nodes, base stations and a hybrid case of processing sensed data at either sensors or at base stations, and showed that dynamic allocation of bandwidth in FiWi access networks and processing data at its origin leads to lowering the congestion of network traffic at base stations and reducing the average end-to-end delay.

show abstract

Animal Human Conflict Resolution Mechanism by Machine Learning in Passive Optical Wireless Networks

Naik

2022

Proceedings of International Conference on Industrial Instrumentation and Control

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Identification of trespasser from the signatures of buried single mode fiber optic sensor cable

Cited by 8 publications

References 9 publications

Improving Animal-Human Cohabitation with Machine Learning in Fiber-Wireless Networks

Improving Animal-Human Cohabitation with Machine Learning in Fiber-Wireless Networks

Improving Animal-Human Cohabitation withMachine Learning in Fiber-Wireless Networks

Animal Human Conflict Resolution Mechanism by Machine Learning in Passive Optical Wireless Networks

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