Localized and Precise Boundary Detection in 3-D Wireless Sensor Networks

Zhou, Hongyu; Xia, Sheng-Yuan; Jin, Miao; Wu, Hongyi

doi:10.1109/tnet.2014.2344663

Cited by 17 publications

(8 citation statements)

References 25 publications

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“…The approach faces the misinterpreted as a B-N due to projection, so it uses the Isolated F ragment F iltering (IF F ) approach to remove false positives. The limitation of paper [37] is its assumption that nodes have prior global information about coordinates (by creating a localized coordinate system) and embedding a 3D network on the 2D plane. In Localized boundary detection and parametrization (LBDP ) algorithm [38] a convex-hull based algorithm [39] is proposed for 3D B-N detection.…”

Section: Related Workmentioning

confidence: 99%

Angle Based Critical Nodes Detection (ABCND) for Reliable Industrial Wireless Sensor Networks

Shukla

2022

Preprint

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Node failure in the Wireless Sensor Networks (WSN) topology may lead to economic loss, endanger people, and cause environmental damage. It is found that reliability and security are the two most essential concerns in IWSN. Reliability can be achieved by adequately managing network topology using the structural approaches, where the critical wireless sensor nodes are precisely detected and protected. In this paper, we address the problem of critical node detection and present two-phase algorithms. In Phase-I, a 2D critical node (C-N) detection algorithm is proposed, which uses only RSSI information of the neighbor. This algorithm is also extended to work in 3D topology. In Phase II, a correlation-based reliable approach is proposed to increase the node resilience against node failure to achieve low overhead. The proposed algorithms (ABCND) requires mathcalO (log (N)) time for convergence and mathcalO (delta (log N)) for critical node detection, N represents the number of IoT devices, and delta is the cost required to forward and backward message. We compare our algorithm with the current state-of-the-art on C-N detection algorithms. The simulation results show that the proposed ABCND consumes 50% less energy to detect C-N and 90% to 95% reliable C-N.

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Section: Related Workmentioning

confidence: 99%

Angle Based Critical Nodes Detection (ABCND) for Reliable Industrial Wireless Sensor Networks

Shukla

2022

Preprint

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“…Zhou et al also proposed a two-step localized algorithm for precise boundary detection [23]. In the first step, an Unit Ball Fitting (UBF) process is used to coarsely identify the boundary nodes.…”

Section: Boundary Detection In Volume Networkmentioning

confidence: 99%

“…The sensors surrounding an inner node are the 1-hop neighbors of it that are located on the surface or in the body of a ball that using the inner sensor as its center and the sensor's communication range as its ball radius. Different from an unit ball been used in UBF in [23], the ball does not need to be fully filled with nodes, and the 1-hop neighbors of an inner sensor v roughly shape a latitude-circle-liked closed path r v in the ball. The routes on the path are ensured by the connections between nodes.…”

Section: Identifying Boundary Nodes In Surface Coveragementioning

confidence: 99%

Boundary Node Identification in Three Dimensional Wireless Sensor Networks for Surface Coverage

Wei

Song

Zheng

et al. 2019

IEICE Trans. Inf. & Syst.

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With the existing of coverage holes, the Quality of Service (such as event response, package delay, and the life time et al.) of a Wireless Sensor Network (WSN) may become weaker. In order to recover the holes, one can locate them by identifying the boundary nodes on their edges. Little effort has been made to distinguish the boundary nodes in a model where wireless sensors are randomly deployed on a threedimensional surface. In this paper, we propose a distributed method which contains three steps in succession. It first projects the 1-hop neighborhood of a sensor to the plane. Then, it sorts the projected nodes according to their angles and finds out if there exists any ring formed by them. At last, the algorithm validates a circle to confirm that it is a ring surrounding the node. Our solution simulates the behavior of rotating a semicircle plate around a sensor under the guidance of its neighbors. Different from the existing results, our method transforms a three-dimensional problem into a two-dimensional one and maintaining its original topology, and it does not rely on any complex Hamiltonian Cycle finding to test the existence of a circle in the neighborhood of a sensor. Simulation results show our method outperforms others at the correctness and effectiveness in identifying the nodes on the edges of a three-dimensional WSN.

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“…In fact, they are similar somehow when events occur in the whole network region. A boundary detection mechanism in three-dimensional wireless networks has been proposed in [ 18 ], where sensor nodes on the boundaries are identified based on local information within a one-hop neighborhood. An algorithm that locally constructs planarized triangular meshes is developed and extended from two-to three-dimensions for producing the boundary surface.…”

Section: Related Work and Comparisonmentioning

confidence: 99%

“…Event coverage detection and location determination have been explored relatively extensively for two- and three-dimensional terrestrial WSNs [ 18 , 19 , 20 ]. Generally, sensor nodes are either static or moving along pre-determined itineraries and can be localized through global positioning systems or other localization techniques [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks

Zhou

Xing

Duan

et al. 2015

Sensors

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With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady.

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Localized and Precise Boundary Detection in 3-D Wireless Sensor Networks

Cited by 17 publications

References 25 publications

Angle Based Critical Nodes Detection (ABCND) for Reliable Industrial Wireless Sensor Networks

Angle Based Critical Nodes Detection (ABCND) for Reliable Industrial Wireless Sensor Networks

Boundary Node Identification in Three Dimensional Wireless Sensor Networks for Surface Coverage

Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks

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