Redundant Node Management in Wireless Sensor Networks with Multiple Sensor Types

Maduranga, Mwp; Saengudomlert, Poompat; Bandara, H. M. N. Dilum

doi:10.1109/nitc.2018.8550076

Cited by 4 publications

(9 citation statements)

References 14 publications

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“…Several works [1]- [3] to compute the backup set of WSN exist. A 2D omni-directional sensing model i.e.…”

Section: Related Workmentioning

confidence: 99%

“…A 2D omni-directional sensing model i.e. circular sensing model is used in [1]- [3] and in [4]- [7]. The backup sets in [1]- [3] are computed considering 2D modelling of the field of view (FoV) of WSN.…”

Section: Related Workmentioning

confidence: 99%

“…The approaches APP_5 and APP_6 use the 3D coverage model of VSN which is a more practical camera coverage model than the 2D omni-directional (circular) coverage model of WSN as considered in [1]- [7] and 2D coverage model of VSN as considered in [8]- [15]. Unlike [4] the proposed approaches are centralized.…”

Section: Significance Of the Proposed Approaches App_5 And App_6mentioning

confidence: 99%

“…Many works [1]- [7] have already been reported to compute a backup set of wireless sensor nodes (WSN). A 2D coverage model of WSN is used in [1]- [7] and a 2D coverage model of VSN is used in [8]- [15] for computing backup sets. But such 2D modelling does not represent practical camera coverage.…”

Section: Introductionmentioning

confidence: 99%

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Coverage Aware Scheduling Strategies for 3D Wireless Video Sensor Nodes to Enhance Network Lifetime

Bairagi¹,

Mitra

Bhattacharya

2021

IEEE Access

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High-density wireless video sensor nodes (VSNs) having limited battery power are deployed randomly in the disaster-hit area for capturing visual data, but its local processing and transmission consume high energy. High deployment density of those VSNs results in a larger overlap in the coverage area across VSNs that can be utilized to cover the sensing region of some VSNs and shut off such VSNs to decrease energy consumption and increase network lifetime without losing much area coverage. Two advanced approaches (APP_5 and APP_6) with realistic 3D rectangular pyramid camera coverage of VSN monitoring 2D target area is proposed in this paper. These approaches reduce the number of active VSNs in the target area and energy consumption maintaining the overall coverage area above some threshold value ensuring network connectivity. The approaches are compared with the three state-of-the-art approaches EX_1, EX_2 and EX_3 in the same simulation setup. Observed that for 150 deployed VSNs over the target area of size 75x75 square meters, APP_5 and APP_6 reduce energy consumption by 6.98% and 18.6% respectively from the existing approach EX_3 (producing a better result among three existing approaches in terms of energy consumption). Reducing the number of active VSNs helps decrease energy consumption at the expense of reduced area coverage. For the same node density, both APP_5 and APP_6 lose a little amount of area coverage (i.e. 0.93% and 0.95%) than the existing approach EX_2 (producing a better result among three existing approaches in terms of percentage of area coverage). Additionally, both the proposed approaches (having the same communication overhead) establish superiority by 3.19%/7.83%/4.25% from EX_1/EX_2/(EX_3) in terms of communication overhead for 100 deployed VSNs on the same target area. Finally, APP_6 substantiates superiority in terms of reduced energy consumption (11.97%) than APP_5 losing a very little percentage (0.02%) of area coverage for 150 deployed VSNs.

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“…Several works [1]- [3] to compute the backup set of WSN exist. A 2D omni-directional sensing model i.e.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Significance Of the Proposed Approaches App_5 And App_6mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Coverage Aware Scheduling Strategies for 3D Wireless Video Sensor Nodes to Enhance Network Lifetime

Bairagi¹,

Mitra

Bhattacharya

2021

IEEE Access

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“…This approach increases reliability in data delivery by a sensor network. In [30], authors have suggested a scheme to set off redundant sensors. This model is implemented over traditional energy-based protocol for wireless sensor networks.…”

Section: Related Workmentioning

confidence: 99%

Grouping and Sponsoring Centric Green Coverage Model for Internet of Things

Kumar

AlShboul

et al. 2021

Sensors

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Recently, green computing has received significant attention for Internet of Things (IoT) environments due to the growing computing demands under tiny sensor enabled smart services. The related literature on green computing majorly focuses on a cover set approach that works efficiently for target coverage, but it is not applicable in case of area coverage. In this paper, we present a new variant of a cover set approach called a grouping and sponsoring aware IoT framework (GS-IoT) that is suitable for area coverage. We achieve non-overlapping coverage for an entire sensing region employing sectorial sensing. Non-overlapping coverage not only guarantees a sufficiently good coverage in case of large number of sensors deployed randomly, but also maximizes the life span of the whole network with appropriate scheduling of sensors. A deployment model for distribution of sensors is developed to ensure a minimum threshold density of sensors in the sensing region. In particular, a fast converging grouping (FCG) algorithm is developed to group sensors in order to ensure minimal overlapping. A sponsoring aware sectorial coverage (SSC) algorithm is developed to set off redundant sensors and to balance the overall network energy consumption. GS-IoT framework effectively combines both the algorithms for smart services. The simulation experimental results attest to the benefit of the proposed framework as compared to the state-of-the-art techniques in terms of various metrics for smart IoT environments including rate of overlapping, response time, coverage, active sensors, and life span of the overall network.

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Improving the Deployment of WSNs by Localized Detection of Covered Redundant Nodes in Industry 4.0 Applications

Aljubori

Akram

Challenger

2022

Sensors

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Wireless sensor networks can be used as cost-effective monitoring and automation platforms in smart manufacturing and Industry 4.0. Maximizing the covered area and increasing the network lifetime are two challenging tasks in wireless sensor networks. A feasible solution for maximizing the coverage area and network lifetime is detecting and relocating the covered redundant nodes. A covered redundant node is a node whose covered area is also covered by the other active nodes in the network. After identifying the covered redundant nodes, putting them in sleep mode can increase the network lifetime. In addition, moving the detected redundant nodes to the uncovered locations can improve the overall covered area by the sensor nodes. However, finding the redundant nodes is an NP-complete problem. In this paper, we propose a localized distributed algorithm for identifying the redundant nodes based on the 2-hop local neighborhood information of the nodes. The proposed algorithm uses the existing connections between the neighbors of each sensor node to decide the redundancy of the node. The algorithm is localized and does not need the entire topology of the network or the coordinates of the nodes.

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Redundant Node Management in Wireless Sensor Networks with Multiple Sensor Types

Cited by 4 publications

References 14 publications

Coverage Aware Scheduling Strategies for 3D Wireless Video Sensor Nodes to Enhance Network Lifetime

Coverage Aware Scheduling Strategies for 3D Wireless Video Sensor Nodes to Enhance Network Lifetime

Grouping and Sponsoring Centric Green Coverage Model for Internet of Things

Improving the Deployment of WSNs by Localized Detection of Covered Redundant Nodes in Industry 4.0 Applications

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