Correlation-Based Sensor Activity Scheduling Mechanisms for Wireless Sensor Networks

Saad, Ghina; Harb, Hassan; Jaoude, Chady Abou; Jaber, Ali

doi:10.1109/aiccsa47632.2019.9035254

Cited by 3 publications

(7 citation statements)

References 17 publications

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“…That's why in Fig. 3 c 4 and the line of sight #» D have not been shown. Let P is the projection of P(x,y,z)(which is the position of the VSN v) on z=0.…”

Section: Coverage Model Network Model and Some Definitions A Coverage Modelmentioning

confidence: 92%

“…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%

“…Two correlation-based sensor scheduling algorithms are proposed in [4]. The scheduling algorithms are cluster-based.…”

Section: Related Workmentioning

confidence: 99%

“…combination of distributed and centralized) and grid-based. But heuristics in [4]- [7] are based on the 2D omni-directional sensing mode of WSNs which is not a practical camera coverage model and they also lack any collision handling mechanism to prevent message loss due to collision. A distributed duty cycling approach is proposed in [9]- [16].…”

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. This reduces complications associated with cluster formation, cluster head election and delay.…”

Section: Significance Of the Proposed Approaches App_5 And App_6mentioning

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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“…That's why in Fig. 3 c 4 and the line of sight #» D have not been shown. Let P is the projection of P(x,y,z)(which is the position of the VSN v) on z=0.…”

Section: Coverage Model Network Model and Some Definitions A Coverage Modelmentioning

confidence: 92%

Section: Related Workmentioning

confidence: 99%

“…Two correlation-based sensor scheduling algorithms are proposed in [4]. The scheduling algorithms are cluster-based.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Significance Of the Proposed Approaches App_5 And App_6mentioning

confidence: 99%

See 3 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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Multi‐hop similarity‐based‐clustering framework for IoT‐Oriented Software‐Defined wireless sensor networks

Shafique

Asad

Aslam

et al. 2022

IET Wireless Sensor Systems

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The performance of Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) depends on the routing protocol and the deployment technique in modern applications. In a plethora of IoT-WSNs applications, the IoT nodes are essential equipment to prolong the network lifetime with limited resources. Data similarity-based clustering protocols exploit the temporal correlation among the neighbouring sensor nodes through the subset of data. In bendy supervision, IoT-based Software Defined WSNs provide an optimistic resolution by allowing the control logic to be separated from the sensor nodes. The benefit of this SDN-based IoT architecture, allows the unified control of the entire IoT network, making it easier to implement on-demand network management protocols and applications. To this end, in this paper, we design a Multi-hop Similarity-based Clustering framework for IoT-oriented Software-Defined wireless sensor Networks (MSCSDNs). In particular, we construct data-similar application-aware clusters in order to minimise the communication overhead. Also, we adapt inter-cluster and intra-cluster multi-hop communication using adaptive normalised least mean square and merged them with the proposed MSCSDN framework that helps prolong the network lifespan. The proposed framework is compared with the state-of-the-art approaches in terms of network lifespan, stability period, instability period, report delay, report delivery, and cluster leader nodes generations. The MSCSDN achieves optimal data accuracy concerning the collected data. K E Y W O R D S internet of things, multi-hoping, similarity-based clustering, software defined networkingThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Node Scheduling Scheme for Wireless Sensor Networks with Partial Coverage

Bajaber

2023

JTEC

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Sensor nodes in a wireless sensor network are distributed across an area for data collection. These nodes have basic capabilities in terms of interfaces and components, and they often operate in dynamic, hostile environments. Sensor networks present numerous challenges: they are dispersed, generate constant high rates data streams, function in dynamic and time-changing situations; and may involve a large number of sensors. Sensor nodes have enough power to transmit their readings to a central high-performance computing unit for processing. Sensor networks generate data streams, which are sequences of real-time data records characterized by their high data rates that consume significant network computing resources. However, only a few studies address the issue of collecting highly redundant data, leading to nodes wasting energy by sending redundant information to a central high-performance computing unit. Improved scheduling tactics can help reduce energy consumption in sensor nodes. This research developed a Node Scheduling Scheme for Wireless Sensor Networks with Partial Coverage (NSPC). Partial coverage can be obtained by dividing the area of interest into smaller sub-regions and determining the monitoring intensity for each sub-region by sensor nodes. Various strategies, such as clustering and scheduling, can be employed to accomplish partial coverage. Considering partial coverage when designing a WSN is crucial, as it can enhance network stability and reliability while reducing the cost and energy consumption of each sensor nodes.

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Correlation-Based Sensor Activity Scheduling Mechanisms for Wireless Sensor Networks

Cited by 3 publications

References 17 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

Multi‐hop similarity‐based‐clustering framework for IoT‐Oriented Software‐Defined wireless sensor networks

Node Scheduling Scheme for Wireless Sensor Networks with Partial Coverage

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