Toward robust and energy-efficient clustering wireless sensor networks: A double-stage scale-free topology evolution model

Fu, Xiuwen; Pace, Pasquale; Aloi, Gianluca; Li, Wenfeng; Fortino, Giancarlo

doi:10.1016/j.comnet.2021.108521

Cited by 30 publications

(5 citation statements)

References 37 publications

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“…For instance, numerous clusters are used so as to form a topology that can resist faults in highly mobile environments such as MANETs. 26 It's findings from simulation-based studies have best energy consumption, latency among others. q. Topology-aware Routing: In order to maximize utilization of resources, routing protocols are designed in such a way to adapt themselves to changes in network topology.…”

Section: Recent Developments In Wsn Topology Controlmentioning

confidence: 99%

Optimizing Wireless Sensor Networks through Ant Colony-Based Localized Mesh Topology

Gupta,

Chopra,

Bala

et al. 2024

J. Electrochem. Soc.

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The growing need for faster and more effective data handling and data transfer capabilities of a network require energy-efficient and more powerful smart sensor devices. These devices have become increasingly necessary in wireless sensor networks (WSN). The architecture or topology of WSN can greatly impact the organizational efficacy and connectivity among the sensor nodes employed within the given area. It is foremost important to implement a robust network topology which must be capable of ensuring continuous and reliable communication within the whole network. This research paper presents an effective approach based on Ant colony scheme to optimize mesh network topology ant colony optimization (ACO) is used to place the sensor nodes optimally in the given area of the whole network. The proposed research carries out comprehensive performance evaluation of the network under various QoS parameters such as bandwidth, throughput, delay, residual energy and routing load. These parameters shows the effectiveness or adaptability of the network with different and dynamically changing communication requirements suggested by this topology scheme. The network based on the proposed method has been simulated several times and the achieved simulation patterns have been analyzed under the mentioned QoS constraints.

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Section: Recent Developments In Wsn Topology Controlmentioning

confidence: 99%

Optimizing Wireless Sensor Networks through Ant Colony-Based Localized Mesh Topology

Gupta,

Chopra,

Bala

et al. 2024

J. Electrochem. Soc.

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“…In order to better understand the research progress of distributed filtering in the field of LSR, Table 1 compares the existing methods according to six aspects. [27][28][29] As shown in Table 1, the existing methods focus on estimation consensus. However, the optimal (estimation accuracy) is ignored.…”

Section: Related Workmentioning

confidence: 99%

“…In order to better understand the research progress of distributed filtering in the field of LSR, Table 1 compares the existing methods according to six aspects. 27–29…”

Section: Introductionmentioning

confidence: 99%

Distributed filtering in sensor networks based on linear minimum mean square error criterion with limited sensing range

Shao

2022

International Journal of Distributed Sensor Networks

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One of the fundamental problems in sensor networks is to estimate and track the target states of interest that evolve in the sensing field. Distributed filtering is an effective tool to deal with state estimation in which each sensor only communicates information with its neighbors in sensor networks without the requirement of a fusion center. However, in the majority of the existing distributed filters, it is assumed that typically all sensors possess unlimited field of view to observe the target states. This is quite restrictive since practical sensors have limited sensing range. In this article, we consider distributed filtering based on linear minimum mean square error criterion in sensor networks with limited sensing range. To achieve the optimal filter and consensus, two types of strategies based on linear minimum mean square error criterion are proposed, that is, linear minimum mean square error filter based on measurement and linear minimum mean square error filter based on estimate, according to the difference of the neighbor sensor information received by the sensor. In linear minimum mean square error filter based on measurement, the sensor node collects measurement from its neighbors, whereas in linear minimum mean square error filter based on estimate, the sensor node collects estimate from its neighbors. The stability and computational complexity of linear minimum mean square error filter are analyzed. Numerical experimental results further verify the effectiveness of the proposed methods.

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“…Efficient management of critical energy resources to cope with varying link conditions in Wireless Sensor Network (WSN) as well as WBAN has always been the point of concern for researchers. In [21] Fu et al proposed a robust and energy-efficient scalefree double stage topology evolution model for WSN. A trade-off among latency, energy efficiency, and routing survivability is made by Fu et al in [22] while making routing decisions in WSN.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement learning based effective communication strategies for energy harvested WBAN

et al. 2022

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Toward robust and energy-efficient clustering wireless sensor networks: A double-stage scale-free topology evolution model

Cited by 30 publications

References 37 publications

Optimizing Wireless Sensor Networks through Ant Colony-Based Localized Mesh Topology

Optimizing Wireless Sensor Networks through Ant Colony-Based Localized Mesh Topology

Distributed filtering in sensor networks based on linear minimum mean square error criterion with limited sensing range

Reinforcement learning based effective communication strategies for energy harvested WBAN

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