The performance of QoS in wireless sensor networks

Cedeño, Néstor Zamora; Asqui, Orlando Philco; Chaw, Emily Estupiñan

doi:10.23919/cisti.2019.8760756

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…Here, the degree of ambiguity in membership is determined using member functions (MFs), such as trapezoidal and triangular. The "min" function is utilized for the "and" operator in this manner, the "max" function is used for the "or" operator, and the centroid failure is used for the process [26]. The fuzzy structure is given in Figure 2.…”

Section: Super Cluster Head Selectionmentioning

confidence: 99%

Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

Bali

Gill²,

Choudhary³

et al. 2022

Energies

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In Wireless Sensor Networks (WSNs), routing algorithms can provide energy efficiency. However, due to unbalanced energy consumption for all nodes, the network lifetime is still prone to degradation. Hence, energy efficient routing was developed in this article by selecting cluster heads (CH) with the help of adaptive whale optimization (AWOA) which was used to reduce time-consumption delays. The multi-objective function was developed for CH selection. The clusters were then created using the distance function. After establishing groupings, the supercluster head (SCH) was selected using the benefit of a fuzzy inference system (FIS) which was used to collect data for all CHs and send them to the base station (BS). Finally, for the data-transfer procedure, hop count routing was used. An Oppositional-based Whale optimization algorithm (OWOA) was developed for multi-constrained QoS routing with the help of AWOA. The performance of the proposed OWOA methodology was analyzed according to the following metrics: delay, delivery ratio, energy, NLT, and throughput and compared with conventional techniques such as particle swarm optimization, genetic algorithm, and Whale optimization algorithm.

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Section: Super Cluster Head Selectionmentioning

confidence: 99%

Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

Bali

Gill²,

Choudhary³

et al. 2022

Energies

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“…Many studies have been conducted on the different aspects of QoS of sensor networks, such as power-saving [11][12][13][14][15][16][17][18][19], routing [20][21][22][23][24][25][26], throughput of networks [27], enhancement performance [28,29] and coverage [30][31][32], and. The main goal of this study was to autonomously control the number of active sensors.…”

Section: Related Workmentioning

confidence: 99%

A High-Robust Sensor Activity Control Algorithm for Wireless Sensor Networks

Tsai

Shen

et al. 2022

Sensors

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In wireless sensor networks, it is important to use the right number of sensors to optimize the network and consider the key design and cost. Due to the limited power of sensors, important issues include how to control the state of the sensor through an automatic control algorithm and how to power-save and efficiently distribute work. However, sensor nodes are usually deployed in dangerous or inaccessible locations. Therefore, it is difficult and impractical to supply power to sensors through humans. In this study, we propose a high reliability control algorithm with fast convergence and strong self-organization ability called the sensor activity control algorithm (SACA), which can efficiently control the number of sensors in the active state and extend their use time. In the next round, SACA considers the relationship between the total number of active sensors and the target value and determines the state of the sensor. The data transmission technology of random access is used between the sensor and the base station. Therefore, the sensor in the sleep state does not need to receive the feedback packet from the base station. The sensor can achieve true dormancy and power-saving effects. The experimental results show that SACA has fast convergence, strong self-organization capabilities, and power-saving advantages.

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“…In [26] evaluated some parameters of the QoS for performance of the WSN network under the AODV routing protocol in the NS-3 simulator. It analyzes 4 metrics (packet sent, received and lost, throughput, packet delivery rate and loss packets.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, QoSensing metrics in proposed method are analyzed in both star and mesh topology. In order to show the efficiency of our proposed method, we compare our proposed method with existing works QoS with AODV [26], multi-hop routing [30], Data fusion method [32], and MDR method [34] held on WSN QoSensing improvement.…”

Section: Analysis Of Throughputmentioning

confidence: 99%

4TQS: Four Tiers OVER Quality of Sensing with Energy Improvement in Wireless Sensor Network

Asqui¹,

Cedeño²

2021

IJCNS

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This paper presents four algorithms for QoSensing (4TQS) and routing on dynamics Wireless Sensor Networks. Since many types of researches held on QoSensing improvement in terms of coverage and connectivity enhancement. In a WSN with a density of 120 static and 30 mobile nodes, the major power-consuming activities are mobility and communication. Such power constraint has a great effect on the node activities and the same network. We analyzed the occurrence of coverage holes which is, regions inside the area of interest that are void of operational nodes for sensing and/or routing purposes. 4TQS method shows clustering phase through neuro-fuzzy based affinity propagation NFAP, sleep scheduling phase with ESLS algorithm and the assist of coverage enhancement phase in which HAHP is involved. Routing phase and connectivity metrics are improved with DTMR algorithm. Involvement of four efficient algorithms results in improved QoSensing in WSN. We analyzed our proposed method in both Star and Mesh topology in order to evaluate the QoSensing metrics in both topologies. Our method shows promising results in QoSensing metrics or performance in dynamics WSNs.

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The performance of QoS in wireless sensor networks

Cited by 6 publications

References 8 publications

Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

A High-Robust Sensor Activity Control Algorithm for Wireless Sensor Networks

4TQS: Four Tiers OVER Quality of Sensing with Energy Improvement in Wireless Sensor Network

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