Energy Efficient PSO-based routing protocol for large WSN

Qabouche, Hicham; Sahel, Aïcha; Badri, Abdelmajid; Mourabit, Ilham El

doi:10.1109/iraset52964.2022.9738078

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…The properties of the Matlab installation environment are as follows: Windows 10 64-bit operating system, Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz, 16GB RAM. To validate the model, our proposed model's simulation result is compared with the traditional model LEACH [41], DEEC [42] and state-of-the-art meta-heuristic algorithm based on PSO [43] and hybrid models PSO-GA [45] and PSO-HSA [46]. The parameters used for this simulation are given in Table 3.…”

Section: Simulation Resultsmentioning

confidence: 99%

Bio-Inspired Energy Efficient Cluster-based Routing Protocol for IoT in Disaster Scenarios

Ahmed,

Hossain,

Chong

et al. 2024

Preprint

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The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-adverse situations. Clustering-based communication helps reduce a node’s energy decay during data transmission and enhances the network’s lifetime. Many hybrid combination algorithms are proposed for clustering and the routing protocol to improve the network lifetime in disaster scenarios. However, the performance of these protocols is widely varied based on the underlying network configuration and the optimisation parameters considered. In this research, we have used the clustering parameters that are most relevant to disaster scenarios, such as the node’s residual energy, distance to sink, and network coverage. Then, we proposed a bio-inspired hybrid (BAO-PSO) algorithm where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm is compared with the benchmark protocols LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime are considered performance matrices. The simulation result showed that the proposed algorithm increases network lifetime by 10%-20% by comparison with the benchmark algorithm.

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Section: Simulation Resultsmentioning

confidence: 99%

Bio-Inspired Energy Efficient Cluster-based Routing Protocol for IoT in Disaster Scenarios

Ahmed,

Hossain,

Chong

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The PSO approach is an evolutionary optimization method inspired by flocks of birds and schools of fish. It is often implemented in various engineering applications due to its superior computational efficiency [40][41][42]. The PSO approach converges faster and more consistently than other populationbased stochastic optimization approaches, such as the genetic algorithm (GA) and ant colony optimization (ACO) [30,31,33,[43][44][45].…”

Section: Pso Controller Design and Tuningmentioning

confidence: 99%

Dynamic Voltage Stability Enhancement in Electric Vehicle Battery Charger Using Particle Swarm Optimization

et al. 2022

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Electric vehicles (EVs) are poised to lead the transportation sector as the primary choice of automobile due to their efficiency and environmental benefits. EVs with enhanced autonomy and reduced pricing have become feasible in the market, enabling a gradual transition for higher EV penetration. However, electric vehicles require highly efficient and stabilized charging stations in urban areas to ensure the vehicle's charging time is not compromised. In this regard, the Vienna rectifier with a voltage-oriented controller (VOC) plays a significant role in improving the power quality of the utility grid for EV battery charger applications. The low stability of the battery charger's output voltage and current is due to the trial-and-error method used to select the PI controller gains. In order to improve the voltage and current stability, the particle swarm optimization (PSO) technique is used to optimize VOC's PI controller gains. The code composer studio (CCS) platform integrates the PSO technique for EV battery chargers in the experimental setup. The Vienna rectifier with VOC for EV battery charger is implemented using TMS 320F28337 digital signal controller in the test board. Findings indicate that the PSO optimized VOC improves the output voltage and current stability by 12% compared to the existing trial-anderror technique. Furthermore, the proposed system is tested in an experimental setup that provides input current THD to less than 5% for different load variations (up to 1.5kW) to meet the IEEE-519 standards. Results from simulations and experimental setup verify that the proposed PSO-PI controller-based Vienna rectifier significantly improves EV battery chargers' output voltage and current stability.INTEX TERMS Vienna rectifier, voltage oriented controller (VOC), particle swarm optimization (PSO), electric vehicles, charging stations

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“…Meanwhile, when it comes to vast monitored fields, the risk of data loss increases, adopting direct transmissions to the sink. Which may create isolated CHs which cannot reach the sink because it is out of range, resulting excessive energy consumption and data loss [20].…”

Section: Introductionmentioning

confidence: 99%

Novel reliable and dynamic energy-aware routing protocol for large scale wireless sensor networks

Qabouche

Sahel

Badri

et al. 2022

IJECE

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<p>Wireless sensor networks (WSN) are made up of an important number of sensors, called nodes, distributed in random way in a concerned monitoring area. All sensor nodes in the network are mounted with limited energy sources, which makes energy harvesting on top of the list of issues in WSN. A poor communication architecture can result in excessive consumption, reducing the network lifetime and throughput. Centralizing data collection and the introduction of gateways (GTs), to help cluster heads (CHs), improved WSN life time significantly. However, in vast regions, misplacement and poor distribution of GTs wastes a huge amount of energy and decreases network’s performances. In this work, we describe a reliable and dynamic with energy-awareness routing (RDEAR) protocol that provides a new GT’s election approach taking into consideration CHs density, transmission distance and energy. Applied on 20 different networks, RDEAR reduced the overall energy consumption, increased stability zone and network life time as well as other compared metrics. Our proposed approach increased network’s throughput up to 75.92% , 67.7% and 9.78% compared to the low energy adaptive clustering hierarchy (LEACH), distributed energy efficient clustering (DEEC) and static multihop routing (SMR), protocols, respectively.</p>

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Energy Efficient PSO-based routing protocol for large WSN

Cited by 6 publications

References 22 publications

Bio-Inspired Energy Efficient Cluster-based Routing Protocol for IoT in Disaster Scenarios

Bio-Inspired Energy Efficient Cluster-based Routing Protocol for IoT in Disaster Scenarios

Dynamic Voltage Stability Enhancement in Electric Vehicle Battery Charger Using Particle Swarm Optimization

Novel reliable and dynamic energy-aware routing protocol for large scale wireless sensor networks

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