Optimization of wireless sensor network

Padmaja, P.; Marutheswar, G. V.

doi:10.1109/iceeot.2016.7754938

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…From the above, we can note that the cluster-based version of the routing protocol proposed in this paper works better in a large-scale network, while for a small network(less or equal to 50 devices), the flat-based version can be preferred. On the other hand, energy consumption mainly affects the network performance; this is to say, the usage of devices' energy can have a good or bad influence on the overall network performance [33][34][35][36].…”

Section: Energy Efficiency and Network Lifetime Evaluationmentioning

confidence: 99%

EER-RL: Energy-Efficient Routing Based on Reinforcement Learning

Mutombo

Lee

et al. 2021

Mobile Information Systems

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Wireless sensor devices are the backbone of the Internet of things (IoT), enabling real-world objects and human beings to be connected to the Internet and interact with each other to improve citizens’ living conditions. However, IoT devices are memory and power-constrained and do not allow high computational applications, whereas the routing task is what makes an object to be part of an IoT network despite of being a high power-consuming task. Therefore, energy efficiency is a crucial factor to consider when designing a routing protocol for IoT wireless networks. In this paper, we propose EER-RL, an energy-efficient routing protocol based on reinforcement learning. Reinforcement learning (RL) allows devices to adapt to network changes, such as mobility and energy level, and improve routing decisions. The performance of the proposed protocol is compared with other existing energy-efficient routing protocols, and the results show that the proposed protocol performs better in terms of energy efficiency and network lifetime and scalability.

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Section: Energy Efficiency and Network Lifetime Evaluationmentioning

confidence: 99%

EER-RL: Energy-Efficient Routing Based on Reinforcement Learning

Mutombo

Lee

et al. 2021

Mobile Information Systems

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show abstract

“…BSN has improved memory and computing ability compared to normal sensor-node, enables it to perform complex processing and classification procedures before sending data to the supervisory party via a communication network [10]. Transmitting data from the field to the BSN using a routing algorithm presents the most critical operation in terms of communication and energy-consuming affecting WSN performances and lifetime [11].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Head Cluster Selection in WSN by Human-Based Optimization Techniques

Faris¹,

Mahmood²,

Alomari³

et al. 2022

Computers, Materials &Amp; Continua

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Wireless sensor networks (WSNs) are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data, and transmit it in a collaborative manner to one or more processing centers wirelessly using a routing protocol. Energy dissipation is one of the most challenging issues due to the limited power supply at the sensor node. All routing protocols are large consumers of energy, as they represent the main source of energy cost through data exchange operation. Clusterbased hierarchical routing algorithms are known for their good performance in energy conservation during active data exchange in WSNs. The most common of this type of protocol is the Low-Energy Adaptive Clustering Hierarchy (LEACH), which suffers from the problem of the pseudo-random selection of cluster head resulting in large power dissipation. This critical issue can be addressed by using an optimization algorithm to improve the LEACH cluster heads selection process, thus increasing the network lifespan. This paper proposes the LEACH-CHIO, a centralized cluster-based energyaware protocol based on the Coronavirus Herd Immunity Optimizer (CHIO) algorithm. CHIO is a newly emerging human-based optimization algorithm that is expected to achieve significant improvement in the LEACH cluster heads selection process. LEACH-CHIO is implemented and its performance is verified by simulating different wireless sensor network scenarios, which consist of a variable number of nodes ranging from 20 to 100. To evaluate the algorithm performances, three evaluation indicators have been examined, namely, power consumption, number of live nodes, and number of incoming packets. The simulation results demonstrated the superiority of the proposed protocol over basic LEACH protocol for the three indicators.

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Detection of Malicious Node in Wireless Sensor Network

Padmaja¹,

Marutheswar

2017

2017 IEEE 7th International Advance Computing Conference (IACC)

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Optimization of wireless sensor network

Cited by 4 publications

References 7 publications

EER-RL: Energy-Efficient Routing Based on Reinforcement Learning

EER-RL: Energy-Efficient Routing Based on Reinforcement Learning

Optimization of Head Cluster Selection in WSN by Human-Based Optimization Techniques

Detection of Malicious Node in Wireless Sensor Network

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