An approach by adopting multi-objective clustering and data collection along with node sleep scheduling for energy efficient and delay aware WSN

Guruprakash, B.; Balasubramanian, C.; Sukumar, R.

doi:10.1007/s12083-019-00779-3

Cited by 21 publications

(11 citation statements)

References 36 publications

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“…Then, in order to reduce energy consumption, this method has used a sleep scheduling scheme. Also, this method has used a selective track search algorithm to collect data in an optimal manner to improve lifetime and other quality of service factors in WSN [16]. Malakar et al have proposed an intelligent method for CH selection in WSN using teaching-learning-based optimization (TLBO) that has tried to optimize several conflicting objectives of the network by selecting CH efficiently and dynamically in each iteration of the network [17].…”

Section: Related Workmentioning

confidence: 99%

WSN Routing Protocol Using a Multiobjective Greedy Approach

Nabavi

Eraghi

Torkestani

2021

Wireless Communications and Mobile Computing

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Due to the widespread use of communication networks and the ease of transmitting and gathering information through these networks, wireless sensor networks (WSN) have become increasingly popular. Usability in any environment without the need for environmental monitoring and engineering of these networks has led to their increasing usage in various fields. Routing information from the sensor node to sink, so that node energy is consumed uniformly and network life is not reduced, is one of the most important challenges in wireless sensor networks. Most wireless networks have no infrastructure, and embedded sensor nodes have limited power. Thus, the early termination of the wireless node’s energy based on the transmission of messages over the network can disrupt the entire network process. In this paper, the object is designed to find the optimal path in WSN based on the multiobjective greedy approach to the near optimal path. The proposed model is presented in this method to transfer sensed data of the sensor network to the base station for the desired applications. In this method, the sensor nodes are identified as adjacent nodes based on their distance. The energy of all nodes initially is approximately equal, which decreases with the transfer of information between the nodes. In this way, when a node senses a message, it checks several factors for transmitting information to its adjacent nodes and selects the node with the largest amount of factors as the next hop. The simulation results show that the energy consumption in the network grids is almost symmetrically presented, and the network lifetime is reduced with a gentle slope that provides optimum energy consumption in the networks. Also, the packet transmission delay in the network reaches 450 milliseconds for the transmission of information between 15 nodes and 650 connections. Besides, network throughput increases by approximately 97%. It also shows better performance compared to other previous methods in terms of evaluation criteria.

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Section: Related Workmentioning

confidence: 99%

WSN Routing Protocol Using a Multiobjective Greedy Approach

Nabavi

Eraghi

Torkestani

2021

Wireless Communications and Mobile Computing

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“…The arrangement of sensor nodes is the primary step in setting up a sensor network. Since WSNs contain many sensor nodes, the nodes must be placed in clusters [30], where the area of each specific node cannot be wholly ensured a priori. In this manner, the number of nodes deployed to cover the complete observed zone is mostly higher.…”

Section: State Of the Artmentioning

confidence: 99%

Improved Localized Sleep Scheduling Techniques to Prolong WSN Lifetime

Tarasia

Swain

Roy

et al. 2021

SCPE

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A standard Wireless Sensor Networks(WSNs) comprises of low-cost sensor nodes embedded with small batteries. To enhance the network lifetime of WSN, the number of active nodes among the deployed nodes should be minimum. Along with this, it must be ensured that coverage of the targeted area would not get affected by the currently active nodes. Considering different applications of WSN, there is still a demand for full coverage or partial coverage of the deployed area. Irrespective of the circumstances, a proper sleep scheduling algorithm needs to be followed. Else, the active nodes will be tuckered out of the battery. Random distribution of the sensor nodes in a common area may have multiple active nodes. It is essential to identify the redundant number of active nodes and put them into sleep to conserve energy. This paper has proposed a methodology where the active sensor nodes form a hierarchical structure that heals itself by following a level-wise approach. In the meantime, it also detects the total number of redundant nodes in the coverage area. The performance of the proposed protocol is evaluated using the Castalia simulator. The simulation results show that the proposed level-wise periodic tree construction approach increases the network's durability in conjunction with the level wise approach.

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“…Alterna-tively, the route problem can commonly be represented including a multidimensional optimization issue in which throughput must be maximized while latency is minimized. In recent years, many bioinspired evolutionary techniques (swarm intelligence) have piqued interest, as an instance Ant Colony Optimization (ACO) and PSO, for determining optimal paths in WSN-based IoT applications [55][56][57][58][59]. The basic idea underlying these algorithms is to calculate the cost/fitness iteratively and calculate a function over a population initially and the cost or the fitness on a fresh population, usually acquired by performing operations to populations.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the researcher's task has been to create an energy effective routing protocol with better QoS parameters, as shown in [55][56][57]. As a result, several studies have been proposed by academics and academicians in order to determine the best way while taking into account QoS metrics like throughput, Packet Delivery Fraction (PDF), and delay [58]. Liu et al proposed an accurate multipath routing protocol with two-path selection matrices assumed link stability and the value of time constraints into account, resulting in low link interruption probability and delay [59].…”

Section: Introductionmentioning

confidence: 99%

An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm

Ghawy

Amran

AlSalman

et al. 2022

Wireless Communications and Mobile Computing

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Improving wireless communication and artificial intelligence technologies by using Internet of Things (Itoh) paradigm has been contributed in developing a wide range of different applications. However, the exponential growth of smart phones and Internet of Things (IoT) devices in wireless sensor networks (WSNs) is becoming an emerging challenge that adds some limitations on Quality of Service (QoS) requirements. End-to-end latency, energy consumption, and packet loss during transmission are the main QoS requirements that could be affected by increasing the number of IoT applications connected through WSNs. To address these limitations, an effective routing protocol needs to be designed for boosting the performance of WSNs and QoS metrics. In this paper, an optimization approach using Particle Swarm Optimization (PSO) algorithm is proposed to develop a multipath protocol, called a Particle Swarm Optimization Routing Protocol (MPSORP). The MPSORP is used for WSN-based IoT applications with a large volume of traffic loads and unfairness in network flow. For evaluating the developed protocol, an experiment is conducted using NS-2 simulator with different configurations and parameters. Furthermore, the performance of MPSORP is compared with AODV and DSDV routing protocols. The experimental results of this comparison demonstrated that the proposed approach achieves several advantages such as saving energy, low end-to-end delay, high packet delivery ratio, high throughput, and low normalization load.

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An approach by adopting multi-objective clustering and data collection along with node sleep scheduling for energy efficient and delay aware WSN

Cited by 21 publications

References 36 publications

WSN Routing Protocol Using a Multiobjective Greedy Approach

WSN Routing Protocol Using a Multiobjective Greedy Approach

Improved Localized Sleep Scheduling Techniques to Prolong WSN Lifetime

An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm

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