CS-CGMP: Clustering Scheme Using Canada Geese Migration Principle for Routing in Wireless Sensor Networks

Kavitha, A.; Guravaiah, Koppala; Velusamy, R. Leela

doi:10.1007/s11277-020-07632-4

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…The simulation experiments of the proposed approach were conducted to validate the performance of data reduction strategy DR-NAP; it has been implanted in GSA-based clustering routing strategy (GSA-CRS) 31 and clustering scheme using Canada Geese Migration Principle (CS-CGMP). 51 The obtained results of the proposed approaches were compared with the above-mentioned approaches which have no data reduction strategy. WSN scenario given in Intel Berkeley Research Lab 50 has been used for simulation as shown in Figure 3.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

DR‐NAP: Data reduction strategy using neural adaptation phenomenon in wireless sensor networks

Kavitha

Guravaiah²,

Velusamy

et al. 2023

Int J Communication

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Internet of Things (IoT) has got significant popularity among the researchers' community as they have been applied in numerous application domains. Most of the IoT applications are implemented with the help of wireless sensor networks (WSNs). These WSNs use different sensor nodes with a limited battery power supply. Hence, the energy of the sensor node is considered as one of the primary constraints of WSN. Besides, data communication in WSN dissipates more energy than processing the data. In most WSNs applications, the sensed data generated from the same location sensor nodes are identical or timeseries/periodical data. This redundant data transmission leads to more energy consumption. To reduce the energy consumption, a data reduction strategy using neural adaptation phenomenon (DR-NAP) has been proposed to decrease the communication energy in routing data to the BS in WSN. The neural adaptation phenomenon has been utilized for designing a simple data reduction scheme to decrease the amount of data transmitted. In this way, the sensor node energy is saved and the lifetime of the network is enhanced. The proposed approach has been implanted in the existing gravitational search algorithm (GSA)-based clustered routing for WSN. The sensed data are transmitted to CH and BS using DR-NAP. Real sensor data from the Intel Berkeley Research lab have been used for conducting the experiments. The experiment results show 47.82% and 51.96% of improvement in network lifetime when compared with GSA-based clustered routing and clustering scheme using Canada Geese Migration Principle (CS-CGMP) for routing, respectively.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

DR‐NAP: Data reduction strategy using neural adaptation phenomenon in wireless sensor networks

Kavitha

Guravaiah²,

Velusamy

et al. 2023

Int J Communication

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“…In equation (11), the hop (u, v) with the minimum value of the fitness function is regarded as the final traffic load transfer path. In equation ( 12), the constraint C 0 .…”

Section: Intersegment Routingmentioning

confidence: 99%

“…7 The first type is hybrid transmission, such as Wang and colleagues, 8,9 in which sensor nodes have two data transmitting modes: (1) single-hop (SH) transmission, in which every node sends data to the sink by an one-hop way and does not need any relay node; and (2) multiple-hop (MH) transmission, in which most packets are sent to the destination by a relay way, that is, at least two hops of data delivery. The second type is power control, such as Zhang et al, 10 Kavitha et al, 11 and Lai et al, 12 in which sensor nodes with less data delivery tasks use shorter communication range while the reverse is performed for those with more data delivery tasks. The third type is multifactor routing, such as Liu et al, 13 Peng et al, 14 and Qin et al, 15 in which data delivery paths are determined according to some key factors, for example, the remaining energy of nodes, the distance between two nodes, and the density distribution of nodes.…”

Section: Introductionmentioning

confidence: 99%

GLBR: A novel global load balancing routing scheme based on intelligent computing in partially disconnected wireless sensor networks

Sun

Liao

Zeng

et al. 2022

International Journal of Distributed Sensor Networks

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Load balancing is of great significance to extend the longevity of wireless sensor networks, due to the inherent imbalanced energy overhead in such networks. However, existing solutions cannot balance the load distribution in partially disconnected wireless sensor networks. For example, if a network is partitioned into several segments with different area sizes, some areas have much more traffic load than other areas. In this article, we propose a load-balanced routing scheme, which aims to balance energy consumption within each segment and among different segments. First, we adopt unequal transmission distances to build initial routing for intrasegment load balancing. Second, we adopt the genetic algorithm to build extra routing between different segments for intersegment load balancing. The unique character of our work is twofold. On one hand, we investigate partitioned wireless sensor networks where there are several isolated segments. On the other hand, we pursue load balancing from a global perspective rather than from a local one. Some simulations verify the effectiveness and the advantages of our scheme in terms of extra deployment cost, system longevity, and load balancing degree.

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“…Those cluster heads having lower residual energy is rotated with cluster heads having high energy. This ensures that the energy consumption is uniform, and the lifetime of the nodes can be extended as all the nodes bear the same energy in themselves [18]. Sundar Rajina et al [11] suggested that network operations be managed using a load saver system with a opportunistic energy-efficient routing with load balancing (OE2-LB) that eliminates data collection delays and avoids data collection.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Spider Monkey Optimization for Energy Efficient clustering and Routing in wireless sensor networks

Samiayya

Ramalingam

2021

Preprint

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In wireless sensor network (WSN), the gateways far away from the base station (BS) uses the gateways nearer to the BS to forward the data. It causes heavy traffic to the gateways in proximity with the BS. They need to manage this heavy traffic load but it leads to additional energy consumption and reduction in network lifetime. In order to overcome these issues, loads around the gateways need to be balanced. In this paper, multi objective based spider monkey optimization (MOSMO) has been presented to balance the load and to improve the network lifetime through energy efficient routing and clustering. The objective functions such as routing fitness and clustering fitness have been considered for optimal routing and clustering. The routing fitness function is found by incorporating both the minimum distance traversed by the gateways and minimum number of the gateway hops. The clustering fitness function is the minimum fitness function of gateways. The fitness function of each gateway is computed based on both the mean load of gateways as well as the distance between gateways and BS. The performance of the proposed MOSMO based routing and clustering scheme is compared with the existing Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) based routing and clustering scheme. The QoS features such as delay, energy consumption, delivery ratio, throughput and network lifetime with various node density are analyzed. The proposed work is simulated using MATLAB. The results show that, the reduction in delay and energy consumption is about 18% and 17% respectively whereas improvement in delivery ratio, throughput and network life time is about 15%, 24% and 19% respectively when compared to the existing PSO and GWO methods.

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CS-CGMP: Clustering Scheme Using Canada Geese Migration Principle for Routing in Wireless Sensor Networks

Cited by 5 publications

References 17 publications

DR‐NAP: Data reduction strategy using neural adaptation phenomenon in wireless sensor networks

DR‐NAP: Data reduction strategy using neural adaptation phenomenon in wireless sensor networks

GLBR: A novel global load balancing routing scheme based on intelligent computing in partially disconnected wireless sensor networks

Multi-Objective Spider Monkey Optimization for Energy Efficient clustering and Routing in wireless sensor networks

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