Optimal LEACH Protocol with Improved Bat Algorithm in Wireless Sensor Networks

doi:10.3837/tiis.2019.05.013

Cited by 18 publications

(12 citation statements)

References 32 publications

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“…In [35], the concept of advanced nodes and an enhanced cluster head election rule set are used to optimize the LEACH protocol, and the meta-heuristic particle swarm enhancement algorithm is used to initially cluster the sensor nodes. Cai [36] proposes an improved FTBA algorithm for fusion curve strategy, which improves local and global search ability to select cluster heads to extend the lifetime of the network. Jourdan [37] used genetic algorithm to optimize the energy of WSN for the first time.…”

Section: Sink Nodementioning

confidence: 99%

A many-objective optimization WSN energy balance model

2020

KSII TIIS

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Wireless sensor network (WSN) is a distributed network composed of many sensory nodes. It is precisely due to the clustering unevenness and cluster head election randomness that the energy consumption of WSN is excessive. Therefore, a many-objective optimization WSN energy balance model is proposed for the first time in the clustering stage of LEACH protocol. The four objective is considered that the cluster distance, the sink node distance, the overall energy consumption of the network and the network energy consumption balance to select the cluster head, which to better balance the energy consumption of the WSN network and extend the network lifetime. A many-objective optimization algorithm to optimize the model (LEACH-ABF) is designed, which combines adaptive balanced function strategy with penalty-based boundary selection intersection strategy to optimize the clustering method of LEACH. The experimental results show that LEACH-ABF can balance network energy consumption effectively and extend the network lifetime when compared with other algorithms.

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Section: Sink Nodementioning

confidence: 99%

A many-objective optimization WSN energy balance model

2020

KSII TIIS

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“…In the LEACH routing protocol, this paper adopts the energy consumption model used in literature. 47 For the LEACH protocol's prerequisites, please refer to the work of Cai et al 40 LEACH (low-energy adaptive clustering hierarchy) protocol is the first clustering routing algorithm and is proposed by Heinzelman et al Well, generally speaking, a cluster consists of a unique cluster head and a large amount of cluster members. The members can only communicate with their own cluster heads.…”

Section: Low-energy Adaptive Clustering Hierarchy (Leach)mentioning

confidence: 99%

“…To verify the improved LEACH protocol, the paper compared with standard LEACH protocol, LEACH protocol combined with LEACH, standard BA (SBA-LEACH), CS-LEACH, and LEACH protocol combined with fast BA with triangle flip and curve strategy (FTBA-TC-LEACH). 40 Please refer to Figure 9, Figure 10, and Figure 11 for the comparison of the results.…”

Section: Leach Protocol Algorithm With Mcgbmentioning

confidence: 99%

An improved energy‐low clustering hierarchy protocol based on ensemble algorithm

Xie

Liu

Zhao

2019

Concurrency and Computation

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Summary LEACH (low‐energy adaptive clustering hierarchy) protocol is an important part of wireless sensor network (WSN) and consisting of a good deal of energy‐constrained nodes. LEACH is widely used but also faces various challenges and constraints. The biggest constraint is that the energy is limited and the network life is finite. In the article, an improved ensemble algorithm based on energy‐efficient LEACH is proposed. The ensemble algorithm benefits from the cuckoo search algorithm (CS), glowworm swarm optimization (GSO), and bacterial foraging optimization (BFO). The paper enhances local and global search capabilities by modify the CS GSO and BFO algorithm. To verify results, this paper performs statistical comparatives with oriented cuckoo search (OCS), bat algorithm with characteristics of Levy flights, particle swarm optimization, and adaptive differential evolution algorithm. Comparison results shows that the new method is effective. Moreover, the research also showed the universal applicability of the ensemble strategy in solving diverse problems by using above different optimization algorithms. To further exploit our mind, this paper applies the improved coupling algorithm to LEACH for selecting the cluster‐head node. Computational experiments illustrate that the method proposed in this paper can provide very competitive results compared to these bio‐inspired LEACH, both in terms of reducing the network energy consumption and improving the lifecycle of nodes.

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“…(1) Bat Algorithm (BA) [46] (2) Particle Swarm Optimizer (PSO) [48] (3) Fast Triangle Flip Bat Algorithm (FTBA) [47] (4) Fast Triangle Flip Bat Algorithm with Curve Strategy (FTBA-TC) [39] Table 4 presents the values of mean error function which are achieved by the four algorithms on the CEC2013 test suite. And the results show that FTBA-TCR has the best performance.…”

Section: Comparison Of Ftba-tcr With Other Algorithmsmentioning

confidence: 99%

“…Cai [39] proposed the fast bat algorithm which adopted the triangle reversal curve strategy (FTBA-TC), which is combined with the fast triangular flip and curve decline strategy. In this paper, we continue to improve FTBA-TC so that the algorithm can balance the proportion of global and local searches.…”

Section: Introductionmentioning

confidence: 99%

Fast triangle flip bat algorithm based on curve strategy and rank transformation to improve DV-Hop performance

Cai¹,

Geng²,

Wang³

et al. 2019

KSII TIIS

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The information of localization is a fundamental requirement in wireless sensor network (WSN). The method of distance vector-hop (DV-Hop), a range-free localization algorithm, can locate the ordinary nodes by utilizing the connectivity and multi-hop transmission. However, the error of the estimated distance between the beacon nodes and ordinary nodes is too large. In order to enhance the positioning precision of DV-Hop, fast triangle flip bat algorithm, which is based on curve strategy and rank transformation (FTBA-TCR) is proposed. The rank is introduced to directly select individuals in the population of each generation, which arranges all individuals according to their merits and a threshold is set to get the better solution. To test the algorithm performance, the CEC2013 test suite is used to check out the algorithm's performance. Meanwhile, there are four other algorithms are compared with the proposed algorithm. The results show that our algorithm is greater than other algorithms. And this algorithm is used to enhance the performance of DV-Hop algorithm. The results show that the proposed algorithm receives the lower average localization error and the best performance by comparing with the other algorithms.

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Optimal LEACH Protocol with Improved Bat Algorithm in Wireless Sensor Networks

Cited by 18 publications

References 32 publications

A many-objective optimization WSN energy balance model

A many-objective optimization WSN energy balance model

An improved energy‐low clustering hierarchy protocol based on ensemble algorithm

Fast triangle flip bat algorithm based on curve strategy and rank transformation to improve DV-Hop performance

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