Energy-Efficient and Load-Balanced Clustering Routing Protocol for Wireless Sensor Networks Using a Chaotic Genetic Algorithm

Wang, Chuhang; Liu, Xiaoli; Hu, Huangshui; Han, Youjia; Yao, Meiqin

doi:10.1109/access.2020.3020158

Cited by 47 publications

(27 citation statements)

References 35 publications

(191 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For CSO, the maximum generation is set to be 60, the number of roosters is between

percentages randomly, and the number of hens is between

randomly. The list of other parameters and their values is listed in Table 3 , which is similar to paper [ 29 , 31 ].…”

Section: Resultsmentioning

confidence: 99%

“…In this section, a comparison of the HIOA with LEACH-FL [ 27 ], CRGA [ 29 ] and EC-PSO [ 28 ] algorithms has been analyzed to show the effectiveness in terms of network lifetime, average energy consumed per node at each round and the total average energy consumed per round.…”

Section: Resultsmentioning

confidence: 99%

“…However, the authors completely ignore the node distance from the base station, which causes it to affect the fitness function tremendously and ultimately consume uneven energy in each time slot. In [ 29 ], the authors proposed an enhanced version of a cluster-based genetic algorithm referred to as CRCGA by coding the fitness function to minimize the energy consumption and load balancing of the network. This proposed algorithm mainly considers three factors: formation of clusters, finding the best route and then maintaining the clusters appropriately in each time slot.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach

Kumar

Kashyap

et al. 2022

Sensors

View full text Add to dashboard Cite

Clustering is a promising technique for optimizing energy consumption in sensor-enabled Internet of Things (IoT) networks. Uneven distribution of cluster heads (CHs) across the network, repeatedly choosing the same IoT nodes as CHs and identifying cluster heads in the communication range of other CHs are the major problems leading to higher energy consumption in IoT networks. In this paper, using fuzzy logic, bio-inspired chicken swarm optimization (CSO) and a genetic algorithm, an optimal cluster formation is presented as a Hybrid Intelligent Optimization Algorithm (HIOA) to minimize overall energy consumption in an IoT network. In HIOA, the key idea for formation of IoT nodes as clusters depends on finding chromosomes having a minimum value fitness function with relevant network parameters. The fitness function includes minimization of inter- and intra-cluster distance to reduce the interface and minimum energy consumption over communication per round. The hierarchical order classification of CSO utilizes the crossover and mutation operation of the genetic approach to increase the population diversity that ultimately solves the uneven distribution of CHs and turnout to be balanced network load. The proposed HIOA algorithm is simulated over MATLAB2019A and its performance over CSO parameters is analyzed, and it is found that the best fitness value of the proposed algorithm HIOA is obtained though setting up the parameters, number of rooster, number of hen’s and swarm updating frequency. Further, comparative results proved that HIOA is more effective than traditional bio-inspired algorithms in terms of node death percentage, average residual energy and network lifetime by 12%, 19% and 23%.

show abstract

“…For CSO, the maximum generation is set to be 60, the number of roosters is between

percentages randomly, and the number of hens is between

randomly. The list of other parameters and their values is listed in Table 3 , which is similar to paper [ 29 , 31 ].…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach

Kumar

Kashyap

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Genetic manipulation involves four main processes: coding, selection, crossover, and mutation. In TAGA, real number coding is used instead of binary coding to represent chromosomes, and a new fitness function is designed to determine the quality of chromosomes [35]. In addition, random crossover and dynamic mutation methods from the literature [32], [33] are used to enhance the population diversity and improve the algorithm convergence.…”

Section: B Routing Based On Agamentioning

confidence: 99%

Energy-Aware and Trust-Based Secure Routing Protocol for Wireless Sensor Networks Using Adaptive Genetic Algorithm

Han

Guo

2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

Due to their working environments, limited resources and communication characteristics, wireless sensor networks face some challenges including energy optimization and security enhancement to extend the network lifetime and guarantee the network security. Therefore, an energy-aware and trust-based routing protocol for wireless sensor networks using adaptive genetic algorithm called TAGA is proposed to not only resist common routing attacks and special trust attacks, but also minimize the energy consumption caused by data transmission. To this end, TAGA constructs the nodes' comprehensive trust values based on their direct trust values considering the volatilization and adaptive penalty factors, and indirect trust values with the filtering mechanisms. In addition, a novel threshold function is presented to select the optimal cluster heads, which considers the dynamic changes of the nodes' comprehensive trust values and residual energy. Finally, a genetic algorithm with adaptive crossover probability and mutation probability is applied to find the optimal secure routing for the cluster heads. The simulation results show that TAGA can reduce the number of packets discarded by malicious nodes when facing common attacks and special trust attacks, and effectively improve the energy efficiency compared to the relative secure routing protocols EOSR and IASR.INDEX TERMS Wireless sensor networks, secure routing, comprehensive trust, direct trust.

show abstract

“…The genetic algorithm utilizes its fitness function to optimize the network's performance, thus if the network performance is decreased then the genetic algorithm alters the route such that network's efficiency is increased. Wang et al [44] also present a genetic algorithm that selects the best cluster-heads and combines them in a single chromosome to find the optimal routing path, with a fitness function that considers load balancing among SNs and minimum energy consumption.…”

Section: Background and Related Workmentioning

confidence: 99%

Adaptive Probabilistic Model for Energy-Efficient Distance-based Clustering in WSNs (Adapt-P): A LEACH-based Analytical Study

Suleiman,

Hamdan

2021

Preprint

View full text Add to dashboard Cite

Network lifetime and energy consumption of data transmission have been primary Quality of Service (QoS) obligations in Wireless Sensor Networks (WSNs). The environment of a WSN is often organized into clusters to mitigate the management complexity of such obligations. However, the distance between Sensor Nodes (SNs) and the number of clusters per round are vital factors that affect QoS performance of a WSN. The designer's conundrum resolves around the desire to sustain a balance between the limited residual energy of SNs and the demand for prolonged network lifetime. Any imbalance in controlling conflicting objectives may result in either QoS penalties due to draining SN energies which leaves WSN's environment unevenly covered, or an over-cost environment that is significantly difficult to distribute and operate. Low-Energy Adaptive Clustering Hierarchy (LEACH) is a well-known distributed clustering algorithm proposed to tackle such difficulties. Multiple LEACH-based algorithms have also been proposed to enhance QoS requirements. Proposed algorithms typically focus on residual energies of SNs to compute a probability function that selects cluster-heads. Some algorithms form an optimal energy-efficient path toward a destination SN. Nevertheless, these algorithms do not consider variations in network's state at run-time. Such a state changes in an adaptive manner according to existing network structures and conditions. Thus, cluster-heads per round are not elected adaptively depending on the state and distances between SNs. To tackle such complications, this paper proposes an energy-efficient adaptive distance-based clustering called Adapt-P, in which an adaptive probability function is developed to formulate clusters. A near-optimal distance between each cluster-head and its cluster-members is formulated so that energy consumption of the network is mitigated and accordingly the network lifetime is maximized. Distances between and residual energies of SNs are employed to obtain a maximum number of cluster-heads to be elected per round. The cluster-head selection probability is adapted at the end of each round based on the maximum number of cluster-heads permitted per round found a priori and the number of existing alive SNs in the network. The Adapt-P based algorithms proposed in this paper improve the performance of LEACH algorithm in term adaptivity and network lifetime.

show abstract

Energy-Efficient and Load-Balanced Clustering Routing Protocol for Wireless Sensor Networks Using a Chaotic Genetic Algorithm

Cited by 47 publications

References 35 publications

Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach

Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach

Energy-Aware and Trust-Based Secure Routing Protocol for Wireless Sensor Networks Using Adaptive Genetic Algorithm

Adaptive Probabilistic Model for Energy-Efficient Distance-based Clustering in WSNs (Adapt-P): A LEACH-based Analytical Study

Contact Info

Product

Resources

About