scholarly journals Minimization of Energy Consumption for Routing in High-Density Wireless Sensor Networks Based on Adaptive Clone Elite Genetic Algorithm

2021 ◽  
Vol 1924 (1) ◽  
pp. 012029
Author(s):  
Jing Xiao ◽  
Chaoqun Li ◽  
Yao Zhang ◽  
Jie Zhou ◽  
Yang Liu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
High Density ◽  
Wireless Sensor

Lifetime Maximization Routing Based on Genetic Algorithm for Wireless Sensor Networks

2011 ◽  
Vol 230-232 ◽  
pp. 283-287
Author(s):  
You Rong Chen ◽  
Tiao Juan Ren ◽  
Zhang Quan Wang ◽  
Yi Feng Ping
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Data Gathering ◽  
Transmission Probability ◽  
Wireless Sensor ◽  
Total Energy Consumption ◽  
Lifetime Maximization

To prolong network lifetime, lifetime maximization routing based on genetic algorithm (GALMR) for wireless sensor networks is proposed. Energy consumption model and node transmission probability are used to calculate the total energy consumption of nodes in a data gathering cycle. Then, lifetime maximization routing is formulated as maximization optimization problem. The select, crosss, and mutation operations in genetic algorithm are used to find the optimal network lifetime and node transmission probability. Simulation results show that GALMR algorithm are convergence and can prolong network lifetime. Under certain conditions, GALMR outperforms PEDAP-PA, LET, Sum-w and Ratio-w algorithms.

scholarly journals Clone Chaotic Parallel Evolutionary Algorithm for Low-Energy Clustering in High-Density Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rui Yang ◽  
Mengying Xu ◽  
Jie Zhou
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Convergence Speed ◽  
High Density ◽  
Low Energy ◽  
Wireless Sensor ◽  
Initial Population ◽  
Clustering Model ◽  
Chaotic Operator

Because the sensors are constrained in energy capabilities, low-energy clustering has become a challenging problem in high-density wireless sensor networks (HDWSNs). Usually, sensor nodes tend to be tiny devices along with constrained clustering abilities. To have a low communication energy consumption, a low-energy clustering scheme should be designed properly. In this work, a new cloned chaotic parallel evolution algorithm (CCPEA) is proposed, and a low-energy clustering model is established to lower the communication energy consumption of HDWSNs. By introducing CCPEA into the low-energy clustering, an objective function is designed for evaluating the communication energy consumption. For this problem, we define a clone operator to minimize the communication energy consumption of HDWSNs, use the chaotic operator to randomly generate the initial population to expand the search range to avoid local optimization, and find the parallel operator to speed up the convergence speed. In the experiment, the effect of CCPEA is compared to heuristic approaches of particle swarm optimization (PSO) and simulated annealing (SA) for the HDWSNs with different numbers of sensors. Simulation experiments demonstrate that the presented CCPEA method achieves a lower communication energy consumption and faster convergence speed than PSO and SA.

Genetic optimization of hybrid clustering algorithm in mobile wireless sensor networks

Sensor Review ◽  
2018 ◽  
Vol 38 (4) ◽  
pp. 526-533 ◽  
Author(s):  
Sangeetha M. ◽  
Sabari A.
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Clustering Algorithm ◽  
Wireless Sensor ◽  
Mobile Wireless ◽  
Content Type ◽  
Mobile Wireless Sensor

Purpose This paper aims to provide a prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). MWSNs have characteristics of dynamic topology due to the factors such as energy consumption and node movement that lead to create a problem in lifetime of the sensor network. Node clustering in wireless sensor networks (WSNs) helps in extending the network life time by reducing the nodes’ communication energy and balancing their remaining energy. It is necessary to have an effective clustering algorithm for adapting the topology changes and improve the network lifetime. Design/methodology/approach This work consists of two centralized dynamic genetic algorithm-constructed algorithms for achieving the objective in MWSNs. The first algorithm is based on improved Unequal Clustering-Genetic Algorithm, and the second algorithm is Hybrid K-means Clustering-Genetic Algorithm. Findings Simulation results show that improved genetic centralized clustering algorithm helps to find the good cluster configuration and number of cluster heads to limit the node energy consumption and enhance network lifetime. Research limitations/implications In this work, each node transmits and receives packets at the same energy level throughout the solution. The proposed approach was implemented in centralized clustering only. Practical implications The main reason for the research efforts and rapid development of MWSNs occupies a broad range of circumstances in military operations. Social implications The research highly gains impacts toward mobile-based applications. Originality/value A new fitness function is proposed to improve the network lifetime, energy consumption and packet transmissions of MWSNs.

scholarly journals An Improved Niche Chaotic Genetic Algorithm for Low-Energy Clustering Problem in Large-Scale Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Min Tian ◽  
Jie Zhou ◽  
Xin Lv
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Large Scale ◽  
Low Energy ◽  
Wireless Sensor ◽  
Mixed Integer ◽  
Clustering Problem ◽  
Systems Simulation

Large-scale wireless sensor networks consist of a large number of tiny sensors that have sensing, computation, wireless communication, and free-infrastructure abilities. The low-energy clustering scheme is usually designed for large-scale wireless sensor networks to improve the communication energy efficiency. However, the low-energy clustering problem can be formulated as a nonlinear mixed integer combinatorial optimization problem. In this paper, we propose a low-energy clustering approach based on improved niche chaotic genetic algorithm (INCGA) for minimizing the communication energy consumption. We formulate our objective function to minimize the communication energy consumption under multiple constraints. Although suboptimal for LSWSN systems, simulation results show that the proposed INCGA algorithm allows to reduce the communication energy consumption with lower complexity compared to the QEA (quantum evolutionary algorithm) and PSO (particle swarm optimization) approaches.

scholarly journals Provide a Way to Reduction of Energy Consumption in Wireless Sensor Networks using the concepts of Fuzzy Logic and Genetic Algorithm

2015 ◽  
Vol 10 (9) ◽  
pp. 2495-2506
Author(s):  
Sayyed Hedayat Tarighi Nejad ◽  
Reza Alinaghian
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Sensor Network ◽  
Fuzzy System ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Reduction Of Energy Consumption ◽  
Main Station

Wireless sensor networks are a collection of small sensor nodes that can monitor and sense of their surroundings and sending data to a main station. The limited energy of nodes is a major challenge of sensor networks that affect the survival of the network. Thus, as yet is presented several methods to optimization of energy consumption and increasing the lifetime of a sensor network. In this paper, using fuzzy system design and system optimization by genetic algorithm is presented approach to select the best cluster head in sensor networks. Using random data set has been addressed to evaluate of fuzzy-genetic system presented in this paper and finally, MSE rate or mean error of sending the messages using proposed fuzzy system in comparison with LEACH method is calculated in select the cluster head. The results of evaluations is representative of a reduction the MSE metric in proposed method in comparison with LEACH method for select the cluster head. Reduce of MSE directly is effective on energy consumption and lifetime of wireless sensor network and can cause the reduction of energy consumption and increase the lifetime of the networks.

scholarly journals Optimal Selection of the Cluster Head in Wireless Sensor Networks by Combining the Multiobjective Genetic Algorithm and the Gravitational Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Seyed Reza Nabavi ◽  
Vahid Ostovari Moghadam ◽  
Mohammad Yahyaei Feriz Hendi ◽  
Amirhossein Ghasemi
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Multiobjective Genetic Algorithm ◽  
Gravitational Search

With the development of various applications of wireless sensor networks, they have been widely used in different areas. These networks are established autonomously and easily in most environments without any infrastructure and collect information of environment phenomenon for proper performance and analysis of events and transmit them to the base stations. The wireless sensor networks are comprised of various sensor nodes that play the role of the sensor node and the relay node in relationship with each other. On the other hand, the lack of infrastructure in these networks constrains the sources such that the nodes are supplied by a battery of limited energy. Considering the establishment of the network in impassable areas, it is not possible to recharge or change the batteries. Thus, energy saving in these networks is an essential challenge. Considering that the energy consumption rate while sensing information and receiving information packets from another node is constant, the sensor nodes consume maximum energy while performing data transmission. Therefore, the routing methods try to reduce energy consumption based on organized approaches. One of the promising solutions for reducing energy consumption in wireless sensor networks is to cluster the nodes and select the cluster head based on the information transmission parameters such that the average energy consumption of the nodes is reduced and the network lifetime is increased. Thus, in this study, a novel optimization approach has been presented for clustering the wireless sensor networks using the multiobjective genetic algorithm and the gravitational search algorithm. The multiobjective genetic algorithm based on reducing the intracluster distances and reducing the energy consumption of the cluster nodes is used to select the cluster head, and the nearly optimal routing based on the gravitational search algorithm is used to transfer information between the cluster head nodes and the sink node. The implementation results show that considering the capabilities of the multiobjective genetic algorithm and the gravitational search algorithm, the proposed method has improved energy consumption, efficiency, data delivery rate, and information packet transmission rate compared to the previous methods.

scholarly journals proposing a new clustering algorithm to Reduction of Energy Consumption using Fuzzy Logic and Genetic Algorithm for Wireless Sensor Networks.

2016 ◽  
Vol 11 (2) ◽  
pp. 2702-2719
Author(s):  
Sayyed Hedayat Tarighi Nejad ◽  
Reza Alinaghian ◽  
Mehdi Sadeghzadeh
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Sensor Network ◽  
Network Lifetime ◽  
Fuzzy System ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Reduction Of Energy Consumption

The large-scale deployment of wireless sensor networks  and the need for data aggregation necessitate efficient organization of the network topology for the purpose of balancing the load and prolonging the network lifetime. Clustering is one of the important methods for prolonging the network lifetime in wireless sensor networks. It involves grouping of sensor nodes into clusters and electing cluster heads for all the clusters. Clustering has proven to be an effective approach for organizing the network into a connected hierarchy.In this paper, using fuzzy system design and system optimization by genetic algorithm and colony of ants is presented approach to select the best cluster head in sensor networks. Using design and simulation a sensor network has been addressed to evaluation the presented fuzzy system in this paper, and finally the amount of energy consumption using proposed fuzzy system in comparison with LEACH method is calculated in select the cluster head. The result of evaluations is representative of a reduction of energy consumption in the proposed method in comparison with LEATCH method for select the cluster head. The reduction of energy consumption directly is effective on lifetime of wireless sensor network and can cause increase the lifetime these networks.

Balancing Energy Consumption in Heterogeneous Wireless Sensor Networks Using Genetic Algorithm

2015 ◽  
Vol 19 (12) ◽  
pp. 2194-2197 ◽  
Author(s):  
Mohamed Elhoseny ◽  
Xiaohui Yuan ◽  
Zhengtao Yu ◽  
Cunli Mao ◽  
Hamdy K. El-Minir ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Heterogeneous Wireless Sensor Networks ◽  
Balancing Energy

An Improved Genetic Algorithm Based Annulus-sector Clustering Routing Protocol for Wireless Sensor Networks

2021 ◽  
Author(s):  
Wang Chu-hang ◽  
Liu Xiao-li ◽  
Youjia Han ◽  
Hu Huang-shui ◽  
Wu Sha-sha
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Fitness Function ◽  
Minimum Energy ◽  
Wireless Sensor ◽  
Optimal Routing ◽  
Improved Genetic Algorithm

Abstract In wireless sensor networks, uniform cluster formation and optimal routing paths finding are always the two most important factors for clustering routing protocols to minimize the network energy consumption and balance the network load. In this paper, an improved genetic algorithm based annulus-sector clustering routing protocol called GACRP is proposed. In GACRP, the circular network is divided into sectors with the same size for each annulus, whose number is determined by calculating the minimum energy consumption of each annulus. Each annulus-sector forms a cluster and the best node in this annulus-sector is selected as cluster head. Moreover, an improved genetic algorithm with a novel fitness function considering energy and load balance is presented to find the optimal routing path for each CH, and an adaptive round time is calculated for maintaining the clusters. Simulation results show that GACRP can significantly improve the network energy efficiency and prolong the network lifetime as well as mitigate the hot spot problem.

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