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![Figure 8: Comparison of M2NGA with LA2D-GA presented in [14] and common...](profile/S-Hamed-Javadi/publication/234166526/figure/fig3/AS:271298756608030@1441694072542/Comparison-of-M2NGA-with-LA2D-GA-presented-in-14-and-common-2-tiered-clustering_Q320.jpg)
In this study, an optimal method of clustering homogeneous wireless sensor networks using a multi-objective two-nested genetic algorithm is presented. The top level algorithm is a multi-objective genetic algorithm (GA) whose goal is to obtain clustering schemes in which the network lifetime is optimized for different delay values. The low level GA...
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... These objective functions were designed to select nodes in clusters that minimize energy consumption and maximize lifetime for each node. Peiravi and Javadi (12) proposed an optimal solution for clustering WSNs using a multi-objective two-nested GA, referred to as M2NGA. This proposed algorithm is executed by the Base Station (BS) node to optimize both network lifetime and minimize delay in WSN (wireless sensor network). ...
... To determine the best routing option in WSNs, the authors of Arya et al. (2015) presented a routing method based on the ant colony algorithm. In (Peiravi et al. 2013), clustering algorithms are created using a multi-objective genetic algorithm (MOGA) to maximize network lifetime for various delays. The prairie dog optimization (PDO), a new nature-inspired metaheuristic that replicates prairie dog behavior in its natural context, was recently created by the authors of . ...
... Optimization Focus Outcome Eberhart and Kennedy (1995) Particle Swarm Optimization (PSO) Efficacy as an optimization approach Quick convergence toward optimum positions, but convergence speed may slow close to a minimum Arya et al. (2015) Ant colony optimization (ACO) Routing Optimal routing Peiravi et al. (2013) Genetic Algorithm (GA) Clustering Optimal clustering Prairie dog optimization (PDO) Exploration and Exploitation Optimal solutions for problems like unknown global optima Dwarf mongoose optimization (DMO) Suitability and effectiveness as an optimization approach ...
... In order to maximize routing performance, the authors of Senthilkumar and Manickam (2017) (2022) proposed an approach for cluster head or gateway selection and balancing routing load can have an insignificant impact on energy consumption and overall network lifetime. Furthermore, to improve network routing performance, researchers have applied numerous optimization methods such as PSO (Eberhart and Kennedy 1995;Kuila and Jana 2014), ACO (Arya et al. 2015), GA (Peiravi et al. 2013) and linked graphs (Xia et al. 2016). The authors of Du and Wang (2011), introduced the Ant Colony optimization based uneven clustering technique, which uses the Ant colony optimization strategy to optimize multi-hop routing. ...
Although the fifth generation (5G) is crucial to the current Internet of Things (IoT), the increase of automated IoT systems and data-centric services that need thousands of microseconds of latency, terabytes of data every second, and more than 10⁷ IoT connections per km² would be beyond the capability of current 5G networks. In order to fulfil the requirements of future IoT networks, this research presents an intelligent clustering and routing approach for IoT networks (ICR-IoT) that minimizes energy consumption and server latency, which are crucial factors in ensuring quality of service to the end users. Load balancing and energy efficiency in IoT-edge computing systems are NP- hard problems. This paper presents a novel approach called ICR-IoT for intelligent clustering and routing in internet of things networks, with the goal of minimizing energy consumption and server latency to ensure quality of service for end users. The approach uses a metaheuristic called the Butterfly Optimization Algorithm (BOA) to solve the NP-hard problems of load balancing and energy efficiency in IoT edge computing systems, and a dynamic routing approach to handle changing network conditions such as node energy. ICR-IoT presents novel parameters, packet uniformity (Pu) and Lifetime uniformity (Lu) of the data aggregators to improve the overall 6G IoT network performance. An experiment set is created to thoroughly assess the performance of the proposed work with various sensor node and gateway configurations. We have implemented our code in Python 3.10 on a 64-bit system having 8 GB RAM and 2.00 GHz processor. The proposed approach was tested and found to perform better than the BPSO approach, with improvements in lifetime and energy uniformity of the network. The lifetime and energy uniformity has been improved by 13.6% and 27.07%, respectively. In general, the ICR-IoT approach has the potential to enhance routing and clustering performance, as well as quality of service and network lifetime, in 6G-IoT networks.
... When a GA is applied to an optimization problem, especially to one with two decision variable vectors, the following strategy is often adopted [2][3][4][5][6][7]. An individual of the GA is expressed by the value of one of the decision variable vectors. ...
... Concrete examples of the problem defined in Sect. 2.1 include the clustering problems [3,5], the drone delivery scheduling problem (drone problem) [30], and the vehicle routing problem [31]. This subsection describes one of the clustering problems and the drone problem. ...
... It is evaluated to be good if the determination of x 2 results in generating a good solution. For generating such a good solution, metaheuristics or greedy algorithms determine the value of x 2 [2][3][4][5][6][7]. However, the metaheuristics are time-consuming, and the greedy algorithms are not general purpose. ...
This paper addresses an optimization problem with two decision variable vectors. It is assumed that this problem can be divided into multiple subproblems when an arbitrary value is given to the first decision variable vector. This assumption is often made when the system targeted for the optimization is large scale and complicated. Conventional genetic algorithms (GAs) for the problem are combined with metaheuristics or greedy algorithms but are time-consuming or not general purpose. This paper proposes a less time-consuming and general-purpose GA with a neural network model. The neural network estimates the optimal objective function values of the subproblems. The proposed method is novel and fundamentally differs from other GAs introducing a machine learning model in using a pre-training model: the model is trained before execution of the GA. Experimental results show that the proposed method is more effective than other GAs and an exact method.
... A new coverage control format founded on a selective non-dominated sorting genetic algorithm, which is known as (NSGA-II), was implemented within a heterogeneous WSN. The algorithm was applied in a distributed manner by developing a cluster-based architecture [37,38]. Vijayalakshmi and Anandan investigated the choice of the best path within routing that develops a network's energy efficiency and network lifespan. ...
Designing an efficient wireless sensor network (WSN) system is considered a challenging problem due to the limited energy supply per sensor node. In this paper, the performance of several bi-objective optimization algorithms in providing energy-efficient clustering solutions that can extend the lifetime of sensor nodes were investigated. Specifically, we considered the use of the Moth–Flame Optimization (MFO) algorithm and the Salp Swarm Algorithm (SSA), as well as the Whale Optimization Algorithm (WOA), in providing efficient cluster-head selection decisions. Compared to a reference scheme using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, the simulation results showed that integrating the MFO, SSA or WOA algorithms into WSN clustering protocols could significantly extend the WSN lifetime, which improved the nodes’ residual energy, the number of alive nodes, the fitness function and the network throughput. The results also revealed that the MFO algorithm outperformed the other algorithms in terms of energy efficiency.
... These approaches choose CH by combining density, energy, distance, and the node parameter into single selection criteria. Peiravi et al. [11] deliberate GAbased clustering techniques in order to prolong the network lifespan. This strategy raises the communication overhead, but it is the only one that works for stationary WSNs [12]. ...
Recent advancements in the field of the Internet of medical things (IoMT) have enabled the real-time monitoring and treatment of patients with communicable infectious diseases while minimizing human intervention. However, IoMT devices face challenges such as unbalanced energy consumption, memory constraints, computation power, and low latency, which can deter the efficient transfer of patient monitoring data. Thus, there is an urgent need to establish an energy-efficient infrastructure for IoMT devices to remotely monitor and collect data on communicable diseases. For this, a genetic algorithm (GA) based dynamic transmission of data for communicable diseases in the IoMT environment is proposed in this paper. The energy utilization of the IoMT is enhanced by considering the GA evolutionary processing based on the dynamic sensor range. The proposed work incorporates a periphery of the fixed area for deploying the IoMT devices to settle the energy hole problem. Multiple sinks and direct information collection concepts are also introduced which further improve the performance and reduce the movement of data packets. The proposed protocols not only optimize energy usage but also provide a robust approach for massive data collection and communication.
... Ferentinos & Tsiligiridis [19] presented a multiobjective GA-based optimization methodology for WSN design and energy management, in which each sensor node also connects to its nearest CH. Peiravi et al. [20] used a two-nested GA for sensor clustering of WSNs to optimize the network lifetime. Nayak & Vathasavai [21] developed a clustering algorithm based on GA to optimize the WSN lifetime by considering distance and energy as parameters of the fitness function. ...
Optimal placement of wireless structural health monitoring (SHM) sensors has to consider modal identification accuracy and power efficiency. In this study, two-tier wireless sensor network (WSN)-based SHM systems with clusters of sensors are investigated to overcome this difficulty. Each cluster contains a number of sensor nodes and a cluster head (CH). The lower tier is composed of sensors communicating with their associated CHs, and the upper tier is composed of the network of CHs. The first step is the optimal placement of sensors in the lower tier via the effective independence method by considering the modal identification accuracy. The second step is the optimal placement of CHs in the upper tier by considering power efficiency. The sensors in the lower tier are partitioned into clusters before determining the optimal locations of CHs in the upper tier. Two approaches, a constrained K-means clustering approach and a genetic algorithm (GA)-based clustering approach, are proposed in this study to cluster sensors in the lower tier by considering two constraints: (1) the maximum data transmission distance of each sensor; (2) the maximum number of sensors in each cluster. Given that each CH can only manage a limited number of sensors, these constraints should be considered in practice to avoid overload of CHs. The CHs in the upper tier are located at the centers of the clusters determined after clustering sensors in the lower tier. The two proposed approaches aim to construct a balanced size of clusters by minimizing the number of clusters (or CHs) and the total sum of the squared distance between each sensor and its associated CH under the two constraints. Accordingly, the energy consumption in each cluster is decreased and balanced, and the network lifetime is extended. A numerical example is studied to demonstrate the feasibility of using the two proposed clustering approaches for sensor clustering in WSN-based SHM systems. In this example, the performances of the two proposed clustering approaches and the K-means clustering method are also compared. The two proposed clustering approaches outperform the K-means clustering method in terms of constructing balanced size of clusters for a small number of clusters. Doi: 10.28991/CEJ-2022-08-12-01 Full Text: PDF
... Compared to ST-WSNs, TT-WSNs are more balanced in energy consumption for SNs, although they are more complex in network topology formation. A genetic algorithm-based clustering method was proposed to optimize the lifetime of a TT-WSN with different delays in [13]. In their study, a multi-objective and top level GA was applied to obtain clustering schemes to optimize the network lifetime for different delay values. ...
Lifetime optimization is one of the key issues among the many challenges of wireless sensor networks. The introduction of a small number of high-performance relay nodes can effectively improve the quality of the network services. However, how to deploy these nodes reasonably to fully enhance the network lifetime becomes a very difficult problem. In this study, a modified and enhanced Artificial Bee Colony is proposed to maximize the lifetime of a two-tiered wireless sensor network by optimal deployment of relay nodes. First, the dimension of the problem is introduced into the candidate search equation and the local search is adjusted according to the fitness of the problem and number of iterations, which helps to balance the exploration and exploitation ability of the algorithm. Second, in order to prevent the algorithm from falling into local convergence, a dynamic search balance strategy is proposed instead of the scout bee phase in the original Artificial Bee Colony. Then, a feasible solution formation method is proposed to ensure that the relay nodes can form the upper-layer backbone of the network. Finally, we employ this algorithm on a test dataset obtained from the literature. The simulation results show that the proposed algorithm for two-tiered wireless sensor network lifetime optimization can obtain higher and stable average network lifetime and more reasonable relay node deployment compared to other classical and state-of-the-art algorithms, verifying the competitive performance of the proposed algorithm.
... Using optimization algorithms, the models determine the path of the mobile anchor, which facilitates localization ratio maximization and localization error minimization. Some recent studies have described the use of natural heuristic artificial intelligence (AI) methods [31][32][33][34][35][36][37][38] for optimizing WSNs' energy consumption. Examples include using improved particle swarm algorithms and virtual force algorithms to solve the sensor nodes' positioning problem [31]; using a multiobjective GA to optimize the network lifetime for designing energy-efficient WSNs [32]; using the ant-based routing algorithm to maximize energy efficiency [33]; and using GAs to establish a set of ideal parameters (including energy consumption and route lifetime) [34]. ...
... Some recent studies have described the use of natural heuristic artificial intelligence (AI) methods [31][32][33][34][35][36][37][38] for optimizing WSNs' energy consumption. Examples include using improved particle swarm algorithms and virtual force algorithms to solve the sensor nodes' positioning problem [31]; using a multiobjective GA to optimize the network lifetime for designing energy-efficient WSNs [32]; using the ant-based routing algorithm to maximize energy efficiency [33]; and using GAs to establish a set of ideal parameters (including energy consumption and route lifetime) [34]. Regarding energy cost optimization, sensitivity area, and network reliability, one study made performance comparisons of GAs and swarm intelligence algorithms, namely the bees algorithm and the firefly algorithm [35]. ...
... Because the energy of each sensor node is limited, unless the battery life of sensor nodes can be extended, determining how to reduce the energy consumption of WSNs is crucial. The natural heuristic AI method was used to provide a reasonable and rapid solution for optimizing WSN performance [32][33][34][35][36][37][38]. GAs and swarm intelligence algorithms are fast and widely used intelligent optimization techniques for determining the optimal combination of parameters for minimizing the energy consumption of WSNs [32,34,35,38]. ...
This paper addresses the challenge of moving objects in a mobile wireless sensor network, considering the deployment of a limited number of mobile wireless sensor nodes within a predetermined area to provide coverage for moving objects traveling on a predetermined trajectory. Because of the insufficient number and limited sensing range of mobile wireless sensors, the entire object’s trajectory cannot be covered by all deployed sensors. To address this problem and provide complete coverage, sensors must move from one point of the trajectory to another. The frequent movement quickly depletes the sensors’ batteries. Therefore, solving the moving object coverage problem requires an optimized movement repertoire where (1) the total moving distance is minimized and (2) the remaining energy is also as balanced as possible for mobile sensing. Herein, we used a genetic algorithm (GA) and a discrete particle swarm optimization algorithm (DPSO) to manage the complexity of the problem, compute feasible and quasi-optimal trajectories for mobile sensors, and determine the demand for movement among nodes. Simulations revealed that the GA produced trajectories significantly superior to those produced by the DPSO in terms of total traveled distance and balance of residual energy.
... The Genetic Algorithm is an Evolutionary Algorithm which used an Adaptive Strategy and a Global Optimization technique for finding a global optimum answers [67]. It was developed to simulate the principle of "the survival to the fittest" that found by "Darwinian evolution" and "Mendel's theory of genetics". ...
... This process will repeated until stopping condition is achieved. The stopping condition could be either "a predefined number of iterations of the executing algorithm or convergence during a predefined number of iterations" [67]. ...
In today's Network, high performance routing and quality of services (QoS) is a major problem. Wireless Sensor Networks (WSN), however, vary in terms of so limited resources from conventional wireless networks. Within this respect, the limitation of energy poses a challenging problem when implementing WSNs applications. The sensors in the WSN depend heavily on the power resources provided as a battery. These batteries are typically not replaceable or / or rechargeable for a number of purposes. The main source of energy consumption for WSN applications is the task of data transfer. Therefore, several methods have been implemented to minimize energy usage and increase the life time of the network. So, in recent years, several new techniques have been proposed using Artificial Intelligence to resolve network routing problems, particularly in WSN. In this dissertation, An Intelligent approaches, as many previous works have been proposed for enhancing the WSN Routing Protocols. The intelligent approaches have been used in all three phases of WSN Routing Protocol: Clustering phase to structure the sensor nodes in the WSN and selecting Cluster Head using Fuzzy C Mean (FCM), Genetic Algorithm (GA), and proposed Fuzzy Genetic Algorithm (FGA), Intra-Cluster phase to reduce the active nodes in the clusters using Multi Objective Genetic Algorithm (MOGA), and to select New Cluster Head when the energy of the Current Cluster Head has been depleted using proposed Fuzzy Cluster Head Rotation (FCHR), and Inter-Cluster Phase to select the best route to the Sink / Base Station (BS) using the proposed Multi Objective Ant Colony Optimization (MOACO), Multi Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Dijkstra. The three Phases is implemented based on 3 particular parameters, these parameters are the Residual Energy, Distance to the Sink / Base Station, Distance to the Cluster Center, and Node Centrality. The versions of the proposed routing protocol were compared with some relevant other protocols, which are LEACH and Advanced-LEACH (A-LEACH) protocols. The tool of simulation was MATLAB version 2019a, and the simulations were carried out for different dimensions of the network, different numbers of sensor nodes, and various Cluster Number. The results of many simulation experiments show some improvement in network lifetime, network throughput, and energy consumption, over some other simulated protocols. Eventually, the improvement in the network lifetime and for Last Node Die (LND) metric, for different versions according to with/without scheduling of the proposed Routing Protocols, was 87.5% over the LEACH and 58.34% over A-LEACH protocols respectively.
... The lifetime and the throughput of the DE-LEACH were proved to be superior by 23% and 15% compared to the classical LEACH scheme. Then, an Ant Colony-based Clustering Scheme (AC-CS) was proposed with the merits of pheromone update parameters for the purpose of balancing the exploitation and exploration of sensor nodes under cluster head selection [12]. This AC-CS incorporated an improved scout bee phase that aided in enhancing the global optimization process towards the selection of cluster heads. ...
The rapid advancement of technologies of wireless sensor network is gaining maximized attentioned across the scientific community due to its reliable coverage in real life applications. It has evolved as an indispensable technology with diverisifed capabilities as it facilitates potential information to the end users regarding a region of target under real time monitoring process. However, the characteristics of WSNs such as resource-constrained nature and infrastructure-less deployment has the possibility of introducing diversified problems that influences the network performance. Moreover, the process of handling the issues of suitable cluster head selection, energy stability and network lifetime improvement are still considered as herculean task of concern. In this paper, a Squirrel Search Optimization-based Cluster Head Selection Technique (SSO-CHST) is proposed for prolonging the lifetime in the sensor networks by utilizing a gliding factor that aids in the better determination of cluster head selection during the process of data aggregation and dissemination. It estimates the fitness value of sensor nodes and arranges them in ascending order, such that the node with least fitness value is identified as the cluster memner. On the other hand, the sensor nodes with high fitness value is confirmed as the potential cluster head. The simulation results of the proposed SSO-CHST with minimum number of rounds used for selecting cluster head confirmed better throughput of 13.48% and improved network lifetime of 17.92% with minimized energy consumptions of 15.29%, remarkable to the benchmarked schemes.
