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Communication quality is compromised when wireless sensor network (WSN) operate in harsh environments, which can be improved by supplementing the nodes. This paper proposes a deployment strategy to optimize the placement of new nodes in WSN, specifically in complex environments with limited communication. To achieve this, the paper introduces the concept of strong connectivity relationships and presents a novel metaheuristic algorithm, namely double-state differential evolution (DSDE), which divides the optimization process into two states and adopts different optimization strategies. The proposed DSDE can improve the overall network communication glowing at a lower computing cost. Extensive experiments show that the proposed DSDE has better performance than state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks are booming with the development of computer technology, network communication technology, and sensor technology. However, the question of how to use fewer nodes to achieve maximum coverage still exists. In this paper, a two-layer Surrogate-Assisted Phasmatodea Population Evolution (SAPPE) is proposed for 3D coverage of wireless sensors by combining the characteristics of meta-heuristic algorithms and surrogate models. In this algorithm, Radial Basis Function Networks are used to construct the surrogate model, and the two surrogate models are global surrogate-assisted and local surrogate-assisted, respectively. The global-surrogate model is used to smooth the fitness function, and the local surrogate-assisted model is used to find the optimal value accurately. They use the same archive DataBase (DB) to store particle positions and true fitness values. However, the number of particles involved in the creation of the surrogate-assisted model is different. Seven benchmark functions are used to test and analyze the algorithm, and the results show that the algorithm has good performance. Also, the algorithm verified the significance of the algorithm using Wilcoxon rank test. The result shows that the proposed algorithm is effective compared with PPE, PSO, PPSO, FMO, and BA. This paper compares the number of nodes and coverage radius using different algorithms to ensure maximum coverage. The result shows that the SAPPE algorithm has better performance in terms of 3D coverage. [ABSTRACT FROM AUTHOR]

In this paper, the establishment of efficientWireless Sensor Network (WSN) networks has been projected to minimize the consumption of energy using a new Self-adaptive Multi-Objective Weighted Approach (SMOWA) algorithm for solving a multi-objective problem. The Different WSN nodes deployment policies have been proposed and applied in this paper to design an efficientWireless Sensor Network to minimize energy consumption. After that, the cluster head for each cluster has been selected with the help of the duty cycle. After configuring the WSN networks, the SMOWA algorithms have been developed to obtain the minimum energy consumption for the networks. Energy minimization, as well as the amount of day-saving, has been calculated for the differentWSNswhich has been configured through different deployment policies. The major finding of the research paper is to improve the durability of Wireless Sensor Network (i) applying different deployment strategies: (Random, S pattern and nautilus shell pattern), and (ii) using a new Meta-heuristic algorithm (SMOWA Algorithm). In this research, the lifetime of WSN has been increased to a significant level. To choose the best result set from all the obtained results set some constraints such as "equivalent distribution", "number of repetitions", "maximum amount energy storage by a node" has been set to an allowable range. [ABSTRACT FROM AUTHOR]

Directional sensor networks (DSNs) are ad-hoc networks which are utilized in different industrial applications. Their usual engagements are to monitor and to perform the coverage of all specific targets in the observing fields permanently. These kinds of networks include numerous configurable directional sensors in which they can be utilized in one of the possible directions along with the one of their adjustable ranges. Although the energy harvesting methodology is being applied for these battery-hungry applications, the battery management and network lifetime maximization are still prominent challenges. In this paper, the network lifetime extension is formulated to a discrete optimization problem which is a famous non-deterministic polynomial time hardness (NP-Hard) problem. To solve this combinatorial problem, a discrete cuckoo search algorithm (D-CSA) is designed and is called in several rounds. A cover is a sub set of configured sensors capable of monitoring all targets in the observing field. In each round, the most efficient cover is constituted along with its activation time. In the determined activation time, the sensors in the cover are scheduled in wakeup mode whereas others are set in sleep mode to save energy. Despite other meta-heuristic algorithms, this proposed algorithm utilizes the novel defined discrete walking around procedures that makes to reach a good balance between exploration and exploitation in this complex search space. The proposed algorithm has been tested in different scenarios to be evaluated. The simulation results in the variety circumstances prove the superiority of the proposed algorithm is about 20.29%, 19.55%, 14.40%, 14.51%, 7.70% and 8.03% in term of average lifespan improvement against H-MNLAR, Hm-LifMax-BC, GA, ACOSC, H-GATS, and HDPSO algorithms, respectively. The results also show the high potential scalability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Summary: Designing an energy efficient and durable wireless sensor networks (WSNs) is a key challenge as it personifies potential and reactive functionalities in harsh antagonistic environment at which wired system deployment is completely infeasible. Majority of the clustering mechanisms contributed to the literature concentrated on augmenting network lifetime and energy stability. However, energy consumption incurred by cluster heads (CHs) are high and thereby results in minimized network lifetime and frequent CHs selection. In this paper, a modified whale‐dragonfly optimization algorithm and self‐adaptive cuckoo search‐based clustering strategy (MWIDOA‐SACS) is proposed for sustaining energy stability and augment network lifetime. In specific, MWIDOA‐SACS is included for exploiting the fitness values that aids in determining two optimal nodes that are selected as optimal CH and cluster router (CR) nodes in the network. In MWIDOA, the search conduct of dragon flies is completely updated through whale optimization algorithm (WOA) for preventing load balancing at CHs. It minimized the overhead of CH by adopting CHs and CR for collecting information from cluster members and transmitting the aggregated data from CHs to the base station (BS). It included self‐adaptive cuckoo search (SACS) for achieving sink mobility using radius, energy stability, received signal strength, and throughput for achieving optimal data transmission process after partitioning the network into unequal clusters. Simulation experiments of the proposed MWIDOA‐SACS confirmed better performance in terms of total residual energy by 21.28% and network lifetime by 26.32%, compared to the competitive CH selection strategies. [ABSTRACT FROM AUTHOR]

Wireless sensor networks use the concept of connected dominating sets that can form virtual backbones for effective routing and broadcasting. In this paper, we propose an optimization algorithm that configures a connected dominating sets in order to balance the load of nodes to increase network lifetime and to perform effective routing. The proposed optimization algorithm in this paper uses the metaheuristic method of tabu search algorithm, and is designed to balance the number of dominatees in each dominator in the constituted linked dominance set. By constructing load-balanced connected dominating sets with the proposed algorithm, it is possible to extend the network lifetime by balancing the load of the dominators. The performance of the proposed tabu search algorithm was evaluated the items related to load balancing on the wireless sensor network, and it was confirmed in the performance evaluation result that the performance was superior to the previously proposed method. [ABSTRACT FROM AUTHOR]

UWB positioning can be modeled as an optimization problem for a fitness function, which is resolved through the conventional whale optimization algorithm. The fitness function used in the conventional whale positioning algorithm does not use the range measurement data between labels, and the range measurement data is incomplete, which leads to a limited improvement in its positioning accuracy. Consequently, a novel whale cooperative positioning algorithm that can increase positioning accuracy is presented in this paper. First, a fitness function is established during the positioning process that contains the range measurement data between the labels to improve positioning accuracy. Then, the conventional whale positioning method provides a good initial position for the labels, enabling the proposed algorithm to quickly search for the optimal position. Finally, Levy flight mode is adopted to prevent the positioning result from converging to the local optimal solution. According to simulation results, the proposed algorithm can provide better positioning accuracy than the conventional whale positioning algorithm while realizing simultaneous positioning of all the labels. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms are widely used in complex problems that cannot be solved by traditional computing methods due to their powerful optimization capabilities. However, for high-complexity problems, the fitness function evaluation may take hours or even days to complete. The surrogate-assisted meta-heuristic algorithm effectively solves this kind of long solution time for the fitness function. Therefore, this paper proposes an efficient surrogate-assisted hybrid meta-heuristic algorithm by combining the surrogate-assisted model with gannet optimization algorithm (GOA) and the differential evolution (DE) algorithm, abbreviated as SAGD. We explicitly propose a new add-point strategy based on information from historical surrogate models, using information from historical surrogate models to allow the selection of better candidates for the evaluation of true fitness values and the local radial basis function (RBF) surrogate to model the landscape of the objective function. The control strategy selects two efficient meta-heuristic algorithms to predict the training model samples and perform updates. A generation-based optimal restart strategy is also incorporated in SAGD to select suitable samples to restart the meta-heuristic algorithm. We tested the SAGD algorithm using seven commonly used benchmark functions and the wireless sensor network (WSN) coverage problem. The results show that the SAGD algorithm performs well in solving expensive optimization problems. [ABSTRACT FROM AUTHOR]

In spite of the severe limitations on the resources of the sensor nodes such as memory, computational power, transmission range and battery, the application areas of Wireless Sensor Networks (WSNs) are increasing day by day. The main challenge in WSNs is energy consumption. It becomes significant when a large number of nodes are deployed. Although clustering is one of the solutions to cater to this problem, but it suffers from severe energy consumption due to the non-uniform selection of CHs and frequent re-clustering. In this paper, we propose a heuristic and fuzzy based load balanced, scalable clustering algorithm for WSNs called HFLBSC. In this algorithm, we have segregated the network into a layered structure using the area under intersection over union curve. We have selected the CHs by considering residual energy and distance threshold. We have stalled the frequent re-clustering by utilizing the decision made with the help of fuzzy logic. Our proposed scheme is capable enough to elongate the network lifetime. Simulation results confirm that on an average, HFLBSC is 32% better in terms of FND, 38% less energy consumption, 25% more alive nodes, 72% less deviation in residual energy than LEACH, FM-SCHEL, and MIWOCA. [ABSTRACT FROM AUTHOR]

Due to the wide range of applications, Wireless Sensor Networks (WSN) are increased in day to day life and becomes popular. WSN has marked its importance in both practical and research domains. Energy is the most significant resource, the important challenge in WSN is to extend its lifetime. The energy reduction is a key to extend the network's lifetime. Clustering of sensor nodes is one of the well-known and proved methods for achieving scalable and energy conserving WSN. In this paper, an energy efficient protocol is proposed using metaheuristic Echo location-based BAT algorithm (ECHO-BAT). ECHO-BAT works in two stages. First Stage clusters the sensor nodes and identifies tentative Cluster Head (CH) along with the entropy value using BAT algorithm. The second stage aims to find the nodes if any, with high residual energy within each cluster. CHs will be replaced by the member node with high residual energy with an objective to choose the CH with high energy to prolong the network's lifetime. The performance of the proposed work is compared with Low-Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Zoning Clustering Algorithm (PEZCA) and Chaotic Firefly Algorithm CH (CFACH) in terms of lifetime of network, death of first nodes, death of 125th node, death of the last node, network throughput and execution time. Simulation results show that ECHO-BAT outperforms the other methods in all the considered measures. The overall delivery ratio has also significantly optimized and improved by approximately 8%, proving the proposed approach to be an energy efficient WSN. [ABSTRACT FROM AUTHOR]

Energy is the most valuable resource in WSN. As the power storage capability of the tiny WSN is very limited, efficient mechanisms for energy minimization should be implemented to increase the overall lifetime of the network. Efficient clusterization of the area, cluster head selection leads toward the energy minimization of the whole network. Previously very few research works had been reported where the use of serve points had been considered. In this research work, the WSN has been configured depending upon many important parameters like (1) the frequency of serve points in a particular area and (2) WSN node deployment strategy in the target area. The main goal of this paper is to construct an efficient wireless sensor network. The efficiency of the network directly depends on the minimization technique of the consumed energy. So by minimizing the consumed energy an efficient wireless sensor network has been established. In this research, the specific region (target area) has been clustered using a modified K-mean algorithm to fix the position of the sink node. Deployment of WSN nodes in the target area has been done using least distance connect first (LDCF) deployment strategy. The cluster head has been selected for each cluster for each duty cycle. After configuring the WSN by using a modified k-mean algorithm and (LDCF) deployment strategy, a modified metaheuristic hybrid algorithm (GA-SAMP-MWPSO) has been used to get the optimized energy consumption for the network. Ultimately the consumed energy and the total day-life of the network have been calculated. The result has been compared with the existing literature, and it has been noted that the proposed technique yields 15.5544456 day-life of the network where the existing literature's approach gives 6.333777 day-life of the network. The proposed algorithm delivers a better outcome. [ABSTRACT FROM AUTHOR]

Summary: For the past 5 years, localization has become an emerging area in the field of wireless sensor networks (WSNs), which can be applicable for diverse applications, such as outdoor environments and monitoring of objects located in indoor environments. The major constraint in localization is allocating a location for each node as diverse sensor nodes are employed to retrieve the information in WSN. Recent research works in node localization have generally moved by multihop range‐free localization approaches for achieving high accuracy through reducing the localization error among the actual and estimated position of nodes. The conventional approaches are aimed to improve the localization accuracy without considering efficiency concerned with algorithm's convergence time and energy costs. The main intent of this paper is to model the WSN localization model, which focuses on the localization of anchor nodes concerning the target nodes. The multiobjective function is developed by considering the distance function, received signal strength (RSS), and energy. Here, a hybrid metaheuristic algorithm named hybrid cuckoo–red deer algorithm (HC‐RDA) with the integration of red deer algorithm (RDA) and cuckoo search algorithm (CSA) is adopted for localization of unknown nodes. The fitness function of the developed model is the minimization of the derived multiobjective function. Both simulation studies and theoretical analysis demonstrate that the proposed approach can improve localization performance with a better convergence rate, thus ensuring high localization accuracy compared to conventional metaheuristic models. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a set of interrelated devices on the Internet platform. It can receive and send data to make human life more efficient and convenient. The main challenge in the IoT network is energy consumption in nodes. Clustering is a proper data collection method in the IoT that selectively reduces energy consumption by forming IoT nodes into clusters. The Cluster Head (CH) can control all Cluster Member (CM) nodes, and all intra-cluster and inter-cluster connections are made through it. Today, metaheuristic algorithms solve many problems, including clustering, because they have good performance and are noticeable practical effects. This paper uses the artificial fish swarm algorithm, an effective algorithm to solve optimization problems based on imitation of fish behavior. The cost function contains the residual energy of the nodes, the sum of the distances, and the degree of each node. The simulation results on the dataset showed that the proposed method increases network lifetime value by at least 12.5% and reduces latency by at least 14%. [ABSTRACT FROM AUTHOR]

Summary: Energy‐efficient clustering and routing are two well‐known optimization problems, mainly employed to achieve energy efficiency and maximum network lifetime in wireless sensor networks (WSNs). The clustering and routing processes can be considered as an NP‐hard problem, and metaheuristic algorithms can be applied to resolve it. In this paper, a dynamic clustering and process protocol based on multi‐objective particle swarm optimization with Levy distribution (MOPSO‐L) algorithm. Since the parameters in WSN are related to one another, multi‐objective parameters should be included in the process of cluster head selection and routing. The proposed MOPSO‐L technique is presented for organizing the clusters and CH chosen by merging consolidated and shared models. The MOPSO‐L algorithm incorporates the benefits of PSO algorithm along with the merits of Levy distribution to escape from trapping into local optima. The presented model undergoes comparison with existing techniques under three different scenarios based on the location of the BS with respect to average energy consumption, number of data transmission, and network lifetime. The experimental outcome reveals that the proposed model attains extended network lifetime as well as efficient energy over its comparatives. [ABSTRACT FROM AUTHOR]

Recent breakthroughs in wireless power transfer technology have been promising for empowering and enabling seamless operation of wireless sensors and developing sustainable systems such as wireless rechargeable sensor networks (WRSNs). Relying on this technology and focusing on WRSNs, this paper proposes an adaptive charging scheduling algorithm called the three-stage fuzzy metaheuristic algorithm (TSFM) by using the whale optimization algorithm (WOA) and a multiobjective function designed to meet the application requirements of WRSNs in smart cities. The TSFM algorithm benefits from the WOA for the automated configuration and optimization of the fuzzy rule-base table in a three-stage fuzzy inference system for clustering, routing, and scheduling mobile charging simultaneously. Unlike the other methods, the TSFM algorithm determines the ratio of effect of fuzzy input parameters of every stage based on application and environment in addition to considering effective parameters for every stage, especially for charging scheduling (e.g. , the energy of a charge-request node, the distance between a charge-request node and a mobile charging (MC), the density of a charge-request node, the average energy consumption rate of a charge-request node, and the time elapsed after a charge request). Furthermore, for the proper distribution of energy in a network, better responsiveness, and optimal use of a charging robot, two energy thresholds are defined in the TSFM algorithm, namely as the charging request issue threshold and the charging operation completion threshold. The TSFM algorithm is adaptable to applications, environments, and requirements of WRSNs not only by using the features of nodes (as the fuzzy input parameters) but also by considering the application-specific features, environments, and infrastructure (e.g. , network size, number of nodes, base station (BS) positions, and designer's goals) within the process of optimizing tunable parameters. The TSFM algorithm was evaluated and compared with some common methods such as FCFS, NJNP, and ESS in terms of functional metrics such as energy utility, charging latency, survival rate, death rate of nodes, system stability, number of packets received by BS, number of request nodes, number of alive nodes, average residual energy, adaptability analysis, and statistical validation in two scenarios and six applications. According to the simulation results, the proposed TSFM algorithm improved the performance criteria and outperformed the other methods by far in terms of application. For instance, the TSFM algorithm improved the minimum system stability rate by 167%, 0.73%, and 123% compared with FCFS, NJNP, and ESS methods, respectively. • TSFM is first method for analyzing mutual effects of network strategies in WRSNs via fuzzy-metaheuristic. • TSFM adopts optimization algorithms to adjust the effects of fuzzy mobile charging input parameters in WRSN. • TSFM is the first method for employing and matching WRSNs within the platform of smart cities. • In TSFM, charging problem as a multiobjective function focusing on application of WRSNs in smart cities. • As indicated by simulation results, the proposed TSFM algorithm outperforms other schemes. [ABSTRACT FROM AUTHOR]

Coverage is a fundamental issue in wireless sensor networks (WSNs). It plays a important role in network efficiency and performance. When sensor nodes are randomly scattered in the network environment, an ON/OFF scheduling mechanism can be designed for these nodes to ensure network coverage and increase the network lifetime. In this paper, we propose an appropriate and optimal area coverage method. The proposed area coverage scheme includes four phases: (1) Calculating the overlap between the sensing ranges of sensor nodes in the network. In this phase, we present a novel, distributed, and efficient method based on the digital matrix so that each sensor node can estimate the overlap between its sensing range and other neighboring nodes. (2) Designing a fuzzy scheduling mechanism. In this phase, an ON/OFF scheduling mechanism is designed using fuzzy logic. In this fuzzy system, if a sensor node has a high energy level, a low distance to the base station, and a low overlap between its sensing range and other neighboring nodes, then this node will be in the ON state for more time. (3) Predicting the node replacement time. In this phase, we seek to provide a suitable method to estimate the death time of sensor nodes and prevent possible holes in the network, and thus the data transmission process is not disturbed. (4) Reconstructing and covering the holes created in the network. In this phase, the goal is to find the best replacement strategy of mobile nodes to maximize the coverage rate and minimize the number of mobile sensor nodes used for covering the hole. For this purpose, we apply the shuffled frog-leaping algorithm (SFLA) and propose an appropriate multi-objective fitness function. To evaluate the performance of the proposed scheme, we simulate it using NS2 simulator and compare our scheme with three methods, including CCM-RL, CCA, and PCLA. The simulation results show that our proposed scheme outperformed the other methods in terms of the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime. [ABSTRACT FROM AUTHOR]

Learning enthusiasm-based Teaching Learning Based Optimization (LebTLBO) is a metaheuristic inspired by the classroom teaching and learning method of TLBO. In recent years, it has been effectively used in several applications of science and engineering. In the conventional TLBO and most of its versions, all the learners have the same probability of getting knowledge from others. LebTLBO is motivated by the different probabilities of acquiring knowledge by the learner from others and introduced a learning enthusiasm mechanism into the basic TLBO. In this work, to achieve the enhanced performance of conventional LebTLBO by balancing the exploration and exploitation capabilities, an improved LebTLBO algorithm is proposed. The exploration of LebTLBO has been enhanced by the incorporation of the Opposition Based Learning strategy. Exploitation has been improved by Local Neighborhood Search inspired by the experience of the best solution so far discovered in a local neighborhood of the present solution. On the CEC2019 benchmark functions, the suggested technique is assessed, and computational findings show that it provides promising outcomes over other algorithms. Finally, improved LebTLBO is employed in three engineering problems and the competitive findings demonstrate its potential for a real-world problem such as the localization problem in Wireless Sensor Networks. [ABSTRACT FROM AUTHOR]