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The design of underwater wireless sensor networks (UWSNs) faces many challenges, including power consumption, storage, battery life, and transmission bandwidth. UWSNs usually either use node clustering or multi-hop routing as their energy-efficient optimization algorithms. The cluster optimization technique will organize the sensor nodes into a cluster network, with each cluster led by a cluster head (CH). In contrast, the multi-hop optimization algorithm will create a multi-hop network by sending data to the base station (BS) while switching between different sensor nodes. However, the overburdens of CH nodes impact the performance of the cluster optimization method, whereas the overburdens of nodes close to the BS impact the performance of the multi-hop optimization algorithm. Therefore, clustering and routing procedures can be considered as a simultaneous NP-hard problem that metaheuristic algorithms can address. With this motivation, this paper proposes an energy-efficient clustering and multi-hop routing protocol using the metaheuristic-based algorithm to increase energy efficiency in UWSNs and lengthen the network life. However, the existing metaheuristic-based methods use two separate algorithms for clustering and multi-hop routing, increasing computational complexity, different initialization, and difficulty in hyperparameters' tuning. In order to address the mentioned shortcomings, this paper proposes a novel hierarchical structure called hierarchical chimp optimization (HChOA) for both clustering and multi-hop routing processes. The proposed HChOA is validated using various metrics after being simulated using an extended set of experiments. Results are compared to those from LEACH, TEEN, MPSO, PSO, and IPSO-GWO to validate the impact of the HChOA. According to the findings, the HChOA performed better than other lifespan and energy usage benchmarks. [ABSTRACT FROM AUTHOR]

The meta-heuristic algorithms require a lot of fitness calculations to get good enough solutions, which constitutes an obstacle to solving computationally complex practical problems. Recently, it has been found that surrogate-assisted meta-heuristic algorithms show potential in solving expensive complex optimization problems. This paper proposes an efficient surrogate-assisted Taguchi salp swarm algorithm (SATSSA) to solve expensive complex optimization problems. The SATSSA uses a combination of the local surrogate-assisted model (LSAM), global surrogate-assisted model (GSAM), and k-means clustering surrogate-assisted model (KCSAM) to fit the fitness function. To enhance the prediction ability of the model, an improved salp swarm algorithm with the Taguchi method (TSSA) is proposed to update and predict the model. GSAM is mainly used to capture the entire landscape of the search space. KCSAM is designed to capture part of the search space to improve the exploration capability of the algorithm. LSAM is used to capture the contours around the optimal individual. The proposed SATSSA is compared with other four excellent algorithms on 30D, 50D, and 100D benchmark functions. In addition, SATSSA is also applied to intrusion detection. Simulation results show that SATSSA is effective in improving detection rate and reducing false alarm rate. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) as an emerging technology is widely used in various applications such as remote healthcare, smart environment, and intelligent transportation systems. It is necessary to address users' concerns about cost, ease of use, privacy, and comprehensive security to grow the popularity of this technology. Intrusion Detection System (IDS) plays an indispensable role in security and preventing unauthorized users to access authorized network resources through analyzing network patterns. Several techniques such as metaheuristic algorithms, machine learning, fuzzy logic, and artificial intelligence algorithms can be applied to increase the accuracy of IDS, feature selection, and network patterns classification. In this paper, a hybrid intrusion detection system based on Support Vector Machine (SVM) and Grey Wolf Optimization (GWO) is presented that utilizes the advantages of these algorithms. In the proposed approach, the support vector machine has been used to train and differentiate anomaly records from normal records and grey wolf optimization has been used to find the kernel function, feature selection, and adjust optimal parameters for the SVM in order to improve the classification. The conducted simulations prove that the proposed approach outperforms in terms of detection accuracy, precision, recall, and F-score on both NSL-KDD and TON_IoT datasets. [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]

Clustering sensor nodes is an efficient approach of modeling the routing algorithms for the Wireless Sensor Networks (WSNs) that enhance the energy efficiency and network lifetime. The cluster heads (CHs) present in the clustered WSNs represent the key nodes. Since they accomplish several tasks, more energy is being consumed. Hence, it remains a significant conflict for choosing the optimal CHs. Most of the WSN routing protocol's labels particular conflicts with the "Low-Energy Adaptive Clustering Hierarchy" (LEACH) protocol. The challenge for the WSN protocols is to maintain high scalability and expand the lifespan of the network. The major challenge in enhancing the WSNs is energy efficiency. Since communication occupies many portions of the energy consumption, and efficient solution to handle this problem is effective routing. The main intention of this paper is to develop the energy-efficient routing for WSN using a hybrid meta-heuristic-based LEACH protocol. Here, the optimal CH selection is accomplished by the hybrid meta-heuristic algorithm with Sail Fish Optimization (SFO) and Jaya Algorithm (JA), and the so formed algorithm is called Limit-based Jaya Sail Fish Optimization (L-JSFO). A Multi-objective function is considered with significant constraints like "energy, distance, delay, trusts of nodes, load, and Quality of Service (QoS)". Finally, this paper compares the proposed meta-heuristic-based LEACH protocol against other WSN routing protocols. The simulation outcomes described that the proposed protocol exceeded the benchmarked protocols in terms of energy efficiency and load balancing, and proves the enhanced network lifetime. [ABSTRACT FROM AUTHOR]

Wireless powered communication network (WPCN) is a promising technique to resolve the power constraint issue faced by wireless nodes at the same time it also provides green and a safer solution. This paper studies, mutual sharing between WPCN and simultaneous wireless information and power transfer model where optimal resource allocation occurs between licensed/bandwidth group and an unlicensed/power group to enhance the system performance. The main aim of this paper is to maximize system weighted sum rate and minimize power consumption during transmission by optimal time allocation and energy beamforming vector allocation. These multiobjective combinatorial problems are solved using joint metaheuristic particle swarm optimization-cuckoo search algorithm to provide an optimal solution. The results are compared using a conventional mathematical optimization algorithm to set as a baseline for performance parameters. The obtained results demonstrate that the efficient joint metaheuristic approach provides a significant gain in the system performance and also offers a computationally less complex approach for solving a complicated non-convex multiobjective problem of wireless powered network. [ABSTRACT FROM AUTHOR]

This paper investigates the application of non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave) transmission in the fifth-generation (5G) of heterogeneous cellular networks (HetNets). Due to the high penetration loss of mmWave, we propose that a small cell base station (SBS) serves small cell user (SCU) equipment in the mmWave band, and a macrocell base station (MBS) serves macrocell users (MCUs) in the microwave band. Cell association, user grouping, and power allocation are fundamental challenges in NOMA-based transmission. We formulate optimization problems for SCU and MCU to maximize the energy efficiency (EE) subject to the required minimum data rates and maximum transmission powers. User grouping algorithms are introduced to demonstrate the significance of selecting the best users. To allocate transmission powers, we formulate an EE maximization problem that is non-convex and NP-hard. We propose to use the Bat algorithm, which is one of the popular and efficient metaheuristic algorithms in solving non-convex problems. Analytical expressions for cell association and outage probabilities are derived. We present Monte Carlo simulation results to validate the analytical expressions and show that: (1) choosing the best user among far users influences the overall EE of system impressively; (2) the optimum values of transmission powers can be obtained by Bat algorithm; (3) the proposed grouping algorithms with power allocation methods outperform the other scenarios in terms of EE and spectral efficiency (SE). [ABSTRACT FROM AUTHOR]

The cell assignment problem is combinatorial, with increased complexity when it is tackled considering resource allocation. This paper models joint cell assignment and resource allocation for cellular heterogeneous networks, and formalizes cell assignment as an optimization problem. Exact algorithms can find optimal solutions to the cell assignment problem, but their execution time increases drastically with realistic network deployments. In turn, heuristics are able to find solutions in reasonable execution times, but they get usually stuck in local optima, thus failing to find optimal solutions. Metaheuristic approaches have been successful in finding solutions closer to the optimum one to combinatorial problems for large instances. In this paper we propose a fast and efficient heuristic that yields very competitive cell assignment solutions compared to those obtained with three of the most widely-used metaheuristics, which are known to find solutions close to the optimum due to the nature of their search space exploration. Our heuristic approach adds energy expenditure reduction in its algorithmic design. Through simulation and formal statistical analysis, the proposed scheme has been proved to produce efficient assignments in terms of the number of served users, resource allocation and energy savings, while being an order of magnitude faster than metaheuritsic-based approaches. [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]

This paper focuses on applying a wireless power transfer WPT technology in an electric vehicle battery charger. The wireless charger system is based on the principle of resonance inductive coupling power through a coreless transformer. The WPT system is considered as a multi parameter and multi constrained nonlinear system. The main contribution in this paper is the use of PSO and GA metaheuristic algorithms in the optimization of a transformer design regarding the impact of a lateral misalignment and the separation distance between the primary and secondary coils. To find the best global solution which is considered as the maximum efficiency in the complex system, both algorithms are compared. A perturbation-and-observation-based tracking system is developed through an efficiency sensing system to act on the misalignment issue and the car position. An additional PSO controller is performed to control the duty cycle of the boost converter in order to follows the maximum efficiency operating points of a WPT system. The discrepancy is the use of the resonant inductive coupling as a source of the MPPT so as to perturb the car position and observe the transferred. Furthermore, the modeling of a contactless transformer is optimized using metaheuristic algorithm. [ABSTRACT FROM AUTHOR]

Clustering of sensor nodes is one of the prominent methods applied to Wireless Sensor Networks (WSN). In the cluster-based WSN scenario, the sensor nodes are assembled to generate clusters. The sensor nodes are composed of limited battery power. Therefore, energy efficiency in WSN is crucial. A load of sensor node and its distance from base station (BS) are the significant factors of energy consumption. Therefore, load balancing according to the transmission distance is necessary for WSN. In this paper, we propose a load-balanced clustering algorithm using Fuzzy C means (FCM) algorithm and an energy-efficient routing approach using BAT-algorithm (FC-RBAT). The cluster heads (CHs) are selected according to the score of the sensor node from each cluster. After selection of the CHs, the BAT-inspired routing algorithm is applied on the CHs. The best routing path from each CH to the BS is obtained from the proposed approach. The simulations are conducted on evaluation factors such as energy consumption, active sensor nodes per round, the sustainability of the network and the standard deviation of a load of the sensor node. It is observed that FC-RBAT outperforms compared algorithms, namely EAUCF, DUCF and SGA, under the evaluation factors. [ABSTRACT FROM AUTHOR]

Millimeter waves (mmWaves) with very wide frequency bands are proposed for 5G new radio to deliver higher data-speed and capacity. Transmission using mmWaves suffers significant path loss and can be compensated by employing directional antennas. The narrow-beam directional antennas act as spatial filters that filter out multipaths falling outside their beam area. This, together with the insignificance of diffraction and diffused scattering in urban outdoors causes mmWave outdoor channel multipath to be spatially sparse with few specular reflections, posing unique requirements on channel modeling. In this paper, we have derived a low complexity mmWave directional channel model based on ray tracing for the urban microcell street canyon environment. A comprehensive characterization of outdoor links in various realistic scenarios including, line of sight (LOS)/non-LOS, beam aligned/unaligned and road canyon with/without crossroads is presented. The model captures azimuth and elevation directions of propagation in 3D plane by using a highly directional horn antenna design. Atmospheric absorption loss of mmWave is also modeled to enhance the model's accuracy and generality. The model is validated against measurements reported in the literature. Furthermore, the channel is studied for varying propagation conditions, antenna beamwidths and polarizations, transmitter/receiver heights and positions, and street deployment parameters. Additionally, we propose a metaheuristic algorithm called particle swarm optimization to simultaneously optimize the deployment parameters that minimize path loss as the objective function. The proposed model helps in mmWave system evaluation without the necessity for any costly measurement setup or complex off-the-shelf ray tracing model. [ABSTRACT FROM AUTHOR]

Green communication plays a significant role in sensor enabled IoT. Energy is at the focal point in the smart application of the IoT which enables the sensors to be work. The faster energy depletion causes the hindrance in efficient functioning of the sensors. An energy-efficient scheme is required to prevent the faster reduction of energy from sensors in IoT. Metaheuristic techniques are most effective to solve such problems with the near-optimal solution as heuristic techniques are not suitable for such problem, they may turn into NP-hard problem in dense area sensor networks. Most of the optimization-based energy-efficient techniques suffer from unstable energy depletion problem because nodes near to the base station (BS) have more traffic load. In this paper, an energy balanced integrated atom swarm and electromagnetic force optimization (iASEF) scheme is proposed to overcome the energy depletion problem. The iASEF consists of (a) A linear programing problem of clustering that consisting of an objective function and constraints based on node degree, intra-cluster distance, residual energy of node and inter-cluster distance, (b) optimal routing problem. Atom search optimization has been employed to solve linear problem to find optimal cluster head (CH) among sensors. Electromagnetic force optimization has been used to solve routing problem to find next hop for data forwarding between the CH and BS. The simulation results demonstrate that the proposed iASEF scheme achieves substantial enhancement over the state-of-art algorithms. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms have popularly been used to solve a wide range of complex engineering optimization problems. In order to solve the problems with two or more objectives multi-objective algorithms (MOAs) are used. Application of MOAs to solve multiple conflicting objectives yields a Pareto-optimal solution set. In this paper, we propose a multi-objective decomposition-based moth flame optimization (MOMFO/D) algorithm, that decomposes the objectives into multiple single objectives which are optimized simultaneously. The algorithm is used to solve the relay node placement problem, that is modeled as a bi-objective problem with the goal of minimization of average intra-cluster distance and average hop-count to improve the network lifetime. The Pareto-optimal fronts obtained through the simulations are evaluated using three distinct quality indicators namely the Inverted Generational Distance, Spacing Metric and Maximum Spread in order to evaluate the performance. The obtained results considered over a number of runs are compared with other existing optimizers in the literature such as multi-objective non-dominated sorted moth flame optimizer, and multi-objective evolutionary algorithm based on decomposition. The results demonstrate the superiority in the performance of the proposed algorithm over others. The statistical analysis of the experimental work has been carried out by conducting Friedman's and Quade test. [ABSTRACT FROM AUTHOR]

Location-based services in wireless sensor networks demand precise information of locations of sensor nodes. Range-based localization, a problem formulated as a two-dimensional optimization problem, has been addressed in this paper as a multistage exercise using bio-inspired metaheuristics. A modified version of the shuffled frog leaping algorithm (MSFLA) has been developed for accurate sensor localization. The results of MSFLA have been compared with those of geometric trilateration, artificial bee colony and particle swarm optimization algorithms. Dependance of localization accuracies achieved by these algorithms on the environmental noise has been investigated. Simulation results show that MSFLA delivers the estimates of the locations over 30% more accurately than the geometric trilateration method does in noisy environments. However, they involve higher computational expenses. The MSFLA delivers the most accurate localization results; but, it requires the longest computational time. [ABSTRACT FROM AUTHOR]

This paper presents a feasibility study of 60 GHz indoor WLANs. We evaluate 60 GHz performance in a typical academic office building under the primary assumption that 60 GHz WLAN APs and clients will be equipped with relatively wide-beam antennas to cope with client mobility. In contrast to previous works which measured performance at a single layer using custom, non-standard compliant hardware, we investigate performance across multiple layers using primarily 802.11ad-compliant wide-beam COTS devices. Our study shows that the large number of reflective surfaces in typical indoor WLAN environments combined with wider beams makes performance highly unpredictable and invalidates several assumptions that hold true in static, narrow-beam, Line-Of-Sight scenarios. Additionally, we present the first measurements, to our best knowledge, of power consumption of an 802.11ad NIC and examine the impact of a number of factors on power consumption. [ABSTRACT FROM AUTHOR]

The location management is one of the most important tasks in current Public Land Mobile Networks because of the number of mobile subscribers has increased exponentially over the last decade. That is why systems that automatically optimize the operations involved in the location management (subscriber location update and paging) are becoming more necessary. There are several works in which different metaheuristics have been applied to optimize the location management tasks. In these works, the objective functions of the location update and paging were linearly combined into a single objective function with the goal of optimizing these two tasks by using Single-Objective Optimization Algorithms. In this paper, in order to avoid the drawbacks associated with the linear aggregation of the objective functions, we have adapted and modified two Multi-objective Evolutionary Algorithms: Non-dominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm 2. Furthermore, we have performed an in-depth analysis of the Location Areas scheme and its relation to the user's call and mobility patterns. This study concludes that the location areas are as small as possible due to the fast increase of the paging cost, and that the cells with higher mobile activity are located in the center of its location area. Moreover, results show that our algorithms outperform the single-objective optimization algorithms proposed by other authors in the two most complex test networks, as well as the advantages of using a multi-objective approach. [ABSTRACT FROM AUTHOR]

The explosive growth of cellular networks makes their deployment and maintenance more and more complex, time consuming, and expensive. Self-Organizing Networks have been recognized as a promising way to alleviate this problem by minimizing human intervention in such processes. This paper introduces a novel multiobjective framework, based on evolutionary optimization, aiming at improving network performance and users Quality of Service. By tuning the transmitted power at each cell, average intercell interference levels are minimized. The design of the proposed scheme is feasible for distributed implementations in Long Term Evolution (LTE) and LTE-Advanced networks and its operation is compatible with current specifications. The framework is able to provide effective network-specific optimization and obtained results show that gains in terms of network capacity and cell edge performance are 5 and 10 %, respectively. Energy savings always accompanied such enhancements with reductions up to 35 %. [ABSTRACT FROM AUTHOR]