Low Earth orbit (LEO) satellites can be used to assist maritime wireless communications for wide-area data transmission. However, the extensive coverage of LEO satellites, combined with the openness of channels, can cause the communication process to suffer from security risks. This paper presents a LEO satellite-maritime communication system assisted by low-altitude unmanned aerial vehicle (UAV) friendly-jamming to ensure data security at the physical layer. Since such a system requires balancing the conflicting performance metrics of secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor-critic (TransSAC) algorithm, which is a generative artificial intelligence-enabled deep reinforcement learning approach to solve the reformulated problem, thus capturing strong temporal correlations and diversely exploring weights. Simulation results demonstrate that the TransSAC algorithm outperforms comparative approaches and algorithms, maximizing the secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results identify more suitable constraints for the system.

Low-altitude wireless networks (LAWNs) have emerged as a viable solution for maritime communications. In these maritime LAWNs, uncrewed aerial vehicles (UAVs) serve as practical low-altitude platforms for wireless communications due to their flexibility and ease of deployment. However, the open and clear UAV communication channels make maritime LAWNs vulnerable to eavesdropping attacks. Existing security approaches often assume eavesdroppers follow predefined trajectories, which fail to capture the dynamic mobility patterns of eavesdroppers in realistic maritime environments. To address this challenge, we consider a low-altitude maritime communication system that employs intelligent jamming to counter dynamic eavesdroppers with uncertain positions to enhance the physical layer security. Since such a system requires balancing the conflicting performance metrics of the secrecy rate and energy consumption of UAVs, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP). To solve this dynamic and long-term optimization problem, we first reformulate it as a partially observable Markov decision process (POMDP). We then propose a novel soft actor-critic with conditional variational autoencoder (SAC-CVAE) algorithm, which is a deep reinforcement learning algorithm improved by generative artificial intelligence. Specifically, the SAC-CVAE algorithm employs advantage-conditioned latent representations to disentangle and optimize policies, while enhancing computational efficiency by reducing the state space dimension. Simulation results demonstrate that our proposed intelligent jamming approach achieves secure and energy-efficient maritime communications. Furthermore, comparison results show that the proposed SAC-CVAE algorithm outperforms baseline methods across various eavesdropper movement patterns, simultaneously maximizing the secrecy rate and minimizing the energy consumption of UAVs.

Autonomous aerial vehicles (AAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of AAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the AAV relay for remote maritime communications. However, due to the open nature of AAV channels, security issue requires special consideration. This article proposes a dual AAV cluster-assisted system via CB to achieve physical-layer security in maritime wireless communications. Specifically, one AAV cluster forms a maritime AAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other AAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multiobjective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of AAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method.

Given that the locations of base stations (BSs) remain fixed after installation, direct data forwarding to remote user equipment (UE) becomes challenging. Unmanned aerial vehicles (UAVs) offer a hopeful solution as mobile relays for next generation wireless communications to realize data forwarding with the flexible and cost-effective deployment. However, the limited onboard energy of UAVs and slow progress in energy storage technology pose significant challenges to achieving energy-efficient communication. Therefore, in this article, we investigate a wireless communication network utilizing a UAV as a high-altitude relay for data forwarding, and formulate a UAV relay deployment optimization problem (URDOP) to minimize the energy consumption of data forwarding and UAV hovering by optimizing UAV deployment, including the locations and number of UAV hover points. Given that the URDOP is a mixed-integer programming problem, conventional gradient-based approaches face limitations. To address this, we propose a self-adaptive differential evolution with a variable population size (SaDEVPS) algorithm to solve the URDOP. The performance of proposed SaDEVPS is verified through simulations, and the results show that it can successfully decrease the energy consumption of system when compared to other benchmark algorithms.

Maritime wireless communications as the promising applications gradually spur people's interest. However, they are suffering from critical security issues due to the feature of open channels. This paper propose to utilize a group of unmanned aerial vehicles (UAVs) as a jammer to achieve physical layer security during the maritime wireless communications. Specifically, these UAVs form a maritime UAV-enabled virtual antenna array (UVAA), which can be allowed to transmit jamming signals to the eavesdropper. In the designed system, we formulate a maritime wireless communication multi-objective optimization problem (MWCMOP) to simultaneously achieve the maximization of the signal-to-interference-plus-noise ratio (SINR) of the legal vessel, minimization of the SINR of the eavesdropping vessel, and minimization of the total flying energy cost of UAVs. It is challenging to solve the formulated MWCMOP since it is complex and NP-hard. Thus, we present a novel improved evolutionary algorithm to deal with the problem. Simulation results show that the presented algorithm is superior to other peer algorithms and it is capable of searching a transmission-maximizing flight scheme.

The goal of feature selection in machine learning is to simultaneously maintain more classification accuracy, while reducing lager amount of attributes. In this paper, we firstly design a fitness function that achieves both objectives jointly. Then we come up with a chaos-based binary dragonfly algorithm (CBDA) that incorporates several improvements over the conventional dragonfly algorithm (DA) for developing a wrapper-based feature selection method to solve the fitness function. Specifically, the CBDA innovatively introduces three improved factors, namely the chaotic map, evolutionary population dynamics (EPD) mechanism, and binarization strategy on the basis of conventional DA to balance the exploitation and exploration capabilities of the algorithm and make it more suitable to handle the formulated problem. We conduct experiments on 24 well-known data sets from the UCI repository with three ablated versions of CBDA targeting different components of the algorithm in order to explain their contributions in CBDA and also with five established comparative algorithms in terms of fitness value, classification accuracy, CPU running time, and number of selected features. The results show that the proposed CBDA has remarkable advantages in most of the tested data sets.

Feature selection is a complicated multi-objective optimization problem with aims at reaching to the best subset of features while remaining a high accuracy in the field of machine learning, which is considered to be a difficult task. In this paper, we design a fitness function to jointly optimize the classification accuracy and the selected features in the linear weighting manner. Then, we propose two hybrid meta-heuristic methods which are the hybrid basic bald eagle search-particle swarm optimization (HBBP) and hybrid chaos-based bald eagle search-particle swarm optimization (HCBP) that alleviate the drawbacks of bald eagle search (BES) by utilizing the advantages of particle swarm optimization (PSO) to efficiently optimize the designed fitness function. Specifically, HBBP is proposed to overcome the disadvantages of the originals (i.e., BES and PSO) and HCBP is proposed to further improve the performance of HBBP. Moreover, a binary optimization is utilized to effectively transfer the solution space from continuous to binary. To evaluate the effectiveness, 17 well-known data sets from the UCI repository are employed as well as a set of well-established algorithms from the literature are adopted to jointly confirm the effectiveness of the proposed methods in terms of fitness value, classification accuracy, computational time and selected features. The results support the superiority of the proposed hybrid methods against the basic optimizers and the comparative algorithms on the most tested data sets.

Unmanned aerial vehicles (UAVs) are widely used as relay platforms to assist in maritime wireless communications. However, due to the open channel of UAV communications, security issue needs to be paid extra attention. This paper proposes a dual-UAVs jamming-aided system to implement physical layer encryption in maritime wireless communication. Specifically, one UAV acts as a relay to transmit data to the legitimate vessel and a set of UAVs can form a maritime UAV-enabled virtual antenna array (MUVAA) as a jammer to send jamming signals to the eavesdropper. In this system, an encrypted and energy-efficient maritime communication multi-objective optimization problem (EEMCMOP) is formulated to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the overall flight energy consumption of UAVs. Since the EEMCMOP is proven to be NP-hard, we propose an enhanced evolutionary computation algorithm with some upgraded characteristics to solve the problem. Simulation results indicate that the performance of the proposed algorithm is superior to other benchmark algorithms.

Internet of Things (IoT) has gradually been applied to various fields, including industries and agriculture, and plays an increasingly important role in society. However, the limited coverage of terrestrial IoT network restricts the communication performance of IoT devices, making the network inefficient. Unmanned aerial vehicles (UAVs) have the potential to be an efficient solution to improve the communication efficiency of the terrestrial IoT devices. Thus, we formulate a UAV-assisted data collection multi-objective optimization problem (UAVDCMOP) to jointly maximize the average transmission rate, minimize the total time of UAVs, and minimize the average energy consumed by UAVs via determining the optimal positions of UAVs. To this end, we propose an improved multi-objective grey wolf-based optimization (IMOGWO) algorithm with chaotic mapping initialization operator and inversion opposition generation operator, making it suitable for optimizing the formulated UAVDCMOP. Simulation results demonstrate that the proposed approach contributes to enhance the system average transmission rate and energy efficiency, and it has superior performance compared to other approaches.

Unmanned aerial vehicle (UAV) communications and networks are promising technologies in the forthcoming 5G/6G wireless communications. However, they have challenges for realizing secure communications. In this paper, we consider to construct a virtual antenna array consists UAV elements and use collaborative beamforming (CB) to achieve the UAV secure communications with different base stations (BSs), subject to the known and unknown eavesdroppers on the ground. To achieve a better secure performance, the UAV elements can fly to optimal positions with optimal excitation current weights for performing CB transmissions. However, this leads to extra motion energy consumption. We formulate a physical layer secure communication multi-objective optimization problem (MOP) of UAV networks to simultaneously improve the total secrecy rates, total maximum sidelobe level (SLL) and total motion energy consumption of UAVs by jointly optimizing the positions and excitation current weights of UAVs, and the order of communicating with different BSs. Due to the complexity and NP-hardness of the formulated MOP, we propose an improved multi-objective dragonfly algorithm with chaotic solution initialization and hybrid solution update operators (IMODACH) and a parallel-IMODACH (P-IMODACH) to solve the problem. Simulation results verify that the proposed approaches can effectively solve the formulated MOP and it has better performance than some other benchmark algorithms and approaches. Moreover, some unexpected circumstances are considered and discussed.

Unmanned aerial vehicle (UAV)-enabled wireless powered communication networks (WPCN) are promising technologies in Internet of Things (IoTs). However, energy-constrained devices and connectivity in complex environments are two major challenges for IoTs. We consider a UAV-enabled WPCN scenario that a UAV can connect with the ground IoT devices (IoTDs). To connect and fly faster, UAV needs to be scheduled reasonably and the corresponding trajectory should be optimized. Thus, we formulate a UAV scheduling and trajectory optimization problem (USTOP) to minimize the total time so that improving the charging and transmission efficiency. Since conventional methods are difficult to solve USTOP, we propose an improved simulated annealing (ISA) with the variable size changing mechanism, the conflict resolution mechanism and the hybrid evolution method to solve it. Simulation results verify the effectiveness and performance of ISA under different scales of the network, and the stability of the proposed algorithm is verified.

Unmanned aerial vehicles (UAVs) have the advantages of low-cost and flexible deployment. It can be used as the aerial transmitter in forests scenarios. However, forest terrain has severe multi-path reflections and may have several eavesdroppers, which degrade the performance of the communications. In this paper, we consider a collaborative UAV secure communication model under a forest channel, where multiple UAVs form a virtual antenna array (VAA) to communicate with a ground base station (BS) and circumvent the influence of the eavesdropper. Moreover, we formulate a multi-objective optimization problem (MOP) to simultaneously optimize the secure performance and energy efficiency of the considered system. Then, we propose an enhanced multi-objective particle swarm optimization algorithm (EMOPSO) which is based on chaos theory, linear weight reduction method and optimal set-based mutation approach to solve the formulated MOP. Simulation results show that the proposed method is effective and the proposed EMOPSO outperforms other benchmark algorithms.

Unmanned aerial vehicle (UAV) communication is a promising technology in 5G/6G wireless communications. However, there are several challenges for ensuring secure communications in practical scenarios. In this paper, we consider a UAV-enabled communication scenario that a UAV needs to maintain secure communication with the ground communication nodes (GCNs), subject to the known ground eavesdropping nodes (GENs). UAV needs to select optimal communication positions and avoid obstacles. We formulate a UAV secrecy scheduling optimization problem (USSOP) to maximize the average secrecy rate and the minimum secrecy rate jointly. Then, we propose a particle swarm optimization with $\underline {normal}$ distribution initialization, $\underline {differential}$ mechanism and $\underline {avoiding}$ obstacles operator (PSONDA) to solve the USSOP. Simulation results show that this method performs better than other comparison algorithms.

Automated guided vehicle (AGV) communications and networks have attracted extensive attention and have broad prospects in the field of wireless transmission. Unmanned aerial vehicle (UAV) can be used as flight base station (BS) to receive information from the ground AGVs. In this paper, we study an AGV-UAV communication scenario, in which a group of AGVs form an AGV-based virtual antenna array (AVAA) and communicate with different UAVs based on distributed collaborative beamforming (DCB). We formulate an AGV-UAV communication multi-objective optimization problem (AUCMOP) to simultaneously maximize the total transmission rate, minimize the total repositioning time of AGVs and minimize the total motion energy consumptions of AGVs by optimizing the positions, excitation current weights and moving speeds of AGVs, as well as the sequence for communicating with different UAVs. The formulated AUCMOP is complicated, and thus we propose an improved multi-objective ant lion optimization algorithm with Chebyshev chaos-opposition based learning solution initialization and hybrid solution update method (IMOALOCH). Simulation results show that the proposed IMOALOCH can solve the formulated AUCMOP effectively and it is superior to other comparison algorithms.

Feature selection aims at reducing the number of attributes while achieving a high classification accuracy in machine learning. In this paper, we design a fitness function to jointly reduce the number of the selected features and enhance the accuracy. Then, we propose a chaotic binary dragonfly algorithm (CBDA) with several improved factors on the conventional dragonfly algorithm (DA) for developing a wrapper-based feature selection method to solve the fitness function. Specifically, the CBDA introduces three improved factors that are the chaotic map, evolutionary population dynamics mechanism and binarization strategy to make the algorithm more suitable for the problem. Experiments are conducted to evaluate the performance of the proposed CBDA on 24 well-known data sets from the UCI repository, and the results demonstrate that the proposed CBDA outperforms other comparative algorithms on the majority of the tested data sets.

Automated guided vehicle (AGV) has the advantages of high endurance, autonomy and security, which render them appealing for applications such as monitoring and sensing networks. However, AGVs may be limited in energy and transmission range. Unmanned Aerial Vehicle (UAV) is a promising platform that can assist terrestrial networks. In this work, we aim to adopt a UAV swarm to assist the data forwarding of AGVs and propose a novel data transmission framework based on collaborative beamforming (CB) where the AGVs and UAVs jointly construct a virtual antenna array (VAA) to transmit data to the remote air base stations (ABSs). Specifically, we formulate a terrestrial and air collaboratively data transmission multiobjective optimization problem (TATFMOP) to optimize the excitation current weights and locations of the AGVs and UAVs, and the communication order of different remote ABSs. Since TATFMOP is an NP-hard problem, we present an extended multiobjective ant-lion optimization (EMOALOIB) with opposition-based population initialization and black hole update operators to solve the problem. Simulations results demonstrate that the proposed EMOALOIB outperforms other existing benchmark algorithms and can obtain more valuable solutions.

The fifth generation (5G) mobile technology is one of the means to support wireless communication capabilities in Internet of Things (IoT), which has been widely used in multifarious scenarios. However, the insufficient terrestrial networks limit the deployment of IoT devices, which makes the integration between the devices and the looming 5G infrastructures more difficult. Unmanned Aerial Vehicles (UAVs) have the potential to facilitate the integration of them and overcome the limitations of terrestrial infrastructures since they can be deployed as the air auxiliary base stations (AABS) for IoT. In this work, we aim to determine the optimal number of UAVs while considering communication-related parameters such as average throughput of UAVs and association between the devices and UAVs. First, we formulate a joint deployment optimization problem of UAVs (JDOPUAV) to simultaneously minimize the number of UAVs, maximize the average throughput of UAV-device pairs and maximize the lowest throughput of UAV-device pairs by optimizing the positions of UAVs. Then, an improved biogeography-based optimization with flexible local selection, chaos mechanism and intrusion operator (IBBOFCI) is proposed to solve the formulated JDOPUAV. Simulation results verify that the proposed IBBOFCI is more effective for the problem compared to other methods.

Due to the flexibility and high line-of-sight (LoS) probability, unmanned aerial vehicles (UAVs) can play an important role in 5G/6G networks. In this work, we study a novel air-to-air (A2A) communication scheme, in which two UAV swarms perform two UAV-enabled virtual antenna arrays (UVAAs) for exchanging data by using collaborative beamforming (CB). In order to improve the transmission efficiency and save the energy consumptions of the UAVs, we formulate an A2A communication multi-objective optimization problem (A2ACMOP) to simultaneously enhance the duplex transmission rates and reduce the total energy consumptions of the UAV swarms by deploying the UAVs and adjusting their excitation current weights. Due to the complexity and NP-hardness of the formulated A2ACMOP, we propose an improved non-dominated sorting genetic algorithm-III (INSGA-III) with opposition-based learning solution initialization and hybrid solution update operators to solve the problem. Simulation results verify that the proposed INSGA-III can effectively solve the formulated A2ACMOP and it has better performance than some other benchmark strategies.

Wireless rechargeable sensor networks with a charging unmanned aerial vehicle (CUAV) have broad application prospects in the power supply of the rechargeable sensor nodes (SNs). However, how to schedule a CUAV and design the trajectory to improve the charging efficiency of the entire system is still a vital problem. In this article, we formulate a joint-CUAV scheduling and trajectory optimization problem (JSTOP) to simultaneously minimize the hovering points of CUAV, the number of the repeatedly covered SNs, and the flying distance of CUAV for charging all SNs. Due to the complexity of JSTOP, it is decomposed into two optimization subproblems that are CUAV scheduling optimization problem (CSOP) and CUAV trajectory optimization problem (CTOP). CSOP is a hybrid optimization problem that consists of the continuous and discrete solution space, and the solution dimension in CSOP is not fixed since it should be changed with the number of hovering points of CUAV. Moreover, CTOP is a completely discrete optimization problem. Thus, we propose a particle swarm optimization (PSO) with a flexible dimension mechanism, a$K$-means operator, and a punishment-compensation mechanism (PSOFKP) and a PSO with a discretization factor, a 2-opt operator, and a path crossover reduction mechanism (PSOD2P) to solve the converted CSOP and CTOP, respectively. Simulation results evaluate the benefits of PSOFKP and PSOD2P under different scales and settings of the network, and the stability of the proposed algorithms is verified.

Unmanned-aerial-vehicle (UAV)-aided wireless communication in Internet of Things (IoT) applications is becoming the focus of attention of researchers. This article investigates a UAV-assisted communication system for serving IoT, in which multiple rotary-wing UAVs are employed to communicate with multiple terrestrial IoT devices. Specifically, we formulate a UAV deployment multiobjective optimization problem (UAVDMOP) to simultaneously maximize the minimum throughput of the UAV–device pairs, maximize the total throughput of the whole system, and minimize the total energy consumptions of UAVs via a joint optimization of the locations of UAVs, transmission power of UAVs, and association relationship between the UAVs and IoT devices. UAVDMOP consists of both discrete and continuous solution spaces, which is difficult to be solved. Thus, we propose an improved discrete and continuous multiobjective evolutionary algorithm based on decomposition (IDCMOEA/D) with a hybrid solution initialization operation and a hybrid solution reproduction operation for increasing the performance of the algorithm so that making it more suitable for dealing with the UAVDMOP. Simulation results demonstrate that the proposed method is effective to enhance the throughput and energy efficiency of the system and it has superior performance compared to other methods.

Unmanned aerial vehicle (UAV) is a promising communication platform to assist terrestrial networks. In this work, we aim to provide relay communication to the blocked or low-quality terrestrial networks via an aerial relay. Nevertheless, major issues of the considered system are the worrying security and limited service time. Thus, we study a novel aerial relay system via collaborative beamforming (CB) by exploiting a UAV-enabled virtual antenna array (UVAA) to achieve a secure and energy-efficient communication for remote ground users (GUs). Specifically, we formulate a secure and energy-efficient communication multi-objective optimization problem (SECMOP) to circumvent the effects of the known and unknown eavesdroppers and minimize the propulsion energy consumption of UAVs, by optimizing the hovering positions and excitation current weights of UAVs and the scheduling for communicating with the remote GUs. The formulated SECMOP is challenging and proven to be NP-hard. Thus, we propose an improved evolutionary computation method with several enhanced designs to solve this problem. Simulation results demonstrate the benefits of the proposed IMODAOM against various benchmark algorithms. Moreover, we find that the UVAA-based relay can achieve substantial energy consumption reduction as compared to the multi-hop relay scheme.

Unmanned aerial vehicles (UAVs) have attracted growing attention in enhancing the performance of mobile wireless sensor networks (MWSNs) since they can act as the aerial base stations (ABSs) and have the autonomous nature to collect data. In this paper, we consider to construct a virtual antenna array (VAA) consists of mobile sensor nodes (MSNs) and adopt the collaborative beamforming (CB) to achieve the long-distance and efficient uplink data transmissions with the ABSs. First, we formulate a high data transmission rate multi-objective optimization problem (HDTRMOP) of the CB-based UAV-assisted MWSN to simultaneously improve the total transmission rates, suppress the total maximum sidelobe levels (SLLs) and reduce the total motion energy consumptions of MSNs by jointly optimizing the positions and excitation current weights of MSN-enabled VAA, and the order of communicating with different ABSs. Then, we propose an improved non-dominated sorting genetic algorithm-III (INSGA-III) with chaos initialization, average grade mechanism and hybrid-solution generate strategy to solve the problem. Simulation results verify that the proposed algorithm can effectively solve the formulated HDTRMOP and it has better performance than some other benchmark methods.

Wireless rechargeable sensor networks (WRSNs) with a charging UAV (CUAV) have the broad application prospects for the power supply of the rechargeable sensor nodes (SNs). However, how to schedule the CUAV so that improving the charging efficiency of the whole system is still a vital problem. In this paper, we formulate a scheduling optimization problem of CUAV (SOPCUAV) to jointly reduce the hovering number of the CUAV and the duplicate coverage of SNs for enhancing the charging performance. Then, we propose an improved particle swarm optimization (IPSO) algorithm with the flexible dimension mechanism, using K - means operator to find the hovering position of CUAV and punishment and compensation mechanism to solve the formulated SOPCUAV. Simulation results demonstrate the effectiveness and performance of the proposed algorithm.

Collaborative beamforming (CB) in wireless sensor networks (WSNs) based on a virtual node antenna array (VNAA) can increase the transmission distance and enhance the energy efficiency of sensor nodes. However, a VNAA cannot be pre-designed like the conventional antenna arrays due to the randomly deployed sensor nodes, thereby causing a high sidelobe level (SLL) which increases the interferences. In this article, we formulate a hybrid discrete and continuous optimization problem (HDCOP) for reducing the maximum SLL. HDCOP requires to solve both the discrete and the continuous problems simultaneously, and we propose both centralized and consensus-based distributed CB strategies for solving HDCOP. For the centralized strategy, we convert HDCOP into two sub-optimization problems, and propose a discrete cuckoo search (CS) algorithm for the node location selection optimization and a continuous CS algorithm to optimize the excitation current weights of the selected nodes. For the distributed strategy, we propose a parallel distributed CS algorithm to solve the discrete and continuous parts of HDCOP simultaneously. Moreover, we propose two operating mechanisms based on these two algorithms. Simulation results verify the effectiveness of the proposed strategies for reducing the maximum SLL of CB in WSNs. Moreover, the proposed CB strategies have better performance in terms of the energy efficiency compared with other approaches such as the cross-entropy optimization-based method.

Wireless power transfer based on charging unmanned aerial vehicles (CUAVs) is a promising method for enhancing the lifetime of wireless rechargeable sensor networks (WRSNs). However, how to deploy the CUAVs so that enhancing the charging efficiency is still a key issue. In this work, we formulate a CUAV deployment optimization problem (CUAVDOP) to jointly increase the number of the sensor nodes that within the charging scopes of CUAVs, improve the minimum charging efficiency in the network and reduce the motion energy consumptions of CUAVs. Moreover, the formulated CUAVDOP is analyzed and proofed as NP-hard. Then, we propose an improved firefly algorithm (IFA) to solve the formulated CUAVDOP. IFA introduces two improved items that are the attraction model and adaptive step size factor to enhance the performance of conventional firefly algorithm, so that making it more suitable for CUAVDOP. Simulation results demonstrate that the proposed algorithm is effective for the formulated joint optimization. Moreover, the performance of IFA is better than some other algorithms.

Mobile wireless sensor networks (MWSNs) are resource constrained, and have limited energy and transmission range. Distributed collaborative beamforming (DCB) in MWSNs based on a virtual node antenna array (VNAA) can increase the transmission distance and enhance the energy efficiency of a single sensor node. To achieve a lower maximum sidelobe level (SLL), sensor nodes can move to optimal locations with optimal excitation current weights for DCB. However, this leads to an extra motion energy consumption. In this article, we construct a multiobjective optimization framework (MOF) to jointly optimize the maximum SLL, transmission power, and motion energy consumption of the DCB nodes in MWSNs. Moreover, an improved nondominated sorting genetic algorithm-II (INSGA-II) and a distributed parallel INSGA-II (DPINSGA-II) are proposed for solving the formulated MOF. In addition, a simple but practical DCB scheduling mechanism is proposed. The simulation results show that the maximum SLL, transmission power, and motion energy consumption of the VNAA can be effectively optimized by the proposed algorithms.

Mobile wireless sensor networks (MWSN) are resource constrained, and have limited energy and transmission range. Distributed collaborative beamforming (DCB) in MWSN based on a virtual node antenna array (VNAA) can increase the transmission distance and enhance energy efficiency of a single sensor node. To achieve a lower maximum sidelobe level (SLL), sensor nodes can move to optimal locations with optimal excitation currents for DCB. However, this leads to an extra motion energy consumption. In this paper, we construct a multi-objective optimization framework to jointly optimize the maximum SLL, the transmission power and the motion energy consumption of the DCB nodes in MWSN. Moreover, an improved non-dorminated sorting genetic algorithm-II (INSGAII) is proposed for solving the optimization problem. Simulation results show that the maximum SLL, the transmission power and the motion energy consumption of the VNAA can be effectively optimized by the proposed algorithms.

A wireless rechargeable sensor network (WRSN) consists of sensor nodes that can harvest energy from the wireless charging nodes (WCNs) for prolonging the network lifetime. This study deals with the WCN deployment optimization problem in WRSNs. We present an optimization framework that simultaneously maximizes the coverage and the charging efficiency. Moreover, an improved firefly algorithm (IFA) is proposed for solving the WCN deployment optimization problem. IFA adopts a novel adaptive attractiveness factor and introduces a dynamic location update mechanism to enhance the performance of the normal firefly algorithm (FA). We compare the proposed IFA with several benchmark algorithms in two different scenarios. Simulation results show that the proposed algorithm outperforms other comparative algorithms in both accuracy and convergence rate.

A novel approach to suppress the maximum sidelobe level (SLL) with specific half power beam width (HPBW) of concentric circular antenna array (CCAA) is proposed. The approach is based on the cuckoo search (CS) algorithm, which is an effective optimization method for continuous problems. However, the sparse array synthesis is a discrete problem, so an improved discrete cuckoo search algorithm (IDCSA) is presented by introducing the nest location coding discretization, mapping method based on jumping path, and improved egg elimination mechanism, thereby optimizing the beam pattern of the CCAA. Simulation results show that IDCSA can obtain a lower maximum SLL with the same HPBW compared with other algorithms. Moreover, IDCSA has a faster convergence rate. In addition, the thinning rate of the antenna array can reach more than 50%, thereby resulting in cost savings after optimization.