The Internet of Things (IoT) is widely applied for forest monitoring, since the sensor nodes (SNs) in IoT network are low cost and have computing ability to process the monitoring data. To further improve the performance of forest monitoring, uncrewed aerial vehicles (UAVs) are employed as the data processors to enhance computing capability. However, efficient forest monitoring with limited energy budget and computing resource presents a significant challenge. For this purpose, this article formulates a multiobjective optimization framework to simultaneously consider three optimization objectives, which are minimizing the maximum computing delay, minimizing the total motion energy consumption, and minimizing the maximum computing resource, corresponding to efficient forest monitoring, energy consumption reduction, and computing resource control, respectively. Due to the hybrid solution space that consists of continuous and discrete solutions, we propose a diffusion-model-enhanced improved multiobjective grey wolf optimizer (IMOGWO) to solve the formulated framework. The simulation results show that the proposed IMOGWO outperforms other benchmarks for solving the formulated framework. Specifically, for a small-scale network with 6 UAVs and 50 SNs, compared to the suboptimal benchmark, IMOGWO reduces the motion energy consumption and the computing resource by 53.32% and 9.83%, respectively, while maintaining computing delay at the same level. Similarly, for a large-scale network with 8 UAVs and 100 SNs, IMOGWO achieves reductions of 41.81% in motion energy consumption and 7.93% in computing resource, with the computing delay also remaining comparable.

Cooperative reconfigurable intelligent surfaces (RISs) are promising technologies for 6 G networks to support a great number of users. Compared with the fixed RISs, the properly deployed RISs may improve the communication performance with less communication energy consumption, thereby improving the energy efficiency. In this paper, we consider a cooperative unmanned aerial vehicle-mounted RISs (UAV-RISs)-assisted cellular network, where multiple RISs are carried and enhanced by UAVs to serve multiple ground users (GUs) simultaneously such that achieving the three-dimensional (3D) mobility and opportunistic deployment. Specifically, we formulate an energy-efficient communication problem based on multi-objective optimization framework (EEComm-MOF) to jointly consider the beamforming vector of base station (BS), the location deployment and the discrete phase shifts of UAV-RIS system so as to simultaneously maximize the minimum available rate over all GUs, maximize the total available rate of all GUs, and minimize the total energy consumption of the system, while the transmit power constraint of BS is considered. To comprehensively solve EEComm-MOF which is an NP-hard and non-convex problem with constraints, a non-dominated sorting genetic algorithm-II with a continuous solution processing mechanism, a discrete solution processing mechanism, and a complex solution processing mechanism (INSGA-II-CDC) is proposed. Simulations results demonstrate that the proposed INSGA-II-CDC can solve EEComm-MOF effectively and outperforms other benchmarks under different parameter settings. Moreover, the stability of INSGA-II-CDC and the effectiveness of the improved mechanisms are verified. Finally, the implementability analysis of the algorithm is given.

Unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services close to mobile devices (MDs). However, meeting the demands of computation-intensive and delay-sensitive tasks for MDs poses several challenges, including the demand-supply contradiction between MDs and MEC servers, the demand-supply heterogeneity between MDs and MEC servers, the trajectory control requirements on energy efficiency and timeliness, and the different timescale dynamics of the network. To address these issues, we first present a hierarchical architecture by incorporating terrestrial-aerial computing capabilities and leveraging UAV flexibility. Furthermore, we formulate a joint computing resource allocation, computation offloading, and trajectory control problem to maximize the system utility. Since the problem is a non-convex mixed integer nonlinear programming (MINLP), we propose a two timescale joint computing resource allocation, computation offloading, and trajectory control (TJCCT) approach. In the short time scale, we propose a price-incentive method for on-demand computing resource allocation and a matching mechanism-based method for computation offloading. In the long time scale, we propose a convex optimization-based method for UAV trajectory control. Besides, we prove the stability, optimality, and polynomial complexity of TJCCT. Simulation results demonstrate that TJCCT outperforms the comparative algorithms in terms of the utility of the system, the QoE of MDs, and the revenue of MEC servers.

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.

The point of interest (POI) recommendation algorithm in location based social network (LBSN) can assist people to find more appealing locations and satisfy their specific demands. However, it is challengeable to infer user’s preference due to the sparsity of the user’s check-in data. To address the problem and improve recommendation performance, this paper proposes an improved context-aware weighted matrix factorization algorithm for POI recommendation (ICWMF). It takes advantage of time factor, geographical information, and social relationship to obtain user’s preference for locations. Firstly, the Ebbinghaus forgetting curve is employed to model the influence of time attenuation, so as to reflect that user preferences change over time. In order to assign dynamic weights to unvisited POI and infer user preference, we build the implicit feedback term by modeling the geographical influence from user perspective and the social relationship. In addition, the Gaussian model is employed to construct proximity location relationship to represent the probability of locations being discovered by users. Then, it is taken as the regularization term to avoid overfitting. Finally, the objective function of weighted matrix factorization is reconstructed with the implicit feedback term and the regularization term we designed. ICWMF naturally learns two potential feature matrices during weighted matrix decomposition based on new designed objective function to achieve better recommendation results. The results of simulation experiments on Brightkite and Gowalla dataset indicate that ICWMF outperforms other four comparison methods in terms of precision and recall.

Vehicular edge computing (VEC) is emerging as a promising architecture of vehicular networks (VNs) by deploying the cloud computing resources at the edge of the VNs. However, efficient resource management and task offloading in the VEC network is challenging. In this work, we first present a hierarchical framework that coordinates the heterogeneity among tasks and servers to improve the resource utilization for servers and service satisfaction for vehicles. Moreover, we formulate a joint resource allocation and task offloading problem (JRATOP), aiming to jointly optimize the intra-VEC server resource allocation and inter-VEC server load-balanced offloading by stimulating the horizontal and vertical collaboration among vehicles, VEC servers, and cloud server. Since the formulated JRATOP is NP-hard, we propose a cooperative resource allocation and task offloading algorithm named BARGAIN-MATCH, which consists of a bargaining-based incentive approach for intra-server resource allocation and a matching method-based horizontal-vertical collaboration approach for inter-server task offloading. Besides, BARGAIN-MATCH is proved to be stable, weak Pareto optimal, and polynomial complex. Simulation results demonstrate that the proposed approach achieves superior system utility and efficiency compared to the other methods, especially when the system workload is heavy.

Unmanned aerial vehicles (UAVs) are playing a pivotal role in wireless networks due to their high mobility and on-demand deployment advantages. However, the UAV-enabled communications are susceptible to be wiretapped by eavesdroppers due to the strong line-of-sight (LoS) dominated air-ground channel. In this paper, we consider a UAV-enabled secure communication scenario, in which a group of UAVs form a UAV-enabled virtual antenna array (UVAA) to transmit information towards the remote base stations (BSs) via collaborative beamforming (CB), while multiple known and unknown eavesdroppers aiming to wiretap the information. Specifically, a secure communication multi-objective optimization problem (SCMOP) is formulated to achieve the maximization of the worst-case secrecy rate, the minimization of the maximum sidelobe level (SLL) as well as the minimization of the flight energy consumption of UAVs by obtaining optimal locations and excitation current weights concerning the UAVs as well as determining an optimal receiver BS that can achieve superior communication performance. To solve the formulated SCMOP which is demonstrated to be non-convex and NP-hard, an improved multi-objective salp swarm algorithm (IMSSA) with several specific operating factors is proposed. Simulations results demonstrate that the proposed IMSSA can deal with the formulated SCMOP effectively and outperforms other benchmark strategies. Moreover, the multi-hop relay is introduced to verify the reasonability of the UVAA system, and two benchmark schemes of the formulated SCMOP are introduced to demonstrate the necessity of the formulated SCMOP. In addition, the performance of the UVAA system under certain unexpected circumstances is estimated. Finally, experimental implementation is conducted by using a Raspberry Pi and the results demonstrate the practicality of the proposed CB-based secure communication approach in real-world scenarios.

Unmanned aerial vehicles (UAVs)-aided device-to-device (D2D) networks have attracted great interests with the development of 5G/6G communications, while there are several challenges about resource scheduling in UAVs-aided D2D networks. In this work, we formulate a UAVs-aided D2D network resource scheduling optimization problem (NetResSOP) to comprehensively consider the number of deployed UAVs, UAV positions, UAV transmission powers, UAV flight velocities, communication channels, and UAV-device pair assignment so as to maximize the D2D network capacity, minimize the number of deployed UAVs, and minimize the average energy consumption over all UAVs simultaneously. The formulated NetResSOP is a mixed-integer programming problem (MIPP) and an NP-hard problem, which means that it is difficult to be solved in polynomial time. Moreover, there are trade-offs between the optimization objectives, and hence it is also difficult to find an optimal solution that can simultaneously make all objectives be optimal. Thus, we propose a non-dominated sorting genetic algorithm-III with a Flexible solution dimension mechanism, a Discrete part generation mechanism, and a UAV number adjustment mechanism (NSGA-III-FDU) for solving the problem comprehensively. Simulation results demonstrate the effectiveness and the stability of the proposed NSGA-III-FDU under different scales and settings of the D2D networks.

In this paper, we propose collaborative beamforming (CB) in unmanned aerial vehicle (UAV)-assisted communication networks to improve transmission data rate with minimum energy consumption. Specifically, CB allows a group of UAVs forming a virtual element antenna array (VEAA) and transmitting data collaboratively in a synchronous manner through a high-gain mainlobe (ML) beam. The goal is to optimize the deployment locations of UAVs in the VEAA and excitation current weights for performing CB transmissions considering the energy cost for UAV deployment. Accordingly, we formulate an Energy-Efficient Communication Multi-objective Optimization Problem (EECMOP) to jointly maximize the transmission rate and minimize the maximum sidelobe level (SLL) as well as UAV energy consumption. Then, we propose an Enhanced Multi Objective Ant Lion Optimizer (EMOALO) algorithm which incorporates a chaos theory to develop chaotic initialization and adjustable mode operators for solving the problem. Simulation results demonstrate the effectiveness of the EMOALO algorithm in improving energy efficiency for UAV-assisted communication networks.

Unmanned aerial vehicle (UAV)-enabled wireless powered communication networks (WPCNs) are promising technologies in 5G/6G wireless communications, while there are several challenges about UAV power allocation and scheduling to enhance the energy utilization efficiency, considering the existence of obstacles. In this work, we consider a UAV-enabled WPCN scenario that a UAV needs to cover the ground wireless devices (WDs). During the coverage process, the UAV needs to collect data from the WDs and charge them simultaneously. To this end, we formulate a joint-UAV power and three-dimensional (3D) trajectory optimization problem (JUPTTOP) to simultaneously increase the total number of the covered WDs, increase the time efficiency, and reduce the total flying distance of UAV so as to improve the energy utilization efficiency in the network. Due to the difficulties and complexities, we decompose it into two sub optimization problems, which are the UAV power allocation optimization problem (UPAOP) and UAV 3D trajectory optimization problem (UTTOP), respectively. Then, we propose an improved non-dominated sorting genetic algorithm-II with$K$-means initialization operator and Variable dimension mechanism (NSGA-II-KV) for solving the UPAOP. For UTTOP, we first introduce a pretreatment method, and then use an improved particle swarm optimization with Normal distribution initialization, Genetic mechanism, Differential mechanism and Pursuit operator (PSO-NGDP) to deal with this sub optimization problem. Simulation results verify the effectiveness of the proposed strategies under different scales and settings of the networks.

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.

Visible light communication (VLC) based on unmanned aerial vehicles (UAVs) can simultaneously transmit data and lighting, which has been considered as a promising technology for the next generation wireless networks. In this paper, we construct a system consisting of UAV elements to fairly communicate with receiving plane. However, the unreasonable layout of UAVs may lead to the uneven distribution of the received optical power on the same receiving plane, which cannot guarantee the fairness of the communication between the UAVs and receiving plane. Besides, the transmission power of the UAVs has direct effects on the strength of the received optical signal in VLC communication. Therefore, we formulate an optical power coverage optimization problem (OPCOP) to achieve more uniform received optical power coverage by jointly considering the positions and appropriate power adjustment factor of transmission power of UAVs. Then, an improved cuckoo search with c haotic solution initialization operation and m utation mechanisms (ICSCM) algorithm is proposed to solve the formulated optimization problem. ICSCM introduces the chaotic solution initialization operation for increasing the performance of initial solution, and employs two mutation mechanisms, which are mutation operator of differential evolution (DE) algorithm and Gaussian perturbation to enhance the exploration ability of conventional cuckoo search (CS). Simulations are conducted and the results verify that the received optical power of receivers distributed on the same receiving plane obtained by ICSCM can be more uniform than other comparison methods.

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.

In recent years, the dramatic increase in vehicles and the limited resources of VEC servers make it challenging for vehicles to execute intensive and sensitive tasks on the local own CPU. The mobile edge computing (MEC) is viewed as a promising paradigm by deploying the cloud resources on roadside road side units (RSU). However, compared to cloud server, MEC servers have limited resources. Moreover, the vehicular tasks with different priorities have different requirements on the edge resources. In this work, we propose a priority -aware collaborative task offloading and resource allocation approach for vehicular edge computing networks (VECN). Specifically, we propose a variant grey wolf optimizer (VGWO) algorithm for resource optimization and a dynamic task offloading strategy (DOS) algorithm for task offloading. Simulation results show that the proposed VGWO algorithm outperforms the basic swarm intelligence optimization algorithm, and the collaborative offloading method is able to effectively reduce the task processing latency and energy consumption.

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) 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.

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) 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.

Unmanned aerial vehicle (UAV) communications and networks are promising technologies in the forthcoming fifth-generation wireless communications. However, they have the 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 consumptions. We formulate a secure communication multi-objective optimization problem (MOP) of UAV networks to simultaneously improve the total secrecy rates, total maximum sidelobe levels (SLLs) and total motion energy consumptions 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) to solve the problem. Simulation results verify that the proposed IMODACH can effectively solve the formulated MOP and it has better performance than some other benchmark approaches.

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.

Unmanned aerial vehicles (UAVs) are usually resource constrained, and have the limited communication and energy storage capacity. Collaborative beamforming (CB) in UAV networks based on a virtual node antenna array (VNAA) can enhance the signal-to-noise-ratio (SNR) and energy efficiency of a single UAV node. The UAV nodes can move to better locations for constructing the VNAA to achieve a maximum SNR of CB. However, this will result in an extra motion energy consumption. In this paper, we formulate a joint optimization problem to simultaneously optimize the received SNR and motion energy consumption of UAVs for CB. Then, a mended particle swarm optimization with weed optimization mechanism (PSOWOM) algorithm is proposed to solve the formulated joint optimization problem. Simulation results verify the effectiveness of the proposed algorithm.

In the communication of devices based on C-V2X, packet error rate (PER) is an important metric to measure the communication performance of a device. As for the packet loss phenomenon, we usually focus on why the receiver did not successfully receive the message, and rarely focus on whether the transmitter actually sent the message.We usually consider the messages sending situation recorded by the application layer as the messages that should be received by the receiver (packets that are known to be not sent by the application layer due to application layer congestion control, etc., are not in the scope of this paper). However, in the actual communication process, there are some discrepancies between the real packets sent from the bottom layer and the application layer's records. In the 2020 C-V2X Large-scale Pilot Demonstration, when we analyzed the results and calculated the received PER of the devices, we were confused whether some of the devices did not send all the packets successfully. Based on this confusion, we defined the concept of transmitter traffic to represent the actual packet sending situation of the device. We designed a method to calculate transmitter traffic by using the "large-scale" data available, and conducted statistics on the transmitter traffic of more than 40 terminal companies, more than 10 chip module companies, and more than 50 participating devices. We analyzed the statistical results, and analyzed the possible reasons for the unsuccessful transmitter traffic.

Anonymisation technique has been extensively studied and widely applied for privacy-preserving data publishing. However, most existing methods ignore personal anonymity requirements. In these approaches, the microdata consist of three categories of attribute: explicit-identifier, quasi-identifier and sensitive attribute. In fact, the data sensitivity should be determined by individuals. An attribute is semi-sensitive if it contains both QI and sensitive values. In this paper, we propose a novel anonymisation approach, called local anatomy, to address personalised privacy protection. Local anatomy partitions the tuples who consider the value as sensitive into buckets inside each attribute. We conduct some experiments to illustrate that local anatomy can protect all the sensitive values and preserve great information utility. Additionally, we also present the concept of intelligent anonymisation system as our direction of future work.

Unmanned aerial vehicle (UAV) communications and networks are of utmost concern. However, they have challenges such as the limited on-board energy and restricted transmit power. In this paper, we study a UAV-enabled communication scenario that a set of UAVs perform a virtual antenna array (VAA) to communicate with different remote base stations (BSs) by using collaborative beamforming (CB). To achieve a better transmission performance, the UAV elements can fly to optimal positions by using optimal speeds and adjust to optimal excitation current weights for performing CB transmissions. However, there are some trade-offs between energy consumption and transmission performance. Thus, we formulate a time and energy minimization communication multi-objective optimization problem (TEMCMOP) of CB in UAV networks to simultaneously minimize the total transmission time, total performing time of VAAs and total motion and hovering energy consumptions of UAVs by jointly optimizing the positions, flight speeds and excitation current weights of UAVs, as well as the order of communicating with different BSs. Due to the complexity and NP-hardness of the formulated TEMCMOP, we propose an improved multi-objective ant lion optimization (IMOALO) algorithm with chaos-opposition based learning solution initialization and hybrid solution update operators to solve the problem. Simulation results verify that the proposed IMOALO can effectively solve the formulated TEMCMOP and it has better performance than some other benchmark approaches.

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.

Vehicular ad hoc networks (VANETs) can improve the safety during the traffic by enabling cooperative communication among the vehicles. The media access control (MAC) protocol should be well designed so that cooperative messages can be exchanged efficiently and reliably. Because vehicles move fast on the road, the network topology changes rapidly, which makes it harder to design the MAC protocol. This article introduces SCMAC, a slotted-contention-based time-division multiple access MAC protocol. SCMAC combines the advantages of the contention-based protocols and the contention-free protocols, and hence, can accommodate different traffic densities and channel conditions. Each time slot is divided into two periods: 1) reservation period (RP) and 2) transmission period (TP) in the protocol. Nodes compete in the RP to confirm whether the channel can be used before the transmission can take place in the TP. Analysis and simulation results are also presented to evaluate the performance of SCMAC in various scenarios. The results show that SCMAC can adapt different traffic densities and channel conditions and can provide more real time and efficient services compared to the other protocols.

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.

Conventional collaborative beamforming with virtual node antenna array often results in high maximum sidelobe level (SLL) due to the unexpected node positions. In this paper, a hybrid optimization approach (HOA) for the SLL suppression and transmission power reduction is proposed. The proposed HOA organizes the node locations according to the concentric circular antenna array for location optimization. Then, a novel algorithm called variation particle chicken swarm optimization (VPCSO) is proposed to further optimize the transmission power weight of the selected array nodes. Simulations are conducted and the results show that the proposed location optimization approach is effective, and the maximum SLL of the beam patterns obtained by VPCSO is lower than that of other algorithms. Moreover, the overall transmission power weights obtained by the proposed VPCSO is the lowest among all the comparison methods.

Antenna arrays can enhance the directivity and save the transmission power of a communication system. Beam pattern optimization for reducing the maximum sidelobe level (SLL) is a classical electromagnetic problem in antenna arrays. In this paper, a novel improved chicken swarm optimization (ICSO) algorithm is proposed to suppress the maximum SLL of the linear antenna array (LAA), the circular antenna array (CAA) and the random antenna array (RAA). Three improved factors that are the global search, the weighting and the local search factors are introduced into the update method of the roosters, the hens and the chicks of the conventional chicken swarm optimization (CSO), respectively, to achieve better optimization results. Simulations are conducted to verify the performance of the proposed ICSO for suppressing the maximum SLL, and the results show that the proposed ICSO can obtain lower maximum SLL in LAA, CAA and RAA cases compared with several benchmark algorithms. Moreover, the stability of ICSO is evaluated and the results show that it outperforms the other algorithms.

Research on discovering the community structure has become a popular issue in the field of network analysis.In this paper, an improved discrete bat algorithm is proposed to solve the community detection problem. First, an ordered adjacent list method is used to encode the position of bat for population initialization. In the proposed method, Modularity is applied as the objective function. All the bats are divided into some groups based on their fitness, and the bat position in each group is updated based on operators we defined. It will expand the search area and improve the diversity of population. Local optimal solution and global optimal solution can be generated through strategy of dividing and merging bat position. Simulations and comparison results based on synthetic and real networks are performed to prove the effectiveness and accuracy of the proposed method in detecting community structures in networks.

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.

The sparse synthesis of the concentric circular antenna array (CCAA) is a very important technology because it is able to reduce the cost of the antenna array. In this paper, we first formulate a multi-objective optimization problem to jointly reduce the maximum sidelobe level (SLL) and the number of the switched-on elements of the CCAA. Then, we propose a novel enhanced non-dominated sorting genetic algorithm-II (ENSGA-II) to solve this problem. ENSGA-II introduces a hierarchy mechanism to improve the population utilization of the conventional non-dominated sorting genetic algorithm, thereby enhancing the accuracy and the convergence rate of the algorithm. Simulation results show that ENSGA-II obtains a lower maximum SLL with the similar number the switched-off elements compared with other algorithms. Moreover, ENSGA-II has a faster convergence rate.

Distributed cooperative control requires that every participant shares a consistent view of objectives and the world. Information is periodically disseminated over a noisy time-varying network topology so that all the agents asymptotically converge to a common value. However, the strict global consensus is of excessive resource consumption and not mandatory for the majority of coordination tasks. To better satisfy such quantitative requirements of consensus in the practical multi-agent systems, this paper proposes a real time and distance-driven consensus quantification model especially for C-ITS applications. This model encodes agents' spatial location distribution into their mutual consensus quantification through introducing their inter-distance into consensus calculation. Accordingly, this paper proposes a distance-driven-consensus-based power adaptive control method as a practical use case of the quantitative framework of consensus, by which agents can autonomously optimize the transmit power through balancing the desired consensus benefit and power cost according to the real timely predicted local consensus. We perform extensive numerical calculations to investigate the effectiveness and the applicability of the consensus quantification framework and the power adaptive control method. The results show that the model can effectively capture the real time consensus fluctuation as the multi-agent systems evolve and can provide reliable decision basis to cooperative control, in such way to restrict the consensus extent to a target value and to tradeoff between the anticipated consensus level and the paid cost accordingly.

Generalization is an effective technique for protecting confidential information of individuals, and has been studied by proposing numerous algorithms. However, the previous works do not separate the protection against identity disclosure and sensitive disclosure. Thus, when the requirement of attribute protection is higher than that of identity protection, generalization for l-diversity causes overprotection for identity and large mounts of information utility loss. This paper presents a novel approach, called cross-bucket generalization, as a solution to meet the problem. The rationale is to divide microdata into equivalence groups and buckets. First, it provides separate protection for identity and sensitive values, and the level of protection can be flexibly adjusted based on actual demands. Second, the sizes of equivalence groups and buckets are minimized as far as possible by only satisfying the protection requirements, which avoid the overprotection for identity and reduce information loss. The experiments we conducted illustrate the effectiveness of our solution.

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.

Abstract The Notice of Proposed Rulemaking 13‐22 released by Federal Communications Commission unlocks the Dedicated Short Range Communication (DSRC) spectrum for Wi‐Fi availability, which undoubtedly brings unpredictable effects to the new‐emerging vehicular applications and services. To efficiently harmonize the spectrum operation between DSRC and Wi‐Fi networks, several dynamic spectrum‐sharing schemes are already proposed to improve the spectral efficiency over a limited bandwidth situation and as well to satisfy the ever‐increasing demand for bandwidth resource. Different from most previous literature that mainly focused on the performance analysis of cellular‐network‐centric spectrum sharing, we aim to analyze the performance of the mainstream dynamic spectrum‐sharing schemes specially designed for the coexistence of DSRC and Wi‐Fi networks against various combinations of network parameters through a hybrid network model and performance indicators. We employ the Poisson point process to model a hybrid network where DSRC vehicles and Wi‐Fi devices coexist, and introduce the performance indicators of spectrum efficiency and data rate to assess the utility of different spectrum sharing candidates. Through the presented hybrid model and performance indicators, we collect extensive numerical and simulation results to investigate four typical spectrum allocation schemes for DSRC and Wi‐Fi coexistence, that is non‐sharing scheme, original sharing scheme, and Qualcomm's and Cisco's proposals, respectively. The results show that the dynamic spectrum sharing in the 5.9‐GHz band can significantly raise the performance of Wi‐Fi network without excessively degrading the DSRC system, and especially the Cisco's proposal prefers to protect the DSRC profit while the Qualcomm's draft favors Wi‐Fi exclusively. Copyright © 2016 John Wiley & Sons, Ltd.

Information exchange is not easily secured in the emergency cases where the normal telecommunication infrastructure might have been collapsed. When vehicles are moving on a highway, communications between the vehicles and the base stations always result in a high delay that causes a vehicle to fail to verify all the messages received from the neighbors in real time. These situations may result in message losses and even security risks. To address these issues, we propose a scheme that combines the technologies of trusted network connect and multi-secret sharing to securing authentication and verification for ad hoc vehicles SAV4AV, in which a new vehicle is permitted to flexibly join in a platoon through collaborating with t existing vehicles and thereby to accomplish identity authentication and integrity verification. We list several possible attacks and provide a detailed security analysis on how to avoid these threats in SAV4AV. Moreover, we perform extensive simulations to investigate the performance of SAV4AV against various network scenarios with respect to time consumption and network throughput. Copyright © 2014 John Wiley & Sons, Ltd.

In order to effectively determine whether a vehicle is turning or not, we proposed a method to map arbitrary consecutive GPS heading information to 2 dimensional feature space. Then we applied K-means clustering algorithm to divide the feature space into 2 classes: going straight and turning. After that, we used supervised learning algorithm to analyze these labeled data and build a model to recognize vehicle moving state. The experimental results showed that the model built in this way has good generalization. Based on the above research achievement, we designed and implemented a vehicle moving state recognition learning system for dynamic in-car navigation systems and applied this learning system to the map-matching field. The improved map-matching algorithm was tested on a complex urban road network and the result showed that the new algorithm can significantly improve the performance of the junction match.

This paper presents a heterogeneous network interconnection model based on a kind of translucent routing gateway and Linux virtual device to tackle with the communication issue about heterogeneous communication between various devices and Internet in the vehicular networks. In this model, an in-vehicle CAN node is regarded as a usual node in the Internet and will be assigned an IPv6 address and a MAC address as well. This mechanism will make the external terminals connect to these nodes without the perception of the in-vehicle gateway, which will implement the remote condition monitoring and fault diagnosis on a specified unit in the in-vehicle networks. Experiment result shows that this model may establish the communication between the nodes and various devices in the heterogeneous networks.

Order-k Markov Model can be used in many fields such as Natural Language Understanding, Coding, Mobile Path Prediction and so on to make prediction and then control. But the model has to face the problem of state space expansion. Taking the mobile path prediction as the research background, the paper firstly proposes a Step-k Markov model and validates its feasibility. Secondly, a hybrid Markov predictor model is put forward based on the Step-k Markov model. The complexity of the Hybrid Markov Model is O(N) while the Order-k Markov model is O(N2). And the memory demand of the hybrid Markov model is O(N2) while Order-k Markov model is O(N3). Finally, it is proved that the hybrid Markov predictor can get close performance with Order-k Markov Predictor at much lower expense by conditional entropy analysis and user mobility data analysis. Also, it can alleviate the zero probability problem in Order-k Markov model to some extent. The hybrid Markov predictor is more practical than Order-k Markov predictor under WLAN.

Path prediction is an important issue in QoS of wireless networks. The paper points out problems in some existed path prediction schemes, especially the state space expansion problem in order-k Markov predictor. And it firstly proposes a step-k Markov model and validates its feasibility. Secondly, a hybrid Markov predictor model and its improved models are put forward based on the step-k Markov model. Because of the order-2 Markov model's best performance in order-k Markov models, the Hybrid Markov model takes the order-2 Markov model as its target. The state space's complexity of the Hybrid Markov Model is 0(N) while the order-2 Markov model is O(N2). And the memory demand of the hybrid Markov model is O(N2) while Order-2 Markov model is O(N3). Finally, it is proved that the hybrid Markov predictor can get close performance with order-2 Markov predictor at much lower expense by conditional entropy analysis and user mobility data analysis. Also it can alleviate the zero probability problem in order-k Markov model to some extent. The hybrid Markov predictor is more practical than order-k Markov predictors under WLAN.