Semantic segmentation of high-resolution remote sensing imagery plays a critical role in applications such as disaster assessment. However, deploying large models on Autonomous Aerial Vehicles (AAVs) remains challenging due to inherent conflicts among accuracy, model size, and computational efficiency. To address these challenges, we propose FMC-ULite, a novel lightweight architecture designed to achieve a better balance between accuracy and efficiency for real-time processing. Our model incorporates four key innovations, including a Fast Fourier Transform (FFT)-based fusion module for enhanced edge feature extraction and noise suppression in the frequency domain, a simplified MobileNetV3-Large encoder that substantially reduces parameter count, a cross-layer feature fusion (CLFF) module to effectively integrate multi-scale semantic and detail information, and an attention-gated decoder with multi-scale dilated convolutions to prioritize critical disaster regions. Furthermore, an adaptive combined loss function is introduced to alleviate class imbalance. Experiments conducted on the RescueNet dataset show that our model achieves competitive accuracy compared to advanced lightweight methods under a comparable parameter budget, demonstrating its strong suitability for real-time disaster assessment using AAVs.

Vehicular Edge Computing (VEC) leverages promising technologies, namely the vehicle-to-vehicle (V2V) computation offloading approach and edge service caching, to address latency-sensitive tasks. The V2V offloading method efficiently harnesses idle resources from neighboring vehicles. Edge service caching facilitates the offloading task through pre-caching pertinent service data. However, formulating an efficient caching mechanism to support V2V offloading poses significant challenges, given the dynamic vehicle environment, varying computational resources, and limited caching resources of Roadside Units (RSUs). This paper introduces a collaborative caching and offloading (CACO) scheme. First, to mitigate resource wastage caused by inter-vehicle communication interruptions, we employ Generative Adversarial Network (GAN) for trajectory prediction. This process generates a relationship matrix, predicting the stability of inter-vehicle link connections to assist in V2V offloading decisions. Second, to circumvent redundant uploads and computations for recurring offloading tasks, we analyze the popularity of historical offloading tasks using the Page-Hinkley test (PHT) technique, caching frequently offloaded tasks to reduce the processing latency of offloading tasks. Subsequently, a matching scheme for caching and offloading contents is devised. Finally, the Deep Reinforcement Learning (DRL) algorithm is employed to train the offloading strategy. Results from extensive experiments substantiate that CACO attains superior performance in both system computational latency and offloading success rate.

Recently, fixed-wing unmanned aerial vehicles (UAVs) are able to extend the communications mission time and ease of deployment due to their powerful onboard capabilities and flexibility, and reflective intelligent surfaces (RISs) are capable of reflecting links to avoid obstacles and thus improve channel gain. Therefore, RIS-assisted fixed-wing UAVs are widely used in wireless communications. Nevertheless, fixed-wing UAVs have limited energy, so improving energy efficiency is critical. This article focuses on energy efficiency optimization problems under RIS-assisted fixed-wing UAV communications. In communications coverage systems, the flight trajectory of fixed-wing UAVs and service scheduling to ground nodes (GNs) significantly impact energy efficiency. Existing work often adopts circular trajectory and traditional deep reinforcement learning (DRL) algorithms for optimizing trajectory and service scheduling. However, circular trajectory can not adapted to the GN distribution well. In addition, the traditional DRL algorithm has two drawbacks: 1) the efficiency of exploring the empirical process is low and 2) the accuracy of handling the hybrid action space needs to be higher. Thus, we propose the midpoint iteration convex hull (MICH) algorithm based on the Graham scan to design trajectories that can be adapted to the distribution of the GNs. In addition, we propose the action screening virtual and real experience (AS-VRE) mechanism and the N-steps hybrid deep Q and policy network (NsHQPN) algorithm to address the low-exploration efficiency and the low-fetch accuracy in handling the hybrid action space. Experiments show that our proposed MICH algorithm, AS-VRE mechanism, and NsHQPN algorithm can effectively improve the system energy efficiency and outperform other baseline schemes.

Reconfigurable-intelligent-surface (RIS)-assisted autonomous aerial vehicles (AAVs) communications technology improves energy efficiency by reflecting signals. This article utilizes RIS and deep reinforcement learning (DRL) to optimize the scheduling of ground terminals (GTs), AAV trajectories, resource allocation, and time slot lengths to maximize system energy efficiency. Three flaws of the existing DRL algorithm are also addressed to seek higher energy efficiency further. First, DRL faces exploration challenges due to the complexity of the solution space, resulting in low rewards. We propose the ant colony DRL (ACDRL) algorithm, which optimizes the scheduling order of the GTs using the ant colony optimization (ACO) algorithm and feeds the results back to the DRL to optimize the subsequent decision making, thus reducing the exploration overhead. Second, to reduce the degree of local optimization when dealing with hybrid action space planning, we propose a hybrid discrete-continuous DRL (HDCDRL) algorithm to improve action accuracy. Finally, to better generalize the model to similar tasks, we propose the transfer-DRL (T-DRL) model to reduce the training time when the task changes. Experimental results show that our proposed solution outperforms the benchmark solution.

Satellite-terrestrial integrated networks have recently gained substantial interest due to their exceptional coverage, lower transmission delay, robust storage, and computing power. However, existing task offloading schemes often fail to effectively manage task dependencies, resulting in incorrect execution sequences, increased end-to-end delay, and excessive energy consumption. To address these challenges, we propose a dependency-aware task offloading framework for jointly optimizing delay and energy consumption in satellite-terrestrial collaborative networks with mobile-edge computing (MEC). First, we construct a directed acyclic graph (DAG) based dependency-aware task offloading framework aimed at reducing delay and energy consumption. Second, to reduce the frequency of low Earth orbit (LEO) satellite access, we design a cluster head selection strategy (CHSS), which leverages DAG-based task dependencies to optimize the association between Internet of Things (IoT) devices and LEO satellites. Finally, we formulate system delay and energy consumption as a cost-minimization problem, modeling it as a Markov decision process (MDP). We also propose a novel hybrid deep reinforcement learning (DRL) algorithm to effectively handle DAG structures and optimize task offloading decisions, thereby minimizing the total cost. Extensive simulation results confirm the effectiveness of the proposed method, demonstrating that the proposed algorithm significantly outperforms others by reducing system delay by 18.07% and decreasing energy consumption by 21.15% on average, respectively.

With the development of artificial intelligence technology, Autonomous aerial vehicles (AAV) have the ability to sense the environment. multiple object tracking (MOT) in AAV video is a very important vision task with a wide variety of applications. However, there are still many challenges in MOT in AAV video. First, the movement of the onboard camera in the three-dimensional (3-D) direction during the tracking process, as well as the unpredictable measurement noise characteristics of AAVs flying at high speeds, can lead to significant deviations in the prediction of the object’s position. Second, the applicability of the traditional detection algorithm decreases when the object is small and dense in the AAV viewpoint during detection. Finally, the traditional intersection over union (IoU) matching approach does not take into account the effects of the height and width of the box, and the matching results are inaccurate for the prediction and detection box. In order to address these challenges, we recommend the adaptive DeepSort (ADT) algorithm to reduce the prediction bias due to camera movement and difficulty in predetermining measurement noise characteristics, the hybrid attention transformer-YOLOv8 (HAT-YOLOv8) algorithm to enhance the detection capability of tiny objects, and the IoU of height and width (HWIoU) matching algorithm, which improves the matching accuracy and thus the tracking accuracy. Experimental results show that our proposed solution outperforms the baseline solution. It outperforms the current mainstream StrongSort in MOTA, HOTA and IDF1 by 2.86%, 0.9%, and 9.36%. Code repository link:https://github.com/networkcommunication/.

The flocking movement is a fundamental and crucial operation in multi-AAVs systems, encompassing navigation and obstacle avoidance. However, traditional flocking algorithms typically rely on rigid rules and exhibit limited adaptability to diverse environments. Reinforcement learning (RL) effectively addresses this issue as a flexible and model-free framework. In this article, RL techniques are utilized to achieve navigation and obstacle avoidance for a swarm of AAVs. To enhance training efficiency, we propose an improved algorithm called heuristic guides TD3 (HGTD3) by integrating heuristic guides with the twin delayed deep deterministic policy gradient (TD3), aiming to address the protracted learning periods commonly observed in traditional RL methods. Considering the network-constrained environment, we propose the negative interference flocking algorithm (NIFA): the network interference flocking algorithm and an AAV flocking algorithm designed based on the sparrow search algorithm. NIFA can guide the losing AAV to follow the swarm and at the same time maintain the overall navigation and avoidance efficiency. Finally, we demonstrate the scalability and adaptability of HGTD3-NIFA in a simulation experiment in terms of multi-AAVs flocking and navigation.

Autonomous aerial vehicles (AAVs) have found widespread utility in the field of multi-target tracking (MTT) due to their inherent advantages, such as ease of deployment, flexible maneuverability, and cooperative communication capabilities. AAVs can perform tasks ranging from regional surveillance to tracking and search-and-rescue operations in hazardous environments. Nonetheless, the issue of how to efficiently coordinate multiple AAVs to track diverse mobile targets remains a critical concern. This paper focuses on MTT with multi-AAV, aiming at optimizing system performance in scenarios where network availability is limited. In contrast to treating sensor perception as a monolithic process, we propose a scheme for cooperative sensing and data processing. This scheme is designed to reduce system response latency in environmental information sensing and multidimensional data processing for multiple AAVs. Furthermore, in contrast to assuming linear target movement and single-step target position prediction, we introduce a multi-agent deep reinforcement learning (MADRL) framework combined with multi-step prediction extended Kalman Filter (MP-EKF). This framework is tailored to enhance tracking precision, especially when targets’ trajectories are curved, which can reduce AAV flight displacement if the target’s position can be predicted multiple steps later. In addition, unlike using latency as a real-time application to measure the “freshness” of information, the Age of Information (AoI) is introduced for considering the waiting time of transmission and calculation between multiple AAVs. This assessment method is utilized to comprehensively evaluate and mitigate data latency within the MADRL algorithm. Finally, extensive simulation experiments demonstrate that the proposed scheme significantly outperforms both baseline methods and state-of-the-art approaches in terms of AoI, system latency, and energy consumption.

Unmanned aerial vehicles(UAVs), or drones, are garnering considerable focus in the realm of wireless communications research because to their notable characteristics, including exceptional mobility, versatile deployment capabilities, and robustness in maintaining line-of-sight (LoS) links. This paper studies a UAV-assisted bidirectional relaying system for multi-pair user devices (UDs), where a rotary-wing UAV is used to serve as a mobile relay for providing information transmission between UDs belonging to a pair on the ground. The UDs in a pair communicate with each other via the UAV relay employing the physical-layer network coding (PNC) technique. To trade off fair communications with system energy consumption, we jointly optimize transmission scheduling and association, UAV relay and UD transmission power, and UAV trajectory to maximize system energy efficiency during UAV relay communications. Due to the formulated problem being mixed-integer and nonconvex programming, it proves to be excessively complex to solve. For ease of solution, this problem is initially decomposed into three sub-problems. Next, by adopting the block coordinate descent (BCD) method, the successive convex approximation (SCA) method, and the Dinkelbach method, an efficient iterative algorithm is proposed that alternately solves variables of each sub-problem while fixing others. The numerical results demonstrate that our designed scheme is capable of substantially improving the system energy efficiency in comparison with other baseline schemes and benchmark schemes.

Vehicle-to-Everything (V2X) technology plays a pivotal role in enabling real-time traffic coordination and safety, warning, and decision support. Within V2X, the Basic Safety Message (BSM) serves as the core to transmit critical vehicle status, location, and intention information to provide a foundation for ensuring reliable traffic safety and coordination mechanisms. Data accuracy stands as a key to the effectiveness and reliability of the V2X system, in which the transmission of error data can potentially result in severe traffic accidents. During vehicular operation, sensors may generate error data owing to looseness or external conditions. However, immediate sensor replacement is often impractical or infeasible. Therefore, this paper introduces a collaborative scheme involving vehicles, Road Side Units (RSUs), and Data Center (DC) to jointly enhance the accuracy of vehicle-transmitted BSMs. Our scheme involves analyzing statistical features of vehicle driving information to detect error BSMs. Subsequently, these detected errors are corrected by leveraging historical data from the vehicle and its relative relationship with surrounding vehicles. In addition, we propose a time optimization method to reduce the average processing time of each data by RSUs. The extensive experimental results demonstrate that the proposed scheme can accurately detect error BSMs and effectively correct error BSMs. The entire scheme also meets the requisite computational latency requirements.

The emergence of unmanned aerial vehicles (UAVs) ushers in a new era for mobile edge computing (MEC), significantly expanding its range of service and potential applications. Due to the limited storage capacity and energy budget of UAVs, it is crucial to determine a reasonable service caching and task offloading strategy. Service caching means that task-related programs and the associated databases are cached on edge servers. In this paper, we consider the time latency and energy consumption caused by frequent changes to the service caching, aiming to jointly optimize the computational offloading, resource allocation, and service caching in multi-UAV assisted MEC systems at different time scales. The objective of this optimization is to reduce the overall system delay while staying within the energy limitations of both the UAVs and ground devices. An improved service caching policy (SCP) is proposed, which is based on task popularity and utilizes the greedy dual size frequency (GDSF) algorithm. The SCP is combined with the twin delayed deep deterministic policy gradient (TD3) algorithm to propose an innovative dual timescale TD3 (DTTD3) algorithm. The numerical outcomes obtained from a substantial number of simulation experiments demonstrate that DTTD3 outperforms existing benchmark methods in terms of convergence and parameter optimization.

With the proliferation of mobile applications, the explosion of mobile data traffic imposes a significant burden on backhaul links with limited capacity in heterogeneous cellular networks (HetNets). To alleviate this challenge, content caching based on popularity at Small Base Stations (SBSs) has emerged as a promising solution. However, accurately predicting the file popularity profile for SBSs remains a key challenge due to variations in content characteristics and user preferences. Moreover, factors such as content size and the length of time slots (that is, the time duration of the update cycle for SBSs) critically impact the performance of caching schemes with limited storage capacity. In this paper, arealism-orientedintelligent caching (RETINA) is proposed to address the problem of content caching with unknown file popularity profiles, considering varying content sizes and time slots lengths. Our simulation results demonstrate that RETINA can significantly enhance the cache hit rate by 4%–12% compared to existing content caching schemes.

Multi-Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) technology can provide users with more flexible and efficient computing services. Most of the existing research works realize multi-UAV collaboration through single-hop or multi-hop task relaying. Although the delay and energy consumption incurred in transmitting mission results to the destination node is relatively small, the UAV node changes dynamically and the result feedback should not be ignored. At the same time, when the remaining power of the UAVs swarms is unevenly distributed, when one UAV runs out of power, the users in the area it serves will fall into congestion again. In order to solve these problems, we propose an energy-aware computation offloading and routing (ECOR) strategy with result feedback. ECOR allows tasks to select the best node by flying ad-hoc network (FANET) multi-hop routing, thus increasing the lifetime of the entire network and the number of tasks completed during the lifetime, i.e., the lifetime value. Meanwhile, we propose to embed the remaining energy and reliability information of the UAV in the Hello message, thus making the UAV energy-aware. Considering the dynamics of the environment and the complexity of the optimization objective, we propose to use Dueling DQN to optimize offloading and routing decisions. After the task is successfully offloaded, the system will enter the result transmission phase to relay the task result to the destination node. Simulation results show that ECOR significantly outperforms other strategies in terms of cumulative rewards, energy efficiency, and task delivery rates.

In wireless networks, meeting the performance requirements of all tasks solely with Internet of Things (IoT) devices is challenging due to their limited computational power and battery capacity. Given their flexibility and mobility, the application of unmanned aerial vehicles (UAVs) in the context of mobile edge computing (MEC) has garnered significant interest within the sector. However, UAVs also face constraints in terms of resources like storage and computational power. Therefore, it is vital to develop effective UAV assistance solutions to provide long-term demands of in-network services. The dynamic scheduling and computation offloading of UAVs is the subject of this paper. Specifically, we propose a deep deterministic policy gradient algorithm based on a greedy strategy (DDPGG) to jointly optimize dynamic scheduling, device association, and task allocation of UAVs, with the goal of minimizing the weighted sum of total system energy consumption and time delay. The problem is formulated as a nonlinear programming problem involving mixed integers. The simulation results demonstrate that the DDPGG algorithm we have proposed exhibits a higher level of performance in comparison to its competitors.

Supporting mobile edge computing (MEC) in satellite-terrestrial networks (STNs) provides essential offloading services for devices for the Internet of Things (IoT) devices in remote areas. However, when terrestrial demands for computing resources are high, the MEC servers on visible LEO satellites may suffer from insufficient capacity, while those on more distant LEO satellites remain underutilized. To address this issue, this article investigates cooperative task offloading across multiple LEO satellites within an MEC-based STN. We propose a Quality-of-Service (QoS)-aware offloading decision and resource allocation scheme supported by a software-defined network (SDN) for a STN architecture. This architecture integrates the LEO Walker constellation with satellite ground stations (SGSs), with the aim of providing edge computing services to IoT devices in remote areas. To meet the task’s QoS requirements, the tasks can be offloaded to either SGS or LEO satellites within the constellation. To address the challenges of a vast state space and complex action space within the system, we introduce the QOS-aware multihop task offloading in satellite-terrestrial edge networks (OUTSIDE) algorithm, which combines the global search capabilities of genetic algorithms with the local refinement strengths of the Lagrangian multiplier method to minimize the total task computation latency while satisfying QoS demands. Finally, comparative analysis and simulation experiments were conducted. These demonstrate that the OUTSIDE algorithm outperforms other approaches in terms of efficiency and effectiveness.

In the scenario of limited energy supply, Unmanned Aerial Vehicles (UAVs) enabled communication systems must make efficient use of energy in order to provide long-term service. In this paper, we propose a global energy efficiency maximization (GREEN) strategy for multi-UAV enabled communication systems. In such systems, a group of UAVs communicates with their associated ground terminals (GTs) by using a UAV-enabled interference channel (UAV-IC). In particular, we optimize the UAVs' trajectory control by jointly considering both the communication throughput and the total energy consumption of the whole system. We aim to maximize the global energy efficiency (GEE) of a task for multi-UAV communications, in which the problem is challenging to optimally solve due to its non-convex nature and strongly coupled variables. To tackle this problem, first, we investigate and propose a global energy-efficient optimization problem based on the fly-hover-communicate protocol. Second, we extend our proposed solution from the single UAV-enabled system to multiple UAV-GT pairs cases. In addition, we consider the general scenario in which the UAVs also communicate while flying. Based on the successive convex approximation technique and the path discretization method, the GREEN strategy is designed for optimizing UAV trajectories in this scenario. The simulation results show that the proposed strategy can achieve significantly higher GEE than the benchmark schemes for multi-UAV enabled communications.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is a promising technology to provide computational services to ground terminal devices (TDs) in remote areas or for emergency incidents with its flexibility and mobility. This paper aims to maximize the UAV’s energy efficiency while considering the fairness of offloading. We formulate this optimization problem by jointly considering the UAV flight time, the UAV 3D trajectory, the TD binary offloading decisions, and the time allocated to TDs, which is a mixed-integer nonlinear programming problem. The problem is transformed into two sub-problems and we propose a PDDQNLP (parametrized dueling deep Q-network and linear programming) algorithm based on the combination of deep reinforcement learning (DRL) and linear programming (LP) to address them. For the first sub-problem, a DRL-based algorithm is used to optimize the TD offloading decisions, the UAV trajectory, and the UAV flight time. The action space is hybrid that contains discrete actions (e.g., binary offloading) and continuous actions (e.g., UAV flight time). Therefore, we parameterize the action space and propose the PDDQN algorithm, which combines the DDPG algorithm for handling the continuous action space and the Dueling DQN algorithm for handling the discrete action space. According to the solution obtained from the first sub-problem, the LP is used to adjust the time allocated to TDs in the second sub-problem. The numerical results show that the PDDQNLP algorithm outperforms its counterparts.

Device-to-device (D2D) content caching is a promising technology to mitigate the backhaul pressure, and reduce the contents transmission delay. In this paper, to improve the content hit rate (CHR) and the utilization efficiency of the limited caching capacity, we put forward a caching content placement strategy by predicting the user preference and the content popularity, where unmanned aerial vehicles (UAVs) are introduced into the D2D networks to provide computation offloading services to the users. A dynamic resource allocation optimization algorithm (DRAOA) is proposed to deploy UAVs and plan UAVs trajectory adaptively according to the users' task requirements. Simulation results show that the proposed caching content placement policy outperforms the existing baselines. Additionally, the DRAOA can effectively improve the network capacity and mitigate the computation delay compared to the other two DRL algorithms.

Vehicular Edge Computing (VEC) is a computing paradigm that brings Mobile Edge Computing (MEC) to the road and vehicular scenarios by providing low-latency and high-efficiency computation services. One key technology of VEC is task offloading, which allows vehicles to send computation tasks to surrounding Roadside Units (RSUs) for execution, thereby reducing service delay. However, the existing task offloading schemes face the important challenges because the vehicles with time-varying trajectories and limited computing resources need to process massive data with high complexity and diversity. In this paper, we propose a Cooperative Task Qffloading Scheme (CTOS) based on Cybertwin-assisted VEC. Specially, a novel Cybertwin-assisted VEC network architecture is established by applying the combination of the Digital-Twins (DT) and the Generative Adversarial Network (GAN). With the powerful prediction capability of GAN, the data of DT is advanced with the physical entity, which is an effective assistant for task offloading. Then, we leverage the distributed Deep Reinforcement Learning (DRL) to make offloading decisions, which consider the limited resources of RSUs and the cooperation of vehicles. The simulation results demonstrate that the proposed scheme can achieve excellent performance in terms of system stability and efficiency.

The rapid development of 6G can help to bring autonomous driving closed to the reality. Drivers and passengers will have more time for work and leisure spending in the vehicles, further generating a lot of data requirements. However, edge resources from small base stations are insufficient to match the wide variety of services of the future vehicular networks. Besides, due to the high-speed nature of the vehicles, users have to switch the connections among different base stations, whereas such way will cause external latency during the data request. Therefore, it is vital to enable the local cache of vehicle users to realize the reliable autonomous driving. In this paper, we consider caching the contents in the local cache, small base station, and edge server. In practice, the request preference of some single users may be different from a whole region. To maximize the efficiency of content cache, we design a strategy that uses reinforcement learning algorithm to optimize cache schemes on different devices. The experimental results demonstrate that our strategy can enhance the cache hit ratio by 10%-20% compared with the well-known counterparts.

In the areas after natural disaster strikes, the ground communication network can be failed, due to the damage of the communication infrastructure. However, during or after the natural disasters such as earthquakes or tsunamis, ground vehicles may not enter the affected areas easily to set up mobile base stations. Unmanned Aerial Vehicles (UAVs) can be an alternate to provide emergency coverage for ground nodes (GNs). Therefore, how to determine the best location for UAV to achieve the maximum coverage is a key issue. In this paper, an adaptive UAV deployment scheme is proposed to solve the coverage problem of UAV- aided GNs communication. The objective is to optimize the location of the UAV to cover as many GNs as possible and reduce communication energy consumption. We construct a unique analysis method assisted by the collected ground information to solve this problem. First, we propose an information collection method based on the communication probability of Line-of-Sight (LoS) to guarantee the integrity of ground information acquisition. Then, based on the results of the information collection, a virtual obstacle model is built around each GN. Meanwhile, the UAV’s coverage problem is decomposed from the horizontal and vertical dimensions to simplify the difficulty of solving the problem. Finally, the best location of the UAV and the optimal transmission power of GNs can be obtained through the iterative methods and the power control, respectively. The extensive simulation results demonstrate that the proposed deployment scheme outperforms its counterparts.

Recently, researchers have used long short-term memory (LSTM) networks and the bi-directional long short-term memory (Bi-LSTM) networks to process sequence data sets such as vehicle positions in software-defined vehicular networks (SDVN). In this paper, we present a three-component intelligent UAV-aided controller placement scheme (CPP) for SDVN. First, we use Bi-LSTM to model the real-time position of vehicles (traffic flow). Second, we implement a dynamic scheme to place controllers and UAVs (DCUPE) in the network based on the predicted flow. Third, in order to collect real-time traffic information and manage the network, we compute trajectories for the UAVs from real-time Bi-LSTM predictions of vehicle positions and an adaptive artificial bee colony algorithm for the traveling salesman problem (IDABC-TSP). We evaluate our proposed design as a function of energy cost, communication delay, and packet delivery ratio. Our experimental results show the effectiveness of our scheme on real geographical topologies.

Energy costs have dramatically increased in data center networks as an increasing number of large-scale Internet applications are used. In software-defined vehicular networks (SDVN), the communication delay between two vehicles and between vehicles and the controller will dramatically climb up as the number of vehicles increases. This requires more controllers to provide communication service to minimize the latency. More controllers lead to high energy costs. Therefore, the number of controllers and their placement, the so-called controller placement problem (CPP), should be addressed. The appropriate placement of controllers can decrease the energy cost, enabling green communication in SDVN. Although CPP has been studied for static networks, it has not been effectively solved in highly dynamic and complex networks. In this article, a novel cost-effective CPP scheme for SDVN is proposed. First, our proposed minimum controller selection mechanism (MOSA) can reduce the number of controllers and guarantee the coverage of the area. Besides, an improved multiobjective artificial bee colony algorithm (IMABC) is proposed based on the original artificial bee colony algorithm. The IMABC can judge which controller should be switched on for data transmission based on real-time traffic flow. A route computation mechanism is proposed to evaluate the performance of our CPP scheme. The experimental results confirm that compared to other existing CPP schemes, our scheme can achieve a higher packet delivery ratio while greatly reducing energy consumption and latency.

Guaranteeing network transmission is one of the most challenging issues in industrial informatization. In the industrial sites without proper networking infrastructure, by deploying unmanned aerial vehicles (UAV), transmission-oriented cyber-physical system (CPS) is an excellent candidate to exploit to provide transmission. In this article, we focus on establishing deterministic network transmission (DNT) using UAV-based CPS, complying with the principles in time sensitive network/deterministic networking in industrial internet. The software-defined networking (SDN) paradigm is adopted for UAVs-based CPS to achieve global optimization. First, we build the coordinate-based global topology view in the SDN controller to manage and control UAVs by integrating UAVs into the view and applying the network positioning method. Then, we introduce a hop-limited time synchronization approach to improve accuracy by reducing synchronization deviation. Last, based on the view, a geometric multipath generating method is proposed to enhance reliability by reducing the joint degree of multiple paths and facilitating convergence. The extensive simulation experiments show that our proposed UAV-CPS allows DNT to provide better reliability with reduced latency.

Drones can be used for many assistance roles in complex communication scenarios and play as the aerial relays to support terrestrial communications. Although a great deal of emphasis has been placed on the drone-assisted networks, existing work focuses on routing protocols without fully exploiting the drones superiority and flexibility. To fill this gap, this paper proposes a collaborative communication scheme for multiple drones to assist the urban vehicular ad-hoc networks (VANETs). In this scheme, drones are distributed regarding the predicted terrestrial traffic condition in order to efficiently alleviate the inevitable problems of conventional VANETs, such as building obstacle, isolated vehicles, and uneven traffic loading. To effectively coordinate multiple drones, this issue is modeled as a multimodal optimization problem to improve the global performance on a certain space. To this end, a succinct swarm-based optimization algorithm, namely Multimodal Nomad Algorithm (MNA) is presented. This algorithm is inspired by the migratory behavior of the nomadic tribes on Mongolia grassland. Based on the floating car data of Chengdu China, extensive experiments are conducted to examine the performance of the MNA-optimized drone-assisted VANET. The results demonstrate that our scheme outperforms its counterparts in terms of hop number, packet delivery ratio, and throughput.