Unmanned maritime surveillance systems (UMSS), consisting of Unmanned Surface Vessels (USVs) and smart buoys, as a typical application of the maritime Communication-Computing Converged Network (CCCN), play a crucial role in combating illegal fishing, smuggling, and piracy. However, the 2D images on which UMSS relies often lack spatial depth, and although 3D point cloud data can compensate for this limitation, complex and harsh weather degrades their quality and increases data volume. This increases the communication and computational burden, making efficient resource allocation far more complex, impairing UMSS’s responsiveness in dynamic maritime conditions. To address these issues, this paper proposes an edge intelligence-based real-time resource allocation framework for UMSS. Unlike existing works that only consider resource allocation, our framework also accounts for the impact of 3D point cloud data quality on system performance in harsh weather conditions. Specifically, it first leverages 3D point cloud data with a dehazing algorithm to mitigate haze-induced data expansion. Then we formulate an optimization problem to dynamically trade off throughput, latency, and Energy Consumption (EC) under varying maritime conditions. Finally, we utilize Deep Deterministic Policy Gradient (DDPG) methods to solve this problem. Additionally, we design task allocation strategies tailored to different maritime services. Simulation results show that our approach reduces average latency by 13%, lowers EC by 20%, and increases throughput by 10%, to guarantee the timely fulfillment of all tasks and improve the efficiency of UMSS under harsh weather conditions.

The rapid advancement of autonomous driving technologies has imposed stringent requirements on low-latency and high-reliability computation, which often exceed the capabilities of onboard processors. Vehicular edge computing (VEC) provides a promising solution by offloading computation to external servers; however, terrestrial infrastructure suffers from fragmented coverage and limited scalability, particularly in highway and rural scenarios. To address these limitations, this paper considers a multi-layer aerial VEC network integrating a high-altitude platform and multiple unmanned aerial vehicles (UAVs) to jointly provide wide-area coverage and proximity services. Different from existing works that primarily focus on latency minimization under homogeneous resources, this paper explicitly models the heterogeneous leasing pricing of aerial platforms and investigates its impact on task offloading decisions. A joint task offloading and resource allocation problem is formulated to minimize the total system cost, defined as a weighted combination of latency and economic expenditure. To ensure the feasibility of UAV-assisted offloading under high mobility, a dwell-time constraint is incorporated to restrict task execution within the effective service duration. The resulting problem is formulated as a mixed-integer nonlinear programming problem, which is solved via a low-complexity iterative algorithm based on Lagrangian duality, linear relaxation, and the alternating direction method of multipliers. Simulation results demonstrate that the proposed scheme achieves significant cost reduction compared with benchmark strategies, especially under high-mobility conditions.

With the rapid advancement of Internet of Things applications, the demand for integrated sensing, communication, computing, and caching (ISC3) functions has surged. However, existing systems optimize these functions independently, leading to suboptimal resource utilization and performance bottlenecks. In this paper, we propose a multi-layer aerial ISC3 architecture where a versatile unmanned aerial vehicle (UAV) provides edge computing and caching services to ground wireless devices (WDs) alongside its radar sensing capabilities. A high-altitude platform maintains the complete service library, delivering required services to the UAV when cache misses occur. Partial data compression is employed to reduce uplink communication overhead, where WDs partially compress their offloaded task data before transmitting to the UAV. The objective is to minimize total system energy consumption by jointly optimizing time scheduling ratios, task offloading ratios, compression selection ratios, service caching decisions, and UAV trajectory, subject to task latency, sensing quality, energy budgets, and cache capacity constraints. An efficient iterative algorithm utilizing specialized optimization techniques such as Lagrangian duality and successive convex approximation is developed to solve the resulting mixed-integer nonlinear programming problem. Extensive simulations demonstrate fast convergence under diverse network configurations, with the proposed scheme consistently outperforming all baselines by 22.5%-67.0% in total energy consumption.

The Industrial Internet of Things (IIoT) networks serve as the foundational infrastructure for real-time communication and data exchange in smart manufacturing. Predicting the spread of malware within IIoT networks is particularly challenging due to uncertainties in infection and recovery rates, which are influenced by dynamic network conditions and device heterogeneity. In this article, we propose an adaptive fuzzy SIR model that incorporates fuzzy logic and gradient descent optimization to address these uncertainties. Specifically, we integrate fuzzy logic with gradient descent, which introduces an adaptive mechanism to handle uncertain infection and recovery rates in real time. This synergy ensures robust parameter tuning under fluctuating network states, significantly improving malware spread prediction. The proposed model dynamically adjusts infection and recovery rates using fuzzy differential equations and real-time data adaptation, enhancing prediction accuracy and resilience to network fluctuations. Experimental results demonstrate the model’s advantages in improving predictive accuracy, convergence speed, and adaptability, making it a robust solution for securing IIoT networks in smart manufacturing.

Cooperative coordination at unsignalized road intersections, which aims to improve the driving safety and traffic throughput for connected and automated vehicles (CAVs), has attracted increasing interests in recent years. However, most existing investigations either suffer from computational complexity or cannot harness the full potential of the road infrastructure. To this end, we first present a dedicated intersection coordination framework, where the involved vehicles hand over their control authorities and follow instructions from a centralized coordinator. Then a unified cooperative trajectory planning problem will be formulated to maximize the traffic throughput while ensuring driving safety. To address the key computational challenges in the real-world deployment, we reformulate this non-convex sequential decision-making problem into a model-free Markov Decision Process (MDP) and tackle it by devising a Twin Delayed Deep Deterministic Policy Gradient (TD3)-based strategy in the deep reinforcement learning (DRL) framework. Simulation and practical experiments show that the proposed strategy could achieve near-optimal performance in sub-static coordination scenarios and significantly improve the traffic throughput in the realistic continuous traffic flow. The most remarkable advantage is that our strategy could reduce the time complexity of computation to milliseconds, and is shown scalable when the road lanes increase.

Benefiting from high mobility, unmanned aerial vehicles (UAVs) can reconstruct wireless connections for affected areas. Most of the existing work has usually ignored the influence of limited airborne energy on the dependability of UAV data transmission. Accordingly, this article proposes an UAV-assisted wireless powered system to achieve dependable data collections in Internet of Things (IoT). Specifically, an UAV leverages energy beamforming to transfer energy to ground users (GUs) in downlink subtimeslot, while the GUs transmit data to the UAV with the harvested energy in uplink subtimeslot. For this system, a joint optimization problem of subtimeslot allocation and UAV route planning is investigated to maximize the system energy efficiency subject to UAV dynamics, time slot duration, and GUs' rate threshold. To tackle the nonconvexity of the formulated problem, a low-complexity alternating iterative algorithm is proposed. The first subproblem optimizes subtimeslot allocation by using the bisection method and Lagrange multiplier method for the fixed UAV route, while the second optimizes the UAV route for the periodic and single flight modes with the given subtimeslot allocation. Then, the two subproblems are alternatively solved until convergence. The simulation results demonstrate that the proposed algorithm can not only optimize the UAV route, but also achieve a good compromise between system throughput and UAV propulsion energy consumption.

As unmanned aerial vehicle (UAV) communication has been widely used in all walks of life, its secrecy issue has also received more and more attention. This article studies the physical-layer security of UAV relaying communication system in multiterminal Internet of Things (IoT) scenarios. Specifically, while receiving the information from the ground base station, the UAV safely forwards the information to one of a group of IoT terminals in the presence of an eavesdropper. Under the constraints of information causality and UAV mobility, our goal is to maximize the minimum average secrecy rate among all IoT terminals. Based on the nonconvex problem, this article proposes a high-efficiency algorithm for joint optimization of UAV trajectory and resource allocation. The simulation results show that the proposed algorithm not only effectively improves information secrecy of IoT terminals, but also enhances the fairness of communication between the IoT terminals.

The sixth-generation (6G) communication requires supporting massive Internet of Things (IoT) devices and extremely differentiated IoT applications for the air–space–ground integrated network. Relying on the aerial superiority, unmanned aerial vehicle (UAV) is capable of acting as an aerial base station (BS) and supporting IoT deployment in remote and disaster areas. A UAV-supported clustered nonorthogonal multiple access (C-NOMA) system is put forward in this article. Specifically, the UAV provides services to IoT terminals as an aerial BS based on the wireless-powered communication (WPC) technique. According to this system, we propose a synergetic scheme for UAV trajectory planning and subslot allocation. Our goal is to maximize the uplink average achievable sum rate of IoT terminals by synergistically planning UAV trajectory and subslot duration, while guaranteeing the uplink achievable sum rate and the UAV mobility constraints. As the formulated problem suffers nonconvexity and complication, an efficient iterative algorithm is proposed to address it. First, for fixed UAV trajectory, all the terminals are clustered and a subslot allocation algorithm based on the Lagrange multiplier and bisection method is proposed. Then, for a fixed clustering state and subslot duration, we optimize the UAV trajectory. Finally, we solve these two subproblems alternatively until the objective function converges. The effectiveness of the proposed scheme in the UAV-supported C-NOMA system is verified by the numerical results.

The 6th generation (6G) communication envisions a highly integrated network where aerial vehicles connect satellites and terrestrial systems. As low altitude vehicle, unmanned aerial vehicle (UAV) is able to quickly establish wireless networks without resorting to terrestrial infrastructure. Due to strong invulnerability, synergistic cooperation and flexible scheduling, the multi-UAV communication system can significantly improve system performance through collaboratively designing multi-UAV trajectory and radio resource scheduling. This article proposes a multi-UAV wireless powered communication (WPC) system for 6G-enabled Internet of Things (IoT). Specifically, each time slot is split into uplink and downlink subslot. In the downlink subslot, multiple UAVs dispatched as aerial communication platforms transfer energy to multiple IoT users. In the uplink subslot, the association between UAVs and users is designed, and then the scheduled user uploads data to the specific UAV by using the harvested energy. According to the proposed system, we propose a collaborative scheme of multi-UAV trajectory optimization and resource scheduling. By synergistically optimizing UAV-user association, subslot duration, user transmit power, and multi-UAV trajectory, we maximize the minimum average achievable rate among all users. Particularly, the nonconvex optimization problem can be efficiently figured out by an alternative iteration algorithm proposed in this article. Finally, numerical results show that our design can not only optimize multi-UAV flight path, but also achieve higher objective value than benchmark schemes.

In order to overcome the shortcomings of orthogonal frequency division multiplexing (OFDM) and prolong the battery life of devices in the Internet of Things, a joint subcarrier and subsymbol allocation-based simultaneous wireless information and power transfer scheme for multiuser generalized frequency division multiplexing (GFDM) system is proposed in this paper. According to the 2-D time-frequency block structure of GFDM, we investigate the problem to maximize sum information decoding (ID) rate by optimizing subcarrier and subsymbol allocation, power allocation and power splitting ratio under the constraints of total transmit power and harvested energy. To solve the nonconvex problem, an iterative algorithm is developed to obtain its optimal solution. The performances of sum ID rate and harvested energy are simulated and evaluated. Simulation results show that the proposed algorithm converges fast. Moreover, the proposed algorithm can not only allocate the subcarriers, subsymbols, and power based on different channel conditions of users, but also outperform the conventional OFDM in sum ID rate on the premise of satisfying the minimum harvested energy of each user.

In the fifth generation mobile networks, generalized frequency division multiplexing (GFDM) is expected as the candidate waveform which can flexibly meet the requirements of diverse applications and scenarios for the Internet of Things (IoT) because of its advantages over orthogonal frequency division multiplexing (OFDM). In order to achieve the reliable data transmission in GFDM-based IoT systems, channel estimation (CE) is a prerequisite. However, the 2-D block modulation and the nonorthogonality between subcarriers for GFDM make it almost impossible that the conventional CE methods suitable for OFDM are directly applied to GFDM. To cope with this problem, a soft decision control strategy-based iterative CE (SDC-ICE) method is proposed in this paper. First, the received signal is equalized by the channel frequency response (CFR) from the pilot-based CE. After GFDM demodulation and Turbo decoding, the feedback log-likelihood ratio is utilized to rebuild symbols for data-aided CE by a redesigned Turbo receiver. Subsequently, the feedback information of both current and former iterations is used to improve the reliability of rebuilt symbols. The CFR obtained from SDC-ICE is used for equalization in the next iteration. The performance of SDC-ICE can be improved by increasing the iterations. Finally, the bit error rate (BER) and mean square error (MSE) performances of SDC-ICE and hard decision control strategy-based iterative CE (HDC-ICE) are simulated and evaluated. Simulation results demonstrate that the proposed method has better BER and MSE performance than HDC-ICE within fewer iterations.

Content caching is a promising approach to enhancing bandwidth utilization and minimizing delivery delay for new‐generation Internet applications. The design of content caching is based on the principles that popular contents are cached at appropriate network edges in order to reduce transmission delay and avoid backhaul bottleneck. In this paper, we propose a cooperative caching replacement and efficiency optimization scheme for IP‐based wireless networks. Wireless edges are designed to establish a one‐hop scope of caching information table for caching replacement in cases when there is not enough cache resource available within its own space. During the course, after receiving the caching request, every caching node should determine the weight of the required contents and provide a response according to the availability of its own caching space. Furthermore, to increase the caching efficiency from a practical perspective, we introduce the concept of quality of user experience (QoE) and try to properly allocate the cache resource of the whole networks to better satisfy user demands. Different caching allocation strategies are devised to be adopted to enhance user QoE in various circumstances. Numerical results are further provided to justify the performance improvement of our proposal from various aspects.

As the indispensable supplement of terrestrial communications, Low Earth Orbit (LEO) satellite network is the crucial part in future space‐terrestrial integrated networks because of its unique advantages. However, the effective and reliable routing for LEO satellite network is an intractable task due to time‐varying topology, frequent link handover, and imbalanced communication load. An Extreme Learning Machine (ELM) based distributed routing (ELMDR) strategy was put forward in this paper. Considering the traffic distribution density on the surface of the earth, ELMDR strategy makes routing decision based on traffic prediction. For traffic prediction, ELM, which is a fast and efficient machine learning algorithm, is adopted to forecast the traffic at satellite node. For the routing decision, mobile agents (MAs) are introduced to simultaneously and independently search for LEO satellite network and determine routing information. Simulation results demonstrate that, in comparison to the conventional Ant Colony Optimization (ACO) algorithm, ELMDR not only sufficiently uses underutilized link, but also reduces delay.

The wireless caching has attracted a lot of attention in recent years, since it can reduce the backhaul cost significantly and improve the user‐perceived experience. The existing works on the wireless caching and transmission mainly focus on the communication scenarios without eavesdroppers. When the eavesdroppers appear, it is of vital importance to investigate the physical‐layer security for the wireless caching aided networks. In this paper, a caching network is studied in the presence of multiple eavesdroppers, which can overhear the secure information transmission. We model the locations of eavesdroppers by a homogeneous Poisson Point Process (PPP), and the eavesdroppers jointly receive and decode contents through the maximum ratio combining (MRC) reception which yields the worst case of wiretap. Moreover, the main performance metric is measured by the average probability of successful transmission, which is the probability of finding and successfully transmitting all the requested files within a radius R. We study the system secure transmission performance by deriving a single integral result, which is significantly affected by the probability of caching each file. Therefore, we extend to build the optimization problem of the probability of caching each file, in order to optimize the system secure transmission performance. This optimization problem is nonconvex, and we turn to use the genetic algorithm (GA) to solve the problem. Finally, simulation and numerical results are provided to validate the proposed studies.

A fair and adaptive call admission control algorithm for multimedia low Earth orbit (LEO) satellite networks was proposed. Based on current call dropping probability of destination cell, this algorithm reserves bandwidth for handoff calls using double threshold method. To avoid the discrimination of quality of service (QoS) caused by the allocation based on fair bandwidth, this algorithm adopts the bandwidth allocation rule based on fair QoS. Simulation results show that the proposed algorithm can accurately and adaptively reserve bandwidth, present satisfactory call blocking probability and greatly reduce handoff call dropping probability, while guarantees the high bandwidth utilization.