Unmanned aerial vehicles (UAVs) play a pivotal role in air-ground collaborative mobile edge computing (MEC) systems. They function as aerial cloudlets, deploying flexibly closer to ground users (GUs) to provide enhanced computational capacity in edge computing scenarios. While extensive studies have optimized resource allocation and UAV trajectories collaboratively to improve energy and offloading efficiency, few have simultaneously addressed the system's communication security and reliability. This paper proposes a joint optimization model to ensure both security and reliability in an energy-efficient UAV-assisted MEC system. Specifically, we introduce an artificial noise generation technique to enhance system security and derive a closed-form expression for the optimal ratio between the generated noise and the data transmission power to ensure secure communication. Additionally, we propose a probabilistic model to characterize the reliability of data transmission and derive the worst-case transmission rate. Furthermore, we present an energy-efficient model for optimizing resource allocation and UAV trajectory planning, with the goal of improving the overall energy efficiency of the UAV-assisted MEC system. Finally, we design an optimization algorithm with polynomial-time complexity based on the augmented Lagrangian multiplier method. Simulation results demonstrate that the proposed method outperforms existing approaches in terms of both global secure energy efficiency and average secure energy efficiency.

In this paper, we propose a novel road side unit (RSU)-assisted cooperative sensing scheme for connected autonomous vehicles (CAVs), with the objective to reduce completion time of sensing tasks. Specifically, LiDAR sensing data of both RSU and CAVs are selectively fused to improve sensing accuracy, and computing resources therein are cooperatively utilized to process tasks in real time. To this end, for each task, we decide whether to compute it at the CAV or at the RSU and allocate resources accordingly. We first formulate a joint task placement and resource allocation problem for minimizing the total task completion time while satisfying sensing accuracy constraint. We then decouple the problem into two subproblems and propose a two-layer algorithm to solve them. The outer layer first makes task placement decision based on the Gibbs sampling theory, while the inner layer makes spectrum and computing resource allocation decisions via greedy-based and convex optimization subroutines, respectively. Simulation results based on the autonomous driving simulator CARLA demonstrate the effectiveness of the proposed scheme in reducing total task completion time, comparing to benchmark schemes.

Trajectory planning and obstacle avoidance technologies for unmanned aerial vehicles (UAVs) are widely applied in Internet of Things-based intelligent urban management, data collection, and related fields, and are increasingly becoming a global research hotspot. However, uncertainties in trajectory planning caused by factors such as sensor measurement noise, model mismatch, and environmental disturbances can compromise the safety and robustness of UAV flights. While existing optimization-based methods build complex nonlinear models, they are often computationally expensive and inefficient. Learning-based methods, on the other hand, demand substantial computational resources. In this paper, we develop a nonlinear chance-constrained trajectory planning model that explicitly accounts for uncertainties, enabling autonomous obstacle avoidance and landing of UAVs on a dynamic platform. We derive the robust equivalent form of the chance constraints to address the solvability of models that include uncertainty factors. We develop a method that combines lossless convexification with the sequential convex programming (SCP) algorithm to achieve low complexity and high-efficiency solutions. Additionally, a real-time planning framework is proposed to address uncertain dynamic environments. We validate the robustness and safety of the proposed algorithm under various dynamic and uncertain scenarios, including different levels of disturbance, moving platforms, and unpredictable obstacles.

Unmanned aerial vehicles (UAVs) have rapidly advanced in applications such as disaster response, infrastructure inspection, and smart city systems. However, cooperative coverage mission planning in dynamic environments poses a persistent challenge in balancing global optimization with real-time responsiveness. To address this, we propose a two-stage task allocation framework based on a dual-ring dynamic scheduler. In the centralized planning phase, an enhanced NSGA-II algorithm is developed, incorporating dual-population initialization, path-exchange crossover, and multi-strategy mutation. Experimental results demonstrate a 49% improvement in the hypervolume indicator over the baseline NSGA-II, with average reductions of 13.33% and 23.71% in total and maximum execution times, respectively. In the dynamic scheduling phase, we design a distributed auction mechanism leveraging a dual-ring communication topology, capable of handling six types of events: UAV failure, UAV addition, node cancel exploration, node urgent exploration, node re-exploration and node in-depth exploration. Through event-driven auctions and group-based bidding, the system maintains a load imbalance under 28% and achieves effective rebalancing even under scenarios with over 50% UAV loss. These results validate the robustness and adaptability of the dual-ring dynamic scheduler in real-time collaborative coverage missions. The proposed method demonstrates significant potential in dynamic, large-scale UAV applications. Source code will be available at: https://github.com/GradualScholar/CoverageMissionPlannin.git

Uncrewed aerial vehicles (UAVs) play a crucial role in mobile edge computing (MEC) within space-air-ground integrated networks. They serve as aerial cloudlets, enabling task processing in close proximity to ground users. While numerous joint trajectory design and resource allocation schemes aim to enhance energy efficiency or computation rate, few focus on improving system reliability, which is often challenged by stochastic channels and node mobility. This paper presents a stochastic modeling perspective to derive a system reliability expression. Our reliability formulation incorporates the impacts of stochastic Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) air-to-ground communication channels, application data load, available bandwidth, offloading time, and transmission power. This comprehensive approach leads to a reliability-oriented joint optimization model that considers not only resource allocation and user data transmission scheduling but also the motion of UAVs. To solve this problem, we propose a low-complexity algorithm. By utilizing augmented Lagrangian multipliers, the algorithm transforms nonlinear constraints into a tractable formulation, enabling the utilization of legacy unconstrained optimization techniques. We provide a proof of convergence for this algorithm. Through simulations, we demonstrate that our proposed method guarantees convergence within finite iterations and improves the average communication reliability in comparison with several other joint optimization schemes.

Tile-based streaming has been proposed to address the challenge of high transmission rate demand in 360° virtual reality (VR) video streaming. However, it suffers from network and viewing behavior dynamics (i.e., head movements), while encoded video tiles have various properties in terms of transmission priority, deadline, and reliability requirement. Hence, a supporting transmission protocol is imperative. In this paper, we propose a customized transmission protocol based on Quick UDP Internet Connections (QUIC) which operates over a VR video network slice in the core network. The QUIC protocol is tailored to accommodate the characteristics of tile-based VR video streaming where explicit mapping relations between requested video tiles and QUIC streams are established. Two customized in-network protocol functionalities including packet filtering and caching-based packet retransmission are proposed, to filter out outdated video data due to field-of-view (FoV) prediction errors under viewing behavior dynamics and to achieve efficient packet retransmissions with disparate transmission reliability requirements. A slice-level packet header is designed to support enhanced slice-based VR video transmission with the proposed protocol functionalities. Key transport parameters are determined via theoretical analysis. Simulation results are presented to demonstrate the effectiveness of our proposed transmission protocol in achieving short average video segment downloading time and high average video segment quality.

In this paper, we present a performance analytical model for end-to-end (E2E) service provisioning (i.e., processing or transmission) of layer-encoded video packets over a network slice in the core network. The disparate service reliability requirements of base layer (BL) and enhancement layer (EL) packets are considered in the proposed analytical model for the E2E packet delays, deadline violation probabilities, and throughputs of BL and EL packets. Specifically, a network function virtualization (NFV) node along the routing path of the video streaming slice is split into two consecutive logical nodes, one for packet processing and the other for transmission, based on which a segment-based analysis framework is proposed for E2E service performance modeling. A two-stage queuing model is established to obtain the approximate steady-state probability distribution of queue length at the first node in the first segment, upon which the BL/EL packet delay, deadline violation probability, and throughput at the segment are derived. In addition, the inter-departure time of successive packets departing from the first segment is analyzed based on an approximate M/D/1 system, and the packet departure process at the first segment is approximated as a Poisson process under the assumption of a large packet service rate of the first node. The independence between two consecutive segments is then achieved for analysis tractability, based on which the E2E performance measures are derived. Extensive simulation results demonstrate the accuracy of our proposed performance analytical model and its effectiveness such as in transport parameter determination.

In this paper, we propose a digital twin (DT)-based user-centric approach for processing sensing data in an integrated sensing and communication (ISAC) system. The considered scenario involves an ISAC device with a lightweight deep neural network (DNN) and a mobile edge computing (MEC) server with a large DNN. After collecting sensing data, the ISAC device either processes the data locally or uploads them to the server for higher-accuracy data processing. To cope with data drifts, the server updates the lightweight DNN when necessary, referred to as continual learning. Our objective is to minimize the long-term average computation cost of the MEC server by jointly optimizing two decisions, i.e., sensing data offloading and sensing data selection for the DNN update. A DT of the ISAC device is constructed to predict the impact of potential decisions on the long-term computation cost of the server, based on which the decisions are made with closed-form formulas. Experiments on executing DNN-based human motion recognition tasks are conducted to demonstrate the outstanding performance of the proposed DT-based approach in computation cost minimization.

To maintain high perception performance among connected and autonomous vehicles (CAVs), in this paper, we propose an accuracy-aware and resource-efficient raw-level cooperative sensing and computing scheme among CAVs and road-side infrastructure. The scheme enables fined-grained partial raw sensing data selection, transmission, fusion, and processing in per-object granularity, by exploiting the parallelism among object classification subtasks associated with each object. A supervised learning model is trained to capture the relationship between the object classification accuracy and the data quality of selected object sensing data, facilitating accuracy-aware sensing data selection. We formulate an optimization problem for joint sensing data selection, subtask placement and resource allocation among multiple object classification subtasks, to minimize the total resource cost while satisfying the delay and accuracy requirements. A genetic algorithm based iterative solution is proposed for the optimization problem. Simulation results demonstrate the accuracy awareness and resource efficiency achieved by the proposed cooperative sensing and computing scheme, in comparison with benchmark solutions.

Cooperative perception (CP) is a key technology to facilitate consistent and accurate situational awareness for connected and autonomous vehicles (CAVs). To tackle the network resource inefficiency issue in traditional broadcast-based CP, unicast-based CP has been proposed to associate CAV pairs for cooperative perception via vehicle-to-vehicle transmission. In this paper, we investigate unicast-based CP among CAV pairs. With the consideration of dynamic perception workloads and channel conditions due to vehicle mobility and dynamic radio resource availability, we propose an adaptive cooperative perception scheme for CAV pairs in a mixed-traffic autonomous driving scenario with both CAVs and human-driven vehicles. We aim to determine when to switch between cooperative perception and stand-alone perception for each CAV pair, and allocate communication and computing resources to cooperative CAV pairs for maximizing the computing efficiency gain under perception task delay requirements. A model-assisted multi-agent reinforcement learning (MARL) solution is developed, which integrates MARL for an adaptive CAV cooperation decision and an optimization model for communication and computing resource allocation. Simulation results demonstrate the effectiveness of the proposed scheme in achieving high computing efficiency gain, as compared with benchmark schemes.

In this paper, we consider an unmanned aerial vehicle (UAV)-assisted post-disaster rescue scenario, where UAV-mounted aerial base stations (ABSs) compute tasks related to post-disaster rescue operations while also providing communication services to ground users (GUs). With the limited computation capacity of ABSs, we aim to minimize the task computation queuing delay and ensure the GU communication rate by jointly optimizing ABS-GU association, task offloading, and ABS trajectory. The problem is formulated as a mixed-integer nonlinear program, and a solution is proposed by integrating Lyapunov optimization and actor-critic based deep reinforcement learning. We utilize a model-based successive convex approximation technique in a critic module to acquire an accurate evaluation of actor module output. Simulation results demonstrate the effectiveness of the proposed approach in reducing the task computation queuing delay.

Driven by the ever-increasing computing capabilities of mobile devices, the next-generation wireless networks are evolving toward a distributed networking and computing platform, which enables in-network computing and unified resource/service provisioning. The evolution leads to a growing research interest in wireless computing networks that operate under both the high dynamics of the wireless environment and the resource heterogeneity, which complicates resource allocation, scheduling among network flows, and overall optimization. In this paper, we aim to study a low-complexity efficient solution to jointly allocate both networking resources (e.g., links to forward packets between connected computing nodes) and computing resources (e.g., computing power at each node for packet processing). We formulate a novel network utility maximization problem under computing and networking resource constraints and develop an enhanced backpressure-based dynamic scheduling and routing algorithm. Finally, we verify the effectiveness of the algorithm with extensive simulations.

The advances in artificial intelligence (AI) and edge computing enable edge intelligence to support pervasive intelligent Internet of Things (IoT) applications in the future wireless networks. We focus on deep neural network (DNN)-based classification tasks, and investigate how to improve the confidence level and delay performance of DNN inference via device-edge collaboration. We first develop a stochastic cumulative DNN inference scheme that aggregates multiple random DNN inference results and generates a cumulative DNN inference result with improved confidence level. Then, based on a computation-efficient DNN model deployment strategy with shared computation between a locally deployed fast DNN model and a full DNN model partitioned between the device and edge, a closed-loop adaptive device-edge collaboration scheme is developed to support cumulative DNN inference for multiple devices. We adaptively determine how to offload DNN inference computation to the edge and how to allocate transmission and edge-computing resources among multiple devices, for Quality-of-Service (QoS) satisfaction in terms of both confidence level and inference delay with resource and energy efficiency. A reinforcement learning (RL) approach is used for adaptive offloading decision, which relies on a resource allocation solution for reward calculation. Simulation results demonstrate the effectiveness of the adaptive device-edge collaboration scheme for cumulative DNN inference, in terms of confidence level improvement, delay violation minimization, network resource efficiency, and device energy efficiency.

Split learning (SL) is a collaborative learning framework, which can train an artificial intelligence (AI) model between a device and an edge server by splitting the AI model into a device-side model and a server-side model at a cut layer. The existing SL approach conducts the training process sequentially across devices, which incurs significant training latency especially when the number of devices is large. In this paper, we design a novel SL scheme to reduce the training latency, namedCluster-basedParallelSL(CPSL) which conducts model training in a “first-parallel-then-sequential” manner. Specifically, the CPSL is to partition devices into several clusters, parallelly train device-side models in each cluster and aggregate them, and then sequentially train the whole AI model across clusters, thereby parallelizing the training process and reducing training latency. Furthermore, we propose a resource management algorithm to minimize the training latency of CPSL considering device heterogeneity and network dynamics in wireless networks. This is achieved by stochastically optimizing the cut layer selection, device clustering, and radio spectrum allocation. The proposed two-timescale algorithm can jointly make the cut layer selection decision in a large timescale and device clustering and radio spectrum allocation decisions in a small timescale. Extensive simulation results on non-independent and identically distributed data demonstrate that the proposed solution can greatly reduce the training latency as compared with the existing SL benchmarks, while adapting to network dynamics.

In this paper, we investigate a computing task offloading problem in a cloud-based autonomous vehicular network (C-AVN), from the perspective of long-term network wide computation load balancing. To capture the task computation load dynamics over time, we describe the problem as an Markov decision process (MDP) with constraints. Specifically, the objective is to minimize the expectation of a long-term total cost for imbalanced base station (BS) computation load and task offloading decision switching, with per-slot computation capacity and offloading latency constraints. To deal with the unknown state transition probability and large state-action spaces, a multi-agent deep Q-learning (MA-DQL) module is designed, in which all the agents cooperatively learn a joint optimal task offloading policy by training individual deep Q-network (DQN) parameters based on local observations. To stabilize the learning performance, a fingerprint-based method is adopted to describe the observation of each agent by including an abstraction of every other agent’s updated state and policy. Simulation results show the effectiveness of the proposed task offloading framework in achieving long-term computation load balancing with controlled offloading switching times and per-slot QoS guarantee.

Software defined networking (SDN) and network function virtualization (NFV) are key enabling technologies in fifth generation (5G) communication networks for embedding service-level customized network slices in a network infrastructure, based on statistical resource demands to satisfy long-term quality of service (QoS) requirements. However, traffic loads in different slices are subject to changes over time, resulting in challenges for consistent QoS provisioning. In this paper, a dynamic flow migration problem for embedded services is studied, to meet end-to-end (E2E) delay requirements with time-varying traffic. A multi-objective mixed integer optimization problem is formulated, addressing the trade-off between load balancing and reconfiguration overhead. The problem is transformed to a tractable mixed integer quadratically constrained programming (MIQCP) problem. It is proved that there is no optimality gap between the two problems; hence, we can obtain the optimum of the original problem by solving the MIQCP problem with some post-processing. To reduce time complexity, a heuristic algorithm based on redistribution of hop delay bounds is proposed to find an efficient solution. Numerical results are presented to demonstrate the aforementioned trade-off, the benefit from flow migration in terms of E2E delay guarantee, as well as the effectiveness and efficiency of the heuristic solution.

Service-oriented virtual network deployment is based on statistical resource demands of different services, while data traffic from each service fluctuates over time. In this paper, a delay-aware flow migration problem for embedded services is studied to meet end-to-end (E2E) delay requirement with time-varying traffic. A non-convex multi-objective mixed integer optimization problem is formulated, addressing the trade-off between maximum load balancing and minimum reconfiguration overhead due to flow migrations, under processing and transmission resource constraints and QoS requirement constraints. Since the original problem is non-solvable in optimization solvers due to unsupported types of quadratic constraints, it is transformed to a tractable mixed integer quadratically constrained programming (MIQCP) problem. The optimality gap between the two problems is proved to be zero, so we can obtain the optimum of the original problem through solving the MIQCP problem with some post-processing. Numerical results are presented to demonstrate the aforementioned trade-off, as well as the benefit from flow migration in terms of E2E delay performance guarantee.

Hash-based message authentication code (HMAC) is widely used in authentication and message integrity. As a Chinese hash algorithm, the SM3 algorithm is gradually winning domestic market value in China. The side channel security of HMAC based on SM3 (HMAC-SM3) is still to be evaluated, especially in hardware implementation, where only intermediate values stored in registers have apparent Hamming distance leakage. In addition, the algorithm structure of SM3 determines the difficulty in HMAC-SM3 side channel analysis. In this paper, a skillful bit-wise chosen-plaintext correlation power attack procedure is proposed for HMAC-SM3 hardware implementation. Real attack experiments on a field programmable gate array (FPGA) board have been performed. Experimental results show that we can recover the key from the hypothesis space of 2 256 based on the proposed procedure.