The integration of edge computing and satellite networks has emerged as a promising solution to support remote terrestrial computation with wide coverage and low latency. However, single-satellite computing leads to uneven resource utilization and degraded service quality. To address this, peer offloading is required to improve both service quality and resource efficiency. Asides from peer offloading, diverse service requests also call for an appropriate service deployment strategy, which should be jointly optimize with offloading decision. In this paper, taking into processing cost and service update cost, we formulate a long-term optimization for service deployment and peer offloading. To pursue long-term performance, the problem is first reformulated into a sequence of time-invariant problems. Since frequent service deployment adjustments incur overhead and may cause service interruption, we decompose the time-invariant problem into a service deployment subproblem and a peer offloading subproblem, optimized at different timescales. A hierarchical method iteratively solve the two subproblems. In particular, we propose an online distributed algorithm for small-timescale peer offloading. Each local peer offloading problem is transformed into a capacity-constrained minimum cost maximum flow problem, enabling a low-complexity solution via the successive shortest path algorithm. We provide theoretical analysis showing that the proposed algorithm asymptotically approaches the offline optimum at the expense of system congestion. Moreover, we show that the performance bound grows with the large-timescale interval. Simulations results validate the theoretical analysis and demonstrate the effectiveness of the propose algorithm in terms of processing cost and service update cost.

This paper aims to develop an effective uplink access scheduling strategy for massive Internet-of-Things (IoT) networks. To better reap the benefits of uplink resources, the BS has to adjust the uplink resources relies on the network states available at the BS. However, in massive IoT networks, the acquisition of network states, including traffic arrivals, channel conditions, and energy supply rate, are typically obtained through in-band feedback from devices. Therefore, the network states available at the BS are differently outdated across devices, as the staleness depends on the time elapsed since each device was last scheduled. This motivates us to develop a proactive scheduling scheme that enables the BS to schedule uplink access under differently outdated states information. To combat the performance loss caused by the outdated states information, we propose a novel primal-dual online learning framework. This framework leverages mini-batch gradient descent for dual updates and employs Online Convex Optimization for proactive primal updates, which effectively predicting current network states based on outdated knowledge. We evaluate the performance of the proposed proactive scheduling scheme against the offline optimum, which is optimized using prior knowledge of network states. The performance analysis shows that the proactive scheme asymptotically approaches to the offline optimum. Simulation results further validate the effectiveness of the proposed algorithm by comparing to other benchmarks.

To address the challenges of low sampling efficiency in obstructed environments, high fingerprint database construction costs, and insufficient positioning accuracy in indoor visible light positioning (VLP) systems, this paper proposes an integrated intelligent framework termed the obstructed environment based intelligent fingerprint positioning (OEIFP). This framework combines adaptive sampling, sparse expansion, and optimized positioning algorithms. The framework first employs the obstructed environment adaptive sampling (OEAS) algorithm to achieve highly representative sparse sampling by incorporating obstacle information, thereby significantly reducing the cost of fingerprint data collection. Subsequently, the variational autoencoder with convolutional encoding (VACE) model accomplishes high-quality reconstruction from sparse to dense fingerprint databases. On this basis, the firefly algorithm-based cross-variation optimized extreme learning machine (FA-CVO-ELM) positioning model is designed, integrating the firefly algorithm (FA) with cross-variation operation (CVO) to perform dual optimization of the input weights and biases of the extreme learning machine (ELM), which enhances positioning accuracy and generalization capability. Simulation and experimental results demonstrate superior performance across diverse obstacle scenarios. The simulation achieves a 99.75% reduction in database construction cost compared to the initial sampled fingerprint database, while maintaining an average positioning error (APE) as low as 0.0423 m. Experimental results show a 97.6% reduction in construction cost with a minimum average positioning error of 0.0503 m. The framework realizes synergistic optimization between adaptive sampling for low-cost dense database construction and high-precision positioning, offering a viable solution for deploying visible light fingerprint positioning systems in obstructed environments.

Autonomous exploration in unknown environments is a fundamental capability for intelligent mobile IoT systems, especially in scenarios where prior environmental information is unavailable. In such settings, mobile IoT devices are required to achieve safe and efficient full-area coverage based solely on onboard sensing and limited computational resources. However, the unpredictable and continuously changing nature of unknown environments poses significant challenges to adaptive and collision-free exploration, particularly for resource-constrained mobile IoT devices. To address these challenges, we propose an autonomous full-area exploration algorithm for mobile IoT devices based on reward strategy cloning. Specifically, a state representation method using color mapping is designed to improve the information intensity of input state in full-area coverage exploration missions. Simultaneously, to address the issue of sparse rewards in full-area exploration missions, we construct an intensive reward shaping function that integrates exploration rewards, collision penalties, and incentives for exploring frontier trends. Furthermore, a lightweight exploration model that maps state to action reward is designed for mobile IoT devices with limited computing power and storage resources. Moreover, we propose a reward-sensitive dynamic ϵ-greedy strategy that adaptively balances exploration and exploitation based on real-time performance trends. Finally, empirical results demonstrate the robustness of the proposed algorithm in exploring various complexities and dynamic environments. In particular, the computational complexity of the proposed exploration model is significantly reduced compared to other models.

Visible light positioning (VLP) has emerged as a promising solution to address the requirements of indoor industrial localization services, such as the smart healthcare and indoor navigation. Increasing LEDs can boost positioning performance while leading to higher energy consumption, increased costs, and potential signal interference issues. To solve this problem, we propose an LED deployment algorithm that improves VLP performance by optimizing the placement of LEDs, thereby avoiding the introduction of excessive LEDs. The proposed algorithm uses the squared position error bound (SPEB) as the metric for deployment to assess the overall positioning performance. Additionally, we utilize Fisher information (FI) to quantify the information received from different LEDs and derive the fusion rules of information ellipses (IEs) to guide the deployment, aiming to maximize the overall received information in the system. To address the non-convex deployment problem, we introduce the confidence region for convex relaxation of the localization area, achieving deployment by minimizing the upper bound of SPEB within the confidence region. Moreover, we derive the localization error bounds to analyze the impact of various key parameters on positioning performance. Convincing simulation results demonstrate the significant improvement in VLP performance achieved with the proposed algorithm.

Visible light positioning (VLP) enables high-precision indoor localization, but single-sensor systems do not scale well to multi-user settings: camera-based methods suffer from motion jitter, while photodiode (PD)-based approaches are vulnerable to ambient light and multipath effects. This paper proposes a camera-photodiode fusion-based cooperative localization algorithm (C-CPD): cameras extract geometric features of a circular luminaire for coarse positioning, and PD-light-emitting diode (LED) modules measure distances to cooperative visible light communication (VLC) units via received signal strength (RSS) to refine estimates, mitigating motion distortions. We derive the Cram´er-Rao lower bound (CRLB) to characterize performance, revealing impacts of focal length, signal-to-noise ratio (SNR), and cooperative VLC units. Simulations show 0.41 cm average error, and experiments demonstrate 3.24 cm real-world error, outperforming baselines in dynamic scenarios. This work highlights multi-sensor fusion and geometric feature exploitation for robust, high-accuracy VLP, with CRLB insights guiding system design.

Visible light positioning (VLP) has become a popular indoor localization technology due to its low energy consumption and high security. Multi-target VLP systems typically utilize LEDs as anchors for localization, while overlooking the potential collaborative gains among targets. In this paper, we derive the Cramér-Rao lower bound (CRLB) for the received signal strength (RSS)-based collaborative VLP systems. It reveals the long-term performance mechanisms and the gains from both collaboration and prior information. Based on the derived CRLB and Fisher information (FI) fusion rules, we propose a collaborative target selection algorithm to mitigate the computational complexity caused by excessive collaborative targets. By integrating collaborative and prior information, we formulate the collaborative localization (CL) problem as an augmented Lagrangian function and solve it through the alternating direction method of multipliers (ADMM). Simulation and experimental results validate the effectiveness of the proposed algorithm and performance analysis.

Federated learning (FL) is a privacy-preserving technique that enables local model training on devices without raw data sharing. However, a critical challenge in FL lies in the communication requirement of uploading the trained models to servers, which can be hindered by interference from ambient devices, particularly in unreliable wireless environments. To address this, hierarchical FL (HFL) introduces an additional intermediate layer where the edge server performs work aggregation from the devices nearby, aiming at reducing the communication load and improving the efficiency of model training. However, existing approaches suffer from two critical limitations. First, they fail to fully quantify the impact of device competition-induced interference on transmission performance, which leads to unacceptably high upload latency and low success upload probability (SUP). Second, they lack a targeted optimization strategy to balance model accuracy and transmission efficiency under dynamic interference conditions. To address these critical limitations and mitigate their adverse impacts on FL performance, we take these gaps as the core motivation of our work and propose a targeted solution. Specifically, we first model the network as a two-layer binomial point process (BPP), which allows us to analyze the network-layer performance and calculate the SUP for the trained model. Based on this model, we propose optimizing cluster selection to balance accuracy and latency, thereby enhancing overall FL performance. We formulate this optimization as a Markov decision process (MDP) and solve it using a twin-delayed deep deterministic policy gradient (TD3)-based cluster selection algorithm (CS-TD3). In addition, to guarantee network-layer performance and enhance the efficiency of HFL, we employ an experimental exhaustive search algorithm to find the best solution within a limited range. The experimental results show that our algorithm overperforms other commonly-used algorithms in terms of HFL accuracy and model transmission latency, achieving a 10.95% improvement over the other methods.

Integrated sensing and communication (ISAC) is emerging as a key technology and research focus for future vehicular communication networks (VCN). It achieves efficient reuse of wireless infrastructure and spectrum resources through the collaborative design of sensing and communication functionalities. However, this integration leads to inevitable mutual interference, and the complexity of channel conditions further complicates the coordination of these functionalities. This paper primarily focuses on the joint sensing-communication performance analysis of ISAC-enabled VCN. Specifically, we model the spatial distribution of roads using a Poisson line process, while the locations of vehicles and roadside units (RSUs) are represented by two one-dimensional Poisson point processes. We characterize the dynamic interference distribution caused by RSUs during the sensing and communication phases and calculate the probability of successful perception (PSP) for a typical pair. Furthermore, for this typical pair, we meticulously derive the communication coverage probability based on the derived PSP for such a pair. To provide a detailed analysis of the interaction between communication and sensing functionalities, we evaluate their trade-off relationship and derive the joint probability of ISAC coverage. Moreover, we perform comprehensive simulations to verify the theoretical results. Additionally, the numerical results demonstrate how different parameters impact the network performance, providing guidance for network deployment and resource allocation under certain performance requirements.

Federated Learning (FL) is an approach that allows nodes with limited resources to collaborate without the need to share their data. However, the computational performance of local devices, the the non-independent and identically distributed (Non-IID) nature of data, and limited communication resources inevitably reduce the model's convergence speed and accuracy. It is urgent to obtain FL models with superior performance under Non-IID data. In this paper, we propose FedCGSU, a federated learning method for client grouping based on similar uncertainty in local client distributions. FedCGSU achieves grouping by leveraging the similarity in local distributions, which reduces the impact of weight divergence among clients with different distributions. It then combines loss values and local data volumes in the aggregation method, which reduces the impact of computationally weak devices on the global convergence speed. Experiments using three public datasets are conducted, demonstrating that the FedCGSU approach exhibits outstanding performance in improving accuracy and accelerating convergence speed.

Federated edge learning (FEL) has become a research hotspot to relieve the computational burden on servers and protect users’ data privacy. In an FEL system, adjusting the client selection and resource allocation scheme can reduce the energy consumption and improve the learning accuracy. However, obtaining a high-learning accuracy and low-energy consumption are primary challenges for FEL when the channel state information (CSI) is imperfect and the resources are dynamic. With this concern, to balance the learning accuracy and energy consumption, we formulate a joint client selection and dynamic resource allocation problem for FEL with imperfect CSI. The optimization problem is formulated as a Markov decision process (MDP) that defines the state space, action space and reward function. To cope with traditional optimization algorithms’ inefficiency in solving the formulated problem, a deep reinforcement learning (DRL)-assisted method is used. We use a Softmax deep double deterministic policy gradient (SD3) framework to train the model. Furthermore, a novel SD3-based FEL algorithm (FL-SD3) is proposed for client selection and dynamic resource allocation. Simulation results show that the proposed FL-SD3 improves the success rate by 11.6%, whilst the accuracy-to-energy (AE) gain is improved by 70.1% compared with some existing methods.

With the emergence of parallel computing systems and distributed time-sensitive applications, it is urgent to provide statistical guarantees for age of information (AoI) in wireless cyber-physical systems (WCPS) with diverse parallelisms. However, most of the existing research on AoI have tended to focus on serial transmission, and the AoI performance of multi-stage parallel systems remains unclear. To help address these research gaps, in this work, we set out to investigate the age of event (AoE) violation probability in a three-stage WCPS with diverse parallelisms such as fork-join and split-merge. We analyze both transient and steady-state characteristics of AoE violation probability (AoEVP). Using these characteristics, we transform the AoEVP minimization problem into an equivalent minimization problem. Moreover, we develop a queuing model to capture the queue dynamics under the max-plus theory of stochastic network calculus (SNC) approach. Based on the max-plus model, we derive a closed-form Chernoff upper bound for the equivalent problem by applying the union bound and the Chernoff inequality. Furthermore, we characterize the service process for different parallelisms applicable to each stage. By solving the Chernoff upper bound with the service moment generation functions (MGFs), we obtain heuristic update period solutions for minimizing the AoEVP of three-stage WCPS. Simulation results validate our analysis and demonstrate that our heuristic update period solutions are near optimal for minimizing the AoEVP of three-stage WCPS with diverse parallelisms. Through the theoretical framework, we further provide insights into how such networks can be designed for better age performance.

Unmanned aerial vehicle (UAV) is expected to bring transformative improvements to the integrated sensing and communication (ISAC) systems, due to its high flexibility, high autonomy, large coverage and strong adaptability to various terrains. Sensory data is gathered by sensing UAVs (SUs) from the coverage area and then relayed to the corresponding fusion center UAVs (FCUs). Afterwards, terrestrial base stations receive the sensory data from FCUs in such air-ground networks. However, due to complex task execution environment and transmission environment, it is challenging to capture the network-layer performance of the sensory data transmission and evaluate the trade-off relationship between sensing and communication. In this work, we model and analyze the network-layer delay violation for an ISAC UAV network to address this challenge. Specifically, the UAV formation is distributed according to a Poisson cluster process (PCP). Then, the successful sensing probability is derived, with which the sensory data traffic can be captured. Under the sensory data flow, the delay violation probability is calculated for the two-stage sensory data transmission queue by exploiting stochastic network calculus (SNC). Furthermore, a delay minimization problem is proposed to reveal the trade-off relationship between sensing and communication under the power allocation strategy. Based on the long-term network-layer queue backlog evaluated, we are devoted to analyze the delay violation probability under an emergency that results in the antenna misalignment for one typical sensing UAV during a certain period. The steady-state and transient analysis for the ISAC UAV network not only illustrate the trade-off relationship between sensing and communication for the network, but also provide insights for on-demand power allocation, network deployment, control module provisioning and sensory data flow control under certain performance requirements.

Distributed edge clouds (ECs) have been recently shown with remarkable advantages in enhancing customized service provisioning by leveraging user proximity and edge resources. However, operating a massive EC network would inevitably incur a huge amount of energy cost to EC providers, which would offset their operating revenue without proper energy cost management. In this paper, we focus on conserving energy cost of ECs by taking advantage of both electricity price-aware geographical task dispatching and dynamic central processing unit (CPU) provisioning according to the spatiotemporal diversities of electricity prices and user task demands. Due to the significant switching cost of turning CPUs and services on/off, we formulate a multi-timescale energy cost minimization problem that integrates both large-timescale CPU provisioning and service placement, and small-timescale geographical task dispatching and CPU resource allocation. The Lagrange dual decomposition theory is exploited to deal with the spatio-temporal variable couplings. A distributed and online mini-batch learning (MBL) algorithm that relies on parameter approximation for large-timescale decision makings is proposed to learn the optimal Lagrange multipliers. Simulation results show the outstanding performance of the MBL algorithm.

This article investigates the resource allocation of a reconfigurable intelligent surface (RIS)-aided joint communication and sensing (JCAS) system in a coal mine scenario. In the JCAS system, an RIS is implemented at the corner of the zigzag tunnels to improve the complicated wireless environment, where ground obstacles frequently block direct links. In addition, a wireless backhaul base station with a limited energy budget is deployed in the depth of the mine to sense the target area and provide Internet of Things (IoT) services and communication services for users. Furthermore, a data center is placed on the ground to analyze the obtained data and route the communication data. Under this deployment, a joint optimization problem of RIS phase-shift matrix, RIS element switches, and area sensing time is proposed. We aim to maximize the successful sensed bits under total completion time, and maximum transmit power constraints. In order to solve this problem, an iterative algorithm is proposed. The successive convex approximation (SCA)-based algorithm is used for the RIS phase-shift matrix optimization subproblem. For the sensing time optimization subproblem, the quadratic approximation method is proposed to optimize the number of area perceptions. The coordinate descent method is utilized to optimize the RIS element switches. Simulation results show that the energy efficiency is improved by up to 38%, and 7% increases the specific data size compared with the benchmark solutions.

How to achieve reliable communication and safety monitoring is very important in coal mines. However, most of the existing transmission strategies and sensing-based monitoring approaches assume a single objective and neglect non-line-of-sight (NLOS) problems brought by winding tunnels or mine collapses. To this end, we first propose a multihop reconfigurable intelligent surface (RIS)-aided joint communication and sensing (JCAS) approach to maximize the energy efficiency of the JCAS access point and the sum sensing rates in order to improve the sensing accuracy. Specifically, we formulate an energy-efficient optimization problem by jointly designing both the phase-shift matrix and the switches status of the RISs as well as the transmit power of the access point. The problem is solved by adopting the successive convex approximation-based alternating optimization algorithm, the second-order optimization method, the Lambert-$w$function, and Newton’s method. Moreover, a sensing-based rate optimization problem is also solved via the Lagrange relaxation method and the Gradient descent method. Simulation results demonstrate that the proposed algorithm has better robustness and higher energy efficiency.

Wireless networks in next generation will provide users ubiquitous computing services with low delay by devices at the network edge, namely mobile edge computing (MEC). The intensive computation tasks can be partially offloaded to the MEC server via the wireless link and then processed through the MEC computation resources to cater for the delay demand. A parallel computation process is formed in the MEC network consists of local computation at MEC users (MUs) and MEC computation at MEC servers. However, the fluctuating wireless channel environment, changeable spatial distribution of MUs and the randomness of MEC servers’ locations make it hard to characterize and guarantee the end-to-end quality of service requirements. In this work, we are devoted to analyze and optimize the overall delay bound for MEC networks under two orthogonal frequency division multiple access (OFDMA) strategies via stochastic network calculus (SNC). Specifically, Poisson cluster process is utilized to capture the randomness of MEC servers’ and users’ spatial locations and to derive the Laplace transform of interference suffered by an MU of interest. The upper bounds for the delay violation probability of two OFDMA strategies are established by exploiting SNC with the Mellin transform of signal-to-interference ratio. Furthermore, we propose an optimal task offloading scheme by minimizing the overall delay, which balances the local computation delay and MEC delay.

Unmanned aerial vehicles (UAV) wireless communications have attracted great interests in 5G networks due to its high mobility, on-demand deployment and low cost. However, it arises new serious concerns about the malicious eavesdropping attacks against UAV communications. In this paper, we study the UAV transmission with a wiretap Rayleigh fading channel, over which UAVs transmit data to a target receiver in an unsafe environment with multiple eavesdroppers. A secure transmission scheme is proposed for satisfying various performance requirements including secrecy and transmission latency, considering the unavailability of wiretappers' instantaneous channel state information (CSI). In particular, secrecy performance is measured by the derivation of secure transmission probability (STP) by physical layer security (PLS) technique. A novel stochastic-network-calculus (SNC) approach is proposed to analyze the service capability of the wiretap channel and as well calculate the latency bounds, whilst obtaining the internal relationship between secrecy and latency. Simulation results verify the theoretical performance bounds, which provide a guidance for designing secure transmission strategies with various performance requirements.

The future 5G wireless network is largely driven by the increasing heavy traffic and spectrum scarcity. Cognitive Radio (CR) techniques provide a potential solution for improving the spectrum efficiency. In this paper, we study the stochastic framework for the CR networks, considering different quality of service (QoS) requirements. To analyze the performance of the CR network, we adopt a poisson point process (PPP) to capture the mobility and randomness of user location. A stochastic-network-calculus (SNC) based approach is proposed to model the wireless transmission and evaluate the network performance. In order to achieve the performance metrics of end-to-end (E2E) delay and backlog in the entire network, we propose a new conception named as effective service process (ESP) which is able to capture the QoS requirements of users. Furthermore, we evaluate the performance in the exponential domain, which can present the E2E analysis more directly. The simulation results verify the theoretical analysis and show that the performance in the CR networks can be derived perfectly with the proposed approach, considering the stochastic traffic arrival and designed service model in our schedule.