In mega low Earth orbit (LEO) satellite constellations, guaranteeing deterministic communication performance is essential for mission-critical applications and reliable service delivery. Unlike terrestrial networks, these large-scale satellite networks present unique challenges: highly dynamic topologies, significant propagation delays, limited on-board processing capabilities, and cumulative jitter uncertainty that increases with hop count. In this case, traditional deterministic mechanisms, i.e., Time-sensitive networking (TSN), become ineffective due to their inability to handle both the scale and dynamics of mega constellations. To address this issue, we propose a TSN-enabled LEO satellite network, whereby we derive single-hop delay upper bounds using network calculus, specifically accounting for high-priority preemption and dynamic satellite characteristics. Then we propose DomainR, a novel domain-based deterministic routing framework that effectively manages large-scale satellite networks through strategic network decomposition and adaptive routing. DomainR consists of two phases: first, a domain-based network compression phase that partitions mega constellations into manageable domains while preserving path continuity and routing efficiency, and, second, a delay budget-aware dynamic routing phase that adaptively allocates delay budgets based on aforementioned delay upper bounds and real-time network conditions. Through rigorous mathematical analysis, we derive the optimal domain sizing rules and establish a theoretical minimum domain count. Comprehensive simulations demonstrate that DomainR significantly reduces end-to-end jitter, improves network resource utilization, and capacity in mega LEO constellations.

6G networks are envisioned to support on-demand AI model downloading to accommodate diverse inference requirements of end users. By proactively caching models at edge nodes, users can retrieve the requested models with low latency for on-device AI inference. However, the substantial size of contemporary AI models poses significant challenges for edge caching under limited storage capacity, as well as for the concurrent delivery of heterogeneous models over wireless channels. To address these challenges, we propose a fine-grained AI model caching and downloading system that exploits parameter reusability, stemming from the common practice of fine-tuning task-specific models from a shared pre-trained model with frozen parameters. This system selectively caches model parameter blocks (PBs) at edge nodes, eliminating redundant storage of reusable parameters across different cached models. Additionally, it incorporates coordinated multipoint (CoMP) broadcasting to simultaneously deliver reusable PBs to multiple users, thereby enhancing downlink spectrum utilization. Under this arrangement, we formulate a model downloading delay minimization problem to jointly optimize PB caching, migration (among edge nodes), and broadcasting beamforming. To tackle this intractable problem, we develop a distributed multi-agent learning framework that enables edge nodes to explicitly learn mutual influence among their actions, thereby facilitating cooperation. Furthermore, a data augmentation approach is proposed to adaptively generate synthetic training samples through a predictive model, boosting sample efficiency and accelerating policy learning. Both theoretical analysis and simulation experiments validate the superior convergence performance of the proposed learning framework. Moreover, experimental results demonstrate that our scheme significantly reduces model downloading delay compared to benchmark methods.

In the routing process of low earth orbit satellite networks, frequent topology changes and complex space environment cause routing interrupted and sudden link failures. Traditional terrestrial routing not only fails to manage this problem but also leads to random network congestion. To address these issues and to achieve high adaptability and stability routing strategy, this paper proposes an intelligent distributed routing algorithm based on multi-agent deep reinforcement learning (MADRL) with Lyapunov optimization. Firstly, we build Lyapunov-based network optimization model and analyze the model stability. Then, we combine the Lyapunov optimization with the MADRL framework to stabilize the network. Each satellite agent selects next hop node according to the queue backlogs and the distance between the next hop node and the destination. Evaluation results show that our proposed Lyapunov optimization aided DRL (LOA-DRL) algorithm has better performance in terms of delivery ratio, average delivery time, throughput and average queue backlogs.

As deep space exploration missions grow in complexity, efficient data transfer in interplanetary networks (IPNs) becomes paramount. However, the vast distances, limited bandwidth, and dynamic nature of IPNs pose significant challenges for the routing and data scheduling of interplanetary data transfers (IP-DTs). To address these challenges, this work proposes a novel distributed, graph neural network (GNN) based multiagent deep reinforcement learning (DRL) approach that can jointly optimize the routing and scheduling of IP-DTs. Our proposal is based on the proximal policy optimization (PPO) framework along with the graph attention networks (GATs). We make the DRL agents for IPN nodes in each subnetwork around a celestial body learn and operate independently, for making intelligent routing and scheduling decisions to properly tradeoff between average end-to-end (E2E) latency and delivery ratio of IP-DTs while ensuring good scalability. Extensive simulations confirm that our proposal handles the routing and scheduling of IP-DTs much better than existing benchmarks. Further, by modifying the interplanetary overlay network (ION) software platform developed by NASA, we build a semi-physical IPN emulator based on Raspberry Pi boards, implement our proposal in it, and conduct experiments with real data transfers between IPN nodes. Experimental results verify that our proposal can work for practical IPNs without causing excessive overheads and prove its advantages.

To solve the sudden inter-satellite link failures of low earth orbit satellite networks (LEO-SNs), a robust routing algorithm against link failures is proposed in this paper. Firstly, we introduce a 2-D Markov model for LEO-SNs to study the problem about how to minimize the probability of encountering link failures in minimum-hop path set. Theoretical results indicate that forwarding in the more-hop direction has a lower probability to encounter link failures. Based on the results, we propose a More-Hop Direction Priority routing algorithm with routing Recovery strategy by Extending Path Area (MHDPREPA). The algorithm consists of three components which are routing preparation, routing calculation and routing recovery. In the routing process, each node aims to avoid encountering link failures when selecting the next hop node. If the node encounters link failures, a routing recovery strategy is adopted to bypass the failed links. Simulation results show that our proposed algorithm can effectively improve delivery ratio and average throughput compared with baseline algorithms.

Low Earth Orbit (LEO) satellites enable various Internet of Things (IoT) applications by providing plentiful observation data at different spatial scales. However, transmitting such a large volume of observation data to the ground for model training is challenging due to the intermittent connection between satellites and ground stations, leading to long training delays for task-oriented artificial intelligence models. Orbital federated learning has been investigated to enable on-board model training sharing model parameters instead of observation data, thus greatly reducing the communication overhead. However, these works underutilize the availability of inter-satellite links and ignore energy consumption. To fill this gap, we propose an energy-efficient federated learning scheme for earth observation applications in LEO satellite systems to minimize learning loss and energy consumption. Specifically, to reduce the effect of data heterogeneity on learning accuracy, we propose a satellite grouping scheme based on satellites' data distribution and communication delay to ground stations. Then, we optimize each satellite's transmission power and computing frequency under the constraint of training time. Experimental results based on popular datasets show the efficacy of the proposed scheme compared to benchmark methods.

We investigate the resource allocation problem of a cell-free massive multiple-input multiple-output system under the condition of colluding eavesdropping by multiple passive eavesdroppers. To address the problem of limited pilot resources, a scheme is proposed to allocate the pilot with the minimum pollution to users based on access point selection and optimize the pilot transmission power to improve the accuracy of channel estimation. Aiming at the secure transmission problem under a colluding eavesdropping environment by multiple passive eavesdroppers, based on the local partial zero-forcing precoding scheme, a transmission power optimization scheme is formulated to maximize the system’s minimum security spectral efficiency. Simulation results show that the proposed scheme can effectively reduce channel estimation error and improve system security.

With the rapid development of wireless communications, integrated sensing and communication (ISAC) has attracted considerable attentions, which enables both data transmission and target detection simultaneously by spectrum sharing. The adaptive Orthogonal Frequency Division Multiplexing (OFDM) shared waveform design can dynamically adjust power allocation based on the preferences of the radar or communication system, which achieves optimal ISAC performance with given static channel conditions. For the perfect channel state information (CSI) is hard to obtain due to the feedback errors, we then propose a robust OFDM shared waveform design, which achieves better performance under the worst-case channel states. The Karush-Kuhn-Tucker (KKT) conditions are formulated and an improved greedy algorithm is introduced to adjust the bit and power allocation on each subcarrier adaptively. Theoretical analysis and simulation results verify the effectiveness of the proposed algorithm for the joint optimization of both radar and communication systems.

Considering the scenarios where the localization accuracy of the agent is required to meet a desired requirement rather than achieve the best result, it is not necessary for all nodes to participate in positioning the agent. In this paper, a reference node (RN) selection algorithm for wireless network localization under desired accuracy is proposed. A robust error bound (REB) is derived as the RN selection metric and the concept of equal-REB contour is given, based upon which the searching region (SR) for selecting RNs is defined. In REB, the measurement errors of distances are taken into consideration and modeled as a Gaussian noise whose variance is proportional to the square of the distance. The proposed RN selection strategy selects nodes from the SR instead of the whole network region iteratively until the localization accuracy meets the desired requirement. Simulations show that the RN-selection algorithm can select the RN sets providing better localization accuracy when using REB metric. Moreover, the improved performance in terms of power conservation of the proposed algorithm is evaluated through simulation results.

With the rapid growth of terminal equipments, the data traffic in the network has grown exponentially. In order to relieve the pressure of cloud computing on link delay, congestion and energy consumption, the promising fog computing is proposed. The fog network consists of several fog clusters. We consider a fog cluster in which a fog controller (FC) aims to schedule the idle fog nodes (FNs) to serve the task node (TN) while guaranteeing the quality of service (QoS) requirements of the TN. We design an ascending-bid auction mechanism to achieve this goal. In this mechanism, the FC is the auctioneer with the reward prices as its strategy and the FNs play the role of bidders with the task sizes as their strategies. The FC uses the bid prices to motivate the FNs to process more data for the TN. The utility function of FNs is proposed, considering the payment from the FC, the cost of task computational delay and energy consumption. The FNs determine the data sizes to be processed by maximizing their utilities. Numerical simulations indicate the satisfactory performance and verify the theoretical analysis, thereby our proposed mechanism results in a win-win solution under the condition of meeting the QoS.

Cognitive radio enabled vehicular ad‐hoc networks (CR‐VANETs) are one of the promising architectures in the future vehicular ad‐hoc networks. In this study, the joint spatial–temporal correlation is exploited to improve decision accuracy of cooperative spectrum sensing (CSS) while reducing overhead introduced by cooperation in the CR‐VANETs. Firstly, a theoretical method is presented to analyse the impact of spatial–temporal correlation on cooperative sensing performance when using soft combining in the CR‐VANET. Then, the expression of the optimal probability of detection with respect to spatial–temporal correlation is given for a target probability of false alarm by employing likelihood ratio test. Additionally, the user selection problem is formulated as an efficient double‐threshold optimisation problem by considering both sensing accuracy and stability to achieve the optimal probability of detection. Finally, a hybrid CSS scheme based on spatial–temporal correlation is designed for the CR‐VANET. Simulation results reveal that the proposed scheme could achieve significant sensing performance gain by selecting a subset of secondary users for combination, and could reduce user selection frequency by employing spatial–temporal diversity.

In this paper, we propose an energy-efficient power control algorithm (EPCA) using a game theory approach, which can be exploited for non-orthogonal multiple access (NOMA) in heterogeneous networks (HetNets). Firstly, we formulate the energy efficiency (EE) maximization problems for users in the macrocell and femtocell, respectively. Then, to reduce information exchanged between the base stations and users, we present a centralized implementation of the EPCA based on a noncooperative game. By studying the properties of the derived game, we develop convex optimization problems to deduce the unique Nash equilibrium (NE). Simulation results show that the proposed EPCA could converge to the equilibrium with higher system-level EE and spectrum efficiency.

Cognitive radio enabled vehicular ad hoc networks (CR- VANETs) is one of the promising architecture in the future VANETs. In this paper, the joint spatial- temporal correlation is exploited to improve decision accuracy of cooperative spectrum sensing while reducing overhead introduced by cooperation in the CR-VANET. Firstly, a theoretical method is presented to analyze the impact of spatial-temporal correlation on cooperative sensing performance when using soft combining in the CR-VANET. Then, the expression of the optimal probability of detection with respect to spatial-temporal correlation is given for a target probability of false alarm by employing likelihood ratio test. Additionally, the user selection problem is formulated as an efficient double threshold optimization problem by considering both sensing accuracy and stability to achieve the optimal probability of detection. Finally, a hybrid cooperative spectrum sensing scheme based on spatial-temporal correlation is designed for the CR-VANET. Simulation results reveal that the proposed scheme could achieve significant sensing performance gain by selecting a subset of secondary users for combination, and could reduce user selection frequency by employing spatial- temporal diversity.

The software‐defined wireless sensor networks (SDWSNs) have been proposed recently to solve the energy limitation of sensor nodes and extend the lifetime of the wireless sensor networks by fast node reconstruction and dynamical resource allocation. In this study, the authors investigate an energy‐efficient resource allocation algorithm in SDWSNs, in which radio resource allocation could be handled at central controllers with powerful storage and computation capacity. In this algorithm, the authors formulate an optimisation problem to minimise the energy consumption, under the individual constraint of quality of service. Then, the initial optimisation problem is transformed using semidefinite relaxation, to achieve centralised adaptive bandwidth and power allocation (CABPA). Additionally, two special cases are derived to reveal the performance of the CABPA. Furthermore, an OpenFlow‐based scheme is proposed for information exchanging and updating to realise the centralised resource allocation. Meanwhile, a distributed scheme with limited information about the whole network is developed to serve as a performance benchmark for the CABPA in the SDWSN. Finally, the simulation results reveal that the proposed CABPA performs better than the other algorithms, and it balances the power and bandwidth utilisation.

In this paper, we propose a game theoretic approach for joint offloading and resource allocation optimization (JORAO) problem in mobile edge computing (MEC) system. This study not only investigates offloading strategy, but also considers cloud and wireless resource allocation. Specially, the concern of the JORAO problem is to minimize the energy consumption and monetary cost from mobile terminals' perspective. However, the JORAO problem is non-convex and NP hard. Therefore, it is formulated as a JORAO game. The existence of Nash equilibrium (NE) is proved for it. To obtain NE, we also concentrate on cloud and wireless resource allocation algorithm (CWRAA), which is the sub- algorithm of the JORAO game. For the CWRAA, on one hand, we take consideration of OFDM sub-channels allocation and uplink power allocation in radio access networks (RAN). On the other hand, the computation resource allocation in MEC is studied. Simulation results show that the distributed JORAO game algorithm can nearly minimize the total cost of all mobile terminals (MTs) with low complexity. In addition, the energy consumption and completion time are less when the size of data becomes larger compared with existing algorithms.

In this paper, we propose a weighted cooperative localization algorithm with the ability to mitigate non-line-of-sight (NLOS) propagations in wireless networks. The link condition indicator (LCI) for each connection is calculated based on the amplitude and delay statistics of channel responses. We partition the ambiguity of link condition into N levels according to the LCI values. With the distance-dependent LOS/NLOS probability suggested by the 3rd Generation Partnership Project (3GPP), the relationship between LOS/NLOS probability and the time-of-arrival (TOA) of inter-node signal transmission is derived. We incorporate this probability into N-level LCI range regions and propose the N probabilistic hard weight (N-PHW) strategy for the cooperative localization, which penalizes the NLOS-induced positive biases by weighting the belief terms introduced by the conventional cooperative localization algorithm, the sum-product algorithm over a wireless network (SPAWN). Simulation results show that the proposed weighted algorithm significantly improves the localization performance in terms of localization accuracy, especially in serious NLOS environments.

In this paper, a positioning system is designed based on the visible light communication (VLC) technology for the tracking of mobile target in the indoor environment. The practical received signal strength (RSS) can be measured for subsequent positioning algorithm by using the designed system. Based on the practical measurements, an empirical equation is proposed to estimate the target's distances relative to the referenced LEDs. Then, taking advantage of the relative distances, the location estimation can be obtained by the trilateration method. Furthermore, Kalman Filter is used for improving the positioning result computed by trilateration method. Finally, experimental results show that the average location errors by adopting the derived empirical formula and trilateration can reach to 3.4cm, and the accuracy of positioning can be promoted to 2.6cm by using Kalman Filter in a real wireless environment.

In this study, the authors propose a semidefinite programming (SDP)‐based localisation and tracking algorithm, which mitigates the non‐line‐of‐sight (NLOS) error of range measurement and calibrates the accumulative error within the inertial sensing data. Both the range measurement in a mixed line‐of‐sight/NLOS environment and the step length estimated from inertial sensing information are approximated parametrically using Gaussian mixture modelling, and a maximum‐likelihood estimator (MLE) is formulated to obtain the optimal position estimation. Since the Gaussian mixture models are non‐linear functions of positions, the MLE is a non‐convex problem, which global optimum is difficult to attain. Then, the non‐convex MLE is transformed into an SDP‐based localisation and tracking problem, relying on Jensen's inequality and semidefinite relaxation. Thus, a sub‐optimal solution to the original MLE can be achieved. Moreover, the Cramer‐Rao lower bound is also derived to serve as a performance indicator for localisation errors. The simulation and experimental results demonstrate the performance of the proposed algorithm. Compared with the existing algorithms, the proposed algorithm owns the best localisation accuracy, and can achieve a sub‐metre level accuracy to a root mean square error of 0.46 m in the real deployments.

In this study, the authors propose an improved localisation algorithm in the software‐defined sensor networks (SDSNs). This algorithm includes a node‐selection strategy under the whole network power constraint, based on the software‐defined networking (SDN) technique for providing the centralised control of the network. The analogous Cramer‐Rao lower bound (A‐CRLB) value is derived for each participating node, which represents a fundamental bound on the variance of the position estimator and is used to evaluate the contribution of each node to localisation accuracy. On the basis of A‐CRLB values, the most helpful nodes for localisation are selected to maximise the sum of the nodes' contributory values to the localisation accuracy. With the global network knowledge provided by the SDN controller in the SDSN, the node‐selection strategy is formulated into a 0‐1 programming problem on the premise of power satisfaction of each node. Furthermore, the proposed node‐selection ‐based localisation algorithm is applied to both noncooperative and cooperative localisation scenarios. Simulation results show that the proposed algorithms provide efficient and effective localisation schemes in SDSNs, and can improve the performance in terms of both the selection convergence speed and the localisation accuracy.

Ultra-wide bandwidth (UWB) and Inertial Navigation (IN) have been adopted in high precision localization and tracking systems. However, the ranging measurements influenced by non-line-of-sight (NLOS) path may degrade the localization accuracy. Besides, inertial measurement errors are within the sensing data and constantly accumulate over the time. To overcome the above problems, we investigate NLOS mitigation for UWB measurements, and calibration for IN estimations. Both ranging measurement and step length estimation are modeled using Gaussian mixture model (GMM), and one maximum likelihood (ML) estimator is developed. Then, the non-convexity estimator is relaxed into a semidefinite programming (SDP), which global minimum can be readily attained. Finally, both simulation and experimental results are provided to illustrate the validity and performance of our proposed SDP-based localization and tracking algorithm.

In this paper, one centralized resource allocation algorithm is proposed to minimize energy consumption in software defined wireless sensor networks (SD-WSNs). The energy consumption problem is formulated as an optimization problem, given quality-of-service (QoS) constraint defined as Signal-to-Interference-plus-Noise Ratio (SINR). Then, the nonconvex optimization problem is relaxed into a semidefinite programming (SDP), which serves as a lower bound. To analyze the tightness of the lower bound, two special cases are introduced. Besides, one distributed approach is also developed to provide a performance benchmark. Furthermore, simulation results are revealed that the proposed centralized algorithm performances better with respect to the energy consumption and bandwidth utilization.

Many applications can be benefit greatly from location- awareness information obtained by positioning and navigation system. Nowadays, a hybrid method combining Ultra-wide bandwidth (UWB) ranging modules and inertial measurement unit (IMU) has been one promising scheme for high precision positioning requirement. However, positioning errors could be terribly affected by the noise of UWB ranging measurements and accumulated errors caused by inertial sensing data. In this paper, a semidefinite programming (SDP) based node localization algorithm is proposed for such hybrid method. The positions of target sensors (TNs) can be determined using the distance estimations from location-aware anchor nodes (ANs) as well as other inertial information (e.g., acceleration and azimuth). Meanwhile, the corresponding Cramer-Rao lower bounds (CRLB) are derived as performance benchmarks. Finally, simulations are provided to illustrate the validity of our hybrid algorithm, which demonstrate that the proposed algorithm achieves superior performance and it could be very promising for high precision positioning service.

Schemes combining Ultra-wide bandwidth (UWB) ranging technology and Inertial Measurement Unit (IMU) have been proposed for high precision positioning and tracking. However, positioning accuracy can be significantly affected by the non-line-of-sight (NLOS) UWB ranging measurements and cumulative inertial sensing error. In this paper, we model the ranging measurement error and the step length as Gaussian Mixture Model (GMM), respectively. Then, we derived a Suboptimal Importance Density (SID) for particle filters, which could resolve the degeneracy of particles and sample impoverishment. Finally, experimental results illustrate the performance gain of the particle filters with the proposed SID.

Cooperative localization has become a promising solution for location-enabled technologies in Wireless Sensor Networks (WSNs). However, it suffers from great energy consumption problem due to the energy-constrained characteristic of the networks. To alleviate this problem, we propose a cluster nodes selection strategy based on the Cramer-Rao lower bound (CRLB) for the cooperative localization algorithm in WSN. We first define clusters for every agent node by setting the received signal strength (RSS) threshold to screen out some less useful nodes, which greatly saves the energy at a cost of only a slight degradation in accuracy. Then, to improve the localization accuracy, the cluster nodes selection strategy catches the nodes that make the biggest contribution to localization results while discarding the least ones based on the derived analogous-CRLB values. Simulations show that the number of nodes participating in the localization is greatly decreased, which means a substantial reduction in energy consumption. In addition, the localization mean absolute error performance is significantly improved by using the proposed nodes selection algorithm.

Localization technologies in wireless sensor networks have been suffering from great energy consumption problem due to the energy-constrained characteristic of the networks. Existing power allocating solutions are mostly distributed for the lack of global network knowledge. In this paper, we investigate localization algorithm with the support of software-defined network (SDN) technique and propose a localization node selection algorithm based on the cramer-rao lower bound (CRLB). By making use of the global network knowledge provided by the SDN controller, we formulate the issues into a 0-1 programming problem on the premise of energy satisfaction. Simulation results show that significant improvement in localization performance can be achieved with our proposed SDN based algorithm.

The array files (AF) API facilitates the manipulation of a large number of records in a message-passing cluster application. Three AF implementations are studied. AFv1 and AFv2 assume the existence of an underlying parallel file system and can be used by PVM or MPI message passing programs. Testing over NSF, PVFS, and Lustre were performed. AFv1 uses separate files on the parallel file system to store different records. AFv2 uses a large file on the parallel file system to store all the records. File locking is used to enforce POSIX file access semantics, and AFv1 and AFv2 provide an application-level locking service for parallel file systems that do not support a native lock subsystem. AFv3 is implemented using MPI-2 and can be used by MPI-2 applications. AFv3 implements parallel I/O with an object-based striping strategy, has no central server, uses a hash function to locate an I/O server, and provides POSIX file access semantics to the user application. AFv2 over PVFS shows somewhat better performance for some workloads with very large records. AFv3 is shown to have better performance than both AFv1 and AFv2 for the broadest range of workloads