Computing Power Networks (CPNs) have become an essential network architecture for supporting emerging applications, where an efficient server deployment scheme is critical to meeting growing demands. However, existing studies on server deployment schemes overlook the significant spatiotemporal variations in renewable energy availability and electricity prices, and inadequately consider network paths and task characteristics, ultimately leading to suboptimal decisions. To bridge this gap, this paper proposes a demand-driven server deployment scheme enabled by a spatiotemporal task scheduling strategy. Firstly, for the task scheduling scheme, we leverage a demand response program to perform triple selection of computing resources, routing paths, and forwarding time. Then, for the server deployment scheme, we obtain the computing resource demand of each CPN node based on the optimized scheduling decisions and further select energy-efficient servers with low procurement costs. Due to the interdependence between the scheduling and deployment schemes, a Multi-Objective Evolutionary Algorithm (MOEA)-based hierarchical solution is developed to iteratively find the optimal deployment solution. Simulation results show that the proposed scheme significantly reduces carbon emissions and annual costs while increasing the proportion of green energy usage, outperforming benchmark methods. The findings demonstrate the effectiveness of integrating scheduling and deployment for building efficient and sustainable CPN infrastructures.

Recently, computing Power Networks (CPNs) have emerged as a critical infrastructure for supporting intelligent services, particularly Large Language Models (LLMs). While integrating renewable energy is imperative for carbon neutrality, it faces significant challenges due to energy intermittency and the rigid reliability requirements of computing resources. The resulting spatiotemporal dynamics create a complex trilateral interdependence among computing, energy, and carbon, complicating precise emission assignment. In this paper, we propose a carbon-tracking hierarchical trading mechanism to address these challenges in green CPNs. Specifically, we formulate a three-stage Stackelberg game to decouple dynamic interactions among consumers, resource providers, and Energy Supply Points (ESPs), facilitating flexible benefit coordination. Furthermore, we simplify the game into convex optimization problems and theoretically prove the existence and uniqueness of the Nash Equilibrium (NE). Simulation results validate the stability of the proposed scheme and demonstrate its superior efficacy in balancing multi-party benefits and reducing carbon emissions compared to existing baselines.

Low Earth Orbit satellite networks play a significant role in providing global ubiquitous services. The application of Inter-Satellite Links (ISLs) has accelerated development in Non-Terrestrial Networks with satellite backhaul. However, ISL-based backhaul does not match the performance of terrestrial fiber links, which makes its impact on end-to-end performance non-negligible. Existing radio resource allocation solutions usually neglect the backhaul performance, potentially failing to meet end-to-end Quality of Service requirements. In this work, we propose ARA-IBA, an access resource allocation scheme with ISL-based backhaul awareness. The satellite network’s backhaul path states, including delay and packet loss rate, are exposed to on-board base stations to enable dynamic resource scheduling. In ARA-IBA, resources are jointly scheduled for Guaranteed Bit Rate (GBR), Delay-critical GBR, and Non-GBR users. For GBR and Delay-critical GBR users, backhaul delay is utilized to optimize end-to-end delay satisfaction. A clustering game is employed to perform fine-grained allocation adjustments. Overloaded Non-GBR users are then scheduled using a pointer network. ARA-IBA optimizes backhaul packet loss rate while maintaining scalability to accommodate varying user numbers. Simulation results demonstrate that the proposed algorithm outperforms conventional methods in terms of users’ delay satisfaction and backhaul packet loss rates.

Service Function Chains (SFCs) in Space–Terrestrial Integrated Networks (STINs) are essential for sixth-generation (6G) applications, enabling flexible and global service delivery. However, the asynchronous evolution of topology and resources introduces cross-timescale inconsistencies that complicate dynamic SFC orchestration. To address this issue, we propose the Flexible-Granularity Time-Expanded Graph (Flex-TEG), which captures topology dynamics at complete-time-slot granularity and adaptively represents resource fluctuations through sub-time slots. Based on Flex-TEG, we design a lightweight flexible-granularity orchestration mechanism that decouples path planning from Virtual Network Function (VNF) deployment and resource scheduling across temporal granularities. The mechanism is instantiated by Successive Convex Approximation-based Offline Request Scheduling (SCA-ORS) and Lightweight Reinforcement Learning-based Online Request Scheduling (LRL-OLRS), enabling efficient cross-scale orchestration with distributed VNF deployment. Extensive simulations show that the proposed methods outperform Time-Expanded Graph (TEG)- and Snapshot Sequence Graph (SSG)-based approaches in model complexity, service latency, request acceptance, and energy consumption.

Integrated satellite-terrestrial networks (ISTNs) are pivotal for ubiquitous 5G/6G connectivity but face severe resource allocation and traffic scheduling challenges due to highly dynamic satellite orbits and heterogeneous traffic demands. To address these issues, this paper proposes a software-defined networking (SDN)-based ISTN architecture featuring a novel two-level temporal hierarchy that effectively decouples long-term multi-path planning from real-time traffic scheduling. First, regarding long-term planning, we formulate a time-aware particle swarm optimization (PSO) algorithm incorporating a penalty mechanism to enforce link-duration constraints derived from orbital dynamics. Second, for real-time scheduling, we design a distributed asynchronous advantage actor-critic (A3C) framework. This hybrid mechanism combines local reinforcement learning at user proxies with lightweight global supervision, thereby mitigating centralized computational bottlenecks and ensuring scalable coordination. Simulation results demonstrate that the proposed method significantly outperforms state-of-the-art techniques in terms of response time, load balancing, and packet delivery reliability.

Low earth orbit (LEO) satellites have recently been applied in various Earth observation applications, generating massive observation data. Promptly transmitting such observation data to the ground is hindered by satellite-ground transmission links with limited data rates. To address this challenge, effective transmission and orbital edge computing schemes are explored to enable continuous transmission and computation offloading via inter-satellite links (ISLs). However, most existing works primarily emphasize the joint optimization of computation and communication resources while ignoring the distribution of observation resources and the coexistence of heterogeneous tasks, thus resulting in degraded performance due to the mismatch between satellite resources and heterogeneous task requirements. This paper proposes cooperatively scheduling satellites for observation, relay, and computation to maximize the number of completed observation tasks with diverse requirements, which is formulated as a mixed-integer linear programming problem constrained by satellites' observation, transmission, and computing resources. An iterative resource-aware scheduling algorithm, JORCA, is designed to find the solution by decomposing the original problem into two sub-problems. Extensive experimental results reveal that JORCA can improve the number of completed observation tasks by up to 40.6% compared to the benchmark policies.

Space-Terrestrial Integrated Networks (STIN) have an important influence on Service Function Chains (SFCs), broadening their service scope and enhancing application performance. However, STIN features a dynamic terrestrial access layer and an inter-satellite layer; it's complex to orchestrate the SFC in such a dynamic network. In this paper, we provide the Virtual Multi-Resource Storage Time Aggregated Graph (VMR-STAG) model and SFC orchestration algorithms for SFC orchestration in STIN. Inspired by the Virtual Topology (VT) method, VMR-STAG aggregates the dual-layer dynamic topology and time-varying resources into a single virtual graph, thus reducing the complexity of the STIN model. Based on VMR-STAG, we propose the Offline Request Orchestration (ORO) and Online Request Orchestration (OLRO) algorithms, designed to minimize SFC migration frequency, service delay, service jitter, and enhance load balancing. Leveraging resource distribution across multiple time slots, these algorithms schedule the long-lasting, high-performance SFC within STIN. Evaluation and simulation results demonstrate that our proposed model and algorithms significantly outperform conventional solutions, achieving significant improvements in model complexity, SFC migration frequency, workload balancing, service delay, and jitter.

Orbital edge computing (OEC) is expected to provide orbital data processing, conserving communication resources and enabling real-time computing responses. In view of the uneven computing resources and computation task requests of low earth orbit (LEO) satellites, computation offloading is widely considered to optimize resource utilization of OEC networks and improve computing performance of tasks. During this process, how diverse computing services are orchestrated plays a non-negligible role in the performance of computation offloading. Therefore, this article introduces a hierarchical framework for joint service orchestration and computation offloading in OEC networks (OEC-H2O). In this framework, computation offloading is addressed on small timescales, using a Markov decision process (MDP) to optimize task processing delay, energy consumption, load balancing, and packet loss. Considering that frequent orchestration of services will bring huge orchestration overhead, service orchestration is optimized on larger timescales, which is modeled as an integer programming problem to enhance computing service capabilities of OEC networks and reduce orchestration costs. A hierarchical approach employing deep reinforcement learning (DRL) and heuristic algorithms aims to iteratively reveal optimal solutions. Extensive simulations are conducted to verify the effectiveness and superiority of the proposed scheme. Finally, we discuss potential open issues and directions for future research.

Multi-layer satellite networks (MLSNs) have become a pivotal architecture for global communication coverage due to their enhanced capacity and spectral efficiency. However, existing studies primarily focus on constellation deployment and static cost optimization, overlooking dynamic operational overheads from cross-layer mobility management and routing control overhead. Addressing this gap, we pioneer the integration of network operation overhead into cost-benefit analysis from an MLSN networking perspective. Specifically, we establish a unified quantification methodology for both construction costs and operational expenditures, proposing a comprehensive cost-benefit evaluation framework for MLSNs. We formulate the MLSN topology and network operation design (MTNOD) problem to co-optimize topology parameters and operation strategies, simultaneously minimizing construction costs and operational overheads while maximizing flow-level capacity and transmission reliability. The Penalty Non-dominated Sorting Genetic Algorithm II (P-NSGA-II) algorithm efficiently generates robust Pareto frontiers, providing satellite operator with quantitative cost-performance trade-off benchmarks. Extensive simulations demonstrate the method's effectiveness in constructing MLSNs with low construction costs and minimal operation overhead.

Segment routing (SR) provides an effective approach to path control with minimal control-plane signaling. This makes SR a strong candidate for routing in the satellite-terrestrial integrated network (STIN), the core infrastructure enabling ubiquitous Internet of Things (IoT) connectivity. However, directly applying existing SR solutions to satellite networks presents significant challenges, which include limited bandwidth and constrained onboard processing capabilities, hindering efficient IoT data transmission. To enable reliable and efficient IoT services, we propose satellite-tailored segment routing (STSR), a novel framework designed specifically for satellite networks in STIN. STSR is built as a lightweight and segment routing over IPv6 (SRv6)-compatible extension. It deploys a customized data plane that enables efficient source routing through bit-based encoding and streamlined processing, which is vital for resource-constrained satellites. Furthermore, we develop a quality of service (QoS)-aware route compression scheme designed to meet diverse IoT service demands. This scheme leverages the computational resources of terrestrial controllers to generate compact, STSR-encoded paths. By accounting for QoS requirements and satellite-specific dynamics, the embedded algorithm enhances routing performance for heterogeneous IoT flows within the satellite network. Simulation and evaluation results demonstrate that STSR outperforms existing SRv6-based approaches in path encoding efficiency, payload transmission efficiency, processing overhead, and traffic engineering (TE) performance.

With the quantum threat looming, IoT deployments urgently require post-quantum cryptographic solutions that deliver strong security within tight resource limits. Lattice-based schemes, including NTRU-type encryption(IEEE Std 1363.1) and fully homomorphic encryption (FHE), are attractive in this setting because they can combine quantum resistance with relatively low computational overhead. However, accurate security assessment of these schemes is important for parameter selection in resource-constrained IoT deployments. This work addresses the ternary Learning With Errors (LWE) problem that underpins these cryptosystems by adapting cryptographic puncturing to lattice attacks. In this paper, we revisit the randomized dimension reduction (RDR) technique originally proposed by May to assess the security of the original NTRU cryptosystem. More specifically, we study how the LWE sample dimension can be reduced while preserving the effective secret-error search space through an explicit puncturing threshold, replacing ad hoc sample-selection rules with a single distribution-aware criterion. We also study LWE with side-channel hints, using elimination-based linear equation solving to construct the reduced hint lattice more transparently before the final embedding step, which is relevant in settings where devices may be physically exposed to leakage. Experimental results demonstrate 41% threshold embedding-dimension reduction for NTRU-like schemes (n, log2 q, h) = (100, 12, 50) and FHE parameters (n, log2 q, h, σe) = (128, 14, 12, 3.2). Relative to standard lattice-estimator evaluations, our refined analysis tightens representative security estimates by 1–3 bits: a CKKS/HEAAN-style parameter set (n, q,w) = (1024, 216, 64) with sparse ternary secret is reduced by 3 bits, while NTRU-Prime (n, q,w) = (653, 4621, 288) and LAC (n, q,w) = (512, 251, 128) parameters are reduced by 1 bit. Additional coefficient-hint experiments on Kyber/ML-KEM parameter settings and representative hint counts from May–Nowakowski show that applying RDR after hint elimination can further reduce the estimated BKZ block size in hint-assisted regimes. These refined estimates provide a more concrete basis for parameter assessment in IoT-style settings.

This paper proposes a dynamic link allocation strategy for multi-layer satellite networks (MLSNs) to address link congestion and load imbalance in the Low Earth Orbit (LEO) layer. By introducing dynamic and stable inter-orbital links (D-IOLs and S-IOLs), the algorithm dynamically adjusts link allocation to respond to sudden traffic changes while ensuring network stability. A greedy algorithm is employed for dynamic central satellite selection, while stable central satellites are optimized based on visibility time. The proposed framework achieves a balance between flexibility and stability by leveraging the complementary advantages of D-IOLs and S-IOLs. Additionally, global load balancing further enhances system performance by ensuring equitable traffic distribution across the network. Simulation results demonstrate that the proposed algorithm significantly reduces link congestion and optimizes load distribution compared to traditional schemes and service-time-based schemes. Notably, the proposed method exhibits superior adaptability, load balancing, and congestion mitigation under high-load scenarios, offering valuable insights for advancing satellite communication systems.

As the scale of Low Earth Orbit (LEO) satellite networks continues to expand, traditional inter-satellite routing models are becoming increasingly unsuitable due to their inability to handle dynamic topologies and complex traffic patterns. Recently, domain-based routing schemes have been proposed to enhance scalability and management efficiency in large-scale LEO satellite networks. However, the uneven distribution of ground hotspot areas leads to imbalanced traffic loads among different domains, causing congestion in certain regions and degrading overall network performance. To address this issue, this paper proposes a distributed cooperative load-balancing mechanism (DCLB) for large-scale satellite networks. The strategy leverages Segment Routing (SR) for autonomous path planning and incorporates an on-demand scheduling mechanism to balance traffic loads globally, thereby mitigating congestion in hotspot regions. By utilizing localized information exchange, our approach enables distributed decision-making without requiring centralized control, making it highly scalable. Simulation results demonstrate that the proposed method effectively reduces congestion in hotspot areas, improves network throughput, and balances link utilization, making it well-suited for large-scale dynamic satellite networks.

With the rapid advancement of technology, intelligent applications increasingly require higher computing power, driving the evolution of the end-edge-cloud Collaborative Scheduling (CS) paradigm. However, this paradigm primarily focuses on vertical collaboration, neglecting horizontal coordination among ubiquitous heterogeneous computing resources, leading to imbalanced resource utilization. Fortunately, the Computing Power Network (CPN) has been introduced to facilitate ubiquitous resource coordination through pervasive networks. The core issue in CPN is the CS of network-wide computing tasks, yet most existing research has largely concentrated on offloading decisions while critical aspects of network routing remain underexplored. Therefore, this paper studies the optimization of joint routing and scheduling within large-scale CPNs. Initially, we develop a CS framework that accommodates the return of task computing results. We then incorporate computing information into the routing domain and introduce a CS mechanism based on Compute-aware Routing (CaR), which narrows the scope of routing exchanges and collaborative scheduling. Ultimately, we present a Deep Reinforcement Learning (DRL)-based algorithm to optimize the long-term CS problems based on balanced CaR, enhancing the success rate of computing tasks and ensuring balanced utilization of computing and network resources. The effectiveness of our proposed mechanism and algorithm is confirmed through simulation experiments.

Industrial Cyber-Physical Systems (CPS) have made significant strides in recent years, driving the future of manufacturing. However, for further advancement in Cloud-Fog Automation (CFA), several challenges remain: rigid sensor sampling, inflexible communication configurations, insufficient coordination between cloud and fog resources, and a lack of integration between sensing, communication, and computing for effective control. To address these issues, this article presents SeCo4, an intelligent control framework for the co-design of sensing, communication, and computing in industrial CPS. The SeCo4 optimization problem is analyzed and divided into two sub-problems: a multi-controller cloud resource competition problem, formulated with a combinatorial auction to enable multi-controller competition for additional cloud resources and improve control performance; and a joint resource optimization problem for sensing, communication, and computing, modeled using a Mixed Integer Programming (MIP) problem to minimize control costs. Given the interdependence of these sub-problems, a hierarchical solution based on the online matching mechanism and the heuristic approach is developed to iteratively find the optimal solution. Finally, extensive simulations demonstrate the effectiveness and superiority of the proposed approach.

With the rapid deployment of large-scale LEO satellite constellations, maintaining network reliability under dynamic topologies and frequent link disruptions has become a critical challenge. Existing monitoring methods often incur excessive communication overhead or depend on centralized control, limiting scalability and responsiveness. This paper presents a treebased active probing and reporting mechanism for efficient faulty link localization. By organizing satellites into multiple multi-branch trees rooted at gateway nodes, each satellite performs periodic probing and hierarchically reports link states to its parent. This structure minimizes reporting overhead and supports distributed subtree reconstruction when link or topology changes occur. Simulation results show that the proposed mechanism significantly reduces management overhead and achieves faster fault recovery than centralized approaches, maintaining over 70% reconstruction success even under large-scale link failures.

Homomorphic encryption (HE) is a kind of algorithm which provides data processing but not data access. Since it was proposed in 1978, as one of the important tools in cryptography, it is broadly used in many scenarios, like privacy protection, cloud computing, federated learning, and so on. Especially in the association rules mining privacy protection schemes, it is often used as a key technology to ensure data security. Recently, Li et al. and Rajasekaran et al. introduced a kind of symmetric HE algorithm in their privacy protection scheme. However, in this article, we find that this symmetric HE algorithm has the possibility to recover its secret key in practical applications. We propose two attacking algorithms based on lattice to recover its secret key $SK=(s<^>{d},q)$ . The core of our attack is to construct a lattice basis using the transformation relation between ciphertexts so that the short vector in the lattice contains the secret key SK. Then, we can use the LLL algorithm to recover the secret key. We prove the feasibility of our attacking algorithms with experiments and the experimental results suggest that all of our algorithms can recover the key within 0.1 s. Besides, we also give some improvements for this symmetric HE algorithm so that the new HE algorithm can resist our attacking algorithms.

The Internet of everything, a potential direction for the next-generation Internet, positions edge collaboration as a promising computing paradigm to address the workload dispersion and resource constraints inherent in traditional edge computing frameworks. However, the increasing complexity of cross-domain networks introduces challenges for efficient task execution and balanced resource utilization in edge collaboration, which remain insufficiently explored. To address these challenges, a next-generation network architecture, the compute power network (CPN), was recently proposed. The CPN leverages ubiquitous connections among heterogeneous resources to optimize task scheduling collaboratively. Building on this concept, we design an edge computing system that integrates CPN to enable dynamic and collaborative task scheduling. Inspired by the sliding window, we develop a dynamic scheduling scheme that prioritizes computing tasks and matches tasks to computing resources in real time. Additionally, we propose an improved deep reinforcement learning (DRL) algorithm to optimize scheduling policies, aiming to improve task success rates, minimize execution delays, and ensure balanced and efficient resource utilization. Lastly, simulation experiments validate the effectiveness of the proposed scheme and algorithm.

With the rapid expansion of Low Earth Orbit (LEO) mega-constellations, efficient network measurement has emerged as a critical challenge, directly impacting network state awareness and fault recovery capabilities. To address the high fault probability caused by the complex space environment and the measurement constraints due to limited onboard resources, this paper presents a network tomography approach for measurement and fault localization in LEO satellite networks. Based on the predictable topology of LEO constellations, our method models the network as an Eulerian graph and constructs an Eulerian measurement circuit that covers all links, forming a minimal candidate-path set for the measurement stage. Subsequently, based on online measurement feedback, the identified faulty paths are segmented. New end-to-end measurement paths are then dynamically generated from these segments to progressively refine the fault set. By iteratively updating link fault probabilities, the proposed approach achieves accurate fault localization under multi-fault conditions with only a few iterations.Simulation results demonstrate that our approach achieves 0 false negatives and 0 false positives, while simultaneously reducing the number of required measurement iterations by approximately 60% and the number of measurement paths by over 50% in multi-fault scenarios, compared to two reference schemes.

Designing secure outsourcing schemes enables resource-constrained Internet of Things (IoT) devices to perform highly complex computational tasks. Solving the quadratic congruence problem is one of the core components in the construction of cryptographic algorithms for IoT. Recently, Rangasamy designed an outsourcing scheme for solving quadratic congruence equations. Interestingly, we find that the scheme has the risk of secret information being cracked. We propose two lattice attack algorithms in this article. In the first attack algorithm, we prove that there is a possibility of leaking secret information in the public parameters of the outsourcing scheme. Specifically, by intercepting the parameters transmitted between the client and the server, and combining the ideas from Fermat's Little Theorem and the Euclidean algorithm, the attacker can obtain multiples of the secret parameter p. Furthermore, after recover the value of p, our attack algorithm is capable of recovering all secret parameters in the quadratic congruence equation. In the second attack algorithm, we note that the authors recommend the randomly chosen value of k to be small in the original outsourcing scheme. However, in this article, we point out that the random value k should not be too small, otherwise, the outsourcing scheme would be wrecked. More precisely, we use the Coppersmith method to provide an approach that can recover all the secret information. Our attack algorithm will succeed once k satisfied $|k| \lt \sqrt {p}$ , so in order to ensure the security of the outsourcing scheme, we propose the recommended selection length of the random value k. Finally, we experimentally verified the two proposed attack algorithms.