With the widespread application of Internet of thingstechnology in smart grid, the integration of numerous intelligent terminals exacerbates network congestion and data loss, leading to increased load tracking deviations. Simultaneously, the automatic generation control (AGC) employed for maintaining supply-demand balance faces high costs, slow response rates, and limited adaptability to renewable energy. Employing gas-to-power technology in conjunction with AGC can enhance overall system efficiency and stability. This paper proposes a power allocation and pricing strategy utilizing a two-level Stackelberg game framework to reduce utility costs while boosting profits for telecom operator and gas company. We develop an electricity cost model for utilities considering regulation errors from direct load control in smart grid. Using an iterative algorithm and backward induction, we derive the Nash equilibrium for the Stackelberg game. Simulation results show that this strategy reduces utility costs and increases profits for telecom operator and gas company.

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.

Time-sensitive networking (TSN) is proposed to satisfy the increasingly stringent demands of Industrial 4.0 for deterministic transmission. This is achieved by generating a series of centrally configured gate control lists to strictly restrict the forwarding time of arriving flows. However, such a centralized scheme requires prior information of all flows, severely impeding TSN from providing an online response to dynamic industrial applications. To solve this problem, we innovatively propose to use admission control (AC) to realize deterministic transmission in the virtualized TSN network. In this approach, AC is distributively executed on each node and link, whereby flows of applications are served by passing through a series of virtual network functions (VNFs). This distributed AC execution is regarded as a VNF embedding (VNE) process. Specifically, we propose a two-stage online framework, Smart Admission Control (SmartAC), to cater to dynamic applications. The first stage, referred to as thestatic stage, obtains a deterministic VNE solution by synthesizing AC decisions of individual TSN nodes and links. The second stage, referred to as thedynamic stage, fine-tunes VNE solutions obtained from thestatic stageto adapt to the harsh environment with insufficient resources or limited VNF migration budgets. Simulation results demonstrate the effectiveness of SmartAC in improving response rate and resource utilization ratio. Notably, SmartAC reduces runtime by 90% compared to existing algorithms and exhibits robustness across different network topologies.

Satellites equipped with computing capabilities serve as invaluable access platforms for 5G and beyond (NextG) non-terrestrial networks (NTNs). They facilitate the continuous execution of resource-intensive edge-assisted deep learning (DL) tasks that are offloaded from Internet-of-Things (IoT) user equipment (UEs) in remote areas. To this end, satellite access network (SAN) resources need to be carefully “sliced”, consid-ering both the constrained energy availability and the scarcity of SAN resources. Existing SAN slicing approaches tend to treat offloaded tasks conventionally, overlooking the intricate semantics associated with DL tasks. In this paper, we propose semantic SAN (SemSAN), the first semantic SAN slicing algorithm for NextG AI-native NTNs. Our keen observations reveal that various DL tasks (i) can tolerate different degrees of image compression, and (ii) may yield equivalent model accuracy when employing DNN models with different sizes. These observations inspire us to further exploit the computation capability of a SAN to support more tasks while still minimizing overall energy consumption. After analyzing the characteristics of this optimization problem, we propose an online greedy SemSAN slicing algorithm to approximate its optimal solution. Extensive experiments verify the effectiveness of SemSAN in energy saving and its ability to support a substantial number of tasks, compared with other baselines.

Driven by the demand of Industry 4.0, the integration of 5G and time-sensitive networking (TSN) is proposed to provide ubiquitous connection and deterministic transmission. However, the heterogeneous access mechanisms and scheduling resolutions between 5G and TSN make it still intractable to schedule 5G and TSN resources jointly. To address this issue, we develop the network function virtualization-enabled 5G-TSN framework to offer unified resource management, where flows are scheduled by network slicing and virtual network function embedding, respectively. Specifically, a novel full-path age of information (FP-AoI) model is proposed as a new metric of the 5G-TSN integrated scheduling by innovatively encapsulating the 5G system as the sampling process of the virtual TSN network. To tackle the long latency tail brought by 5G, the 5G and TSN scheduling is formulated by a risk-aware FP-AoI minimization problem. Then, a decomposition and augmentation-based joint scheduling (DAS) algorithm is proposed to solve this NP-hard problem by decomposing it into three subproblems. The first two subproblems are proved to be convex. For the third subproblem, i.e., TSN scheduling, a FP-AoI-driven TSN scheduling scheme (FvQI) is designed by constructing an augmented logical topology according to constraints of service function chain and TSN characteristics. It realizes the TSN scheduling with low complexity. Simulation results demonstrate that our algorithms offer higher reliability, efficiency, and service acceptance ratio than benchmarks. Moreover, the DAS algorithm achieves a better tradeoff between the performance of time cost and AoI violation ratio with a small optimality gap.

In smart grid, serious packet loss often occurs in the process of information interaction between the Utilities and customers, which results in supply-demand deviation and further increases the cost of the Utilities. In order to improve the information transmission performance of communication networks, the Utilities purchase relay service from telecom operator to help data aggregator units (DAU) transfer information to gateway (GW), so as to improve communication quality and reduce the cost of the Utilities. Second, in order to solve the problem of telecom operators’ energy reduction and reduce the cost of purchasing energy, we utilize energy supply point (ESP) to collect the surplus energy of retail customers for energy supply, and telecom operator pays a certain amount of remuneration to ESP in exchange for ESP to continuously supply energy to telecom operator. Then, we establish a three-stage game method and system model between the Utilities, telecom operator and ESP, and propose the relay power allocation and energy transaction pricing strategy. Due to the real-time change of energy demand, we consider two situations of energy oversupply and conservative supply, and use the backward induction method and iterative algorithm to obtain the equilibrium solution of the Stackelberg game, including the unit energy price of ESP, total power of telecom operator, the proportion of transmission power allocated to the relay service, and payment scheme of the Utilities. Simulation results show that the proposed algorithm can quickly and accurately converge to the optimal solution of the problem, and the method can improve the stability of demand-side regulation, reduce the cost of the Utilities and increase the profit of telecom operator.

Driven by the coexisted heterogeneous communication protocols and diverse quality of service (QoS) requirements of industrial applications, time-sensitive networking (TSN) has been proposed as a promising and unified standard to provide deterministic transmission. However, the traffic scheduling of the TSN network should be orchestrated in a coordinated manner, which is challenging to respond to dynamic applications rapidly. For this, network function virtualization (NFV) has been introduced to eliminate the tight coupling between functions and devices, thus enabling a more flexible network resource allocation. However, NFV cannot be simply applied in a TSN network due to the inherent complex characteristics of TSN. To address this issue, we develop SvTI, a service function chain (SFC) driven queue injection scheme in the NFV-enabled TSN network. The basic idea of SvTI is to jointly model virtual network functions (VNFs) embedding and the TSN multi-queue characteristic by constructing an ordered augmented topology according to the SFC. Thus, the VNF embedding and TSN queue injection problem can be transformed into a shortest path routing problem. Besides, considering the QoS requirements of applications, a TSN queue-based topology division mechanism is proposed to simplify the augmented topology further and improve the algorithm efficiency. Simulation results show that our SvTI obtains a smaller graph size than other works and is more scalable.

Driven by the advantages of the ubiquitous connection of 5G and the determinacy of Time-Sensitive Networking (TSN), the integration of 5G and TSN is expected to provide flexible and deterministic communications for the industry. However, due to the diverse quality of service (QoS) demands of industrial applications, it is challenging to provide suitable QoS mapping across industrial 5G-TSN networks and offer dynamic services via heterogeneous infrastructures. To address this issue, we design a QoS-aware dynamic data injection scheme to realize the interconnection between 5G and TSN under edge-assisted 5G-TSN architecture. To break the tight coupling between applications and infrastructures, we focus on the virtual network function (VNF) mapping problems to facilitate the QoS provisioning for different applications leveraging the network function virtualization (NFV) technique. We first formulate it as a mixed integer linear programming (MILP) with time-sensitive constraints. Then we develop PVMS, a preemption-based two-stage heuristic algorithm for VNF mapping and scheduling in the 5G-TSN network. In particular, we dynamically map the VNFs of arrived applications by greedily searching the earliest available 5G-TSN resources. To further meet the low-latency requirements, we employ a preemption mechanism to provide no-wait transmission for higher priority applications at the cost of the preempted applications being postponed. Simulation results demonstrate that the proposed PVMS has better performance in terms of the acceptance ratio, average delay, and QoS guarantee.

As an emerging communication tool, Time-Sensitive Networking (TSN) is proposed by the IEEE 802.1 TSN Task Group to achieve deterministic data transmission. TSN defines protocols to ensure the determinism of traditional Ethernet by configuring gate control list (GCL). However, calculating schedules of GCL according to different time-sensitive applications, is not well-defined in the standard. In this paper, we focus on the problem of mixed time-sensitive data transmission. To this end, we first model the data into three categories: high time-sensitive (HTS) flows, low time-sensitive (LTS) flows, and best-effort flows. Then based on the models, we propose a coordinated transmission framework to transmit mixed time-sensitive data. Under this framework, we develop a parameter selection approach for choosing the proper cycle time and the scheduling unit. To reduce the impact of HTS data on LTS data flows, we formulate a fine-grained scheduling problem. Then, we design an injection time grouping (ITG) algorithm by grouping the flows with the same period to reduce the computation complexity. Simulation results corroborate the effectiveness of the parameter selection approach and ITG algorithm.