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 performance.

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.

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.

In this paper, we propose an innovative architecture for sharing intelligence in low earth orbit (LEO) satellite edge computing networks. Specifically, we adopt the sharing of intel-ligence to satellites via ground stations to improve the response speed of satellites in processing intelligent services. Considering the burden of frequent transmission of intelligent models over unstable ground-satellite links, the satellites share intelligence with each other in the coalition, which greatly reduces the service response delay. In addition, considering the poor generalization of pre-trained intelligent models transmitted by ground stations, we design a model aggregation scheme with differentiated weights. Each coalition appoints a coalition center satellite, tasked with aggregating models and re-sharing them to individual satellites, thereby enhancing model performance. Then, we propose two low-time complexity algorithms to solve the above problems. Finally, the effectiveness and superiority of the proposed schemes are verified through extensive simulations.

In recent years, Low Earth Orbit (LEO) constellation based networks have attracted wide attention from both the academia and the industry. Broadband access and backhaul becomes a typical application for LEO satellite networks. However, the high-speed motion of satellites brings periodic fluctuation of delays, which is a great violation to the Quality of Service (QoS) provisioning. In this work, inspired by the success of Software Defined Networking (SDN), and considering the dynamics of LEO satellite networks, we propose a low-jitter multiple slots routing in LEO satellite networks to provide low-jitter end-to-end delay path computing and control. The proposed multiple slots routing optimization mechanism takes account of the topology shift as well as the dynamic propagation delay across multiple topology snapshots, and thus achieves low-jitter performance. The performance improvement of the proposed mechanism is validated by the simulation, which promises the value of jitter under 30 ms.

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.

Low earth orbit (LEO) satellite networks can seamlessly supplement terrestrial networks by providing a high capacity, wide coverage, and cost-effective solution. Positioned to play a significant role in the upcoming 5G/6G era thanks to reduced launch expenses, LEO satellite networks offer benefits such as multi-path transmission, aggregated link bandwidth, redundant paths, and enhanced mobility support. These advantages necessitate further exploration in integrated satellite-terrestrial networks. In this work, we leverage network conditions, underlying link status, and real-time service characteristics to achieve effective synergy, aiming to fulfill application requirements. We formulate the service-oriented multi-path scheduling (SOMPS) problem as a bounded multi-knapsack problem and employ dynamic programming methods for its solution. Simulation results demonstrate that our proposed scheme provides high transmission rate, low latency, and customized information delivery for services, in comparison with baseline schemes.

Generative Artificial Intelligence (GAI) is taking the world by storm with its unparalleled content creation ability. Large Language Models (LLMs) are at the forefront of this movement. However, the significant resource demands of LLMs often require cloud hosting, which raises issues regarding privacy, latency, and usage limitations. Although edge intelligence has long been utilized to solve these challenges by enabling real-time AI computation on ubiquitous edge resources close to data sources, most research has focused on traditional AI models and has left a gap in addressing the unique characteristics of LLM inference, such as considerable model size, auto-regressive processes, and self-attention mechanisms. In this paper, we present an edge intelligence optimization problem tailored for LLM inference. Specifically, with the deployment of the batching technique and model quantization on resource-limited edge devices, we formulate an inference model for transformer decoder-based LLMs. Furthermore, our approach aims to maximize the inference throughput via batch scheduling and joint allocation of communication and computation resources, while also considering edge resource constraints and varying user requirements of latency and accuracy. To address this NP-hard problem, we develop an optimal Depth-First Tree-Searching algorithm with online tree-Pruning (DFTSP) that operates within a feasible time complexity. Simulation results indicate that DFTSP surpasses other batching benchmarks in throughput across diverse user settings and quantization techniques, and it reduces time complexity by over 45% compared to the brute-force searching method.

In the large-scale dynamic Low Earth Orbit (LEO) satellite networks, the conventional TCP-based single-path transmission encounters challenges such as prolonged propagation delay, frequent connection failures, and suboptimal resource utilization. In this paper, we propose an Integrated Multi-Path Network Coding (IMPNC) transmission scheme. This scheme leverages multiple paths for end-to-end transmission to achieve bandwidth aggregation and redundant backup. The multi-path transmission is facilitated by Multi-Path Quick UDP Internet Connection (MPQUIC) protocol to adapt to the limited satellite bandwidth and caching resources. The proposed approach involves encoding packets at nodes along the paths, addressing the significant out-of-order problem arising from variable delays on different paths. Additionally, we present a Software Defined Networking (SDN)-based domain clustering architecture, which offers a more streamlined control approach, reducing overall complexity. Furthermore, we formulate the domain clustering problems as mixed-integer nonlinear programming and the coding-based routing problem as a Steiner tree problem. Evaluation results demonstrate that the proposed scheme effectively reduces the latency over 25.1%, enhances bandwidth utilization by 19.6%, and ensures reliable data transmission by reducing retransmission probability by 4.1%.

The Internet of Things (IoT) ecosystem is undergoing a significant evolution through its integration with satellite networks, empowering remote and computation-intensive IoT tasks to leverage computing services via satellite links. Current research in this field predominantly focuses on minimizing latency and energy consumption in computation offloading, yet overlooks the substantial costs incurred by satellite resource utilization. To address this oversight, we introduce a cost-effective hybrid computation offloading (CE-HCO) paradigm in satellite-terrestrial integrated networks (STINs) in this article. First, we propose the 5G-based system framework facilitates gNB and user plane function functionalities on satellites and fosters collaboration between public cloud providers and satellite operators. The framework is in line with the latest 3GPP activities and business models in satellite computing. Then, we formulate the CE-HCO problem, aiming to minimize total computation offloading costs while satisfying diverse user latency requirements and adhering to satellite energy constraints. To tackle this NP-hard problem, we develop an algorithm employing the penalty method and successive convex approximation to simplify the complex mixed-integer nonlinear programming into tractable convex iterations. Simulation results show that our approach outperforms existing baselines in balancing performance and cost, and offer guidance on pricing policies for satellite computing services to promote future commercial growth.

In this paper, we establish a two-timescale framework for the joint service deployment and task scheduling problem in satellite edge computing networks.We aim to optimize the computing performance of networks with diverse quality-of-service (QoS) guarantees for computing tasks. Specifically, to capture the small-timescale network dynamics and task randomness, we formulate the task scheduling problem as a constrained Markov decision process (CMDP) to minimize the energy consumption, load imbalance and packet loss of networks while ensuring the long-term delay. The Lyapunov technique is employed to deal with the delay constraints. A soft actor-critic (SAC)-based deep reinforcement learning (DRL) framework is designed to learn the stationary scheduling policy. We further explore the significant impact of deploying diverse services on the performance of task scheduling in satellite edge computing. Considering that frequent deployment of services will incur huge deployment overhead, we optimize the service deployment on a larger timescale. The optimization problem is modeled as an integer programming problem to improve the service capability of networks and reduce service deployment costs. A heuristic-based atomic orbital search (AOS) approach is proposed to obtain the superior policy with low complexity. Due to the correlation between the problems of two timescales, a hierarchical solution is constructed to iteratively find the excellent solution. Finally, extensive simulations are conducted to validate the effectiveness and superiority of the proposed scheme.

“Read-write-own” based Web3 has been proposed as a promising user-centric Internet to open the new generation of the World Wide Web, where Web3 users can independently manage data and derive value from creating content without relying on intermediaries. Connected and autonomous vehicles (CAVs) in Web3 can trade models in a self-controlled and decentralized credible way, which is a fundamentally and principally innovation based on novel architecture. Effectively implementing such paradigms involves proper model trading strategies. However, reinforcement learning (RL)-based strategies face challenges of poor generalization ability, low feasibility, and the exploration-exploitation dilemma. It is also difficult to define an explicit and appropriate reward function. Therefore, in this paper, we propose an intelligence-based reinforcement learning (IRL) approach for CAVs in Web3. We present a framework to enable model transactions between CAVs. Also, we provide a decentralized identifier (DID)-based identity management system for resource description and data verification to access Web3, followed by the mechanism and supporting smart contracts. Furthermore, we formulate the model trading issue as an active inference to form higher-level cognition about the environment without rewards. Then we use IRL to solve it. And we use “intelligence”, a high-level indicator, to quantify the efficiency of such cognition. It can evaluate the difference between the predicted state and the real state in policy exploration. The proposed scheme shows good generalization and can auto-balance exploration and exploitation, simultaneously achieving outperforming performance on the model trading issue with no rewards. In simulations, the performance of the proposed scheme is compared with existing methods.

In this paper, a clustered parallel training algorithm is designed for personalized federated learning (Per-FL), called CPPer-FL. CPPer-FL improves the communication and training efficiency of Per-FL from two perspectives, namely, less burden for the central server and lower interaction idling delay. CPPer-FL adopts a client-edge-center learning architecture, which offloads the central server's model aggregation and communication burden to distributed edge servers. Also, CPPer-FL redesigns the cascading model synchronization and updating procedure in conventional Per-FL and changes it to a parallel manner, thus improving the interaction efficiency in the training process. Further, for the proposed hierarchical architecture, two approaches are proposed to cater to Per-FL: similarity-based clustering for client-edge association and personalized model aggregation for parallel model updating, such that clients' personal features can be preserved in the training process. The convergence of CPPer-FL has been formally analyzed and proved. Evaluation results validate the communication efficiency, model convergence, and model accuracy improvement.

Recently, MEC has been integrated with satellite networks to process remote terrestrial computation tasks with superior coverage and delay. Since single satellite computation is hard to tackle spatially uneven computation workloads, computation peer offloading among multiple satellites is urgently needed to further improve service quality and resource utilization. However, considering limited resources, deficient energy, and costly overheads of communication and computation, how to enable efficient offloading cooperation in the time-varying satellite networks is a significant challenge. In this paper, we first design a satellite peer offloading scheme, where offloading is performed along multi-hop paths to explore collaborative computing capabilities. Second, we formulate the Multi-Hop Satellite Peer offloading (MHSPO) problem, aiming to jointly minimize the delay and energy consumption under system resources and backlog constraints. Then, to adapt to the network dynamics, the decision-making process with uncertain future workloads is optimized by leveraging the delayed online learning method under the Lyapunov framework. Finally, we develop a practical online distributed algorithm to solve the MHSPO problem, which is proven to achieve close-to-optimal performance. Extensive simulations show that multi-hop peer offloading among satellites improves edge computing performance efficiently.

In the era driven by big data and algorithms, the efficient collaboration of pervasive computing power is crucial for rapidly meeting computing demands and enhancing resource utilization. However, current mainstream end-edge-cloud collaboration faces challenges of computing isolation, adversely affecting resource efficiency and user experience. The Computing Power Network (CPN) is a novel architecture designed to sense and collaborate ubiquitous computing resources through networks. Nevertheless, the expansion of its scope and the integration of networks complicate task scheduling. To address this, we design a collaborative scheduling system that considers the joint selection of computing nodes and network links, aiming to reduce delay, enhance reliability, and ensure long-term load balance. First, we propose a delay-prioritized reliable scheduling policy based on a dual-priority mechanism for forwarding and computing. Second, we define the scheduling problem as a Constrained Markov Decision Process (CMDP) and introduce Lyapunov optimization to transform constraints into instantaneous optimizations, achieving a long-term balanced load of computing and network resources. Lastly, we employ an enhanced Deep Reinforcement Learning (DRL) approach to solve the problem. Performance evaluation demonstrates that compared to standard DRL, the proposed algorithm effectively reduces delay and improves reliability while maintaining long-term load balance, resulting in an overall performance improvement of 54.7%.

Earth observation applications, such as emergency surveillance and disaster relief, are thriving due to the availability of earth observation satellites that provide timely and objective observation data at different spatial and temporal scales. Such observation data is processed at the satellite edge with orbital edge computing, leading to potentially reduced bandwidth cost and transmission delay. However, most existing studies primarily focus on optimizing computation offloading but ignore the consideration of object observation and observation data transmission. To fill this gap, this paper proposes a novel scheduling approach that jointly considers observation satellites, relay satellites, and computing satellites in LEO satellite systems, aiming to maximize the number of completed observation tasks while taking into account various requirements of observation, transmission, and computation resources. Specifically, we first formulate the problem of jointly scheduling observation, relay, and computing satellites to maximize the number of accomplished observation tasks. Then, we decompose the formulated problem into two sub-problems and design a resource-aware algorithm called ORCA to determine the optimal scheduling of observation, relay, and computing satellites. Simulation results demonstrate that ORCA outperforms existing algorithms in terms of completing a number of observation tasks.

The natural mesh network topology of satellite networks makes multi-path transmission prevalent due to its ability to provide bandwidth aggregation and backup using redundant paths. While as one of the significant benefits of multi-path transmission, high reliability requires extensive signaling to achieve excellent performance. So designing an analysis and treatment method to tackle the network overhead and reliability balance remains a major challenge. In addition, massive and burst services in satellite networks will have different demands, which require the network to execute fine-grained path scheduling and management of the multi-path transmission. In this paper, we carefully model the multi-path reliability and network overhead factors under the SDN-based integrated satellite-terrestrial network architecture to characterize the network state. Second, an adaptive multi-path selection scheme is designed to handle the number of active paths, which considers different Quality of Service (QoS) requirements under the limited resources of satellite networks. Then, we formulate the problem as Non-Linear Binary Programming (NLBP) and develop a practical Particle Swarm Optimization (PSO)-based algorithm to solve it. Simulation results show that the proposed scheme can improve satellite networks’ throughput and resource utilization.

Edge intelligence is emerging as a new interdiscipline to push learning intelligence from remote centers to the edge of the network. However, with its widespread deployment, new challenges arise in terms of training efficiency and service of quality (QoS). Massive repetitive model training is ubiquitous due to the inevitable needs of users for the same types of data and training results. Additionally, a smaller volume of data samples will cause the over-fitting of models. To address these issues, driven by the Internet of intelligence, this paper proposes a distributed edge intelligence sharing scheme, which allows distributed edge nodes to quickly and economically improve learning performance by sharing their learned intelligence. Considering the time-varying edge network states including data collection states, computing and communication states, and node reputation states, the distributed intelligence sharing is formulated as a multi-agent Markov decision process (MDP). Then, a novel collective deep reinforcement learning (CDRL) algorithm is designed to obtain the optimal intelligence sharing policy, which consists of local soft actor-critic (SAC) learning at each edge node and collective learning between different edge nodes. Simulation results indicate our proposal outperforms the benchmark schemes in terms of learning efficiency and intelligence sharing efficiency.

User authentication on the blockchain has been considered a promising solution to secure communications in LEO satellite communication systems. Due to resource-limited LEO satellites, the blockchain needs to be deployed in the terrestrial network component of LEO satellite communication systems, consequently resulting in high authentication delays. To fill the gap, we propose to cache the blockchain at LEO satellites and update the blockchain periodically and design a delay-aware cooperative caching scheme for on-chain authentication by considering the query delay and the synchronization delay. Specifically, we first propose to divide LEO satellites into multiple clusters which have the same copy of all the blocks belonging to the blockchain. Then, we model the clustering problem as a coalition formation game. Afterward, we design a distributed delay-aware coalition formation algorithm, which is called DAC, to find an optimal coalition partition. Extensive simulation results show the efficacy of the proposed scheme.

Low earth orbit (LEO) small satellites have attracted great interests in civilian and military applications due to their low cost and high service performance. However, the enormous scale and high dynamism of small satellites pose challenges to network flexibility and scalability. Therefore, the hierarchical satellite network structure is introduced as an effective approach to enhance the satellite network capabilities further. In this regard, small satellites’ clustering is of fundamental importance for designing such a hierarchical structure. Satellite clusters are always prone to instability due to unpredictable link failures and frequent topology changes. In this article, we study the small satellite clustering problem of jointly optimizing the cluster reliability and the network management overhead. A coalition game-theoretic framework is introduced to obtain low computational complexity by adopting the clustering-decision-making process in an automated and fully distributed fashion. A distributed coalition formation algorithm based on the optimization of reliability and management overhead is developed for the clustering problem. Finally, extensive simulations have been conducted, and the results show that our proposed clustering scheme is able to produce better results than the baseline schemes.

By delegating the infrastructure management, such as provisioning or scaling to third-party providers, serverless edge computing has recently been widely adopted in several applications, especially Internet of Things (IoT) applications. Task scheduling is a critical issue in serverless edge computing as it significantly impacts the quality of user experience. In contrast to the centralized scheduling in the cloud center, serverless edge task scheduling is more challenging due to the heterogeneous and resource-constrained nature of edge resources. This article aims to study the distributed task scheduling for the IoT in serverless edge computing networks, in which heterogeneous serverless edge computing nodes are rational individuals with interests to optimize their own scheduling utility while the nodes only have access to local observations. The task scheduling competition process is formulated as a partially observable stochastic game (POSG) to enable serverless edge computing nodes to noncooperatively schedule tasks and allocate computing resources depending on their locally observed system state, which takes into account the associated task generation state, data queue state, communication channel state, and previous computing resource allocation state. To solve the proposed POSG and deal with the partial observability, a multiagent task scheduling algorithm based on the dueling double deep recurrent$Q$-network (D3RQN) method is developed to approximate the optimal task scheduling and resource allocation solution. Finally, extensive simulation experiments are conducted to validate the effectiveness and superiority of the proposed scheme.

Viewport prediction and prefetch have an important influence on VR video streaming performance. This work proposes a novel federated learning-based viewport prediction model training algorithm, ComPer-FedAvg. The proposed algorithm leverages a VR video’s common viewing pattern and users’ personal viewing patterns to train the prediction model in a distributed and privacy-preserving manner. Further, considering the VR video viewport prediction accuracy, a stochastic game is formulated to solve the VR streaming network’s communication resource allocation problem, where limited communication resource blocks are auctioned to users to achieve the optimal overall VR viewing experience. For each user, the auction is decomposed into two disjoint subproblems, namely, the optimal number of data rate requesting and true value claiming (bidding). The optimal true value claiming has been analytically proved to be equal to the VR viewing reward with given data rate. Due to the lack of global information when users request data rate, we reformulate users’ data rate requesting problem as a POMDP problem. A novel deep reinforcement learning algorithm is adopted to solve the problem. Evaluation and simulation results show the proposed viewport prediction and VR streaming schemes outperform conventional solutions in terms of prediction accuracy and VR viewing experience.

Because of the challenges brought by the high dynamic topology of satellite networks to the transport layer, this paper is mainly devoted to a multipath transmission control protocol (MPTCP) path selection scheme based on segment control technology and software-defined networking (SDN). We describe the signaling interaction mode of MPTCP and the process of segment control technology applied in the satellite network. According to the requirements of real-time and accuracy of data transmission, we consider the link delay, stability, and packet loss rate, and construct the scheme as a maximum-flow minimum-cost problem. The experimental results show that the proposed scheme can meet the low delay requirements of delay-sensitive traffic flow, improve bandwidth utilization, and ensure more efficient and reliable data transmission.

Satellite network constellation is promising in providing efficient global Internet access. While the constellation scale, the user population, and service variety in satellite networks are too large, requiring efficient resource allocation and network management. Network virtualization is an efficient solution to achieve preceding objectives, but conventional schemes on terrestrial networks are not well adapted to satellite networks. Therefore, in this work, we establish a network virtualization model considering topology dynamics, quality of service requirement, and resource constraint. Then we formulate Virtual Network Embedding (VNE) into optimization problems, and we propose a multi-constraint virtual network embedding algorithm to solve the problem. Finally, we evaluate the proposed scheme and prove its adaptability to satellite networks.

On-path caching is a building block in Named Data Networking that helps eliminate redundant traffic. The performance of redundancy elimination depends on both Content Store (CS) and Pending Interest Table (PIT), i.e., CS caches content for future reuse, and PIT aggregates repetitive requests in a short period. However, contemporary proactive caching strategies only take account of CS while neglecting PIT. In this work, we integrate both PIT and CS into the proactive caching model, derive how to calculate aggregated request rate, and propose an algorithm to calculate the aggregated request rate across the tree topology. Then we formulate caching placement into optimization problems and solve them with a decomposition-based evolutionary algorithm. The simulation results show that the proposed scheme outperforms conventional solutions.

Device-to-Device (D2D) caching assists Mobile Edge Computing (MEC) based caching in offloading inter-domain traffic by sharing cached items with nearby users, while its performance relies heavily on caching nodes' sharing willingness. In this paper, a Blockchain-based Cache and Delivery Market (CDM) is proposed as an incentive mechanism for the distributed caching system. Under given incentive mechanisms, both D2D and MEC caching nodes' willingness is guaranteed by satisfying their expected reward for cache sharing. Besides, for the distributed CDM, content delivery related transactions are executed by smart contracts. To achieve consensus on transactions and prevent frauds, a consensus protocol among the smart contract execution nodes (SCENE) is necessary. To minimize the latency of reaching consensus while guaranteeing its confidence level, we propose partial Practical Byzantine Fault Tolerance (pPBFT) protocol. Further, the model of cache sharing and transaction execution consensus is proposed, and we further formulate caching placement and SCENE selection as Markov Decision Process problems. Due to the complexity and dynamics of the problems, a deep reinforcement learning approach is adopted to solve the problem. The simulation results show that the proposed schemes outperform conventional solutions in terms of traffic offloading, content retrieval latency, and consensus latency.

Built-in caching in Named Data Networking (NDN) promises to provide efficient content delivery, where the dedicated on-path caching scheme is deployed to serve users' requests on the forwarding path. In this work, to utilize limited caching resources to achieve optimal performance, the caching placement decision is made by jointly considering the content popularity, underlying network topology, forwarding strategy and caching service mechanism in NDN. More specifically, we propose a service-aware caching model. In the model, we first define the Cache Service Matrix (CSM), which describes the position where each user's request is served for each piece of content. In order to make CSM comply with the caching placement, underlying topology, forwarding strategy, and on-path caching service mechanism, we propose an algorithm to calculate CSM under the preceding constraints. With CSM, the utility of caching placement could be derived correctly, and we formulate the optimal caching placement into optimization problems. Moreover, the differential grouping co-evolutionary (DG2-E) algorithm is adopted to decompose and solve the NP-hard optimization problems. Simulation results show the proposed scheme outperforms state of the art solutions in terms of inter-domain traffic reducing and request-response accelerating under arbitrary topologies.