In edge computing, energy-limited distributed edge clients present challenges such as heterogeneity, high energy consumption, and security risks. Traditional blockchain-based federated learning (BFL) struggles to address all three of these challenges simultaneously. This article proposes a Graph-Partitioning Multi-Granularity Federated Learning method on a consortium blockchain, namely GP-MGFL. To reduce the overall communication overhead, we adopt a balanced graph partitioning algorithm while introducing observer and consensus nodes. This method groups clients to minimize high-cost communications and focuses on the guidance effect within each group, thereby ensuring effective guidance with reduced overhead. To fully leverage heterogeneity, we introduce a cross-granularity guidance mechanism. This mechanism involves fine-granularity models guiding coarse-granularity models to enhance the accuracy of the latter models. We also introduce a credit model to adjust the contribution of models to the global model dynamically and to dynamically select leaders responsible for model aggregation. Finally, we implement a prototype system on real physical hardware and compare it with several baselines. Experimental results show that the accuracy of the GP-MGFL algorithm is 5.6% higher than that of ordinary BFL algorithms. In addition, compared to other grouping methods, such as greedy grouping, the accuracy of the proposed method improves by about 1.5%. In scenarios with malicious clients, the maximum accuracy improvement reaches 11.1%. We also analyze and summarize the impact of grouping and the number of clients on the model, as well as the impact of this method on the inherent security of the blockchain itself.

Mobile Edge Caching (MEC) can potentially alleviate Internet transmission congestion by delivering content at the network edge. However, current MEC solutions suffer from low resource utilization efficiency and often fail to meet user Quality of Experience (QoE), primarily due to dynamic user requests and obsessive pursuit of direct caching hits. Given the prevalence of recommendation systems, users often lack precise requests when using recommendation-based applications like TikTok and Taobao, insted passively enjoying recommended content. In this paper, we introduce a recommendation-enabled MEC architecture to enhance resource utilization and QoE. We develop a recommendation-enabled soft caching model and formulate the optimization problem as maximizing joint system revenue. To address this, we propose an attention-assisted federated learning deep Q-network algorithm. We conduct the simulations by using the real-world MIND dataset. The results demonstrate that our proposed algorithm outperforms existing baselines, demonstrating its effectiveness in improving resource utilization and QoE.

Foundation model (FM) has shown great potential in various downstream tasks, such as CLIP, a vision-language pre-trained model that effectively aligns semantics in text and image spaces. Federated learning (FL), as a distributed training paradigm, can provide adequate data for adapting FM while overcoming challenges such as communication pressure and user privacy leakage. However, in edge computing with a large scale of edge devices, vanilla FL faces challenges, including limited computation resources, inadequate bandwidth, non-IID and multi-granularity data caused by the diversity of devices and users. To feasibly and efficiently collaborate with the large and diverse edge devices in adapting FM, we propose a novel personalized and multi-granularity FL framework (PMG-FL) that provides a personalized lightweight prompt for each edge device while considering interactions among same-granularity edge devices and cross-granularity edge devices. In particular, we introduce prompt training to adapt FM locally, which can mitigate computation and communication pressure with few learnable prompt parameters. Based on the prompt parameters, we design a distance-based prompt aggregation mechanism to capture similarities among same-granularity edge devices and aggregate the personalized prompt for each edge device, addressing the challenges posed by non-IID data. Furthermore, we design a cross-granularity guidance mechanism that leverages the correlation of semantic knowledge among edge devices with multigranularity data. Extensive experimental results demonstrate the superiority of PMG-FL over the alternative approaches, with robust performance on both IID and non- IID data.

Lignin is a pivotal building block of lignocellulose biomass and the most abundant aromatic polymer in nature. Selective hydrodeoxygenation (HDO) via the target cleavage of aryl ether C-O bonds is of great importance for lignin biorefinery. However, preserving aromaticity during HDO for efficient production of arenes remains a challenge. Herein, the low electronegativity of Cs was utilized to customize the chemical environment and geometric construction of highly dispersed Cu sites on mesoporous SiO2 nanospheres. The electron density around Cu sites evidently increased while the coordination environment of Cu was precisely tailored. The hydrogenation and deoxygenation activities were effectively balanced. Owing to the synergy between electron-rich Cu delta- sites and the Cs center, the saturated hydrogenation of aromatic rings was efficiently inhibited, and preferential selectivity toward aryl ether C-O bond cleavage was obtained. The selectivity of arenes from anisole reached 83% on 2Cs/Cu@NS-SiO2, while the intrinsic production activity of arenes based on active Cu sites significantly increased to 0.34 mmolmCu -2h-1, more than doubled that of Cu@NS-SiO2 without Cs modification. Besides, a remarkable arene selectivity of 76.3% was achieved among the depolymerized monomers from the HDO of organic solvent-extracted lignin. This study offers a promising approach for valorizing lignin into high-value arenes.

In end-edge-cloud collaborative frameworks, deploying long-term sequence prediction models near edge devices reduces reliance on network resources while enhancing service quality and response speed. However, a key challenge lies in the limited computational capacity of edge devices, necessitating a compromise between model parameter size and prediction accuracy. Additionally, the physical isolation of edge devices, coupled with data processing at the edge, leads to the logical isolation of deployed models, complicating knowledge sharing across devices. We propose the Multi-stage Catalytic Distillation (MCD) framework to address this challenge. MCD utilizes knowledge distillation to share knowledge across models of different scales, thus achieving logical interconnectivity despite physical isolation. Furthermore, the framework introduces an innovative catalytic purification method designed to expedite model convergence and enhance prediction accuracy in edge devices. This approach mitigates the trade-offs between model size and precision, facilitating efficient knowledge transfer in edge computing environments. Within the MCD framework, models across different levels exhibit an enhancement in prediction accuracy while maintaining the parameter count essentially constant. The overall improvement rate amounts to 17.6%.

As far as mobile edge computing is concerned, it is necessary to ensure the data integrity of latency-sensitive applications during the process of computing. While certain research programs have demonstrated efficacy, challenges persist, including the inefficient utilization of computing resources, network backhaul issues, and the occurrence of false-negative detections. To solve these problems, an integrity protection scheme is proposed in this paper on the basis of data right confirmation (DRC). Under this scheme, a two-layer consensus algorithm is developed. The outer algorithm is applied to establish a data authorization mechanism by marking the original data source to avoid the false negative results caused by network attacks from the data source. In addition, blockchain-based mobile edge computing (BMEC) technology is applied to enable data sharing in the context of mobile edge computing while minimizing the network backhaul of edge computing. Based on the Merkle Tree algorithm, the inner layer algorithm is capable not only of accurately locating and promptly repairing damaged data but also of verifying all servers in the mobile edge computing network either regularly or on demand. Finally, our proposal is evaluated against two existing research schemes. The experimental results show that our proposed scheme is not only effective in ensuring data integrity in mobile edge computing, but it is also capable of achieving better performance.

Intelligent Vehicular Ad-hoc Networks (INVANETs) optimize road travel in Intelligent Transportation Systems (ITS), exploiting smart technologies to enhance safety, efficiency, and reduce congestion. Network modeling, as a fundamental tool in network analysis and optimization, is critical to the ongoing enhancement of INVANETs. However, affordable models that can accurately capture topological dependence among network performances in INVANETs and adapt to their dynamics are currently lacking. This paper proposes the INVANETs Routing Modeling Network (IRNet). The IRNet is based on an elaborate Spatio-Temporal Graph Attention Networks (STGAT) to capture the intricate relationships between topology, network states, and Key Performance Indicators (KPIs), e.g., delay, jitter, and drops. IRNet first constructs models for per-packet delay and drops mathematical distributions, then calculates these KPIs for source-destination pairs, and derives global network indicators. It adapts to INVANETs' dynamic topologies using snapshots. Furthermore, IRNet generalizes well across different topologies, routing schemes, and traffic conditions. Evaluations reveal IRNet's superior prediction accuracy, outperforming baseline models with a 17.64% improvement in average-case MRE, highlighting its effectiveness. Use cases are also involved to illustrate IRNet's optimization impact on INVANETs.

Computation offloading is widely used in vehicular edge computing (VEC) networks to satisfy the computational intensity and latency sensitivity requirements. However, many existing offloading algorithms do not comprehensively consider the dynamically changing characteristics of heterogeneous tasks within a roadside unit (RSU), resulting in tasks timeout and being dropped. In this paper, we design a competitive and cooperative computation offloading (C3O) model to reduce task execution latency. Specifically, when intensive heterogeneous tasks are generated, these vehicles jointly compete for the computing resource of a RSU, or they can also offload tasks to the task vehicle (TaV) with free computing resource. Meanwhile, We analyze the latency model of local execution and offloading to RSU or TaV execution and formulate a sequential task offloading decision problem, NP-hard. To address it, we propose a multi-agent reinforcement learning algorithm based on C3O (MARC3O) to intelligently determine the computation offloading policy for each vehicle according to the state of VEC networks. Simulation results demonstrate that the proposed algorithm can significantly reduce task execution latency and improve task completion rates compared with baseline schemes.

Driven by the burgeoning growth of the Internet of Everything and the substantial breakthroughs in deep learning (DL) algorithms, a booming of artificial intelligence (AI) applications keep emerging. Meanwhile, the advance in existing computing paradigms, i.e., cloud computing and edge computing, provide assorted computing solutions to satisfy the increasingly high requirements for ubiquitous AI services. Nevertheless, there are some non-trivial issues in the computing frameworks, including the underutilization of computing power, the self-interest of computing-power trading mechanism, and the inefficiency of AI services management. To tackle the above issues, we propose a computing-power trading framework based on blockchain, also named AI-Bazaar. In AI-Bazaar, the AI consumers play multiple roles and feel free to contribute the computing power rented from the computing-power provider (CPP) for blockchain mining and AI services. Accordingly, we formulate the computing trading problem as a Stackelberg game. Based on the win or learn fast principle (WoLF), we design a profit-balanced multi-agent reinforcement learning (PB-MARL) algorithm to search the AI-Bazaar equilibrium, while finding the balanced profits for AI consumers and CPP. Numerical simulations are carried out to demonstrate the satisfactory performance and effectiveness of the proposed framework.

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS.

Lignin is a renewable carbon resource to produce arenes due to its abundant aromatic structures. For the liquid-phase hydrodeoxygenation (HDO) based on metallic catalysts, the preservation of aromatic rings in lignin or its derivatives remains a challenge. Herein, we synthesized Mn-doped Cu/Al2O3 catalysts from layered double hydroxides (LDHs) for liquid-phase HDO of lignin-derived anisole. Mn doping significantly enhanced the selective deoxygenation of anisole to arenes and inhibited the saturated hydrogenation on Cu/Al2O3. With Mn doping increasing, the surface of Cu particles was modified with MnOx along with enhanced generation of oxygen vacancies (Ov). The evolution of active sites structure led to a controllable adsorption geometry of anisole, which was beneficial for increasing arenes selectivity. As a result, the arenes selectivity obtained on 4Cu/8Mn4AlOx was increased to be more than 6 folds of that value on 4Cu/4Al2O3 over the synergistic sites between metal Cu and Ov generated on MnOx.

Edge clouds have become a de-facto paradigm to deliver low and stable networks to delay-critical applications such as web services and AR/VR. A unique form of edge clouds is those crowdsourced from third parties, e.g., idle PCs or workstations. Such crowdsourced edge platforms can better sink computations closer to users, reduce the purchase cost, and eliminates the carbon generated during manufacturing. Yet, they also face the challenge of out-of-control hardware, e.g., a server dropping in/out anytime. In this paper, we perform the first-of-its-kind measurement of Quality of Service (QoS) for a large-scale crowdsourced edge platform, which covers over 10,000 edge servers, 100,000 users and 10,000,000 user requests. The measurement takes a holistic QoS view: (1) First, we look at how much hardware resources are provided by edge servers, how much time they are available for service deployment, and what are the major abnormal behaviors. (2) Second, we analyze the factors affecting service stability and quantify the resource utilization pattern of containerized services hosted on those edge servers. (3) Third, we investigate the spatial and temporal features of user requests handled by the platform. Many useful and somehow surprising findings are obtained through the above measurements. We also derive insightful implications that could help edge platforms and edge applications to better deliver their services to users.

Lignin utilization is a potential approach for replacing fossil energy and releasing the environment pressure.Herein, we synthesized a series of novel Cu-based catalysts, Cu@NS-SiO 2 （NS = nano sphere） and alkali metals（Na, K, Rb, and Cs） doped Cu@NS-SiO 2 , and applied them in hydrodeoxygenation reaction of anisole. High Cu dispersion was presented on all catalysts. The modification of alkali metals on Cu@NS-SiO 2 significantly enhanced the electron density of Cu sites in the following order: Cs ＞ Rb ＞ K ＞ Na, among which Cs decreased the Cu 2p3/2binding energy most（by 0.7 eV）. Moreover, the modification did not substantially affect the geometric structure of Cu species. This regulable electronic environment of Cu sites was crucial for selective deoxygenation and inhibiting the hydrogenation of aromatic rings in anisole, and thus promoted the selectivity of benzene.Compared with Cu@NS-SiO 2 （～59%）, the highest benzene selectivity was obtained on Cs/10Cu@NS-SiO 2 at ～83%.

The emergence of next-generation latency-critical applications places strict requirements on network latency and stability. Edge cloud, an instantiated paradigm for edge computing, is gaining more and more attention due to its benefits of low latency. In this work, we make an in-depth investigation into the network QoS, especially end-to-end latency, at both spatial and temporal dimensions on a nationwide edge computing platform. Through the measurements, we collect a multi-variable large-scale real-world dataset on latency. We then quantify how the spatial-temporal factors affect the end-to-end latency, and verified the predictability of end-to-end latency. The results reveal the limitation of centralized clouds and illustrate how could edge clouds provide low and stable latency. Our results also point out that existing edge clouds merely increase the density of servers and ignore spatial-temporal factors, so they still suffer from high latency and fluctuations. Based on the observations, we propose a robust prototype edge cloud model based on lessons we learn from the measurement and evaluate its performance in the production environment. The further evaluation result shows that edge clouds achieve 84.1% latency reduction with 0.5ms latency fluctuation and 73.3% QoS improvement compared with the centralized clouds.

Abstract Background This study aimed to compare the postoperative quality of life (PQOL) between non–small–cell lung cancer (NSCLC) patients who underwent video–assisted thoracoscopic sublobar resection (subsegment, segment, or wedge) and lobectomy. Meanwhile, we developed a PQOL scale for patients with NSCLC after optimization. Methods Developing and evaluating the postoperative quality–of–life scale of non–small–cell lung cancer (NSCLC–PQOL) followed by the international principles for developing quality–of–life scale. Therefore, we used the NSCLC–PQOL scale to evaluate the PQOL of patients who underwent different surgeries. Results The overall PQOL of patients who underwent video-assisted thoracoscopic lobectomy and sublobar resection gradually worsened from discharge to 3 months postoperatively and progressively improved from three to 6 months postoperatively. And the sublobar resection group showed better PQOL in chest tightness, breath shortness, breathlessness, cough and expectoration than the lobectomy group, and the differences were statistically significant (P < 0.05). The final version of the NSCLC–PQOL contained three dimensions: “signs–symptoms”, “psychological and psychiatric”, and “social–life” dimensions. Conclusions The sublobar resection group showed better PQOL in “chest tightness”, “breath shortness”, “breathlessness”, “cough”, and “expectoration” than the lobectomy group. Twenty–two items formed a well–behaved PQOL scale after being validated satisfactorily. The scale was a suitable rating tool for evaluating the NSCLC–PQOL of patients. Trial registration As this study was a retrospective study and not a clinical trial, we did not register this study in the Chinese Clinical Trial Registry.

With the breakthroughs of sixth-generation (6G), immersive services are beginning to receive a tremendous amount of interest, that is, 6G immersive services. The 6G immersive services involve various wearable devices to provide a high-quality perception of virtual scenes for users. The active participation of service users (SUs) and service providers (SPs) makes the rapid proliferation of wearable devices. Emerging technologies, such as cloud computing and edge computing, have also promoted the rapid development of the 6G immersive service market, making ubiquitous immersive services possible. However, the proliferation of wearable devices has brought severe challenges, including hierarchical resource provisioning, temporal dependencies between services and resources, as well as heterogeneous resource requirements. To fill this gap, we propose an AI-driven 6G immersive service resource provisioning approach, Almers-6G, from the perspective of large and small regions. In the large region, heterogeneous resources are allocated to satisfy the requirements of perception experience from SUs. The problem of resource provisioning is solved by a context-immersive learning-based Lyapunov optimization algorithm. While in the small region, the well-designed blockchain-based double dutch auction (SDDA) mechanism is used for heterogeneous resources matching and pricing determination. Finally, illustrative simulations are provided to show the effectiveness of the proposed scheme.

Workload prediction in multi-tenant edge cloud platforms (MT-ECP) is vital for efficient application deployment and resource provisioning. However, the heterogeneous application patterns, variable infrastructure performance, and frequent deployments in MT-ECP pose significant challenges for accurate and efficient workload prediction. Clustering-based methods for dynamic MT-ECP modeling often incur excessive costs due to the need to maintain numerous data clusters and models, which leads to excessive costs. Existing end-to-end time series prediction methods are challenging to provide consistent prediction performance in dynamic MT-ECP. In this paper, we propose an end-to-end framework with global pooling and static content awareness, DynEformer, to provide a unified workload prediction scheme for dynamic MT-ECP. Meticulously designed global pooling and information merging mechanisms can effectively identify and utilize global application patterns to drive local workload predictions. The integration of static content-aware mechanisms enhances model robustness in real-world scenarios. Through experiments on five real-world datasets, DynEformer achieved state-of-the-art in the dynamic scene of MT-ECP and provided a unified end-to-end prediction scheme for MT-ECP.

Computing Power Network (CPN) is an evolution of multi-access edge computing. Since the skyrocketing proliferation of CPN s, energy consumption aggravates explosively. However, majority of energy is wasted due to the incomplete analysis of tasks and resources, such as coarse-grained tasks consideration, coarse-grained resources integration, and unfocused complex information. In this paper, we propose a broad attention based fine-grained task offloading approach in green CPNs, i.e., Bat-FG. Specifically, for fine-grained tasks, we establish directed acyclic graphs (DAGs) subtasks offloading problem for green CPNs under the dependency and service constraints. For finegrained resources, decentralized resources are integrated into resource pools. Bridging the gap between fine-grained tasks and resource pools, we design a novel broad attention meta-reinforcement learning approach, i.e., Bat-MRL to focus on the main information for reducing the tasks' latency and energy consumption. Finally, extensive simulations show that Bat-FG significantly reduces 25.6 % task latency and 72.9 % energy consumption.

Energy Internet (EI) is developing and booming rapidly with the increase of distributed energy resources, which is beneficial to address the severe condition of industrial energy. However, there are inevitable credit crises and utility optimization challenges in EI that need to be settled. In this article, we propose a blockchain-assisted software defined energy Internet (BSDEI), where a distributed energy market smart contract is designed to ensure transactions executed reliably and participants’ accounts dealt accurately. In order to jointly optimize the utilities of operators, retailers, and industrial prosumers in BSDEI, we formulate the whole trading process as a three-stage Stackelberg game, with the proof of existence and uniqueness for the Stackelberg equilibrium. Then, we design a hierarchical distributed policy gradient algorithm to solve the Stackelberg game under incomplete information. We implement a blockchain-based industrial energy trading system using a middleware platform. The smart contract is deployed on the consortium blockchain, providing website interfaces for participants to operate. Furthermore, we conduct experiments for analyzing economic benefits. Our system prototype demonstrates the feasibility of BSDEI and the algorithm exceeds about 18% in total mean reward than comparing algorithms.

As shown in Fig. 3.1, the limitations of EI and the complementary advantages of BC are painfully clear. Spontaneously, the appearance of BC-assisted EI would be expected to pave the way for the development of emerging intelligent services. In this chapter, we first discuss the limitations of EI. Then, we elaborate the benefits of BC in EI.