Blockchain sharding has been deemed a promising solution that can substantially improve blockchain scalability. However, developers must overcome two major technical challenges to implement a sharded blockchain. The first challenge is the high cross-shard transaction ratio in blockchain shards. This issue significantly degrades the throughput of a sharded blockchain. The second challenge is the imbalanced workloads across blockchain shards. In a blockchain with imbalanced workloads, some busy shards have to handle an overwhelming number of transactions and thus become congested. Facing these two challenges, a dilemma is that it is difficult to guarantee a lowcross-shard transaction ratioand maintain thebalanced workloadsacross all shards, simultaneously. We believe that a fine-grained account allocation strategy can address this dilemma. To this end, we formulate the tradeoff between these two metrics as a network-partition problem. We then solve this problem by proposing a sharding protocol, namedShardCutter, which includes the following two crucial components: a community-aware account partition algorithm and a fine-tuned account migration mechanism. Finally, experimental results demonstrate that the proposed protocol outperforms other baselines in terms of throughput, makespan, cross-shard transaction ratio, and the workload balance of shards’ transaction pool.

Metaverse is drawing increasing attention from both academia and industry. Interoperability among different metaverse systems has become essential. A cross-metaverse interoperability protocol can enable interoperability across metaverses. However, cross-metaverse protocols often suffer significant cost overhead and transaction latency. For example, in STYLE, a leading cross-metaverse platform, 74% of transaction latency and 97% of the cost overhead are attributed to the relay blockchain rather than the two participating metaverses. To make cross-metaverse efficient, in this paper, we propose a fast and cheap cross-metaverse interoperability protocol namedCrossMeta.CrossMetacan enable direct communication among heterogeneous metaverses rather than depending on a relay blockchain. This is achieved through two components: i) a committee that relays transactions from the source metaverse to the destination metaverse, along with availability proofs, and ii) a smart contract that verifies the proofs provided by the committee. To ensure an honest majority within the selected committee, we propose a dynamic committee selection method based on the chain quality property. Furthermore, we demonstrate that honest brokers achieve a Nash equilibrium. Additionally, we prove that the proposedCrossMetaprotocol satisfies the security properties of atomicity and liveness. To demonstrate the practicality ofCrossMeta, we implemented a prototype of theCrossMetausing two real-world metaverse platforms, i.e.,Axie InfinityandSandbox. The evaluation results show thatCrossMetaoutperforms other cross-metaverse solutions regarding transaction latency and gas fees.

Permissioned blockchains play a significant role in various application scenarios. Applications built on heterogeneous permissioned blockchains need to migrate data from one chain to another, aiming to keep their competitiveness and security. Thus, data migration across heterogeneous chains is a building block of permissioned blockchains. However, existing data migration protocols across heterogeneous chains are rarely used in practice since data migration technologies are insecure. To this end, we propose a data migration protocol across permissioned blockchains, namedDataFly. We design apeg consensus mechanism, which provides consistent data-migration functionality between any two permissioned blockchains. To preserve the confidentiality of data, we invoke two classical cryptographic methods, i.e., i) ECDSA feature and ii) theintegrated signature and public key encryptionscheme. Through combining those two methods, data can be securely migrated from one permissioned blockchain to another without exposing the migrated data to anyone except associated parties. To demonstrate the practicality ofDataFly, we implement a prototype ofDataFlyusing existing popular permissioned blockchains, i.e., Hyperledger Fabric and private enterprise Ethereum. Measurement results demonstrate thatDataFlyoutperforms related works in terms of transaction latency and gas costs.

Previous state-of-the-art studies have demonstrated that adversaries can access sensitive user data by membership inference attacks (MIAs) in Federated Learning (FL). Introducing differential privacy (DP) into the FL framework is an effective way to enhance the privacy of FL. Nevertheless, in differentially private federated learning (DP-FL), local gradients become excessively sparse in certain training rounds. Especially when training with low privacy budgets, there is a risk of introducing excessive noise into clients’ gradients. This issue can lead to a significant degradation in the accuracy of the global model. Thus, how to balance the user's privacy and global model accuracy becomes a challenge in DP-FL. To this end, we propose an approach, known as differential privacy federated aggregation, based on significant gradient protection (DP-FedASGP). DP-FedASGP can mitigate excessive noises by protecting significant gradients and accelerate the convergence of the global model by calculating dynamic aggregation weights for gradients. Experimental results show that DP-FedASGP achieves comparable privacy protection effects to DP-FedAvg and cpSGD (communication-private SGD based on gradient quantization) but outperforms DP-FedSNLC (sparse noise based on clipping losses and privacy budget costs) and FedSMP (sparsified model perturbation). Furthermore, the average global test accuracy of DP-FedASGP across four datasets and three models is about$2.62$%,$4.71$%,$0.45$%, and$0.19$% higher than the above methods, respectively. These improvements indicate that DP-FedASGP is a promising approach for balancing the privacy and accuracy of DP-FL.

Vertical federated learning (VFL) allows parties to build robust shared machine learning models based on learning from distributed features of the same samples, without exposing their own data. However, current VFL solutions are limited in their ability to perform inference on non-overlapping samples, and data stored on clients is often subject to loss due to various unavoidable factors. This leads to incomplete client data, where client missing features (MF) are frequently overlooked in VFL. The main aim of this paper is to propose a VFL framework to handle missing features (MFVFL), which is a tensor decomposition network-based approach that can effectively learn intra- and inter-client feature information from client data with missing features to improve VFL performance. In the proposed MFVFL method each client imputes missing values and encodes features to learn intra-feature information, and the server collects the uploaded feature embeddings as input to our developed low-rank tensor decomposition network to learn inter-feature information. Finally, the server aggregates the representations from tensor decomposition to train a global classifier. In the paper, we theoretically guarantee the convergence of MFVFL. In addition, differential privacy (DP) for data privacy protection is always used, and the proposed framework (MFVFL-DP) can deal with such degraded data by using a tensor robust PCA to alleviate the impact of noise while preserving data privacy. We conduct extensive experiments on six datasets of different sample sizes and feature dimensions, and demonstrate that MFVFL significantly outperforms state-of-the-art methods, especially under high missing ratios. The experimental results also show that MFVFL-DP possesses excellent denoising capabilities and illustrate that the noisy effect by the DP mechanism can be alleviated.

The blockchain technology provides a revolutionary solution for information exchange through its decentralized, tamper-proof, and highly secure characteristics. It has wide application in many industries, with the potential to improve efficiency, reduce costs, and promote innovation. However, the full replication mechanism of blockchain results in the need for each device to store complete blockchain data, leading to inefficient storage. Additionally, as the scale of the blockchain network expands, the increasing data volume and frequent transactions can cause network congestion and latency, posing scalability issues for blockchain. Coded sharding blockchain has been proposed to address these issues. However, the current solutions face challenges such as dealing with malicious nodes and low computational efficiency, which hinder the enhancement of their scalability and computational performance. To resolve these problems, we propose AdaptiveShard by combining coded sharding blockchain with adaptive verifiable coded computing (AVCC). This solution is designed based on the Unspent Transaction Output (UTXO) model and is suitable for cryptocurrency transaction scenarios. Compared to traditional coded sharding blockchain solutions, AdaptiveShard can: 1) enhance the computational performance of coded sharding blockchain during block validation by combining AVCC with Gaussian variant of Freivalds algorithm (GVFA), reducing the decoding complexity toO(N2logN); 2) validate the computation results of each shard using GVFA and replace balance check verification functions with matrix multiplication, reducing the computational complexity of verification toO(√n); 3) reduce the additional number of nodes required to resolve malicious nodes from two to one using verifiable computation; 4) balance the system in the presence of straggler or malicious nodes through dynamic coding techniques, eliminating their impact and improving system reliability. Experiments demonstrate that at t=1000, the throughput is 25.6% higher compared to Polyshard. Compared to the solution without dynamic coding, the solution with dynamic coding can reduce the running time by 9.7% at t=50.

Vehicle Twins (VTs) as digital representations of vehicles can provide users with immersive experiences in vehicular metaverse applications, e.g., Augmented Reality (AR) navigation and embodied intelligence. VT migration is an effective way that migrates the VT when the locations of physical entities keep changing to maintain seamless immersive VT services. However, an efficient VT migration is challenging due to the rapid movement of vehicles, dynamic workloads of Roadside Units (RSUs), and heterogeneous resources of the RSUs. To achieve efficient migration decisions and a minimum latency for the VT migration, we propose a multi-agent split Deep Reinforcement Learning (DRL) framework combined with spatio-temporal trajectory generation. In this framework, multiple split DRL agents utilize split architecture to efficiently determine VT migration decisions. Furthermore, we propose a spatio-temporal trajectory generation algorithm based on trajectory datasets and road network data to simulate vehicle trajectories, enhancing the generalization of the proposed scheme for managing VT migration in dynamic network environments. Finally, experimental results demonstrate that the proposed scheme not only enhances the Quality of Experience (QoE) by 29% but also reduces the computational parameter count by approximately 25% while maintaining similar performances, enhancing users' immersive experiences in vehicular metaverses.

By synergistically integrating mobile networks and embodied artificial intelligence (AI),mobileembodiedAInetworks (MEANETs) represent an advanced paradigm that facilitates autonomous, context-aware, and interactive behaviors within dynamic environments. Nevertheless, the rapid development of MEANETs is accompanied by challenges in trustworthiness and operational efficiency. Fortunately, blockchain technology, with its decentralized and immutable characteristics, offers promising solutions for MEANETs. However, existing block propagation mechanisms suffer from challenges such as low propagation efficiency and weak security for block propagation, which results in delayed transmission of messages or vulnerability to malicious tampering, potentially causing severe accidents in blockchain-enabled MEANETs. Moreover, current block propagation strategies cannot effectively adapt to real-time changes of dynamic topology in MEANETs. Therefore, in this paper, we propose a graph Resfusion model-based trustworthy block propagation optimization framework for consortium blockchain-enabled MEANETs. Specifically, we propose an innovative trust calculation mechanism based on the trust cloud model, which comprehensively accounts for randomness and fuzziness in the validator trust evaluation. Furthermore, by leveraging the strengths of graph neural networks and diffusion models, we develop a graph Resfusion model to effectively and adaptively generate the optimal block propagation trajectory. Simulation results demonstrate that the proposed model outperforms other routing mechanisms in terms of block propagation efficiency and trustworthiness. Additionally, the results highlight its strong adaptability to dynamic environments, making it particularly suitable for rapidly changing MEANETs.

State-of-the-art blockchain sharding solutions, such as Monoxide, can cause severely imbalanced distribution of transaction (TX) workloads across all blockchain shards due to the deployment policy of their accounts. Imbalanced TX distributions then producehot shards, in which the cross-shard TXs may experience an unlimited confirmation latency. Thus, how to address the hot-shard issue and how to reduce cross-shard TXs become significant challenges of blockchain sharding. Through reviewing the related studies, we find that a cross-shard TX protocol that can achieve workload balance among all shards and simultaneously reduce the quantity of cross-shard TXs is still absent from the literature. To this end, we propose BrokerChain, which is a cross-shard blockchain protocol dedicated to account-based state sharding. Essentially, BrokerChain exploits fine-grained state partition and account segmentation. We also elaborate on how BrokerChain handles cross-shard TXs through broker accounts. The security issues and other properties of BrokerChain are analyzed rigorously. Finally, we conduct comprehensive evaluations using an open-source blockchain sharding prototype namedBlockEmulator. The evaluation results show that BrokerChain outperforms other baselines in terms of transaction throughput, transaction confirmation latency, the queue size of the transaction pool, and workload balance.

Numerous blockchain simulators have been proposed to allow researchers to simulate mainstream blockchains. However, we have not yet found a testbed that enables researchers to develop and evaluate their new consensus algorithms or new protocols for blockchain sharding systems. To fill this gap, we developed BlockEmulator, which is designed as an experimental platform, particularly for emulating blockchain sharding mechanisms. BlockEmulator adopts a lightweight blockchain architecture so developers can only focus on implementing their new protocols or mechanisms. Using layered modules and useful programming interfaces offered by BlockEmulator, researchers can implement a new protocol with minimum effort. Through experiments, we test various functionalities of BlockEmulator in two steps. First, we prove the correctness of the emulation results yielded by BlockEmulator by comparing the theoretical analysis with the observed experiment results. Second, other experimental results demonstrate that BlockEmulator can facilitate measuring a series of metrics, including throughput, transaction confirmation latency, cross-shard transaction ratio, the queuing status of transaction pools, workload distribution across blockchain shards, etc. We have made BlockEmulator open-source in Github.

Web 3.0 is viewed as the next generation of the Internet, with the aim of establishing a decentralized network where users can control their digital identities and data. Due to its decentralization feature, blockchain has become a promising solution for secure data storage and retrieval for abundant de-centralized applications in Web 3.0. In this context, decentralized identifiers (DIDs) are rapidly emerging as a key infrastructure for blockchain-based Web 3.0. However, with more and more DIDs generated and stored on the blockchain, it is challenging to support efficient retrieval of DIDs with data integrity assurance. In this paper, we propose a novel authenticated DID retrieval system for blockchain-based Web 3.0. Specifically, a new authenticated data structure (ADS) with the corresponding data verification algorithm is designed to enable efficient retrieval and verification for both DID records and their historical updates. Theoretical analysis has been performed to prove the security and efficiency of our proposed system. We implement our system and conduct experiments to evaluate the performance. Experimental results demonstrate that our proposed scheme exhibits higher system efficiency compared to the baseline solution.

Blockchain interoperability has become an essential functionality, which enables asset/data transfers across different blockchains. Sidechains have been deemed as a key technique to provide interoperability. However, sidechains are rarely used in practice, this is because sidechain technologies are impractical and non cost-efficient. To make sidechains practical, in this paper, we design a universal sidechain construction named USSC, which applies to a variety of blockchains without forking them. USSC also enables interoperability across heterogeneous blockchains regardless of underlying consensus. This is facilitated by three components: i) a committee selection method, ii) a cross-chain certificate, and iii) a cross-chain bridge based on smart contracts. The proposed committee-selection method guarantees an honest majority within a committee. Through a concrete implementation of USSC, we outline how the proof-of-stake (PoS) and the proof-of-work (PoW) blockchains enable asset transfers. Furthermore, our USSC is more storage-efficient because it produces a smaller size of certificate and only needs partial nodes instead of all sidechain nodes following a blockchain. Thus, USSC can reduce the overhead of storage and communication of nodes. In addition, we prove that USSC achieves a secure sidechain construction with desirable security properties. Finally, we develop a proof-of-concept implementation of USSC using Cardano and Ethereum. Experimental results demonstrate that USSC outperforms PoW and PoS sidechains, in terms of the certificate size.

Non-identically distributed (Non-IID) data is a ma-jor challenge in federated learning (FL). Although many related studies have proposed methods to improve FL model performance, they often incur significant resource consumption. These studies typically save gradient states for training correction, some requiring clients to synchronize these states. Given that clients' extra gradient states could be substantial, even several times larger than the model's size, maintaining and synchronizing such large-size gradient states consume considerable memory and communication resources. This paper rigorously explores a substantial reduction in Non-IID methods' resource consumption on clients by reconstructing Non-IID methods' local corrections on the server. A crucial insight driving this reconstruction is to ensure symmetrical execution time for corrections. Motivated by this principle, we introduce Fleet, a lightweight FL framework. Fleet's server performs a two-stage symmetric gradient correction, while clients perform original gradient descents. Experimen-tal results demonstrate Fleet's superior performance over state-of-the-art methods, with resource consumption comparable to lightweight FedAvg on clients. Especially, Fleet excels in training deep models using large datasets. The experimental findings also support Fleet's dynamic scheduling as a plug-and-play module, showcasing its practical potential in real-world applications.

Federated Learning (FL) is a promising distributed machine learning framework that allows clients to collaboratively train a global model without data leakage. The synchronous FL suffers from the inefficient training caused by the slow-speed clients, which are called stragglers. Though asynchronous FL can well address the efficiency challenge, it induces massive system overheads and model degradation. As a framework considering the trade-off between synchronous and asynchronous FL, semi-asynchronous FL gains increasing attention. However, when clients' resources become a bottleneck, an unfair client scheduling may degrade global training accuracy and increase system overheads, especially in heterogeneous environments. In this paper, we propose Libra, which is a new FL framework aiming to achieve fair client scheduling in semi-asynchronous FL mode. Libra restricts devices that train too fast according to the model discrepancy. Furthermore, it selects stale local models according to the number of participating into FL training by clients. Additionally, Libra conducts a biased client selection while considering clients' resources and local losses. The experimental results show that Libra outperforms other baselines in terms of convergence accuracy, system overhead, and fairness of client participation. We also conduct an ablation study to further prove the effectiveness of Libra. In brief, Libra can achieve fair client scheduling and reduce inefficient local updates.

Cross-shard Transactions (CTXs) widely exist in sharded blockchains. CTXs have to endure large confirmation latency because they need to participate in consensus in both their source and destination shards. To diminish CTXs, plenty of state-of-the-art blockchain protocols have been proposed. For example, in BrokerChain [1], some intermediary broker accounts can help turn CTXs into intra-shard transactions through their voluntary liquidity services. Thereby, the original CTXs can be confirmed in blockchain shards quickly. However, we found that BrokerChain is impractical for a sharded blockchain because it does not consider how to recruit a sufficient number of broker accounts. Thus, blockchain clients do not have the motivation to provide token liquidity for others. To address this challenge, we design Broker2Earn, which is essentially a decentralized finance (DeFi) protocol that works as an incentive mechanism for blockchain users who choose to become brokers. Via participating in Broker2Earn, brokers can earn native revenues when they collateralize their tokens to the protocol. Furthermore, Broker2Earn can also benefit the sharded blockchain since it can efficiently spend each staked liquidity provided by brokers on diminishing CTXs. We formulate the core module of Broker2Earn into a revenue-maximization problem, which is proven NP-hard. To solve this problem, we design an online approximation algorithm using the relax-and-rounding technique. We also rigorously analyze the approximation ratio of our online algorithm. Finally, we conduct extensive experiments using real-world Ethereum transactions on both a transaction-driven simulator and an open-source blockchain testbed. The evaluation results show that the proposed Broker2Earn protocol demonstrates a near-optimal performance that outperforms other baselines, in terms of broker revenues and the usage of system liquidity.

Sharding is one of the most promising techniques for improving blockchain scalability. In blockchain state sharding, account migration across shards is crucial to the low ratio of cross-shard transactions and cross-shard workload balance. Through reviewing state-of-the-art protocols proposed to reconfigure blockchain shards via account shuffling, we find that account migration plays a significant role. From the literature, we only find a related work that utilizes the lock mechanism to realize account migration. We call this method the SOTA Lock, in which both the target account’s state and its associated transactions need to be locked when migrating this account between shards. Thereby, SOTA Lock causes a high makespan to the associated transactions. To address these challenges of account migration, we propose a dedicated Fine-tuned Lock protocol. Unlike SOTA Lock, Fine-tuned Lock enables real-time processing of the affected transactions during account migration. Thus, the makespan of associated transactions can be lowered. We implement Fine-tuned Lock protocol using an open-sourced blockchain testbed (i.e., BlockEmulator) and deploy it in Tencent cloud. The experimental results show that the proposed Fine-tuned Lock outperforms the SOTA Lock in terms of transaction makespan. For example, the transaction makespan of Fine-tuned Lock achieves around 30% the makespan of SOTA Lock.

In the Internet of Vehicles (IoV), the data center will receive a large number of data-signature pairs sent by vehicle-mounted communication devices and needs to quickly verify the validity of the signatures. Using aggregated signatures, the validity of multiple signatures can be verified by performing only one verification calculation, which significantly improves the calculation efficiency and is very suitable for the Internet of Vehicles. Unfortunately, security proofs of most certificateless aggregate signature (CLAS) schemes are implemented in the random oracle model (ROM). It is known that schemes with provable security in ROM may be vulnerable in real situations. Therefore, there may be potential security risks when these schemes are applied to the IoVs. In this paper, the system model and security demands of a CLAS scheme for IoVs were put forward. Afterwards, a concrete scheme was constructed and the security proofs were achieved in the standard model (SM). In the end, the advantage enjoyed by new scheme was demonstrated by making a comparison on performance for several schemes.

Previous state-of-the-art studies have proposed various analytical models to understand the double-spending attacks (DSA) occurred in Proof-of-Work (PoW) based Blockchains. Although many insights behind the double-spending attacks have been disclosed, we still believe that advanced versions of DSA can be developed to create new threats for the PoW-based blockchains such as the Bitcoin blockchain. In this paper, we present two new types of double-spending attacks in the context of the PoW-based blockchain and discuss the insights behind them. By considering more practical network parameters, such as the number of confirmation blocks, the hashpower of the double-spending attacker, the amount of coins in the target transaction, and the network-status parameter, we first analyze the success probability of the conventional double-spending attack, named Naive DSA. Based on Naive DSA, we create two new adaptive DSA, i.e., the Adaptive DSA and the Reinforcement Adaptive DSA (RA-DSA). In our analytical models, a double-spending attack is converted into a Markov Decision Process. We then exploit the Stochastic Dynamic Programming (SDP) approach to obtain the optimal attack strategies under Adaptive DSA and RA-DSA. Numerical simulation results demonstrate the correlations between each critical network parameter and the expected attacker's reward. Through the proposed analytical models, we aim to alert the PoW-based blockchain ecosystem that the threat of double-spending attacks is still at a dangerous level. For example, our findings show that the attacker can launch a successful attack with a small hashpower proportion much lower than 51% under RA-DSA. IEEE

With the popularity of the mobile Internet, data is increasingly becoming a new resource. Therefore, the trading of such data resources has become an increasing demand. In this paper, we propose a fair privacy-preserving data trading protocol based on blockchain. Firstly, our data trading protocol achieves fairness by carefully combining the probabilistic approaches and the fully homomorphic encryption techniques. Moreover, our protocol allows online arbitration when misbehavior occurs in the trading process is detected. Note that previous data trading protocols need a Trusted Third Party (TTP) or an offline arbitrator to solve disputes, weakening the trust of those protocols. Secondly, the data validity verification process of our protocol is more flexible. Most Importantly, different from all previous designs which only achieve privacy against communication channel eavesdroppers, our protocol achieves privacy against any eavesdropper and the passive arbitrator. The above-distinguishing properties of our protocol are mainly benefited from the homomorphic encryption and double encryption techniques. In addition, our data trading protocol can be instantiated with post-quantum primitives and thus achieves post-quantum security. To demonstrate the feasibility of the proposed protocol, we conduct a comprehensive evaluation with the instantiated cryptographic primitives based on the Ethereum test network.

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at:https://github.com/Lizonghang/AGOD.

Scalability trilemma is a classical research topic in the area of blockchains. To defeat such trilemma, many previous solutions have been proposed. However, none of those previous solutions can break such scalability trilemma. In this paper, we present a new Layer2 blockchain called W3Chain, which is promising to deliver high transactions per second (TPS) while defeating the scalability trilemma of a public blockchain. To enable the claimed performance, we particularly design our W3Chain by decoupling the correctness of the blockchain into two parts and adopting several crucial technical issues such as the reconfiguration of committees, the design of query APIs, and the handling of cross-shard transactions. We also propose a Time-Beacon Chain (TBChain) to record pivotal data of W3Chain. To show the correctness and safety features, we rigorously analyze multiple properties of W3Chain, including decentralization, scalability, and security under typical attacks. Finally, we conduct extensive simulations using Ethereum's historical transactions to examine the proposed W3Chain. The evaluation results show that our W3Chain can achieve a TPS as high as 10K+, and much lower transaction confirmation latency compared with Ethereum.

Many applications, e.g., digital twins, rely on sensing data from Internet of Things (IoT) networks, which is used to infer event(s) and initiate actions to affect an environment. This gives rise to concerns relating to data integrity and provenance. One possible solution to address these concerns is to employ blockchain. However, blockchain has high resource requirements, thereby making it unsuitable for use on resource-constrained IoT devices. To this end, this paper proposes a novel approach, called two-layer directed acyclic graph (2LDAG), whereby IoT devices only store a digital fingerprint of data generated by their neighbors. Further, it proposes a novel proof-of-path (PoP) protocol that allows an operator or digital twin to verify data in an on-demand manner. The simulation results show 2LDAG has storage and communication cost that is respectively two and three orders of magnitude lower than traditional blockchain and also blockchains that use a DAG structure. Moreover, 2LDAG achieves consensus even when 49% of nodes are malicious.

A number of promising blockchain scalability technologies such as Rollups, facilitate the fast and cost-effective asset transfer by offloading transactions from a mainchain to sidechains. The proposed broker-based decentralized application (dApp) in this paper, named BrokerFi, also employs a sidechain approach that works as a Layer2 solution on top of a Layer1 blockchain. Comparing with conventional sidechain solutions, the distinct feature of BrokerFi is that the sidechain used in BrokerFi is a sharded blockchain, in which users can stably earn money without economic risks when they stake money to BrokerFi.BrokerFi is designed as a dApp that can offer functionalities to enable users to manage their digital assets and earn money if they join BrokerFi’s ecology. Users can change the native tokens issued by BrokerFi using their fiat money. Users can also choose to stake their money in the protocol of BrokerFi and earn profit. We mainly demonstrate the design of BrokerFi in this paper. The significant components of BrokerFi mainly include two parts, i.e., the frontend used by users, and the backend that provides fundamental functionalities for BrokerFi in a Layer2-like sidechain. Experiment results show that the proposed BrokerFi can help clients earn high revenue when their staked tokens follow a low variance.

Sharding is one of the most promising techniques that can improve the scalability and storage issues of blockchain systems. In the past few years, many sharding protocols have been proposed to contribute to the technique matrix of blockchain sharding such as the coordination mechanisms of blockchain shards, the handling of cross-shard transactions, the security guarantee of blockchain shards, etc. Although those previous solutions are crucial for sharded blockchains, we still have not found any systematic implementation of shard reconfiguration, which determines the security of a sharded blockchain because the shuffling of blockchain nodes could prevent malicious nodes from corrupting a shard. However, the implementation of shard reconfiguration is not easy. When reallocating blockchain nodes to designated shards, typical challenges include the following: i) how to synchronize a large size of state data for a newly arrived node, and ii) how to mitigate the large reconfiguration latency of blockchain shards while keeping the liveness and consistency properties of a blockchain system. To overcome those challenges, we propose a dedicated protocol for shard reconfiguration in blockchain sharding using trimmed Merkle Patricia Trie (tMPT). The proposed tMPT-based protocol is designed to guarantee the high efficiency of the reconfiguration of blockchain shards while ensuring the uninterrupted services of the sharded blockchain. We implement the proposed tMPT-based reconfiguration protocol in a prototype, which enables the functionality of blockchain sharding. We then deploy our prototype in Alibaba Cloud. The experimental results show that the proposed tMPT-based protocol outperforms the existing methods in terms of reconfiguration efficiency. For example, the throughput of the proposed protocol shows 198% higher than Ethereum's full sync method.

Federated learning (FL) has been widely used in IoT applications. However, FL is vulnerable to various attacks in its each phase. Existing defense in federated learning mainly focus on the centralized setting. And centralized parameter-server settings require a trusted third party to collect and distribute model parameters. However, the requirement of a trusted third party cannot always be satisfied in many cases. Meanwhile, the centralized settings suffer from the inherent vulnerability of single-point-of-failure (SPOF), in which the whole system cease to function once the parameter server is broken. Therefore, decentralized federated learning has gain great attention recently. Existing conventional defense strategies are mostly designed for the centralized parameter-server architecture. The problem is that these conventional defense strategies cannot cope with new challenges occurred in highly decentralized settings of FL. Firstly, in a trustless setting, malicious participants can cause breakdown to the whole system on the communication level by disrupting model exchanges. Secondly, current defensive methods cannot effectively identify and rule out malicious participants. In either case, a harmful bias hurts the performance even if malicious participants do not perform model-level attacks. Therefore, defensive strategies for the decentralized-manner FL are in urgent need. To this end, we propose a committee-based FL system , named ComAvg , under a trustless setting. ComAvg provides a general coordination scheme for robust aggregation of distributed learning . With reliability assessment scheme to expel abnormal participants and fortified classic model exchange methods, the conventional centralized methods of FL can be easily modified into decentralized versions to cope with the two challenges aforementioned. Finally, we implement a prototype of ComAvg and perform various groups of evaluations on its robustness. The prototype-based evaluation results and theoretical analysis show that the proposed ComAvg is effective against model attacks such as sign-flipping and communication-level isolating attacks.

Blockchain databases have attracted widespread attention but suffer from poor scalability due to underlying non-scalable blockchains. While blockchain sharding is necessary for a scalable blockchain database, it poses a new challenge named on-chain cross-shard database services. Each cross-shard database service (e.g., cross-shard queries or inter-shard load balancing) involves massive cross-shard data exchanges, while the existing cross-shard mechanisms need to process each cross-shard data exchange via the consensus of all nodes in the related shards (i.e., on-chain) to resist a Byzantine environment of blockchain, which eliminates sharding benefits. To tackle the challenge, this paper presents GriDB, the first scalable blockchain database, by designing a novel off-chain cross-shard mechanism for efficient cross-shard database services. Borrowing the idea of off-chain payments, GriDB delegates massive cross-shard data exchange to a few nodes, each of which is randomly picked from a different shard. Considering the Byzantine environment, the untrusted delegates cooperate to generate succinct proof for cross-shard data exchanges, while the consensus is only responsible for the low-cost proof verification. However, different from payments, the database services' verification has more requirements (e.g., completeness, correctness, freshness, and availability); thus, we introduce several new authenticated data structures (ADS). Particularly, we utilize consensus to extend the threat model and reduce the complexity of traditional accumulator-based ADS for verifiable cross-shard queries with a rich set of relational operators. Moreover, we study the necessity of inter-shard load balancing for a scalable blockchain database and design an off-chain and live approach for both efficiency and availability during balancing. An evaluation of our prototype shows the performance of GriDB in terms of scalability in workloads with queries and updates.

The Internet of Things, privacy, and technical constraints increase the demand for edge-based data-driven services, which is one of the major goals of Industry 4.0 and Society 5.0. Big data analysis (BDA) is the preferred approach to unleash hidden knowledge. However, BDA consumes excessive resources and time. These limitations hamper the meaningful adoption of BDA, especially the time and situation critical edge use cases, and hinder the goals of Industry 4.0 and Society 5.0. Automating the BDA process at the edge is a cognitive approach to address the aforementioned concerns. Data science workflow is an indispensable challenge for successful automation. Therefore, we conducted a systematic literature survey on data science workflow platforms as the first contribution. Moreover, we learned that the BDA workflow depends on diversified constraints and undergoes rigorous data-mining stages. These caused an increase in the solution space, dynamic constraints, complexity issues, and NP-hardness of BDA workflow. Graphplan is a heuristic AI-planning technique that can address concerns associated with BDA workflow. Therefore, as the second contribution, we adopted the graphplan to generate a workflow for edge-based BDA automation. Experiments demonstrate that the proposed method achieved our objectives.

In a sharded blockchain, transactions are processed by a number of parallel committees. Thus, the transaction throughput can be largely boosted. A problem is that some groups of blockchain nodes consume large latency to form committees at the beginning of each epoch. Moreover, the heterogeneous processing capabilities of different committees also result in imbalanced consensus latency. Such imbalanced two-phase latency brings a large cumulative age to the transactions pending in transaction pool. Consequently, the blockchain throughput can be significantly degraded. We believe that a good committee-scheduling strategy can reduce the cumulative age of transactions, and thus benefit the throughput. However, we have not yet found a committee-scheduling mechanism that works for accelerating block formation in the context of blockchain sharding. To this end, this paper studies a fine-balanced tradeoff between the transactions’ throughput and their cumulative age in a large-scale sharded blockchain. We formulate this tradeoff as a utility-maximization problem, which is proved NP-hard. To solve this problem, we propose an online distributed Stochastic-Exploration (SE) algorithm, which guarantees a near-optimal system utility. We then rigorously analyze three theoretical properties of the proposed algorithm, including the theoretical convergence time, the probability of committees’ failure due to Sybil attacks, as well as the performance perturbation brought by committees’ offline events. Finally, we evaluate the proposed algorithm using the dataset of real-world blockchain transactions. The simulation results demonstrate that the proposed SE algorithm outperforms other baselines in terms of system utility, the valuable degree of yielded solutions, latency, and throughput performance.

Federated Learning (FL) suffers from Low-quality model training in mobile edge computing, due to the dynamic environment of mobile clients. To the best of our knowledge, most FL frameworks follow the reactive client scheduling, in which the FL parameter server selects participants according to the currently-observed state of clients. Thus, the participants selected by the reactive-manner methods are very likely to fail while training a round of FL. To this end, we propose a proactive Context-aware Federated Learning (ContextFL) mechanism, which consists of two primary modules. Firstly, the state prediction module enables each client device to predict the conditions of both local training and reporting phases of FL locally. Secondly, the decision-making algorithm module is devised using the contextual Multi-Armed Bandit (cMAB) framework, which can help the parameter server select the most appropriate group of mobile clients. Finally, we carried out trace-driven FL experiments using real-world mobility datasets collected from volunteers. The evaluation results demonstrate that the proposed ContextFL mechanism outperforms other baselines in terms of the convergence stability of the global FL model and the ratio of valid participants.

State-of-the-art blockchain sharding solutions, say Monoxide, can induce imbalanced transaction (TX) distributions among all blockchain shards due to their account deployment mechanisms. Imbalanced TX distributions then cause hot shards, in which the cross-shard TXs may experience an unlimited length of confirmation latency. Thus, how to address the hot-shard issue and how to reduce cross-shard TXs become significant challenges of blockchain state sharding. Through reviewing the related studies, we find that a cross-shard TX protocol that can achieve workload balance among all shards and simultaneously reduce the number of cross-shard TXs is still absent from the literature. To this end, we propose BrokerChain, which is a cross-shard blockchain protocol devised for the account/balance-based state sharding. Essentially, BrokerChain exploits fine-grained state partition and account segmentation. We also elaborate on how BrokerChain handles cross-shard TXs through broker accounts. The security issues and other properties of BrokerChain are analyzed substantially. Finally, we conduct comprehensive evaluations using both a cloud-based prototype and a transaction-driven simulator. The evaluation results show that BrokerChain outperforms other solutions in terms of system throughput, transaction confirmation latency, the queue size of transaction pool, and workload balance.

Sharding technique is viewed as the most promising solution to improving blockchain scalability. However, to implement a sharded blockchain, developers have to address two major challenges. The first challenge is that the ratio of cross-shard transactions (TXs) across blockchain shards is very high. This issue significantly degrades the throughput of a blockchain. The second challenge is that the workloads across blockchain shards are largely imbalanced. If workloads are imbalanced, some shards have to handle an overwhelming number of TXs and become congested very possibly. Facing these two challenges, a dilemma is that it is difficult to guarantee a low cross-shard TX ratio and maintain the workload balance across all shards, simultaneously. We believe that a fine-grained account-allocation strategy can address this dilemma. To this end, we first formulate the tradeoff between such two metrics as a network-partition problem. We then solve this problem using a community-aware account partition algorithm. Furthermore, we also propose a sharding protocol, named Transformers, to apply the proposed algorithm into the sharded blockchain system. Finally, trace-driven evaluation results demonstrate that the proposed protocol outperforms other baselines in terms of throughput, latency, cross-shard TX ratio, and the queue size of transaction pool.

Blockchain technology is becoming the cornerstone for the development and deployment of other technologies like Federated Learning (FL) and the Internet of Things (IoT), as it plays a critical role in data sharing and incentives. Blockchains supports decentralization, data-privacy protection, security, and reliability. Assuring secure data sharing in mobile computing and FL is challenging because of untrustworthy participants and unknown data quality. Blockchain provides trust in decentralized environments without requiring trusted third parties. By using smart contracts, blockchain has been able to supporting rich decentralized applications. However, the scalability of blockchain is a challenge that prevents its wide adoption by high-performance applications. To address the blockchain scalability issue, various blockchain sharding technologies and off-chain solutions have been proposed. To improve the network throughput, blockchain sharding divides the entire network into several smaller parallel groups and exploits fast consensus algorithms in blockchain shards. Off-chain solutions, such as payment channel networks (PCNs), transfer the slow on-chain transactions to the off-chain environment, in which transactions can be accelerated. Without consensus and on-chain expensive operations, off-chain scalable solutions significantly reduce transaction costs and increase transaction throughput. This special issue aims to provide a forum for the presentation of state-of-the-art research approaches that advance the construction of intelligent blockchain systems. A total of 27 articles were accepted after a two-round rigorous review process. Based on their topics, we have grouped the accepted articles into four categories: blockchain-based federated learning systems, blockchain and the IoT, blockchain scalability, and high-performance blockchains. In what follows, we introduce these articles and their contributions.

Recent decades have seen a significant increase in the frequency, intensity, and impact of natural disasters and other emergencies, forcing the governments around the world to make emergency response and disaster management national priorities. The growth of extremely large and complex datasets—commonly referred to asbig data—and various advances in information and communications technology and computing now support more effective approaches to humanitarian relief, logistical coordination, overall disaster management, and long-term recovery in connection with natural disasters and emergency events. Leveraging big data and technological advances for emergency management has attracted considerable attention in the research community. However, the desired merging ofbig data and emergency management(BDEM) requires coordinated efforts to align and define interdisciplinary terminologies and methodologies. To date, the key concepts and technologies in this emerging research area have not been coherently discussed in a sufficiently broad and multidisciplinary manner. In this article, an international team presents an overview of the BDEM domain, highlighting a general framework and discussing key challenges from several perspectives. We introduce and summarize typical technologies and applications, organized into the six broad categories of remote sensing, resilient communication networks, mobile communication networks, human mobility and urban sensing, social network analysis, and knowledge graphs. Finally, we outline several directions of future research.

This article studies the PBFT-based sharded permissioned blockchain, which executes in either a local datacenter or a rented cloud platform. In such permissioned blockchain, the transaction (TX) assignment strategy could be malicious such that the network shards may possibly receive imbalanced transactions or even bursty-TX injection attacks. An imbalanced transaction assignment brings serious threats to the stability of the sharded blockchain. A stable sharded blockchain can ensure that each shard processes the arrived transactions timely. Since the system stability is closely related to the blockchain throughput, how to maintain a stable sharded blockchain becomes a challenge. To depict the transaction processing in each network shard, we adopt the Lyapunov Optimization framework. Exploitingdrift-plus-penalty(DPP) technique, we then propose an adaptive resource-allocation algorithm, which can yield the near-optimal solution for each network shard while the shard queues can also be stably maintained. We also rigorously analyze the theoretical boundaries of both the system objective and the queue length of shards. The numerical results show that the proposed algorithm can achieve a better balance between resource consumption and queue stability than other baselines. We particularly evaluate two representative cases of bursty-TX injection attacks, i.e., the continued attacks against all network shards and the drastic attacks against a single network shard. The evaluation results show that the DPP-based algorithm can well alleviate the imbalanced TX assignment, and simultaneously maintain high throughput while consuming fewer resources than other baselines.

As the first decentralized peer-to-peer (P2P) cryptocurrency system allowing people to trade with pseudonymous addresses, Bitcoin has become increasingly popular in recent years. However, the P2P and pseudonymous nature of Bitcoin make transactions on this platform very difficult to track, thus triggering the emergence of various illegal activities in the Bitcoin ecosystem. Particularly,mixing servicesin Bitcoin, originally designed to enhance transaction anonymity, have been widely employed for money laundering to complicate the process of trailing illicit fund. In this article, we focus on the detection of the addresses belonging to mixing services, which is an important task for anti-money laundering in Bitcoin. Specifically, we provide a feature-based network analysis framework to identify statistical properties of mixing services from three levels, namely, network level, account level, and transaction level. To better characterize the transaction patterns of different types of addresses, we propose the concept of attributed temporal heterogeneous motifs (ATH motifs). Moreover, to deal with the issue of imperfect labeling, we tackle the mixing detection task as a positive and unlabeled learning (PU learning) problem and build a detection model by leveraging the considered features. Experiments on real Bitcoin datasets demonstrate the effectiveness of our detection model and the importance of hybrid motifs including ATH motifs in mixing detection.

In a large-scale sharded blockchain, transactions are processed by a number of parallel committees collaboratively. Thus, the blockchain throughput can be strongly boosted. A problem is that some groups of blockchain nodes consume large latency to form committees at the beginning of each epoch. Furthermore, the heterogeneous processing capabilities of different committees also result in unbalanced consensus latency. Such unbalanced two-phase latency brings a large cumulative age to the transactions waited in the final committee. Consequently, the blockchain throughput can be significantly degraded because of the large transaction's cumulative age. We believe that a good committee-scheduling strategy can reduce the cumulative age, and thus benefit the blockchain throughput. However, we have not yet found a committee-scheduling scheme that works for accelerating block formation in the context of blockchain sharding. To this end, this paper studies a fine-balanced tradeoff between the transaction's throughput and their cumulative age in a large-scale sharded blockchain. We formulate this tradeoff as a utility-maximization problem, which is proved NP-hard. To solve this problem, we propose an online distributed Stochastic-Exploration (SE) algorithm, which guarantees a near-optimal system utility. The theoretical convergence time of the proposed algorithm as well as the performance perturbation brought by the committee's failure are also analyzed rigorously. We then evaluate the proposed algorithm using the dataset of blockchain-sharding transactions. The simulation results demonstrate that the proposed SE algorithm shows an overwhelming better performance comparing with other baselines in terms of both system utility and the contributing degree while processing shard transactions.

Bitcoin is currently the cryptocurrency with the largest market share. Many previous studies have explored the security of Bitcoin from the perspective of blockchain mining. Especially on the double-spending attacks (DSA), some state-of-the-art studies have proposed various analytical models, aiming to understand the insights behind the double-spending attacks. However, we believe that advanced versions of DSA can be developed to create new threats for the Bitcoin ecosystem. To this end, this paper mainly presents a new type of double-spending attack named Adaptive DSA in the context of the Bitcoin blockchain, and discloses the associated insights. In our analytical model, the double-spending attack is converted into a Markov Decision Process. We then exploit the Stochastic Dynamic Programming (SDP) approach to obtain the optimal attack strategies towards Adaptive DSA. Through the proposed analytical model and the disclosed insights behind Adaptive DSA, we aim to alert the Bitcoin ecosystem that the threat of double-spending attacks is still at a dangerous level.

Device-free localization (DFL) locates targets without equipping with wireless devices or tag under the Internet-of-Things (IoT) architectures. As an emerging technology, DFL has spawned extensive applications in the IoT environment, such as intrusion detection, mobile robot localization, and location-based services. Current DFL-related machine learning (ML) algorithms still suffer from low localization accuracy and weak dependability/robustness because the group structure has not been considered in their location estimation, which leads to an undependable process. To overcome these challenges, we propose in this work a dependable block-sparse scheme by particularly considering the group structure of signals. An accurate and robust ML algorithm named block-sparse coding with the proximal operator (BSCPO) is proposed for DFL. In addition, a severe Gaussian noise is added in the original sensing signals for preserving network-related privacy as well as improving the dependability of the model. The real-world data-driven experimental results show that the proposed BSCPO achieves robust localization and signal-recovery performance even under severely noisy conditions and outperforms state-of-the-art DFL methods. For single-target localization, BSCPO retains high accuracy when the signal-to-noise ratio exceeds -10 dB. BSCPO is also able to localize accurately under most multitarget localization test cases.

In isolated network domains, global trustworthiness (e.g., consistent network view) is critical to the multiple-domain business partners who aim to perform the trusted corporations depending on each isolated network view. However, to achieve such global trustworthiness across distributed network domains is a challenge. This is because when multiple-domain partners are required to exchange their local domain views with each other, it is difficult to ensure the data trustworthiness among them. In addition, the isolated domain view in each partner is prone to be destroyed by malicious falsification attacks. To this end, we propose a blockchain-based approach that can ensure the trustworthiness among multiple-party domains. In this paper, we mainly present the design and implementation of the proposed trustworthiness-protection system. A cloud-based prototype and a local testbed are developed based on Ethereum. Finally, experimental results demonstrate the effectiveness of the proposed prototype and testbed.

Software Defined Networking (SDN) is beneficial to many applications, such as intra-datacenter communication, inter-datacenter transportation, etc., due to its centralized control. However, this centralized control frequently makes the controller a bottleneck, due to the large amount of interactions between the controller and switches. In this paper, we characterize such interactions as control traffic, and propose RouteStitch to minimize such kind of traffic. RouteStitch exploits existing route entries in switches to build new paths. To this end, RouteStitch first builds a graph model to describe existing route entries. Then, on such a model, a novel minimum color-alternation routing problem is defined to minimize control traffic, after which an optimal algorithm is proposed on a fixed routing path. For general paths, an O(log2L)-competitive online algorithm is designed to build new paths in an online manner that preserves fundamental property of switch Ternary Content Addressable Memory (TCAM) capacity and allowed maximum hop length L. Extensive simulation results based on realistic topology show that RouteStitch has good performance in terms of reducing control traffic, by 40%.

Network Function Virtualization (NFV) technology is viewed as a significant component of both the fifth-generation (5G) communication networks and edge computing. In this paper, through reviewing the state-of-the-art work on applying NFV to edge computing, we identify that an urgent research challenge is to provide the proactive failure recovery mechanism for the stateful NFV. To realize such proactive failure recovery, we propose a prediction-based algorithm for redeploying the stateful NFV instances in real-time when network failures occur. The proposed algorithm is based on relax and rounding technique. The theoretical performance guarantee is also analyzed rigorously. Simulation results show that the proposed failure recovery algorithm outperforms the reactive-manner baselines significantly in terms of redeployment latency.

Fault detection is a fundamental requirement for Industrial Internet of Things (IIoT), such as the process industry. This article first reviews the recent studies focusing on applying the fault detection techniques to the IIoT networks. However, we find that numerous studies focus on the resource utilization and workload allocation. The fault detection toward IIoT facilities is still in its immature stage because the existing approaches are not accurate enough for the stringent fault detection in IIoT networks. To this end, we present a novel algorithm, named Gaussian Bernoulli restricted Boltzmann machines (GBRBMs)-based deep neural network (DNN), to transform the fault detection into a classification problem. The real trace-driven experiments show that the proposed scheme outperforms other baseline machine learning methods. We anticipate that this article can inspire blooming studies on the related topics of smart IIoT networks.

A modern Network Function Virtualization (NFV) service is usually expressed in a service chain that contains a list of ordered network functions, each can run in one or multiple virtual machines. Although lots of efforts have been devoted to service chain deployment, the researchers normally consider a simple model of network functions where different service chains have their own network functions no matter whether some of the network function appliances are interdependent. In this paper, we study the service chain deployment by exploiting two types of correlations between network functions: the Coordination Effect due to information exchanges among multiple VMs running the same network function, and the Traffic-Change Effect where the volume of outgoing traffic is not necessarily equal to the volume of its incoming traffic at each network function because of packet manipulations such as compression and encryption. These two effects have not been studied simultaneously in the context of service chaining. With theobjective to maximize the profit measured by the admitted traffic minus the implementation cost, we first formulate a joint service-function deployment and traffic scheduling (SUPER) problem that is proved to be NP-hard. We then devise an approximation algorithm based on the Markov approximation technique and analyze its theoretical bound on the convergence time. Simulation results show that the proposed algorithm outperforms two existing benchmark algorithms significantly.

Satellite-based communication technology has gained much attention in the past few years, where satellites play mainly the supplementary roles as relay devices to terrestrial communication networks. Unlike previous work, we treat the low-earth-orbit (LEO) satellites as secure data storage mediums. We focus on data acquisition from a LEO satellite based data storage system (also referred to as the LEO based datacenters), which has been considered as a promising and secure paradigm on data storage. Under the LEO based datacenter architecture, one fundamental challenge is to deal with energy-efficient downloading from space to ground while maintaining the system stability. In this paper, we aim to maximize the amount of data admitted while minimizing the energy consumption, when downloading files from LEO based datacenters to meet user demands. To this end, we first formulate a novel optimization problem and develop an online scheduling framework. We then devise a novel coflow-like “Join the first K-shortest Queues (JKQ)” based job-dispatch strategy, which can significantly lower backlogs of queues residing in LEO satellites, thereby improving the system stability. We also analyze the optimality of the proposed approach and system stability. We finally evaluate the performance of the proposed algorithm through conducting emulator based simulations, based on real-world LEO constellation and user demand traces. The simulation results show that the proposed algorithm can dramatically lower the queue backlogs and achieve high energy efficiency.

In the Service-Function-Chaining (SFC) enabled networks, various sophisticated policy-aware network functions, such as intrusion detection, access control and unified threat management, can be realized in either physical middleboxes or virtualized network function (VNF) appliances. In this paper, we study the service chaining towards the hybrid SFC clouds, where both physical appliances and VNF appliances provide services collaboratively. In such hybrid SFC networks, the challenge is how to efficiently steer the service chains for traffic demands while matching their individual policy chains concurrently such that a utility associated with the total admitted traffic rate and the induced overheads can be maximized. We find such problem has not been well solved so far. To this end, we devise a Markov Approximation (MA) based algorithm. The approximation property of the proposed algorithm is also proved. Extensive evaluation results show that the proposed MA algorithm can yield near-optimal solutions and outperform other benchmark algorithms significantly.

In the past decade, network data communication has experienced a rapid growth, which has led to explosive congestion in heterogeneous networks. Moreover, the emerging industrial applications, such as automatic driving put forward higher requirements on both networks and devices. On the contrary, running computation-intensive industrial applications locally are constrained by the limited resources of devices. Correspondingly, fog computing has recently emerged to reduce the congestion of content-centric networks. It has proven to be a good way in industry and traffic for reducing network delay and processing time. In addition, device-to-device offloading is viewed as a promising paradigm to transmit network data in mobile environment, especially for autodriving vehicles. In this paper, jointly taking both the network traffic and computation workload of industrial traffic into consideration, we explore a fundamental tradeoff between energy consumption and service delay when provisioning mobile services in vehicular networks. In particular, when the available resource in mobile vehicles becomes a bottleneck, we propose a novel model to depict the users' willingness of contributing their resources to the public. We then formulate a cost minimization problem by exploiting the framework of Markov decision progress (MDP) and propose the dynamic reinforcement learning scheduling algorithm and the deep dynamic scheduling algorithm to solve the offloading decision problem. By adopting different mobile trajectory traces, we conduct extensive simulations to evaluate the performance of the proposed algorithms. The results show that our proposed algorithms outperform other benchmark schemes in the mobile edge networks.

The concept of the industrial Internet of things (IIoT) is being widely applied to service provisioning in many domains, including smart healthcare, intelligent transportation, autopilot, and the smart grid. However, because of the IIoT devices' limited onboard resources, supporting resource-intensive applications, such as 3D sensing, navigation, AI processing, and big-data analytics, remains a challenging task. In this paper, we study the multi-hop computation-offloading problem for the IIoT-edge-cloud computing model and adopt a game-theoretic approach to achieving Quality of service (QoS)-aware computation offloading in a distributed manner. First, we study the computation-offloading and communication-routing problems with the goal of minimizing each task's computation time and energy consumption, formulating the joint problem as a potential game in which the IIoT devices determine their computation-offloading strategies. Second, we apply a free-bound mechanism that can ensure a finite improvement path to a Nash equilibrium. Third, we propose a multi-hop cooperative-messaging mechanism and develop two QoS-aware distributed algorithms that can achieve the Nash equilibrium. Our simulation results show that our algorithms offer a stable performance gain for IIoT in various scenarios and scale well as the device size increases.

In the era of information, the services of fog computing environment with content delivery are expected to offer users the better satisfaction of Quality-of- Experience (QoE) than that in a conventional environment. Nevertheless, the dataflow and new demands from users increase along with the promising of content-centric computing system in fog computing environment. Therefore, the satisfaction of QoE will become the major challenge. In this article, to enhance the satisfaction of QoE, we propose QoE models to evaluate the quality of service in fog computing environment concerning both system and users. The value of QoE does not only refer to the system cost, but also the Mean Opinion Score (MOS) of users. Therefore, our models could capture influential factors from system cost based on system states and services for users. Specially, we mainly focus on issues of cache allocation and transmission rate. Under this fog computing environment, aiming to the capacity of cache allocation among fog nodes and handle transmission rates under a constrained total system cost and MOS, we devote our efforts to the following two aspects. First, we formulate the QoE as a joint resource allocation problem under different transmission rates to acquire best QoE. Then, we propose a dynamic algorithm based on shortest path tree (SPT), which is suitable for fog computing environment with content delivery frequently. Simulation results reveal that the benefit for using the dynamic allocation (DA) method to allocate resource can achieve high QoE performance.

As the critical supplementary to terrestrial communication networks, the low-earth-orbit (LEO) satellite based communication networks regain growing attentions in recent few years. In this paper, we focus on data gathering for geo- distributed Internet-of-Things (IoT) networks via LEO satellites. Normally, the power supply in IoT data-gathering gateways is a bottleneck resource that constrains the network throughput. Thus, the challenge is how to upload data from IoT gateways to LEO satellites under dynamic uplinks in an energy-efficient way. To address this problem, we first formulate a novel optimization problem, and then propose an online algorithm for green data-uploading in geo-distributed IoT networks. In the proposed framework, we aim to jointly maximize the network throughput and minimize the energy consumption at gateways, while avoiding the buffer overflow at gateways. We finally evaluate the performance of the proposed algorithm through simulations using both real-world and synthetic traces. The simulation results demonstrate that the proposed approach can achieve high efficiency on the power consumption and significantly reduce queue backlogs compared with a benchmark using greedy policy.

With the ubiquitous deployment of the mobile devices and the explosive growth of Internet traffic, an emerging method called association rules mining (ARM) is proposed to solve the problem of mining potential value of existing big data. However, massive ARM methods focus on positive rules which are easy to ignore interesting information because of negative ones. This paper studies a practical problem of combing negative rules in ARM research. Specifically, we propose a rule certainty updating strategy (RCUS) to combine positive rules with negative rules, which consists of two parts: initialization and updating. To solve the large scale problem with negative rules, the proposed strategy decomposes the large scale problem into several relatively small ones by an improved multiobjective evolutionary algorithm (MOEA) with gene representation and certainty. Simulation results show that our method is outstanding when the scale of attributes and examples is increasing.

Cloudlet based network provides a platform to offloading the traffic workload generated by mobile devices. It can significantly reduce the access delay for mobile application users. However, the high dynamic mobility of users brings significant challenges to the service provisioning for mobile applications, especially for the delay-sensitive big data applications. To increase the overall admitted traffic served by local cloudlets, and to reduce the access delay as well as the virtual-machine migration delay, this paper aims to study the adaptively service-provisioning update scheme for a given group of mobile users under a cloudlet-based network. A profit maximization problem is first formulated as an integer linear programming using absolute value manipulation techniques. Then, we propose a framework of heuristic algorithms to solve this problem. The numerical simulation results demonstrate the efficiency of the proposed algorithms.

Software-Defined Networking (SDN) allows controlling applications to install fine-grained forwarding policies in the underlying switches. Ternary Content Addressable Memory (TCAM) enables fast lookups in hardware switches with flexible wildcard rule patterns. However, the performance of packet processing is severely constrained by the capacity of TCAM, which aggravates the processing burden and latency issues. In this paper, we propose a hybrid TCAM architecture which consists of NVM-based TCAM (nvTCAM) and SRAM-based TCAM (sTCAM), utilizing nvTCAM to cache the most popular rules to improve cache-hit-ratio while relying on a very small-size sTCAM to handle cache-miss traffic to effectively decrease update latency. Considering the special rule dependency, we present an efficient Rule Migration Replacement (RMR) policy to make full utilization of both nvTCAM and sTCAM to obtain better performance. Experimental results show that the proposed architecture outperforms current TCAM architectures.

Summary Deep packet inspection (DPI) is important for network security. In this paper, we consider a software‐defined network where several DPI proxy nodes are available for serving flows from ingress switches. These DPI proxy nodes can be implemented in either software or hardware. We study an integrated proxy allocation and routing determining problem with the objective of minimizing the total delay of flows from ingress switches to DPI proxies. This problem is formulated as an integer linear programming problem that is NP‐hard in general. To solve this problem, we design a 2‐phase algorithm that can quickly select proxy and find routing paths for incoming flows. Finally, extensive simulations are conducted to evaluate the performance of our proposed algorithm. Some useful parameter setting insights are obtained.

In the context of Software-Defined Networking (SDN), various sophisticated policy-aware network functions such as intrusion detection, access control and load balancer, can be realized via specified middlebox devices. However, high congestions may occur in specific bottleneck links if middlebox selection and traffic routing are not well jointly planed. To this end, we study a joint optimization of MiddleBox Selection and Routing (MBSR) problem with the objective to maximize the throughput for a specified set of sessions in an SDN network. In order to solve this NP-hard problem, we design a polynomial algorithm using the Markov approximation technique. Numerical results show that the proposed Markov approximation based algorithm outperforms other benchmark algorithms significantly and generates near-optimal solutions.

Facing the spectrum supply-demand gap, heterogeneous network (HetNet) is a promising approach to achieve drastic gains in network coverage and capacity compared with macro-only networks, thus making it especially attractive to network operators. On the other hand, software-defined networking brings a number of advantages along with many challenges. One particular concern is on the resilience for in-band fashioned control plane. Existing approaches mainly rely on a local rerouting policy when performing the routing protection for the target sessions in software-defined networks. However, such a policy would potentially bring congestions in the neighbouring links of the failed one. To this end, we study a weighted cost-minimization problem, where the traffic load balancing and control-channel setup cost are jointly considered. Because this problem is NP-hard, we first propose a near-optimal Markov approximation-based approach for in-band-fashioned software-defined HetNets. We then extend our solution to an online case that handles a single-link failure. We also conduct theoretical analysis on the performance fluctuation due to the single-link failure. We finally carry out experiments by experimental simulation. The extensive numerical results show that the proposed algorithm has fast convergence and high efficiency in resource utilization.

Software-defined networking (SDN) is an emerging network paradigm that simplifies network management by decoupling the control plane and data plane, such that switches become simple data forwarding devices and network management is controlled by logically centralized servers. In SDN-enabled networks, network flow is managed by a set of associated rules that are maintained by switches in their local Ternary Content Addressable Memories (TCAMs) which support high-speed parallel lookup on wildcard patterns. Since TCAM is an expensive hardware and extremely power-hungry, each switch has only limited TCAM space and it is inefficient and even infeasible to maintain all rules at local switches. On the other hand, if we eliminate TCAM occupation by forwarding all packets to the centralized controller for processing, it results in a long delay and heavy processing burden on the controller. In this paper, we strive for the fine balance between rule caching and remote packet processing by formulating a minimum weighted flow provisioning ( MWFP) problem with an objective of minimizing the total cost of TCAM occupation and remote packet processing. We propose an efficient offline algorithm if the network traffic is given, otherwise, we propose two online algorithms with guaranteed competitive ratios. Finally, we conduct extensive experiments by simulations using real network traffic traces. The simulation results demonstrate that our proposed algorithms can significantly reduce the total cost of remote controller processing and TCAM occupation, and the solutions obtained are nearly optimal.

In OpenFlow enabled networks, traffic flow packets are usually processed in data plane by a set of associated forwarding rules maintained by switches in their local flow tables, which are implemented using Ternary Content Addressable Memory (TCAM). While TCAM supports high-speed parallel lookup operations, it is an expensive hardware with high power consumption. Consequently, each switch can only afford limited TCAM space, which is inefficient and even infeasible to maintain all rules locally. On the other hand, forwarding all packets to the centralized controller will induce large network traffic and heavy processing burden on controller. In this paper, we aim to finding a fine-balanced processing in two modes: applying the local cached rules to packets or forwarding them directly to controller. To this end, a Multi-Flow oriented Packets Scheduling (MFPS) problem is formulated with an objective of minimizing the total cost of TCAM occupation and remote packets processing. This problem is proved NP-hard. Then, we propose online distributed algorithms which run at switches and controller, respectively. Finally, synthetic network traffic trace-driven experiments are conducted. The simulation results show that the proposed algorithms outperform the legacy OpenFlow strategies and approach to the optimal performance.

Software-Defined Network (SDN) is a promising network paradigm that separates the control plane and data plane in the network. It has shown great advantages in simplifying network management such that new functions can be easily supported without physical access to the network switches. However, Ternary Content Addressable Memory (TCAM), as a critical hardware storing rules for high-speed packet processing in SDN-enabled devices, can be supplied to each device with very limited quantity because it is expensive and energy-consuming. To efficiently use TCAM resources, we propose a rule multiplexing scheme, in which the same set of rules deployed on each node apply to the whole flow of a session going through but towards different paths. Based on this scheme, we study the rule placement problem with the objective of minimizing rule space occupation for multiple unicast sessions under QoS constraints. We formulate the optimization problem jointly considering routing engineering and rule placement under both existing and our rule multiplexing schemes. Via an extensive review of the state-of-the-art work, to the best of our knowledge, we are the first to study the non-routing-rule placement problem. Finally, extensive simulations are conducted to show that our proposals significantly outperform existing solutions.

The ever-growing cellular traffic demand has laid a heavy burden on cellular networks. The recent rapid development in vehicle-to-vehicle communication techniques makes vehicular delay-tolerant network (VDTN) an attractive candidate for traffic offloading from cellular networks. In this paper, we study a bulk traffic offloading problem with the goal of minimizing the cellular communication cost under the constraint that all the subscribers receive their desired whole content before it expires. It needs to determine the initial offloading points and the dissemination scheme for offloaded traffic in a VDTN. By novelly describing the content delivery process via a contact-based flow model, we formulate the problem in a linear programming (LP) form, based on which an online offloading scheme is proposed to deal with the network dynamics (e.g., vehicle arrival/departure). Furthermore, an offline LP-based analysis is derived to obtain the optimal solution. The high efficiency of our online algorithm is extensively validated by simulation results.

To characterize the delivery performance of message dissemination in Disruption/Delay Tolerant Networks, various methods have been proposed. However, existing work shares a common simplification that the pairwise meeting rate between any two mobile nodes is exponentially distributed. In this paper, instead of relying on such assumption, we jointly consider the deactivation-rate over relay nodes and the number of sinks deployed in the network as the primary system parameters. Then, an ODE-based theoretical framework is proposed in a stochastic manner, which enables to describe how these two parameters affect the performance of a message delivery process using controlled epidemic routing. Via extensive experiments, the high accuracy of our analytical framework are verified.

Service provisioning has been widely regarded as a critical issue to quality-of-service (QoS) of cloud services in datacenters. Conventional studies on service provisioning mainly focus on task mapping, i.e., how to distribute the service-oriented tasks onto the servers to achieve different goals, e.g., makespan minimization. In distributed datacenters, a task is usually routed from its generation point (i.e., control room) to the designated server within a datacenter network. Since the routing delay also has a deep influence on the task makespan, we are motivated to study how to minimize the maximum makespan of all tasks in a duty period by joint optimization of both task mapping and routing. It is formulated as an integer programming with quadratic constraints (IPQC) problem and proved as NP-hard. To tackle the computational complexity of solving IPQC, a heuristic algorithm with polynomial time is proposed. Extensive simulation results show that it performs close to the optimal one and outperforms existing algorithms significantly.

Software-Defined Network (SDN) is a promising network paradigm that separates the control plane and data plane in the network. It has shown great advantages in simplifying network management such that new functions can be easily supported without physical access to the network switches. However, Ternary Content Addressable Memory (TCAM), as a critical hardware storing rules for high-speed packet processing in SDN-enabled devices, can be supplied to each device with very limited quantity because it is expensive and energy-consuming. To efficiently use TCAM resources, we propose a rule multiplexing scheme, in which the same set of rules deployed on each node apply to the whole flow of a session going through but towards different paths. Based on this scheme, we study the rule placement problem with the objective of minimizing rule space occupation for multiple unicast sessions under QoS constraints.We formulate the optimization problem jointly considering routing engineering and rule placement under both existing and our rule multiplexing schemes. Finally, extensive simulations are conducted to show that our proposals significantly outperform existing solutions.

To understand the delivery performance of message dissemination in Disruption Tolerant Networks (DTNs), various methods have been proposed in the literature. However, existing work shares a common simplification that the pairwise meeting rate between any two mobile nodes is exponentially distributed. In this paper, instead of relying on such assumption, we jointly consider the transmission range and Random Direction Mobility (RDM) model to stochastically analyze delivery performance of epidemic routing in terms of percolation ratio and delivery delay. Furthermore, we study a controlled epidemic routing, in which any message stays at a mobile node longer than a predefined lifetime should be removed from the node. It can be considered as an age-structure process described by the Susceptible-Infectious-Recovered (SIR) model. To the best of our knowledge, we are the first to characterize the message propagation process by applying the Delay Differential Equations (DDEs) in DTNs. The correctness of our analysis is validated by extensive simulations.

Content distribution in vehicular networks is essential to many emerging applications. The issues such as content distribution from road side units (RSUs) to vehicles or the cooperation between vehicles have drawn a lot of interests in the literature. However, little work is on packets distribution from content providers to RSUs and many related issues are still under-investigated. In this paper, we consider the problem of minimizing the distribution cost, which is defined as the number of packets that shall be dispatched to RSUs, for deadline-constrained content distribution in vehicular networks. The problem is first formulated as an integer programming problem, based on a link-coloring concept. Then, a heuristic algorithm with low computational complexity is proposed. The high efficiency of the proposed algorithm is extensively validated by the fact that it performs close to the optimal solution obtained by the CPLEX solver.