In a sharded blockchain, cross-shard transactions (CTXs) require longer confirmation latency because CTXs need to be confirmed in the source and destination shards, respectively. Advanced sharded blockchain protocols have been developed to reduce the number of CTXs. For example, brokerchain [1], converts cross-chip transactions into intra-shard transactions by voluntarily pledging the amount of money by the broker. The scheme is built on the ideal premise of voluntary pledges by brokers, and the Broker2Earn (B2E) [2] protocol provides service benefits to brokers in order to solve the problem. However, the B2E protocol also suffers from the problem of unequal revenue distribution. To solve this problem, we propose the Broker2Balance protocol, which ensures that the revenue of brokers is maximized and distributed equitably. We have implemented Broker2Balance protocol using python language and experimental results Broker2Balance protocol solves the problem of unequal distribution of revenue of B2E protocol.

Blockchain sharding improves scalability but suffers from two critical performance bottlenecks during shard reconfiguration: TPS-degradation from ledger synchronization conflicts and Zero-TPS from transaction processing disruptions during key generation. This paper proposes Shardora, a blockchain sharding system that addresses these limitations through two-dimensional parallelism. First, Shardora introduces parallelized dual committee node shuffling that decouples ledger synchronization from transaction processing, eliminating TPS-degradation. A dynamic reputation model ensures security during shuffling. Second, Shardora implements pipelined key pre-generation to avoid Zero-TPS issue. Furthermore, its position-preserving incremental key generation minimizes resource conflicts, maintaining a continuously high TPS during key generation. We prove that Shardora offers theory-guaranteed security. We implement Shardora prototype and deploy it on Alibaba Cloud. Experimental results demonstrate that Shardora addresses the limitations by significantly reducing the overhead of both ledger synchronization and key generation, which shows 90%+ improvement over state-of-the-art sharding schemes. In addition, Shardora shows its superior performance in large-scale network, achieving 31911 TPS in LAN and 8729 TPS in WAN. The code of Shardora is publicly available on GitHub.

Blockchain-based games represent an emerging class of interactive systems where economic incentives shape user behavior. However, most existing designs rely on randomness-driven mechanisms, which limit meaningful interaction and reduce user participation to passive speculation. This reveals a fundamental gap between incentive design and interaction design in current GameFi systems. In this paper, we propose Game2Earn (G2E), an interaction-driven on-chain game framework that rethinks liquidity provisioning as a strategy-based and participatory process. Rather than depending on external randomness, G2E derives outcomes from endogenous blockchain dynamics, enabling transparent, verifiable, and manipulation-resistant interactions without additional trust assumptions. From a human-computer interaction perspective, G2E introduces a novel form of on-chain interaction in which users actively engage through target selection, token staking, and competitive decision-making. This design shifts user behavior from passive betting to active strategy formulation, thereby enhancing perceived agency and sustained engagement. Furthermore, by coupling gameplay with system functionality, user interactions directly contribute to cross-shard transaction processing, aligning individual actions with system-level objectives. We implement G2E on a sharded blockchain architecture and evaluate its impact using real-world transaction data. The results demonstrate that G2E not only maintains stable liquidity and improves the processing efficiency of cross-shard transactions, but also fosters more active and persistent user participation. These findings suggest that embedding interaction design into decentralized systems can bridge the gap between user engagement and infrastructure performance, offering new insights for the design of socio-technical systems.

Blockchain sharding is a pivotal catalyst for scalability. However, the inherent priority disparity between cross-shard transactions (CTXs) and intra-shard transactions (ITXs) leads to persistent processing unfairness, which remains a critical performance bottleneck. While conventional incentive mechanisms (e.g., Justitia) attempt to mitigate this disparity through fee compensation, they predominantly rely on open-loop or semidynamic designs that fail to maintain control precision under volatile workloads and lack rigorous economic safeguards against token inflation. In this paper, we propose Justitia-L, a hierarchical adaptive incentive framework that decouples micro-level delay rectification from macro-level budget management. Specifically, we first develop Justitia-PID, which uses a closed-loop feedback law to reduce the block-level queueing delay deviation between CTXs and ITXs. To ensure economic sustainability, we further formulate the fairness optimization as a constrained Lagrangian dual problem, introducing a dynamic effective “Shadow Price” $(\lambda(t))$ to modulate incentive intensity relative to a global inflation budget. This dual-layer architecture empowers the system with agility against transient traffic bursts while regulating subsidies near long-term economic safety boundaries. Experimental results using real-world Ethereum historical transactions demonstrate that, under the evaluated budget setting, Justitia-L achieves a near-fair CTX/ITX average confirmation-latency ratio of $\mathbf{1. 0 3} \times$ while keeping cumulative subsidies below the global budget, providing a robust balance between processing fairness and inflationary safety.

Blockchain sharding has been deemed a promising solution for substantially improving 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 handle an overwhelming number of transactions, leading to congestion. Facing these two challenges, the dilemma is that it is difficult to guarantee a low cross-shard transaction ratio while maintaining balanced workloads across all shards simultaneously. We believe that a fine-grained account allocation strategy can address this dilemma. To this end, we formulate the trade-off between these two metrics as a network partitioning problem. We then solve this problem by proposing a sharding protocol, named ShardCutter, that includes two crucial components: a community-aware account partitioning algorithm and a fine-tuned account migration mechanism. Finally, experimental results demonstrate that the proposed protocol outperforms baselines in terms of throughput, makespan, cross-shard transaction ratio, and workload balance across shards' transaction pools.

Mainstream blockchains like Bitcoin and Ethereum heavily depend on full nodes to confirm transactions and store the blockchain ledger. As a result, they suffer from low performance and high overhead of maintaining the ledger state. We present a new architecture named Folium, which leverages the proposerfollower design and aims to upgrade the current architecture of most mainstream blockchains. In Folium, heterogenous lowresource nodes can join in a specific given blockchain network and serve as followers for designated proposer nodes. Such the proposer could be a full node of a blockchain. Folium enables proposers to offload the transaction execution and ledger state storage to their followers. Using this proposer-follower architecture, the original full nodes can largely mitigate their storage overhead. Thus, the given blockchain can improve its consensus efficiency and increase its transaction throughput, no matter what consensus protocols the blockchain is using. We implemented Folium on top of three typical blockchain architectures, i.e., a sharding-based blockchain such as Monoxide, an Ethereumbased single-chain architecture, and a PBFT-based single-chain architecture. The experimental results demonstrate that Folium can decrease the transaction execution latency by more than 60% and improve the throughput by 4× compared to singlechain architecture. Moreover, Folium enables significant ledger storage reduction at full nodes-scaling linearly with the number of followers-achieving over 80% reduction in our experimental configuration with 10 followers.

Wireless human gesture recognition has spawned many emerging applications, e.g., intelligent control and sign language communications based on WiFi. By treating wireless signals as images, existing WiFi-based gesture recognition methods adopt 2-Dimensional Convolutional Neural Networks (2D CNNs) to capture the feature of transitions on channels. Although 2D CNNs are practical, such approaches need to pay more attention to the information in the temporal dimension, i.e., the channel of time-series features. To better exploit the information in the temporal dimension, we propose a time-series focused gesture recognition approach using WiFi signals named T-SignFi. Our proposed T-SignFi consists of a preprocessing AdaNorm normalization and a CNN-based 3D sign language recognition model to pay sufficient attention to the features in both temporal and channel dimensions. Experiment results based on the real-world dataset show that, compared to the four Sota methods, our method has significant advantages on all three datasets. Improvements of 4.84%, 3.31%, 3.42%, and 2.73% were achieved on the home and lab data, respectively. Our proposed T-SignFi could also effectively improve recognition performance with high generalization ability. In addition, for a challenging case, i.e., actions combining some body parts, including finger, arm, hand, and head movements, the proposed T-SignFi can also recognize them with high accuracy.

As a major cost in decentralized asset swapping, slippage is commonly measured as the relative deviation between realized and expected outcomes. However, existing studies adopt different benchmarks, leading to ambiguity in terminology and the positioning of prior works. We propose a unified deviation-based framework for understanding slippage, which reconciles existing definitions and enables further developments. Within this framework, the overall deviation can be decomposed into two components: endogenous deviation, arising from the swap itself, and exogenous deviation, which is induced by intervening swaps during the latency period. We review the existing literature along with the two components, covering their theoretical properties, further decompositions, and mitigation approaches. Finally, we outline several directions for future research.

Long Range Wide Area Network (LoRaWAN) has become a practical communication layer for large-scale Internet-of-Things and industrial monitoring systems where battery-powered devices must transmit data reliably over long ranges. Selecting the right spreading factor (SF) remains critical for maintaining link quality and power efficiency, yet existing adaptive data-rate methods often fail under dynamic interference and mobility. This paper presents XSF, an interpretable machine-learning framework that predicts the optimal SF for each device using lightweight multilayer perceptrons (MLP) and majority voting. The model combines physical-layer indicators and device location to adapt transmission settings without changing the LoRaWAN protocol. In this paper, first, a one-time dataset has been generated using ns-3, and five MLP were trained on the dataset. Then a majority voting was employed to choose the best SF. Finally, we utilized SHapley Additive exPlanations (SHAP) analysis for interpretability for SF classification. Furthermore, the pre-trained model was then utilized in ns-3 on the end devices for efficient SF allocation based on newly generated data during simulation. The proposed XSF achieved an average classification accuracy of 85%, with up to 10.7% and 6.0% higher packet success ratio in mobility and static scenarios, respectively, while reducing energy consumption by 33.6% and 20.6% compared with the state-of-the-art baseline AI method.

In a sharded blockchain, cross-shard transactions (CTXs) must endure long confirmation latency because each CTX is split into two sub-CTXs (named as the first half and the second half sub-CTXs). Only when the first-half sub-CTX is confirmed in the source shard can the second-half sub-CTX participate in another consensus and be confirmed in the destination shard. Thus, it is unfair to cross-shard transactions because intra-shard transactions (ITXs) do not need to undergo such cross-shard duplicated consensus. For each CTX, the sharded blockchain must ensure atomicity, meaning that the two sub-transactions associated with it must be confirmed or failed simultaneously. However, guaranteeing atomicity for each CTX is a technical challenge. In particular, reducing the latency between the individual confirmations of the first and second sub-CTXs is deemed a significant challenge. However, we have not found practical solutions that can address this challenge. Thus, to enable the sharded blockchain to process both CTXs and ITXs in a balanced manner within guaranteed consensus iterations, we propose a decentralized online scheduling algorithm (namely LLA) for all transactions by exploiting the preemptive queueing model. The LLA algorithm is executed locally on each shard and can shorten the confirmation latency of CTXs while ensuring their atomicity. We conducted experiments on an open-source blockchain testbed, BlockEmulator. The experimental results show that the proposed LLA algorithm effectively reduces each CTX's queueing latency in transaction pools. For example, compared with a classical sharding protocol, Monoxide, the average latency of CTXs can be reduced by 70.8% while exploiting LLA in a 32-shard blockchain. Index Terms-Blockchain sharding, Transaction scheduling

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 article, we propose a fast and cheap cross-metaverse interoperability protocol named CrossMeta. CrossMeta can 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 proposed CrossMeta protocol satisfies the security properties of atomicity and liveness. To demonstrate the practicality of CrossMeta, we implemented a prototype of the CrossMeta using two real-world metaverse platforms, i.e., Axie Infinity and Sandbox. The evaluation results show that CrossMeta outperforms other cross-metaverse solutions regarding transaction latency and gas fees.

The deployment of embodied artificial intelligence via world-model-based robotics presents a transformative opportunity for blockchain infrastructure, establishing urgent demand for trustworthy data provenance, cross-organizational governance, and incentive-compatible sharing across decentralized ecosystems. Simultaneously, quantum computing advances recognized by the 2025 Nobel Prize in Physics and the Turing Award threaten the cryptographic primitives securing these data economies, creating an interdependent imperative: long-lived verification for embodied AI depends on crypto-agile architectures capable of withstanding quantum adversaries. This tutorial examines blockchain as the coordination layer bridging this dual transition, from financial substrate to foundational Cyber-Physical-Social Systems infrastructure that simultaneously secures against quantum cryptanalysis and enables scalable, trustworthy data economies. The session opens with an immersive AWS Braket demonstration engaging participants with superconducting, trapped-ion, and neutral-atom hardware to assess cryptographic threat timelines and witness ECDSA-to-post-quantum signature transitions. Five integrated modules progress from embodied AI and world-model requirements through quantum hardware reality and evidence-based security migration, to scalable cross-shard architectures via BrokerChain protocols, trustworthy data economies implementing Croissant metadata standards and robotic learning provenance, and industry ecosystem integration for multi-modal cloud deployment. By bridging quantum hardware realities with embodied AI data requirements, this tutorial charts blockchain as unified infrastructure for next-generation decentralized intelligent environments, providing open-source frameworks and roadmaps for architecting quantum-resistant, interoperable, and data-trustworthy systems.

In sharded blockchains, cross-shard transactions (CTXs) inherently suffer from high confirmation latency. This occurs because each CTX is decomposed into two dependent components-a source and a destination sub-transaction-executed sequentially across different shards. This strict dependency exposes CTXs to unbounded delays and severe unfairness compared to independent intra-shard transactions (ITXs). While ensuring atomicity is mandatory, minimizing the latency interval between these sub-transactions remains a critical open challenge. To address this, we propose LADS (Low-latency Atomicity-Driven Scheduling), a decentralized online scheduling framework. LADS conceptualizes the strict temporal dependency as a quantifiable “Atomicity Debt”. By formulating the transaction scheduling as a stochastic optimization problem, we derive an efficient primaldual algorithm using the Lyapunov drift-plus-penalty technique. This allows each shard to make local, dynamic decisions that balance system throughput against the accumulated debt of pending CTXs. Analysis proves that LADS not only ensures strong queue stability but also establishes a deterministic upper bound on the cross-shard atomicity interval, effectively preventing perpetual starvation. We implement LADS on the opensource BlockEmulator testbed and evaluate it using real-world Ethereum traces. Extensive experiments demonstrate that LADS significantly minimizes the average CTX queueing latency and uniquely eliminates systemic discrimination, achieving a solid fairness ratio between ITXs and CTXs even under extreme congestion.

The scalability limitations of mainstream blockchain systems like Bitcoin and Ethereum stem from their reliance on serialized transaction execution and complete ledger maintenance on full nodes, resulting in performance bottlenecks and substantial operational overhead. While sharding has emerged as a promising solution to enhance throughput and scalability through distributed data management, it introduces critical challenges in node coordination and system reliability. In this paper, we provide a comparative analysis of prominent sharding architectures, examining their respective strengths and limitations. The study delves into both intra-shard and cross-shard protocol designs, evaluating their impact on system performance and scalability. We explore recent advancements in shard reorganization, migration protocols, and shard balancing mechanisms. The discussion emphasizes the need for a holistic approach incorporating dynamic sharding, multi-level consensus, and efficient inter-shard coordination. Finally, we outline future research directions for sharding architectures, addressing emerging challenges and opportunities in this evolving field.

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 to O(N(2)logN) ; 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 to O(root 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.

Sharding is a key technology to improve the scalability of blockchain, and cross-shard transaction protocols are the core of sharding to ensure transaction atomicity and consistency. Many scholars have conducted related research, including two-phase commit (2PC), transaction splitting, relay transactions, and deterministic ordering. However, there remains a challenge in balancing the efficiency and atomicity of cross-shard transactions (CTXs). Thus, this paper proposes a parallel and atomicity-guaranteed cross-shard transaction protocol, BMDS-Shard. Firstly, by decoupling cross-shard transactions into synchronized transactions in the source and target shards through beacon nodes, and automatically identifying cross-shard processing flows, we assign higher processing priority within shards, thereby solving the inefficiency of relay transactions. Secondly, based on a multi-beacon node data snapshot mechanism, we ensure instant and reliable state data feedback to the source shard, addressing the insufficient atomicity of transaction splitting. Finally, we have implemented the BMDS-Shard protocol, conducted theoretical analysis, and performed experimental validation on the BlockEmulator platform. Both theoretical and experimental results demonstrate that while guaranteeing transaction atomicity, our protocol reduces cross-shard transaction confirmation latency by 65.82% and 65.06% compared to existing relay transaction protocols Monoxide and BrokerChain, respectively.

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, named DataFly. We design a peg 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) the integrated signature and public key encryption scheme. 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 of DataFly, we implement a prototype of DataFly using existing popular permissioned blockchains, i.e., Hyperledger Fabric and private enterprise Ethereum. Measurement results demonstrate that DataFly outperforms related works in terms of transaction latency and gas costs.

With consumer electronics (CE) development, consumer-electronic WiFi-based human activity recognition (HAR) has been acknowledged as an essential non-intrusive technology in several crucial human-oriented fields, such as metaverse accessing, sign language communication, and healthcare monitoring.By learning from the experience from the CV field, i.e., treating wireless signals as images and extracting the feature based on a 2-dimensional convolutional neural network (2D CNN), existing studies have achieved considerable progress.However, there is a critical difference in feature compositions between WiFi signals and images. Whether existing methods apply to coarse-grained or fine-grained activity recognition, their performance is limited due to the loss of important dynamic temporal features hidden among multiple channels.To overcome these problems, we propose an extended temporal-spatial approach for WiFi-based HAR, named ExtRe, in which sufficient attention is paid to both temporal and spatial-channel characteristics. We also considered a challenging case, i.e., coarse-grained or fine-grained activities.Experiment results based on six datasets show that, compared with the state-of-the-art methods, our ExtRe achieves superior performance. The proposed ExtRe achieves 100% accuracy on a fine-grained dataset. On the coarse-grained dataset, ExtRe improves the accuracy by 0.6% with high stability.In addition, ExtRe has 20.67% less floating point operations (FLOPs).