Edge server deployment is a critical component of mobile edge computing for meeting low-latency requirements while promoting sustainable and energy-efficient operation. Practical deployment is challenging because latency, energy consumption, and load balance are tightly coupled, and the decision space grows rapidly with network scale under time-varying traffic demand. Despite extensive studies, existing approaches often exhibit a scale–adaptivity tradeoff in large-scale scenarios: Scalable placement strategies are typically static under temporal demand variations, whereas adaptive learning-based methods may incur slow convergence or high training cost in high-dimensional deployment spaces. To address this, we propose SC-DQN, a scalable two-stage strategy integrating spectral clustering (SC) and deep Q-network (DQN). SC first partitions base stations using geographic proximity and connectivity information to reduce dimensionality and provide a structured initial deployment. DQN then iteratively refines server placement under dynamic traffic demand, where multiobjective optimization is implemented via a scalarized reward to explicitly control the latency–energy–load tradeoff. Extensive experiments on real-world Shanghai Telecom data demonstrate that SC-DQN achieves improved overall performance compared with representative baselines across multiple scenarios, improving load balancing and reducing latency and energy consumption by up to 28.47%, 34.82%, and 7.08%, respectively.

Crowdsourcing is widely used to solve complex problems by leveraging a large pool of potentially unreliable submitters. To tolerate such unreliability and ensure high-quality outcomes, recent studies have proposed various methods, such as quality-aware incentive mechanisms. However, these approaches only address issues arising from in-system submitter malfeasance or failures, while overlooking the more harmful threat of external bribery attacks. In practice, malicious external parties can bribe submitters to manipulate their submissions, posing serious risks in sensitive fields such as finance, government, and healthcare, where the consequences of successful bribery far outweigh the cost. In response, we propose BFCS, a secure and efficient service framework for Bribery-Free Crowd Sourcing. The core idea of BFCS is to use the “re-encryption” technique to disconnect submitters from their submissions. Specifically, after submitters encrypt their data to protect privacy, they do not send it directly to the requester. Instead, it is first re-encrypted by a trusted randomizer. This process introduces randomness that prevents bribers from tracing a submission's origin, thereby eliminating bribery risks. To further enhance security, BFCS incorporates: (1) a decentralized secure re-encryption protocol to enable secure re-encryption in trust-scarce crowdsourcing environments; (2) an adaptive anonymous reward distribution scheme to preserve the bribery-free property during crowdsourcing reward allocation; and (3) a smart contract-based fair staking mechanism to balance the fairness of reward allocation with bribery-free guarantees. Our theoretical analysis and experiments on three typical crowdsourcing tasks—image annotation, text annotation, and sensor data collection—demonstrate that BFCS is both secure and efficient, making it well suited for real-world deployments and effective in preventing submitter bribery

Emerging applications like smart cities necessitate rapid and localized data processing. To meet this critical requirement, the strategic deployment of edge servers within Mobile Edge Computing (MEC) architectures is essential to improve system performance and user experience. However, despite existing methods attempting to address the edge server deployment problem, challenges remain in large-scale deployment scenarios, including low deployment efficiency and reduced service quality. To address these issues, we propose MF-ESD, a novel edge server deployment strategy that leverages Mean Field Reinforcement Learning (MFRL) to optimize the deployment process. Specifically, we first employ an improved Whale Optimization Algorithm (IWOA) with adaptive weights and differential mutation for preprocessing to find a high-quality initial deployment scheme, thereby alleviating the cold-start problem in MFRL. Subsequently, MFRL utilizes the deployment results from IWOA and employs mean field approximation to determine the optimal edge server deployment strategy, which enhances the search efficiency. We validate the proposed method using real-world datasets provided by Shanghai Telecom and compare it against four baseline algorithms: Random, Top-K, Particle Swarm Optimization (PSO), and ESL. The experimental results show that MF-ESD reduces average latency and energy consumption while significantly improving load balancing performance, outperforming the baseline algorithms.

Network traffic intrusion detection is a critical task for cybersecurity, with many studies relying on large volumes of high-quality data to train detection models. However, real-world datasets often suffer from label noise and class imbalance, which degrade model performance. Existing traffic detection methods typically fail to address both challenges simultaneously, limiting their effectiveness in practical scenarios. To address these challenges, we propose NoiseGuard, a three-pronged solution for handling both label noise and class imbalance in network intrusion detection. First, NoiseGuard identifies high-confidence normal samples based on loss values, then uses these samples to infer and assign pseudo-labels to other normal samples. This step substantially reduces class imbalance by filtering out most normal traffic early on, allowing the system to focus on the remaining, more challenging malicious samples. Next, an enhanced co-teaching mechanism with a dynamically adjusted loss function to refine relatively scarce malicious samples. Finally, once a cleaner dataset is obtained, NoiseGuard employs a re-weighting strategy to further mitigate class imbalance without amplifying noisy labels. We evaluated NoiseGuard using two publicly available datasets, CIC-IDS2017 and DoHBrw-2020. In scenarios with 40% label noise and a 30-fold class imbalance, NoiseGuard achieved F1 scores of 0.77 and 0.98, respectively. These results represent improvements of 13.23% and 10.34% compared to existing state-of-the-art (SOTA) methods.

Machine learning-based network intrusion detection(NID) systems often face concept drift in two forms: unknown class emergence due to the arising of previously unseen classes and feature distribution shifts due to the shifts in existing classes. While current approaches, such as drift detection and model retraining, can effectively address the challenge of unknown categories, they struggle to cope with shifts in feature distributions. Our preliminary analysis reveals that features like packet length vary considerably across different network configurations, resulting in unstable representations and degraded model performance. In this paper, we propose NIDP, a novel approach based on neural pruning designed to improve the robustness of NID models against changes in feature distribution. The NIDP method comprises three key components: 1) Dynamic Drift Anchoring (DDA), which detects drift samples with variant features; 2) Dual-Guided Feature Sculpting (DGFS), which leverages patterns identified by DDA to prune fluctuating features selectively; and 3) Contrastive Representation Forging (CRF), which retrains the model to embed samples from the same category more cohesively. Together, these components improve the model’s adaptability to dynamic network environments. NIDP is evaluated on two widely used NID benchmarks, Kyoto2016 and CIC-IDS2017&2018, as well as two synthetic variants with amplified feature distribution shifts. Compared to state-of-the-art methods, it achieves F1 score improvements of $7.04 \%$ and $40.00 \%$ on the real-world datasets, and demonstrates the fastest performance recovery on the synthetic ones, highlighting its adaptability and robustness.

The acceleration of deep neural networks (DNNs) on field-programmable gate arrays (FPGAs) has gained widespread adoption due to their high performance and energy efficiency. However, the shared hardware nature of FPGAs introduces new security vulnerabilities, especially in multi-tenant cloud and IoT environments. Among these, remote voltage side-channel attacks remain largely underexplored. In this paper, we present a novel attack paradigm that enables remote extraction of sensitive information through voltage fluctuation analysis on Xilinx 7-series FPGAs. Unlike traditional side-channel approaches requiring physical access or external sensors, our method leverages the embedded XADC module to non-invasively capture power-induced voltage variations caused by victim DNN computations. To effectively extract useful information from voltage fluctuations, we develop a customized processing pipeline tailored to the inherently low signal-to-noise ratio present in remote measurements. This pipeline employs dynamic thresholding, image-centric quantization, and multistage denoising to extract meaningful patterns from raw voltage traces. Applying this method, we successfully reconstruct input images with an average Normalized Cross-Correlation (NCC) of 73.86% against the original MNIST inputs, demonstrating high visual similarity and validating the feasibility of the attack. Our findings reveal critical security flaws in the shared power delivery and sensing infrastructure of modern FPGAs. This work underscores the need for hardware-level isolation and mitigation strategies, and expands the attack surface model for future FPGA-based cloud and edge deployments.

Sharding is one of the most effective techniques for addressing scalability challenges in blockchain systems. However, existing sharding schemes often fail to balance security and scalability, primarily due to unavoidable cross-shard communication costs. Some schemes rely on additional roles like TEEs or alliances to streamline cross-shard consensus, introducing security risks such as hardware attacks or node collusion. Others mitigate cross-shard consensus costs by periodically distributing nodes or states based on predefined rules, yet inefficient distribution rules lead to poor scalability. In response, this article proposes SAC, a novelshardingallocation service that efficiently trades scalability and security via a two-stage allocation strategy. First, SAC employs lightweight state graph clustering to group frequently interacting states within the same shards based on historical transaction data, reducing cross-shard transactions significantly. Second, it formulates node allocation as a multi-objective evolutionary problem (MoSA) that jointly maximizes system throughput, minimizes confirmation latency, and balances malicious node distribution. Next, SAC selects FV-MOEA as the foundational solver for MoSA after comprehensive preliminary experiments. Based on this, SAC proposes an improved algorithm, LeFV, to explore optimal shard allocation solutions. Specifically, LeFV retains and mutates low-contributing but potentially high-quality solutions to enhance population diversity. It allows for a wider exploration of shard allocations, thereby identifying optimal ones that effectively balance scalability and security of the sharding system. Extensive experiments on a sophisticated blockchain emulator demonstrate that SAC outperforms two advanced state-of-the-art methods in balancing scalability and security.

Smart contracts in the Ethereum system are stored tamper-resistant, complicating necessary maintenance for offering new functionalities or fixing security vulnerabilities. Previous contract maintenance approaches mainly focus on logic modification using delegatecall-based patterns. While popular, they fail to handle data state updates (like storage layout changes), leading to impracticality and security risks in real-world applications. To address these challenges, this paper introduces SmartUpdater, a novel toolchain designed for transparent, automated, and secure maintenance of stateful smart contracts. SmartUpdater employs a hyperproxy-based contract maintenance pattern, where the hyperproxy serves as a constant entry and ensures that any state/logic modifications remain transparent to end users. SmartUpdater automates the maintenance process in terms of development streamlining, gas cost efficiency, and state migration verifiability. In extensive evaluations, we show that SmartUpdater can reduce gas consumption in contract maintenance compared with actual maintenance approaches. The evaluations point out the potential of SmartUpdater to significantly simplify the maintenance process for developers.

Distributed Validator Technology (DVT) mitigates single points of failure in Ethereum validators by distributing validator duties across a node cluster and making decisions within the cluster through consensus algorithms. However, these systems often fail to ensure economic fairness for participants, where misconduct by a few nodes can lead to financial losses for honest nodes within the cluster. To address this issue, we introduce RobustETH, an innovative DVT implementation that prioritizes operational efficiency and economic fairness for participants. Specifically, RobustETH incorporates an efficient consensus protocol, X-IBFT, to enhance DVT’s operational efficiency. Additionally, we propose a BFT forensic protocol and a reputation-based recluster strategy to accurately penalize malicious nodes and mitigate their impact, thereby safeguarding the financial interests of honest participants. Our theoretical analysis proves RobustETH’s safety, liveness, and accountability. Moreover, through comprehensive evaluations, we demonstrate that RobustETH achieves a 58% reduction in end-to-end latency compared to current state-of-the-art DVT implementations while ensuring economic fairness.

Due to the low-cost nature of cloud computing, many users deploy services on cloud platforms. However, malicious cloud platforms may offer hardware with lower performance than claimed, harming users' interests. Although users can run performance measurements on the cloud to verify the provided performance, existing methods are mostly designed for trusted environments and neglect the integrity of the measurement process. This oversight allows malicious cloud platforms to falsify measurement results easily. In this paper, we propose VEUGM, a verifiable, efficient measurement framework universal to various GPUs. This framework uses the TEE on the CPU side to generate measurement tasks and reliably verify the performance of cloud-based GPUs. Specifically, we devise an adaptive comprehensive performance measurement task that can accurately assess the performance of various GPUs with minimal overhead. Given the typically limited performance of TEEs, we further design a low-overhead measurement integrity verification algorithm to efficiently verify the correctness of measurement task execution within the TEE. Furthermore, we conduct theoretical analysis and extensive evaluations. The results on various GPUs demonstrate that VEUGM not only ensures the verifiability of the measurement task execution process but also reduces the required measurement time by an average of 82.63%.

Mobile edge computing (MEC) enables real-time processing and reduces core network congestion by bringing computing resources closer to data sources. However, optimizing edge server deployments to minimize latency, energy consumption, and load imbalance remains challenging. We propose the SC-DQN strategy, combining spectral clustering and deep Q-network (DQN) techniques. Spectral clustering analyzes the spatial distribution of base stations, grouping them and identifying cluster centers as initial deployment sites. A deep reinforcement learning environment then enables each edge server (agent) to iteratively optimize deployment, minimizing latency, energy consumption, and load imbalance. Experiments with Shanghai Telecom data demonstrate that SC-DQN improves load balancing by 23.05%, reduces latency by 45.32%, and lowers energy consumption by 9.64%, outperforming traditional methods.

Recently, smart contracts have been widely applied in security-sensitive fields yet are fragile to various vulnerabilities and attacks. Regarding this, existing research efforts either statically scrutinize smart contracts’ code or detect suspicious transaction execution flows. However, they either fail to timely protect contracts or only handle a small subset of well-known vulnerabilities. In the paper, we propose$\mathtt {EVM}$-$\mathtt {Shield}$that secures vulnerable smart contracts in real-time via fine-grained access control over sensitive states. The behind rationale is most of attacks aim to manipulate money-related states (e.g., tokens) for profits. Specifically, transaction-level state access control policies are first defined by developers and then translated into EVM-level policies with contract-aware function-level state access permissions. In policy enforcement,$\mathtt {EVM}$-$\mathtt {Shield}$introduces a hybrid storage analyzer to accurately identify (dynamic-allocated) storage locations for policy-involved states and a multi-stage cache based filter to fast revert bad transactions with unexpected state access behaviors. Finally, we conduct thorough experiments using 12 types of real-world contract vulnerabilities and all open-source smart contracts on the first$8M$blocks of Ethereum. The results demonstrate that$\mathtt {EVM}$-$\mathtt {Shield}$outperforms two state-of-the-art runtime analysis tools in terms of attack detection. Extensive performance evaluations with$185M$real-world transactions show that$\mathtt {EVM}$-$\mathtt {Shield}$can block 100% unexpected state accesses at the cost of 8% throughput degradation (compared with the native EVM).

The destruction of communication infrastructure after a disaster makes it impossible for vehicles to timely transmit important data, such as casualty locations, road conditions and rescue demands, which brings great difficulties to ensure safe driving and efficient rescue. Some existing schemes have proposed the use of Unmanned Aerial Vehicles (UAVs) to assist data sharing in the Internet of Vehicles (IoV) to perform instant rescue missions. However, the untrusted network environment after the disaster and the mutual unbelief among rescue vehicles lead to potential security problems in data sharing between vehicles and UAVs. In addition, some selfish or malicious participants may disseminate meaningless or false data, which will not only waste valuable rescue resources in disaster areas but also may threaten the safety of rescue workers. To overcome these challenges, we propose TEBChain, a trusted and efficient data sharing scheme based on blockchain. In TEBChain, a blockchain-based lightweight framework is first designed to guarantee effective data sharing and record all abnormal behavior. Then, we present an improved key update mechanism based on the Boneh-Lynn-Shacham (BLS) threshold signature, which can ensure the trust of shared data among frequently moving vehicles. Furthermore, to facilitate consensus and reduce communication overhead, a lightweight and secure PBFT (LS-PBFT) consensus protocol is proposed to enable efficient rescue of vehicles and UAVs. Finally, the effectiveness and feasibility of our proposed TEBChain are validated through performance comparisons and simulation experiments.

In the trend of network middleboxes as a service, enterprise customers adopt in-the-cloud deep packet inspection (DPI) services to protect networks. As network misconfigurations and hardware failures notoriously exist, recent efforts envision to ensure the execution integrity of DPI services in untrusted clouds. However, they either require enterprise customers to know proprietary DPI rulesets of cloud providers or introduce forbidden overhead in the network context. In the paper, we propose a privacy-preserving and lightweight verification scheme that efficiently checks whether in-the-cloud DPI services run correctly without leaking private DPI rulesets. Particularly, our design introduces one trusted third party to perform privacy-preserving and trustworthy ruleset evaluation and DPI execution verification. Meanwhile, it devises a novel DPI ruleset authentication method that enables tamper-proof DPI operations and facilitates fast proof generation. The proofs can be verified without requiring the verifier to always maintain all rulesets. To further reduce the verification costs while resisting cloud cheating behaviors like bias treatments of packets, it employs a commitment-based delayed sampling mechanism which requires the DPI services to first demonstrate that all packets have been processed before receiving sampling decisions. Moreover, extensive experiments are conducted based on Click modules. The results show that the proposed scheme is practical and only incurs the real-time overhead of 10–20 microseconds.

Sharding is a promising technique to improve the scalability of blockchain systems via processing transactions in parallel. However, there is an overwhelmingly high proportion of cross-shard transactions requiring complicated cross-shard consensus among the involved shards. To solve the problem, most state-of-the-art works adopt a centralized or decentralized coordinator to harmonize the cross-shard consensus. Nevertheless, they usually incur a confirmation latency of at least three consensus rounds and fail to avoid unnecessary communication costs for invalid and conflicting cross-shard transactions. Therefore, we propose DCchain that fully eliminates computation and communication latency for invalid or conflicting transactions. Specifically, DCchain introduces a decentralized coordinator (DC) that maintains verifiable global states. DC directly identifies and aborts invalid and conflicting cross-shard transactions, avoiding additional cross-shard computation and communication overhead. Besides, we present a robust two-tier consensus protocol. It commits valid cross-shard transactions through efficient interaction between DC and involved shards while defeating Byzantine behaviors. Finally, our performance evaluations demonstrate that DCchain's efficiency is superior to a popular existing work by nearly three times at best.

Recently, collaborative learning is proposed to amortize massive computation costs of highly sophisticated artificial intelligence (AI) tasks. To attract lots of participants, researchers investigate blockchains ‘ economic incentives with proof of useful work (PoUW) consensus protocols to motivate substantial numbers of miners in a mining pool to complete AI tasks. However, participants might be untrusted and defraud rewards with as less as possible efforts. In the paper, we propose a robust and efficient proof of learning scheme called RPoL that enables pool managers to verify the training integrity of pool workers for secure pooled mining. Specifically, we devise an address-encoded model and employ a commitment-based secure sampling method to prevent malicious participants from abusing well-trained models or evading the sampling-based verification. Besides, we optimize RPoL via locality-sensitive hashing (LSH) to achieve communication-efficient verification while tolerating inherent reproduction errors of AI tasks. Furthermore, we conduct theoretical analysis and extensive evaluations. The results demonstrate that RPoL preserves high model performance against adversaries with acceptable costs and thus helps the pool win the mining competition among consensus nodes.

In the burgeoning era of digital finance, ensuring user privacy protection within blockchain systems has become a crucial concern. Despite this, existing privacy enhancement methods often lack user-friendliness, as they necessitate users to navigate complex protocols, thereby raising the barrier to entry. This paper introduces EPT, an efficient framework that not only addresses the user privacy concern but also enhances usability. Our method allows users to interact with confidential smart contracts deployed in a Trusted Execution Environment (TEE) by sending private transactions directly to the blockchain. We devise a secure TEE attestation and data privacy scheme for secure private transactions, even when users interact indirectly with the TEE. Further, we utilize a robust data acquisition approach for inputting valid and fresh private transaction-related data into the TEE. Our extensive evaluations demonstrate that EPT can deliver confidential transactions at a gas cost comparable to that of non-confidential block transactions, with a high throughput of 2, 715 tx/s, and an end-to-end latency of around 12 seconds.

While electric vehicles are proliferating rapidly, the number of charging piles is only 1/16 of the number of electric vehicles, showing big tension between small supply of public charging piles and intense charging demand of electric vehicles. To alleviate the tension, it is highly desired to incorporate existing idle private charging piles into a shared charging service. However, existing solutions do not well implement the system due to some non-negligible issues, like poor consensus performance or lack of verification for outputs from blockchain networks. In the article, we propose an efficient and secure trading framework built atop multiple consortium blockchains. It devises a novel node voting mechanism so as to improve consensus efficiency. Meanwhile, it employs a publicly verifiable design based on the$(t,n)$Boneh-Lynn-Shacham (BLS) threshold signature technique, so as to enable entities outside the blockchain to verify the consistency between the output results with the valid consensus. Besides these novel system-level designs, it also introduces a fair and robust trading strategy running by smart contracts on the platform. Furthermore, we implemented a proof-of-concept system and conducted extensive experiments. The results show that the overall latency across chains is reduced by about 26% with the node voting mechanism and the transactions fees are comparable to recent popular cross-chain applications.

Till the end of September 2021, the size of Bitcoin blockchain has reached 366.9GB and continues to increase at an average annual growth rate of 17.6%. The high requirement for storage space prevents new nodes from joining the network, which seriously hinders the development of blockchain technology. In this paper, in order to explore the data characteristics of Bitcoin’s block, we performed statistical experiment on the current 680,000 blocks. The analysis results showed that in nearly 95% of blocks, the number of spent transaction output(STXO) accounts for more than 67% of the total transaction output. Inspired by this, we proposed a hybrid storage scheme to reduce the size of blocks by deleting the transaction data with the STXO ratio over 67% firstly and compressing some fixed-length fields in those transactions. And then, the newly generated block files were deposited to the InterPlanetary File System(IPFS) private network to improve the scalability of Bitcoin network. The experiment results and analysis showed that our scheme achieved a compression ratio of 96.9% and could effectively help the Bitcoin full nodes save 330GB of storage space with guaranteeing the normal operation of Bitcoin network.

Fully homomorphic encryption (FHE) can effectively protect data privacy. It allows users to entrust data operations to a third party on a cloud server without revealing their own privacy. Sorting algorithms are the fundamental technique of managing and processing data. Currently, the sorting algorithms based on the traditional FHE schemes of BGV (Brakerski-Gentry-Vaikuntanathan), BFV (BrakerskiFan-Vercauteren) and CKKS (Cheon-Kim-Kim-Song) are difficult to implement and are inefficient because of their slow bootstrapping techniques. TFHE (Fast Fully Homomorphic Encryption over the Torus), a fast FHE scheme over the torus based on GSW (Gentry-Sahai-Waters), significantly decrease the time cost of bootstrapping. In this paper, we first design the bitwise FHE comparison and swap operations using the bootstrapped binary gates of the TFHE library. With the two operations, a bubble sort algorithm is proposed. By reducing the depth of sorting circuits in the proposed bubble sort, we further present AdditionSort, an efficient sorting algorithm using homomorphic addition, which can support the operations of arbitrary array length and unlimited element size. The experiments show that AdditionSort is nearly 50% faster than the bubble sort when the size of an element exceeds 32 bits.

Range query processing is a common edge computing and service in the Internet of things, which can extract user-interest information from distributed edge devices. How to design lightweight privacy-preserving range query processing methods remains a challenging task. Existing secure range query approaches suffer from both high communication cost and long response time, which makes them unsuitable for edge computing over resource-constrained edge devices. In this article, we propose two privacy-aware fuzzy query processing schemes based on fuzzy theory. Linguistic range variables, fuzzy overlap information, and its recovery mechanism are introduced. In addition, two distributed privacy-aware fuzzy range query processing algorithms are devised. Our approaches not only serve for privacy protection, but also aim to provide other optimal performances in terms of reliability, energy efficiency, and real-time response. Theoretical analysis and experimental evaluations based on real-world datasets validated our motivation.

With the continuous development of blockchain technology, Bitcoin as the first cryptocurrency has drawn massive attention from various sectors. Bitcoin on-chain data storage is over 338GB as of September 1, 2021. According to the exponential growth trend of block data, the size of a Bitcoin full node will exceed 500GB in two years. The huge storage problem makes it difficult for general users to store complete Bitcoin data conveniently, which weakens the decentralization capability of Bitcoin network. In this work, we propose an efficient storage scheme (ESS) based on the distribution characteristics of the unspent transaction outputs in Bitcoin network. ESS sets a UTXO-weight for each block. According to UTXO-weight, it dynamically prunes the blocks which have lower query frequency, improving the scalability of the Bitcoin network. When a new block is generated, ESS enables joining nodes to verify most of the transaction inputs instantly. Only a small amount of transaction outputs of older blocks need to be retrieved from the full node for payment verification. Experimental results demonstrated that ESS can reduce the size of a normal node in current Bitcoin network by about 82.14% at a low communication cost.

As of the end of September 2021, the size of the Bitcoin blockchain has reached 366.9GB and continues to increase at an average annual growth rate of 17.6%. The large-scale demand for storage space constrains new nodes from joining the network, which seriously hinders the development of blockchain technology. In this paper, to explore the data characteristics of the Bitcoin blockchain, we performed a comprehensive statistical experiment on the current 680,000 blocks. The analysis results indicated that in nearly 95% of blocks, the number of spent transaction output (STXO) accounts for more than 67% of the total transaction outputs. Inspired by this feature, we proposed a novel storage scheme to reduce the size of blocks by deleting the transaction data with the STXO ratio over 67% first and compressing fixed-length fields of those transactions. Then, the newly generated block files were deposited to the InterPlanetary File System (IPFS) private network to improve the scalability of the Bitcoin blockchain. The experiments and evalutions showed that the proposed scheme achieved a compression ratio of 96.9% and saved 330GB of storage space for the Bitcoin full nodes while guaranteeing the normal operation of the Bitcoin network.

Line-of-sight (LOS) communications are becoming one of the promising use cases in the near future’s wireless communications thanks to the potential use of very high carrier frequency, for example tera-hertz band. However, most traditional communication techniques have been developed assuming abundant reflected multipaths, and there is relatively not enough in-depth study regarding LOS communications. Therefore, this paper considers LOS communications from the aspect of antenna misalignments assuming multiple-in multiple-out (MIMO) is available. We propose a communication system that utilizes antenna subarrays for estimating the antenna misalignments and other necessary parameters to achieve the best performance in LOS channels. This information is fed back to the transmitter for deriving the optimal precoder. We exploit multiple techniques for enhancing the estimation performance for stable system performance. The results indicate that the performance with ideal information can be closely achieved with the proposed techniques. The proposed techniques are also shown to be flexibly applicable to any shape of Tx antenna arrays.

Ethereum is essentially a transaction-driven state machine, and a smart contract is a piece of executable code on Ethereum. Compared with the scripting language on Bitcoin, the smart contract language solidity, which is Turing-complete and the ex-pressive capabilities are very powerful. However, this attribute also brings many potential security threats, vulnerabilities, and various other issues. In this paper, we propose a novel smart contract security technology, named Jyane, to detect the Reentrancy vulnerability, which is one of the most threatening vulnerabilities to smart contracts. More importantly, Our tool-Jyane is the first path profiling solution for smart contracts. Firstly, we use EVM (Ethereum Virtual Machine) binary bytecode to construct control flow graphs (CFG), then use the improved Ball-Larus Path profiling algorithm (BLPP) to generate IDs for acyclic paths. Finally, after profiling the constructed paths, the suspicious paths can be detected successfully. We evaluate Jyane and other technology through comprehensive test and comparison; the results show that Jyane can profile the actual execution path of smart contracts to detect vulnerabilities with a low false-positive rate accurately. From the results of the evaluation, Jyane marked 27 of 1,226 Ethereum smart contracts selected in 2016 and 2017 as vulnerable contracts, included the vulnerability of the DAO contract which once led to a $60 million loss. Furthermore, compared with some other existing detection tools, Jyane shows broader detection range for Reentrancy vulnerabilities with lower time overhead.

With the continuous development of blockchain technology, Bitcoin as the first cryptocurrency has drawn massive attention from various sectors. A full node on Bitcoin network has to store over 320GB of data as of March 9, 2021. According to the growth rate of Bitcoin transactions, it is estimated that the size of Bitcoin data on a full node will exceed 400GB within two years. The storage problem makes it difficult for common users to easily store all of Bitcoin data, which weakens the decentralization capability of Bitcoin network. In this work, we propose an efficient storage scheme (ESS) based on the distribution characteristics of the unspent transaction outputs in Bitcoin system. It can prune the storage of the transactions for those low query frequency blocks. When a new block is generated, a node using ESS scheme can quickly verify most of the transaction inputs. Only a small amount of transaction outputs of older blocks need to be retrieved from the full node for payment verification. Experimental results demonstrated that our scheme can reduce the size of a normal node in current Bitcoin system by about 85% at a low communication cost.

One of the major challenges in vehicle to everything (V2X) system is robust and low-cost synchronization for ultra-reliable low latency communication under various fading scenarios. To address this issue, this paper presents a maximum likelihood (ML) based algorithm for the sidelink secondary synchronization signal (S-SSS) detection, assuming the sidelink primary synchronization signal (S-PSS) detection has been successfully achieved at an earlier stage. Based on the specific signal structure, the proposed method exploits the channel selectivity in frequency domain (FD) and channel correlation in time domain (TD) to obtain an ML solution. In order to avoid the need to estimate the Doppler frequency and simplify the algorithm, we provide the ML solutions based on the assumptions of infinite or zero Doppler. Furthermore, we propose a practical method with limited TD channel taps that can be efficiently implemented in real systems. Simulation results show that the proposed method significantly improves the performance over the conventional detection methods under different Doppler scenarios.

Millimeter-wave channels are angular sparse. Under practical channels, angle of arrivals (AoAs) tend to change slowly over time, while the path gain corresponding to each AoA may vary relatively fast. To exploit the property of slow AoAs variation, this paper proposes two approaches that consider measurements of current and multiple previous beam sweeping periods to improve channel recovery quality at the user equipment (UE). Given slow AoAs variation, the first approach suggests that UE update the beam sweeping codebook based on estimated AoAs to improve signal to noise ratio for the following beam sweeping. When path gains also vary slowly, the second approach suggests that UE estimate channel AoAs using both current and history measurements, and estimate path gains only using current measurement. The first approach is more robust to channel variation since it only assumes slow AoAs variation. When channel variation is small, the second approach exhibits advantage over the first one, and both outperform closed form simultaneous orthogonal matching pursuit (SOMP) proposed in [1], which only uses current measurement for channel recovery. When channel variation is relatively large, codebook update still brings gains over closed form SOMP.

The antenna location design problem is addressed in line-of-sight channel with superimposed-concentric uniform circular arrays (SC-UCA). The analysis is done by first, converting the capacity maximization to the channel correlation minimization problem, second, calculating channel correlation terms explicitly that need to be minimized, and third, calculating necessary conditions to achieve minimum correlations. The analysis is performed for 4x4 channel matrix, and a generic case with relative antenna rotation is handled and the closed-form solutions are obtained. As another special case, the modified antenna configuration with inner-circle rotation is also considered. With this model, it is shown that the existing results for uniform linear array (ULA) and uniform circular array (UCA) can be obtained. Moreover, it is also shown that it is possible to provide maximum multiple-in multiple-out (MIMO) gain with either non-uniform linear or circular arrays. With detailed analysis and simulations, the flexible design of antenna locations with multiple concentric circular arrays are presented.

This paper considers the problem of channel estimation in hybrid beamforming systems at the user equipment (UE). After the beam sweeping process, UE estimates the channel such that the best beamforming vector can be derived accordingly to improve analog beamforming gain. To exploit the angular sparsity of millimeter-wave (mmWave) channels, a compressed sensing (CS) based channel estimation algorithm, termed as closed form simultaneous orthogonal matching pursuit (SOMP), is proposed. The proposed algorithm works for a class of beamforming codebooks and scenarios with very small number of beam sweeping symbols, with reduced complexity compared with the existing SOMP algorithm [1]. Simulation results show that the proposed algorithm achieves similar performance as that of the SOMP algorithm, and has advantages over the non-CS based channel estimation algorithm.

In this paper, a dynamic codebook selection scheme is proposed for beam sweeping at millimeter wave receiver. The codebook selection problem is formulated as a Partially Observed Markov Decision Process (POMDP) and Q-learning method is used to learn a policy to dynamically select an appropriate codebook from a pre-defined codebook set in each beam sweeping period. A hierarchical structure codebook set design method based on Lloyd's method is proposed to generate a proper codebook set with good coverage and high average beamforming gain. The proposed scheme offers more than 1 dB gain in terms of block error rate (BLER) performance compared with the existing methods which use a fixed codebook in all beam sweeping periods.

In this paper, we propose a novel method for selecting the precoding matrix indicator (PMI) from a DFT-based codebook, such as the one specified for LTE full dimension (FD) MIMO. While conventional approaches are based on explicit codebook search, our method directly estimates the best PMI from the singular vectors of the channel matrix. The key idea is to exploit the DFT structure to formulate the PMI selection problem as the estimation of the linear phase ramping of a sequence. The main advantage of the proposed method is that its complexity does not scale with the number of PMI candidates, while achieving performance comparable or superior to that of state-of-the-art search-based approaches.

This paper addresses the problem of precoding matrix selection in large-scale MIMO cellular systems, where traditional codebook search methods may result in high complexity for the receiver due to the increased codebook size. For this reason, we propose to first compute an unconstrained reference codeword, and then search the codebook for the best approximation of such reference codeword, using low-complexity distance metrics. We consider two possible choices for the distance metric: the chordal distance, previously used in the literature for codebook design, and a newly defined distance function based on the sum of phase differences between the elements of two complex matrices. We show that the proposed method can match or outperform state-of-the-art approaches (based on capacity or mutual information) with significantly lower per-candidate complexity.

Abstract With the objective to minimize the energy consumption for packet based communications in energy‐constrained wireless networks, this paper establishes a theoretical model for the joint optimization of the parameters at the physical layer and data link layer. Multilevel quadrature amplitude modulation (MQAM) and automatic repeat request (ARQ) techniques are considered in the system model. The optimization problem is formulated into a three dimensional nonlinear integer programming (NIP) problem with the modulation order, packet size, and retransmission limit as variables. For the retransmission limit, a simple search method is applied to degenerate the three dimensional problem into a two dimensional NIP problem, for which two optimization algorithms are proposed. One is the successive quadratic programming (SQP) algorithm, combining with the continuous relaxation based branch‐and‐bound method, which can obtain the global optimal solution since the continuous relaxation problem is proved to be hidden convex. The other is a low‐complexity sub‐optimal iterative algorithm, combining with the nearest‐neighboring method, which can be implemented with a polynomial complexity. Numerical examples are given to illustrate the optimization solution, which suggests that the joint optimization of the physical/data link layer parameters contributes noticeably to the energy saving in energy‐constrained wireless networks. Copyright © 2010 John Wiley & Sons, Ltd.

Due to limitations of power, computation capability and storage resources, wireless sensor networks are vulnerable to many attacks. The paper proposed a novel routing protocol algorithm for Wireless sensor network. The proposed routing protocol algorithm can adopt suitable routing technology for the nodes according to distance of nodes to the base station, density of nodes distribution and residual energy of nodes. Comparing the proposed routing protocol algorithm with other routing protocol algorithm through comprehensive analysis, the results show that the proposed routing protocol algorithm is secure and efficient for wireless sensor networks.

In recent years, the security issues of the cognitive radio (CR) networks have drawn a lot of research attentions. Primary user emulation attack (PUEA), as one of common attacks, compromises the spectrum sensing, where a malicious user forestalls vacant channels by impersonating the primary user to prevent other secondary users from accessing the idle frequency bands. In this paper, we propose a new cooperative spectrum sensing scheme, considering the existence of PUEA in CR networks. In the proposed scheme, the sensing information of different secondary users is combined at a fusion center and the combining weights are optimized with the objective of maximizing the detection probability of available channels under the constraint of a required false alarm probability. We also investigate the impact of the channel estimation errors on the detection probability. Simulation and numerical results illustrate the effectiveness of the proposed scheme in cooperative spectrum sensing in the presence of PUEA.

The power adaptation issue for multi-hop sensor networks is considered in this paper to minimize the energy consumption with a given requirement of the average end-to-end bit error rate (BER) performance. To optimize the power adaptation, a nonlinear programming problem is formulated and an analytical result is derived from its Karush-Kuhb-Tucker (KKT) necessary conditions. Numerical examples are presented to compare the networks with the proposed power adaptation scheme and a distributed power adaptation scheme, in which the transmitter of each individual hop adjusts its transmission power based on its own channel state information (CSI) and a specified BER requirement for this hop. A significant energy saving is achieved with the use of the proposed scheme.

A new methodology that applies frequency orthogonality to the spreading code design has been presented. The main purpose is to reduce the receiver complexity of the cyclic prefix code division multiple access (CP-CDMA) over the downlink channel and to enable parallel transmission over the uplink channel. The receiver complexity has been found for requiring full-band FFT and IFFT calculations in the channel equalizer. To handle this problem, we developed the comb spectrum codes organized in frequency orthogonal groups, in each of which the codes have time correlation orthogonality. For the downlink channel, the reduction of complexity in signal processing of a CSCDMA receiver is due to that the separation of groups is simple and each separated group is in the form of a narrower bandwidth CP-CDMA. Thus, the full-band FFT and IFFT can be replaced by the partial FFT and partial IFFT as shown in this proposed approach. For the uplink channel, we assign the code groups to different users to form a group division multiple access (GDMA) for enabling an independent channel equalization of each user at base station. The theoretical analysis and the simulation results agree well with each other and both confirm the effectiveness of this approach.

This paper develops a dual-window technique to reduce the sensitivity to carrier frequency offset (CFO) of orthogonal frequency-division multiplexing. We define two windows and use them alternately onto the subcarriers one another in adjacent for pulse shaping at the transmitter. The receiver selects one of the two windows to maximize the output of the desired subcarriers and suppresses the others by window- matching and anti-matching functions respectively. The efficiency of the proposed method is demonstrated in the theoretical analysis and in the simulations as well.

In the conventional pilot symbol assisted modulation design, a data block is composed of a pilot set and an information data set. For improving the quality of the channel estimates, some iteration methods use the detected information symbols as the additional pilots. We work on orthogonal frequency division multiplexing (OFDM) system to divide the information data set into several segments and assign them with different data rates for exploiting the potential of the iterative channel estimation. The averaged data rate can be increased by the proposed scheme with the minimum mean-square error channel estimator. The simulation results confirm this approach.

In this paper, we propose a new decoding method for LDPC aided by CRC and perturbation for improving FER performance. The CRC is used to check the decoding error of BP algorithm. Once an error is found, the perturbation method will be used to start re-decoding processing. Simulation results show that this method performs within about 1.2 dB of the sphere-packing bound for codeword length of several hundred bits.

In this paper, we consider the capacity behaviors of MC-CDMA and CP-CDMA systems by the uses of MMSE receivers. The averaged capacities are derived for the two systems, having a finite spreading factor, in Rayleigh fading environment. The closed-form solutions have been obtained, which show that the capacity of the MC-CDMA is slightly larger than that of the CP-CDMA. The theoretical predictions are confirmed by the simulation results.