This comprehensive article explores the transformative integration of edge computing and hybrid cloud storage, a technological convergence that is reshaping data processing architectures in the era of exponential data growth. The research delves into the fundamental principles of edge computing and hybrid cloud storage, examining their synergistic relationship in addressing the limitations of traditional centralized cloud computing. By bringing computational resources closer to data sources, this integrated approach significantly reduces latency, enhances processing efficiency by up to 50%, and improves overall system reliability. The article presents detailed case studies in autonomous driving and smart city infrastructure, showcasing real-world applications and benefits. It critically analyzes the challenges inherent in this integration, including security concerns in decentralized architectures, data consistency issues, and cost implications. Furthermore, the article explores future directions, discussing emerging technologies such as AI-powered edge devices, evolving hybrid cloud solutions, and the potential for further optimization. This research provides valuable insights for organizations and researchers navigating the complex landscape of distributed computing, offering a roadmap for leveraging edge computing and hybrid cloud storage to achieve unprecedented levels of performance, scalability, and flexibility in data management and processing.
Blockchain, with its immutability and decentralization, drives innovation in finance and supply chain, but the growing data volume makes storing complete ledger replicas impractical for users, especially in the resource-constrained Internet of Thing (IoT) scenarios. Existing solutions focus on nodes storing only a partial ledger to alleviate storage burdens. Nonetheless, these approaches prioritize storage optimization by minimizing the query cost and lack control over storage cost. Furthermore, these approaches overlook the relationships between network users, thus failing to fully measure the future query cost. Thus, this article proposes BSSN, a blockchain storage technology based on social networks. The combined use of storage cost and query cost is introduced for the first time to formulate the node allocation optimization (NAO) problem, and the multipopulation genetic ant colony (MGAC) algorithm will be employed to derive node allocation strategies. Specifically, we address three technical challenges: 1) to predict the transactions that nodes will participate in the future, we employ the social ties to obtain the access frequencies among users; 2) to strike a balance between the storage cost and query cost, we jointly model the two costs as a multiobjective optimization problem to formulate the NAO problem; and 3) to solve the NP-hard NAO problem, we use the MGAC algorithm, where the storage and query populations collaboratively search for solutions based on four operations. Extensive experiments indicate that compared with existing work, BSSN can reduce the average query cost to 67% with its adjustable storage cost, ensuring a balanced data storage among users.
A. Jabbari, Gowri Ramachandran, Sidra Malik, Raja Jurdak
In the current digital landscape, supply chains have transformed into complex networks driven by the Internet of Things (IoT), necessitating enhanced data sharing and processing capabilities to ensure traceability and transparency. Leveraging Blockchain technology in IoT applications advances reliability and transparency in near-real-time insight extraction processes. However, it raises significant concerns regarding data privacy. Existing privacy-preserving approaches often rely on Smart Contracts for automation and Zero Knowledge Proofs (ZKP) for privacy. However, apart from being inflexible in adopting system changes while effectively protecting data confidentiality, these approaches introduce significant computational expenses and overheads that make them impractical for dynamic supply chain environments. To address these challenges, we propose ZK-DPPS, a framework that ensures zero-knowledge communications without the need for traditional ZKPs. In ZK-DPPS, privacy is preserved through a combination of Fully Homomorphic Encryption (FHE) for computations and Secure Multi-Party Computations (SMPC) for key reconstruction. To ensure that the raw data remains private throughout the entire process, we use FHE to execute computations directly on encrypted data. The "zero-knowledge" aspect of ZK-DPPS refers to the system's ability to process and share data insights without exposing sensitive information, thus offering a practical and efficient alternative to ZKP-based methods. We demonstrate the efficacy of ZK-DPPS through a simulated supply chain scenario, showcasing its ability to tackle the dual challenges of privacy preservation and computational trust in decentralised environments.
Tarek Zaarour, Ahmed Khalid, Preeja Pradeep, Ahmed H. Zahran
Knowledge graphs have proven vital for efficient data management, enhanced search capabilities, and improved decision-making in various information technology domains. However, constructing reliable knowledge graphs in decentralized ecosystems, with distributed autonomous actors, poses significant challenges related to asynchronous transmission, out-of-order knowledge-sharing, device heterogeneity, and trust issues. These challenges are also present in resource orchestration within multi-cloud edge ecosystems where multiple stakeholders must collaborate and share information to enable next-gen smart applications. In this paper, we propose a novel system design that utilizes Distributed Ledger Technology to build knowledge graphs. This approach ensures consistent and trustworthy knowledge sharing among orchestrators in a cloud-edge continuum. Our solution accommodates diverse requirements of both cloud and edge servers, allowing clients to construct complete historic graphs or build filtered sub-graphs. We deploy our solution in a multi-cloud edge environment and construct knowledge graphs representing the system state, including clusters, servers, microservices, and various resources. We validate the feasibility and performance of our solution through a real-world deployment and experiments in a smart shopping use case. Results demonstrate that the proposed solution achieves the claimed benefits with minimal or acceptable delays in comparison to traditional event streaming services.
The IoT devices are growing rapidly, which has led to an exponential rise in the amount of data those devices are producing. There is a pressing need for effective and secure data transfer techniques from IoT devices to the cloud as the amount and complexity of IoT data keep growing. This paper introduces a revolutionary idea that combines fog computing, and blockchain technology and also uses a hybrid consensus mechanism to ensure secured data transmission between IoT and Cloud. Fog computing, a branch of the cloud provides local processing, storage, and communication capabilities. By leveraging fog computing, data transmission latency is reduced, and network congestion is minimized, resulting in improved performance and responsiveness. Blockchain technology is incorporated into the system to guarantee the security of IoT data while it is being transmitted. Blockchain, with its decentralized and immutable nature, provides a transparent and tamper-proof ledger for recording data transactions. Each data transaction from an IoT device is encrypted, timestamped, and appended to the blockchain, creating an auditable and trustworthy record of data transmission. Additionally, a hybrid consensus mechanism using Delegated Proof of Stake and Practical Byzantine Fault Tolerance is employed to validate the transaction. This concept addresses the challenges of data security, latency, and integrity in IoT applications, enabling the development of scalable and trustworthy IoT systems across various industries. The efficiency of the proposed system is validated by evaluating performance metrics such as latency, accuracy, precision, recall, F-score, and verification time, and comparing the results with those of existing approaches. The implemented systems, tailored for the healthcare domain, exhibit security measures and an impressive 18% reduction in latency, while enhancing the accuracy by 15% when compared to the conventional approach, as per the experimental results.
As a new paradigm in the Web3 era, Decentralized Autonomous Organizations (DAOs) not only embodie the spirit of decentralization and collective governance, but also are the forefront of promoting community-led innovation. However, DAOs face challenges in sustainable growth and scalability in community governance, which are closely related to the income distribution model and member participation. Therefore, the Artificial systems, Computational experiments, Parallel execution (ACP) approach is applied to optimize the contribution evaluation and incentive feedback of member behavior through parallel governance and decision-making methods, so as to improve the intelligence of the DAO incentive mechanism. On this basis, the long short-term memory network (LSTM) and combinatorial game theory are used to conduct experimental verification on information sharing within the community. The experimental results show that our proposed method can not only achieve a high degree of information sharing in the community faster than other methods, but also has the ability of autonomous dynamic adjustment. It is of great significance and value to the community governance research and scenario implementation of DAOs.
Sana Naz, Mohsin Javaid Siddiqui, Scott Uk-Jin Lee
To be a stakeholder/validator/token holder is not so difficult in the Proof of Stake (POS)-based blockchain networks; that is why the number of validators is large in these networks. These validators play an essential part in the block creation process in the PoS-based blockchain network. Due to the large validators, the block creation time and communication message broadcasting overhead get increased in the network. Many consensus algorithms use different techniques to reduce the number of validators, such as Delegated Proof of Stake (DPoS) consensus algorithms, which select the set of delegators via stake transactions for the block creation process. In this paper, we propose S&SEM, a secure and speed-up election process to select the ‘z’ number of validators/delegators. The presented election process is based on a traditional voting style with multiple numbers of rounds. The presented election mechanism reduces the possibility of malicious activity in the voting process by introducing a special vote message and a round that checks duplicate votes. We did horizontal scaling in the network to speed up the election process. We designed an improved incentive mechanism for the fairness of the election process. The designed reward and penalty procedure controls the nodes’ behaviors in the network. We simulate the S&SEM, and the result shows that the presented election process is faster and more secure to select delegators than the existing process used by DPOS.
Due to advanced technological development, the application of blockchain is not just for financial services. It has inherent potential uses in various industries, including healthcare, supply chain, industrial goods, E-commerce, and others. Any group or individuals agree in a blockchain network to make transactions on the basis of a consensus mechanism. The critical role of the consensus mechanism is that it improves the efficiency and security of the blockchain system. Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Proof of Authority (PoA), Practical Byzantine Fault Tolerance (PBFT) and some other recent proof of concepts such as Proof of Burn (PoB), Proof of Learning, Proof of Luck (PoLu), etc. are the various existing consensus algorithms. However, there are problems with these algorithms including centralization risk, security, efficiency, and waste of resources. Proof of Work (PoW) consumes high computational power to solve the cryptographic puzzle and makes the system more costly. Proof of Stake (PoS) encourages high-stakes node selected as a validator to propose and validate the new block in the blockchain, which promotes inequality and monopolies. The paper introduces Round Robin Proof of Stake (RRPoS) based on the existing Proof of Stake (PoS) that involves block creation in a round-robin fashion to increase the chances of high- to low-stakes validators for the validation of the proposed block. The proposed consensus algorithm is able to ascertain the existence of malicious validator nodes and eliminate them to enhance system security. The performance analysis demonstrates that RRPoS is better and more efficient than PoW and PoS.
Zero-knowledge layer 2 protocols emerge as a compelling approach to overcoming blockchain scalability issues by processing transactions through the transaction finalization process. During this process, transactions are efficiently processed off the main chain. Besides, both the transaction data and the zero-knowledge proofs of transaction executions are reserved on the main chain, ensuring the availability of transaction data as well as the correctness and verifiability of transaction executions. Hence, any bugs that cause the transaction finalization failure are crucial, as they impair the usability of these protocols and the scalability of blockchains. In this work, we conduct the first systematic study on finalization failure bugs in zero-knowledge layer 2 protocols, and define two kinds of such bugs. Besides, we design fAmulet, the first tool to detect finalization failure bugs in Polygon zkRollup, a prominent zero-knowledge layer 2 protocol, by leveraging fuzzing testing. To trigger finalization failure bugs effectively, we introduce a finalization behavior model to guide our transaction fuzzer to generate and mutate transactions for inducing diverse behaviors across each component (e.g., Sequencer) in the finalization process. Moreover, we define bug oracles according to the distinct bug definitions to accurately detect bugs. Through our evaluation, fAmulet can uncover twelve zero-day finalization failure bugs in Polygon zkRollup, and cover at least 20.8% more branches than baselines. Furthermore, through our preliminary study, fAmulet uncovers a zero-day finalization failure bug in Scroll zkRollup, highlighting the generality of fAmulet to be applied to other zero-knowledge layer 2 protocols. At the time of writing, all our uncovered bugs have been confirmed and fixed by Polygon zkRollup and Scroll zkRollup teams.
Event-driven decentralized systems trigger off-chain functions upon consuming events emitted by decentralized applications deployed on blockchains. However, ensuring dependable, cost-efficient, and flexible event consumption is challenging due to the consensus mechanisms inherent in blockchains. Meanwhile, the need for dependable event consumption has become increasingly critical with the rise of decentralized finance. In this paper, we propose Emer, a reputation-based event consumer that adopts a distributed architecture and leverages a novel asynchronous reputation mechanism to mitigate the risk of missing or inconsistent events. Besides, Emer reduces the communication cost through reputation-based optimization and proves scalable as an infrastructure. Additionally, Emer offers trust condition customization to enable prioritizing sensitivity of timeliness or correctness for specific use cases. Furthermore, we support our claims with simulation analysis and demonstrate its applicability through a real-world scenario in decentralized finance.
Avi Mizrahi, Noam Koren, Ori Rottenstreich, Yuval Cassuto
Merkle trees play a crucial role in blockchain networks in organizing network state. They allow proving a particular value of an entry in the state to a node that maintains only the root of the Merkle trees, a hash-based signature computed over the data in a hierarchical manner. Verification of particular state entries is crucial in reaching a consensus on the execution of a block where state information is required in the processing of its transactions. For instance, a payment transaction should be based on the balance of the two involved accounts. The proof length affects the network communication and is typically logarithmic in the state size. In this paper, we take advantage of typical transaction characteristics for better organizing Merkle trees to improve blockchain network performance. We focus on the common transaction processing where Merkle proofs are jointly provided for multiple accounts. We first provide lower bounds for the communication cost that are based on the distribution of accounts involved in the transactions. We then describe algorithms that consider traffic patterns for significantly reducing it. The algorithms are inspired by various coding methods such as Huffman coding, partition and weight balancing. We also generalize our approach towards the encoding of smart contract transactions that involve an arbitrary number of accounts. Likewise, we rely on real blockchain data to show the savings allowed by our approach. The experimental evaluation is based on transactions from the Ethereum network and demonstrates cost reduction for both payment transactions and smart contract transactions.
Andrey L. Bulgakov, Anna V. Aleshina, Sergey D. Smirnov, Alexey D. Demidov · 6 authors
This article addresses the issues of scalability and security in blockchain networks, with a focus on sharding algorithms and decentralized data storage. Key challenges include the low throughput and high transaction latency in public networks such as Bitcoin and Ethereum. Sharding is examined as a method to enhance performance through data distribution, but it raises concerns regarding node management and reliability. Sharding schemes, such as Elastico, OmniLedger, Pyramid, RepChain, and SSchain, are analyzed, each presenting its own advantages and drawbacks. Alternative architectures like Directed Acyclic Graphs (DAGs) demonstrate potential for improved scalability but require further refinement to ensure decentralization and security. Protocols such as Brokerchain, Meepo, AHL, Benzene, and CycLedger offer unique approaches to addressing performance and transaction consistency issues. This article emphasizes the need for a comprehensive approach, including dynamic sharding, multi-level consensus, and inter-shard coordination. Additionally, a conceptual model is proposed that incorporates the sharding of transactions, states, and networks, which enables greater scalability and efficiency.
Securing interoperable and sovereign data exchange in the Industrial Internet of Things (IIoT) for machine data exploitation by third parties presents a significant challenge. This work addresses this by integrating IOTA Distributed Ledger Technology (DLT) with the International Data Spaces (IDS) Reference Architecture Model (RAM), creating a decentralized data space optimized for IIoT ecosystems. This research demonstrates the practical implementation of core IDS architectural concepts within the IOTA framework, overcoming theoretical DLT limitations and showcasing IOTA’s capability to enhance data sovereignty and interoperability in the IIoT, moving beyond traditional blockchains, which are constrained by scalability and efficiency issues. It sets the stage for future evaluations and broader applicability studies, paving the way for advancements in secure, sovereign, interoperable, and efficient data management.
George Danezis, Lefteris Kokoris-Kogias, Alberto Sonnino, Mingwei Tian
Obelia improves upon structured DAG-based consensus protocols used in proof-of-stake systems, allowing them to effectively scale to accommodate hundreds of validators. Obelia implements a two-tier validator system. A core group of high-stake validators that propose blocks as in current protocols and a larger group of lower-stake auxiliary validators that occasionally author blocks. Obelia incentivizes auxiliary validators to assist recovering core validators and integrates seamlessly with existing protocols. We show that Obelia does not introduce visible overhead compared to the original protocol, even when scaling to hundreds of validators, or when a large number of auxiliary validators are unreliable.
Sushanth Sreenivasamurthy Manakhari, Ajinkya P. Jadhav, Twinkle Paraye, Anurag Gate
In the evolving landscape of digital data management, blockchain technology emerges as a transformative force, particularly through its implementation within Ethereum. This paper delves into the role of Ethereum in enhancing data accessibility across distributed file systems. By leveraging the power of smart contracts, Ethereum introduces a level of automation and reliability previously unattainable in traditional systems. The integration of Ethereum with decentralized storage solutions like the Inter Planetary File System (IPFS) facilitates not only more transparent and efficient access to data but also augments security and trustworthiness. We explore the technical mechanisms by which Ethereum smart contracts automate data operations and how these interactions enhance system performance and user experience. Furthermore, the paper discusses the potential challenges and solutions associated with integrating blockchain technologies into existing data systems, thereby providing insights into their future implications for the global data economy. The findings indicate that Ethereum substantially increases accessibility, reduces operational bottlenecks, and could pave the way for new data governance models that are secure, efficient, and scalable.
Blockchain store states in Log-Structured Merge (LSM) tree-based database. Due to blockchain traceability, the growing ancient states are inevitably stored in the databases. Unfortunately, by default, this process mixescurrentandancientstates in the data layout, increasing unnecessary disk I/O access and slowing transaction execution. This paper proposes MoltDB, a scalable LSM-based database for efficient transaction execution through a novel idea ofancient state segregation, i.e., to segregate current and ancient states in the data layout. However, the frequently generated and uncertainly accessed characteristics of ancient states make the segregation challenging. Thus, we develop an “extract-compact” mechanism to batch extraction process for frequently generated ancient states and the LSM compaction process to relieve additional disk I/O overhead. Moreover, we design an adaptive LSM-based storage for the uncertainly accessed ancient states extracted for on-demand access. We implement MoltDB as a database engine compatible with many mainstream blockchains and integrate it into Ethereum for evaluation. Experimental results show that MoltDB achieves 1.3 × transaction throughput and 30% disk I/O latency savings over the state-of-the-art works.
Ethereum has adopted a rollup-centric roadmap to scale its network while preserving both security and decentralization. Rollups are layer 2 scaling solutions that process transactions off-chain while posting summarized data on-chain to maintain security and reduce costs. Posting data on-chain remains expensive, which led to the introduction of blobs via EIP-4844 that offer a cost-effective solution for data availability (DA). Although blobs significantly reduce DA costs compared to traditional calldata, many cost-sensitive small rollups struggle to fully utilize the fixed blob capacity. Blob sharing, which allows multiple rollups to collaboratively utilize a single blob, has been proposed as a solution to these challenges. In this paper, we empirically analyze nearly six months of data to assess the effectiveness of blob sharing. Our simulation results demonstrate that blob sharing can lower overall costs by approximately $\mathbf{8 0 \%}$ to 99%. These findings imply that the benefits of blob sharing are even greater than initially expected, providing strong incentives for both small and big rollups to actively collaborate in its adoption.
Blockchain technology has recently received a great deal of attention from industry and academia due to its apparent benefits. From the initial foundation based on cryptocurrency to the development of smart contracts, Blockchain technology continues to promise significant business benefits for various industry sectors. Notwithstanding its known benefits, and despite having some protective measures and security features, this technology still faces significant security challenges within its different abstract layers. This work focuses on the critical cybersecurity threats and vulnerabilities inherent to the different layers of the Blockchain architecture, with a view to mitigate against the associated risks. From the perspective of architectural layering, each layer of the Blockchain has its own corresponding security issues. In this work, a seven-layer architecture is used, whereby the various components of each layer are set out, highlighting the related security risks and corresponding countermeasures. A taxonomy is then developed, that establishes the inter-relationships between the vulnerabilities and attacks in a smart contract. A specific emphasis is placed on the issues caused by centralisation within smart contracts, whereby a “one-owner” controls access, thus threatening the very decentralised nature that Blockchain is based upon. Smart contracts with centralised ownership pose major security issues and act as a single point of failure, allowing single individuals, or teams, to have complete control over the Blockchain network. To mitigate against the risks associated with centralised control, decentralised autonomous organisations (DAOs) promote a decentralised decision-making process whereby the power of decision-making is distributed and therefore preventing smart contract ownership monopoly. The main contribution of this thesis is the development of a novel automated decentralised application, “Genuine DAO”, that promises to reduce security risks and improve the performance of Blockchain networks. “Genuine DAO” achieves the reduction in security risks by enforcing automated rules that are encoded in smart contracts thus reinforcing the community-based governance and minimising the threats inherent to centralisation, which can be caused by smart contracts’ owners/developers. Additionally, “Genuine DAO” strengthens the security of the network by guarding against the threats caused by Frontrunning attacks. Three further contributions emanate from this work. The first one is an improvement of the overall performance of the Blockchain network, through gas optimisation, cost reduction, and network throughput. This is achieved by using a Polygon layer 2 scaling solution built on the Ethereum network. The second one is the development of a general taxonomy that compiles the different vulnerabilities, the types of attacks, and the related countermeasures within each of the seven layers of the Blockchain. The third one stems from a deep dive into one layer of the Blockchain namely, the Contract Layer. A model application is developed depicting, in detail, the security risks within the Contract Layer, while enlisting the best practices and tools to adopt in order to mitigate against these risks. The understanding gained from delving into the details of security risks within the Contract Layer reinforced the need for developing countermeasures to alleviate the security risks and vulnerabilities inherent to one-owner control in smart contracts, which ultimately led to the main contribution of this work: Genuine DAO.
This paper surveys innovative protocols that enhance the programming functionality of the Bitcoin blockchain, a key part of the "Bitcoin Ecosystem." Bitcoin utilizes the Unspent Transaction Output (UTXO) model and a stack-based script language for efficient peer-to-peer payments, but it faces limitations in programming capability and throughput. The 2021 Taproot upgrade introduced the Schnorr signature algorithm and P2TR transaction type, significantly improving Bitcoin's privacy and programming capabilities. This upgrade has led to the development of protocols like Ordinals, Atomicals, and BitVM, which enhance Bitcoin's programming functionality and enrich its ecosystem. We explore the technical aspects of the Taproot upgrade and examine Bitcoin Layer 1 protocols that leverage Taproot's features to program non-fungible tokens (NFTs) into transactions, including Ordinals and Atomicals, along with the fungible token standards BRC-20 and ARC-20. Additionally, we categorize certain Bitcoin ecosystem protocols as Layer 2 solutions similar to Ethereum's, analyzing their impact on Bitcoin's performance. By analyzing data from the Bitcoin blockchain, we gather metrics on block capacity, miner fees, and the growth of Taproot transactions. Our findings confirm the positive effects of these protocols on Bitcoin's mainnet, bridging gaps in the literature regarding Bitcoin's programming capabilities and ecosystem protocols and providing valuable insights for practitioners and researchers.
The coming of Distributed Ledger Technologies (DLTs) and blockchain, in the outlook of new technologiesduring the last decade, entailed a disruption in several spheres, such as the economy field and the identificationof entities and individuals, due to the great possibilities provided to them. Although their first application were cryptocurrencies, DLTs have been assimilated in other settings, suchas their introduction in enterprise-grade systems. The technology itself has been evolving, facing some of itslimitations and accommodating the extension of its potential use cases. SIGMA is conceived to broaden knowledge in two of the most novel aspects of DLTs: Layer-2 networksand decentralized self-sovereign identity. Its goal is to increase expertise in these areas to facilitate theirintegration and exploitation by our surrounding companies.
Sharding is a promising technique for increasing a blockchain system’s throughput by enabling parallel transaction processing. The main challenge of state sharding lies in ensuring the atomicity verification of cross-sharding transactions, which results in double communication overhead and increases the transaction’s confirmation time. Previous research has primarily focused on developing cross-shard protocols for the fast and reliable validation of transactions involving multiple shards. These studies typically generate a large number of cross-shard transactions because they primarily use simple address mapping for state sharding, that is, the prefix/suffix of the account address. In this article, we propose a state sharding scheme via density-based partitioning of the account-transaction graph. In order to reduce cross-shard transactions, the scheme groups correlated accounts into the same shard by generating the densest subgraphs, as the graph density describes the correlation among accounts, i.e., how often transactions have occurred among accounts. We formulate the graph density-based state sharding problem, with the goal of maximizing the average density across all shards under the workload constraint. We prove the NP-completeness of the problem. To reduce the complexity of finding the densest subgraph, we propose the pruning-based algorithm that reduces the search space by pre-pruning some invalid edges based on the concept of core number. We also extend the linear deterministic greedy algorithm and PageRank algorithm to handle new transactions in the dynamic scenario. We conduct extensive experiments using real transaction data from Ethereum. The experimental results demonstrate a strong correlation between the shard density and the number of cross-shard transactions, and the pruning-based algorithm can reduce the running time by an order of magnitude.
In the context of Web3.0, the rapid pace of technological innovation has resulted in the emergence of novel computing paradigms. This is a natural result of the exponential growth of technology. Within the ecosystem of Web 3.0, the major emphasis of this research is on rethinking architecture and protocols in order to be ready for the revolution in edge computing. To improve the efficacy, security, and scalability of data processing and transmission, DECABI provides a dynamic edge computing architecture with integrated blockchain characteristics. The study accelerates data transfers without compromising their integrity by using dynamic resource allocation, encrypted data transmission, and a consensus protocol for edge computing (CPEC). The suggested method has much better performance than the alternatives. This exemplifies how the method has the ability to completely alter the way edge computing is carried out in the Web3.0 era. Countless simulations and exhaustive studies confirmed the existence of this option. This study lays the groundwork for a more stable and secure Web3.0 ecosystem, and it also makes a substantial contribution to the current conversation about the future of computer architectures and protocols.
Distributed Ledger Technologies (DLTs) promise decentralization, transparency, and security, yet the reality often falls short due to fundamental governance flaws. Poorly designed governance frameworks leave these systems vulnerable to coercion, vote-buying, centralization of power, and malicious protocol exploits-threats that undermine the very principles of fairness and equity these technologies seek to uphold. This article surveys the state of DLT governance, identifies critical vulnerabilities, and highlights the absence of universally accepted best practices for good governance. By bridging insights from cryptography, social choice theory, and e-voting systems, we not only present a comprehensive taxonomy of governance properties essential for safeguarding DLTs but also point to technical solutions that can deliver these properties in practice. This work underscores the urgent need for robust, transparent, and enforceable governance mechanisms. Ensuring good governance is not merely a technical necessity but a societal imperative to protect the public interest, maintain trust, and realize the transformative potential of DLTs for social good.