Merkle Search Trees: Efficient State-Based CRDTs in Open Networks

Auvolat, Alex; Taı̈ani, François

doi:10.1109/srds47363.2019.00032

Cited by 14 publications

(4 citation statements)

References 24 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After The layer stack is appended, the unique Merkle root is created [95], [96]. Cmndbdoes did not merely utilize a "block + chain" chain information structure but, instead, stores the data gathered by all blocks in a Merkle Tree form [97]- [99] organized by a hash pointer. The block hash pointer is likely to change once the information of any block is altered, guaranteeing that the information cannot tamper.…”

Section: Blockchain-based Wireless Sensor Network Architecture For Detection Of Malicious Nodesmentioning

confidence: 99%

Blockchain-Based Wireless Sensor Networks for Malicious Node Detection: A Survey

et al. 2021

View full text Add to dashboard Cite

Wireless Sensor Networks (WSNs) are broadly applied for various applications in tracking and surveillance due to their ease of use and other distinctive characteristics compelled by real-time cooperation among the sensor nodes. In WSNs, security is becoming a critical issue, as the techniques for malicious node detection adopt a one-time, centralized decision-making approach. With this paradigm, errors are difficult to avoid, and reproducibility and traceability are challenging. Hence, malicious node discovery technologies in conventional WSNs cannot assure traceability and fairness of the detection method. Herein, this paper discusses an in-depth survey of a blockchain-based approach for malicious node detection, an exhaustive examination of the integration of blockchain techniques with WSNs (BWSN), and insights into this novel concept. This survey discusses the architecture, sector-wise applications, and uses of BWSN. Moreover, this survey describes malicious node detection based on BWSN in two parts: 1) the BWSN architecture for detecting the malicious nodes and 2) the smart contract aspects in malicious node detection. Next, this survey explains the contributions of blockchain for WSN data management, which involves online information aggregation and may include auditing, event logs, and storage for information analysis and offline query processing. This survey first presents the conventional WSN solutions then the blockchain-based WSN solutions for data management. Additionally, this survey discusses the contributions of blockchain for WSN security management. It first examines the centralized WSN models for security problems, followed by a discussion of the blockchain-based WSN solutions for security management, such as offering access control, preserving information integrity, guaranteeing privacy, and ensuring WSNs' node longevity.

show abstract

Section: Blockchain-based Wireless Sensor Network Architecture For Detection Of Malicious Nodesmentioning

confidence: 99%

Blockchain-Based Wireless Sensor Networks for Malicious Node Detection: A Survey

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Besides those commercial products, several research projects have emerged. Merkle Search Trees (MSF) [13] implement a key-value store like Dynamo using a state-based CRDT and a Merkle-tree. It builds the Merkle-tree on top of the flat data structure, while OWebSync will make use of the tree-like structure of the data to build the Merkle-tree.…”

Section: Related Workmentioning

confidence: 99%

“…in Riak [12]. Merkle Search Trees [13] are proposed as a solution to the high bandwidth usage. It uses Merkle-trees [14] to replicate a basic key-value store like in Dynamo [15].…”

Section: Introductionmentioning

confidence: 99%

OWebSync: Seamless Synchronization of Distributed Web Clients

Jannes

Lagaisse

Joosen

2021

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

Many enterprise software services are adopting a fully web-based architecture for both internal line-of-business applications and for online customer-facing applications. Although wireless connections are becoming more ubiquitous and faster, mobile employees and customers are often offline due to expected or unexpected network disruptions. Nevertheless, continuous operation of the software is expected. This paper presents OWebSync: a web-based middleware for data synchronization in interactive groupware with fast resynchronization of offline clients and continuous, interactive synchronization of online clients. To automatically resolve conflicts, OWebSync implements a fine-grained data synchronization model and leverages state-based Conflict-free Replicated Data Types. This middleware uses Merkle-trees embedded in the tree-structured data and virtual Merkle-tree levels to achieve the required interactive performance. Our comparative evaluation with available operation-based and delta-state-based middleware solutions shows that OWebSync is especially better in operating in and recovering from offline settings and network disruptions. In addition, OWebSync scales more efficiently over time, as it does not store version vectors or other meta-data for all past clients.

show abstract

“…In this case, the last hash which is the odd one out, will be cloned and hashed with itself. So here, the H(9,10) is cloned to get the hash H(9,10,9,10). Using this logic, the process is repeated until a single element remains.…”

mentioning

confidence: 99%

Voter Verification in an Election Using Merkle Tree

John

Singh

Sabarinathan

2023

IoT, Cloud and Data Science

View full text Add to dashboard Cite

In this paper we propose a new way to verify eligible voters in a blockchain election. Usually verifying a voter in a list of eligible voters is done using linear search-based algorithms which runs with a worst-case time complexity of O(N) or O(kN). Our system shall use a Merkle tree to implement a search algorithm which runs with a worst-case time complexity of O(Log (N)). Under normal circumstances, when there are 1,000,000 eligible voters, the server would have to scan through 1,000,000 voter address hashes in order to verify a voter. In our system, only 20 hashes will be checked in order to verify a voter. The number of hashes required to calculate the number of hashes needed to process can be found out by getting the logarithm value of the number of hashes, to the base 2 since our Merkle tree has a branching factor of 2. A logarithmic algorithm will exponentially reduce the time and make the eligibility verification process more efficient. Since we are hashing the addresses of the voters before creating the tree, the privacy of the voters shall also be maintained. Instead of using a centralized database for handling the vote requests, we shall use a blockchain database in order to make this system decentralized. Making the system decentralized will make it almost impossible to tamper with the results of the election as once the election is deployed, even the owner of the election that is, the address that deploys the smart contract cannot tamper with the results or the functions in the deployed smart contract.

show abstract

Merkle Search Trees: Efficient State-Based CRDTs in Open Networks

Cited by 14 publications

References 24 publications

Blockchain-Based Wireless Sensor Networks for Malicious Node Detection: A Survey

Blockchain-Based Wireless Sensor Networks for Malicious Node Detection: A Survey

OWebSync: Seamless Synchronization of Distributed Web Clients

Voter Verification in an Election Using Merkle Tree

Contact Info

Product

Resources

About