Distributed Ledger Technologies Overview

Updated 7 August 2025

Distributed Ledger Technologies (DLTs) are decentralized systems featuring cryptographic security, immutability, and diverse data models like blockchain and DAGs.
They employ various consensus mechanisms (e.g., PoW, BFT, DAG-based) to balance scalability, efficiency, and security across digital currency, IoT, and industrial applications.
DLTs integrate multi-tier architectures and governance models, enabling secure data storage, interoperability, and compliance in complex decentralized environments.

Distributed Ledger Technologies (DLTs) are decentralized systems that enable a network of independent and potentially mutually distrustful agents to collaboratively maintain, update, and audit a tamper-evident record of state transitions. While DLTs are foundational to digital currencies, their applications now span cyber-physical systems, IoT, data spaces, industrial manufacturing, decentralized identity, and secure AI-driven environments. Rather than a single architecture, DLTs encompass a spectrum of data models (e.g., blockchains, directed acyclic graphs, chains of antichains), consensus mechanisms, and access models. This article synthesizes recent research to provide a comprehensive technical survey of DLT design principles, consensus, architectures, protocol dynamics, integration use cases (including IoT and CPS), semantic data storage, operational efficiency, and the crucial issue of governance.

1. Foundational Principles and Data Structures

DLTs are characterized by an append-only, cryptographically secure, and replicated state machine (Pop et al., 2020). The canonical example, blockchain, consists of time-ordered blocks connected by hash pointers. The block hash construction is:

$H_i = \mathrm{hash}(H_{i-1} \Vert \text{data}_i)$

Blocks typically encapsulate one or more transactions and enforce immutability by chaining.

Emergent DLT architectures generalize this model:

Directed Acyclic Graphs (DAGs) (e.g., IOTA's Tangle): Each transaction approves multiple past transactions, forming a DAG rather than a linear chain. This allows high-parallelism and scalable, fee-less validation (Ferraro et al., 2018, Akhtar, 2023).
Chain of Antichains (Boxchain): Transactions are grouped into mutually incomparable antichains ("boxes") connected to yield total order and recursive confirmation (Lee et al., 2018).
Hashgraph: Utilizes "gossip-about-gossip" and virtual voting to reach asynchronous BFT consensus efficiently in a DAG (Akhtar, 2023).

A general DLT system comprises:

Distributed P2P communication
Replicated storage, often with redundancy and selective disclosure (Pop et al., 2020)
Cryptographic mechanisms (signatures, hashes, Merkle proofs)
Clearly defined access, consensus, and governance models

2. Consensus Algorithms and Security

DLT security and liveness fundamentally depend on the consensus protocol. Exhaustive classifications reveal over thirty consensus schemes, grouped by their operational principle (Alkhodair et al., 2023):

Category	Examples	Fault Tolerance	Typical Applications
Proof-based	PoW (Bitcoin), PoS, DPoS, NPoS, PoET, PoB	up to 51% (PoW), 33% (BFT)	Cryptocurrency, Smart Contracts
Voting/BFT	PBFT, IBFT, FBA, Honey Badger, Tendermint	up to one-third Byzantine	Permissioned, enterprise
DAG-based	IOTA Tangle, Hashgraph, Avalanche	aBFT/Probabilistic	IoT, High-throughput settings
Weight-based	PoA, PoI, PoR, PoC	Application-dependent	Enterprise, IoT, Storage

Performance and security depend on trade-offs among throughput, latency, decentralization, energy efficiency, and attack resilience (Alkhodair et al., 2023, Ferraro et al., 2018).

Blockchain/PoW: Robust against Sybil and double-spend with strong probabilistic finality, but high energy and overhead.
DAG: Enables parallel transaction inclusion, low/no fees (critical for IoT/cyber-physical), with lower confirmation times, but introduces challenges in conflict resolution (e.g., stability against double-spending branches analyzed via DDE models (Ferraro et al., 2018)).
BFT: Guarantees instant finality but at increased communication complexity and reduced scalability.

Consensus protocols are evaluated across attributes such as hardware requirements, pre-trust, scalability, cost, latency, and interoperability, facilitating protocol selection for domain-specific use cases (Alkhodair et al., 2023).

3. Systems Architecture and Design Tiers

DLT-based decentralized applications are best conceptualized as a multi-tier architecture (Pop et al., 2020):

Protocol and Network Tier: Core on-chain mechanisms, asset/tokens, data structures, consensus, peer-to-peer state replication.
Scaling Tier: Off-chain methods (e.g., sharding, sidechains, payment channels, off-chain file systems like IPFS) to ameliorate limitations of storage, throughput, and computational scalability.
Federated Tier: Inter-ledger and interoperability solutions, using mechanisms such as atomic swaps, interledger protocols, and sidechain pegs, allow for asset and data movement between otherwise isolated DLTs.

Privacy is addressed at multiple layers via mechanisms such as zero-knowledge proofs, coin mixers, ring signatures, and selective disclosure (Pop et al., 2020, Charalampidis et al., 2020).

4. Modeling, Dynamics, and Protocol Analysis

Dynamic modeling of novel DLTs enables formal analysis of system stability, security, and compliance:

DAG Fluid Models: The growth of DAG-based DLTs (e.g., Tangle) is captured via delay differential equations:

$\frac{dx_i}{dt}(t) = a(t - h)p_i(t - h) - a(t)u_i(t) \ \frac{dl_i}{dt}(t) = a(t - h)p_i(t - h) - a(t - h)u_i(t - h)$

with

$u_i(t) = \frac{2 x_i(t) l_i(t)}{\sum_{j=1}^d l_j(t)^2}$

where variables describe tip selection, approvals, conflicts (branches), and inherent consensus resolution. Stability is only achieved on a unicursal branch (k = 1); multiple branches result in locally unstable steady states (Ferraro et al., 2018).

Access Control without PoW: In DAG DLTs for IoT, transaction issue is regulated by networking-inspired algorithms (Deficit Round Robin, distributed AIMD rate control), using node "reputation" rather than hash power for Sybil resistance (Cullen et al., 2020).
DLT-Driven Control of Social Compliance: DLTs can orchestrate shared resource access via token-based bonds, automated via dynamic deposit pricing and feedback control—where compliance enforcement becomes a control theory problem (Ferraro et al., 2018).

5. Integration in IoT, Industrial, and Cyber-Physical Systems

DLTs are increasingly foundational in IoT, CPS, and edge/industrial automation:

Suitability for IoT: DAG-based, feeless DLTs (e.g., IOTA's Tangle) enable high-frequency, low-latency microtransactions by lightweight devices (Ferraro et al., 2018, Danzi et al., 2019, Zichichi et al., 2020, Cullen et al., 2020, Nguyen et al., 2022). Trade-offs include latency under load and synchronization protocol complexity in constrained wireless environments.
Industrial Manufacturing: DLTs deliver decentralized trust, machine usage traceability, auditability, and automated billing in shared or flexible manufacturing-as-a-service models. Private DLTs (e.g., Hyperledger Fabric, IOTA, Solana) outperform public ones in resource-constrained settings, minimizing computational overhead and transaction costs (Nguyen et al., 2022).
Edge and Resource Offloading: With permissioned DLTs (e.g., Hyperledger Fabric), smart contracts orchestrate edge compute offloading by integrating live network performance metrics (latency, CPU) directly into offloading decisions (Hernández et al., 2022).
Usage Control and Privacy: Embedding usage control nodes directly into the DLT network, particularly using IoT-suitable DAG platforms (e.g., IOTA), reduces privacy enforcement latency by up to 90% (Denis et al., 2023).

6. Data Models, Semantic Interoperability, and Storage

Deploying DLTs for semantic data/interoperability in data spaces imposes constraints on storage architecture, update, and querying:

Evaluation Criterion	Public DLT (Ethereum)	Private DLT (Fabric)	Hybrid DLT
Write latency (per triple)	3 ms (direct), 38 ms (contract)	~2 ms (batch inserts)	~2 ms (private), + public hash
Disk usage (1.5M triples)	Highest (contract persistent)	Lower (batch, efficient)	Similar to private
Update/query semantics	Weak in direct storage; better with contract	Transactional, efficient	Efficient, with public audit
Auditability	Highest	Controlled/Private	Balanced (audit via hash)

Private DLTs enable transactional batching, flexible data models, and efficient querying but lack public auditability. Hybrid DLTs anchor cryptographic summaries (e.g., SHA-256 hashes of batched RDF data) to a public chain, balancing operational control and external verifiability (Cano-Benito et al., 3 Jul 2025).

Semantic data storage in DLTs remains constrained by transaction costs, state management, and complexities in update semantics (e.g., insert/delete modeled as new string entries in public direct storage).

7. Governance, Security Properties, and Societal Implications

Governance design is a determinant of DLT system robustness and societal legitimacy. Weak governance amplifies vulnerabilities such as vote-buying, coercion, power concentration, and protocol backdoors (Kharman et al., 24 Sep 2024). Core taxonomy:

Participation: Equal suffrage, non-purchasable voting power
Incentives: To vote, propose, distribute liability, with binding outcomes
Social Choice: Pareto efficiency, non-dictatorship, strategy-proofness
Security: Ballot secrecy, verifiability (via ZKPs, digital signatures), coercion/censorship resistance

Technical solutions are drawn from cryptography (ZKPs, distributed encryption, receipt-freeness), identity protocols (Proof of Humanity), and e-voting design. Quadratic voting is proposed to make additional votes quadratically more costly:

$\mathrm{Cost}(v) = v^2$

This discourages vote concentration and reduces susceptibility to bribery. However, inherent limitations from Arrow’s and Gibbard–Satterthwaite’s theorems require engineering acceptably robust trade-offs.

The interplay between transparent/censorship-resistant governance and enforceable, privacy-preserving, and liveness properties is crucial for maintaining public trust and facilitating DLT adoption in domains with strong requirements for fairness, compliance, and inclusion (Kharman et al., 24 Sep 2024).

References

All claims and findings are traceable to the source literature, including key papers (Ferraro et al., 2018, Lee et al., 2018, Danzi et al., 2019, Pop et al., 2020, Cullen et al., 2020, Bonnet et al., 2020, Nguyen et al., 2022, Hernández et al., 2022, Akhtar, 2023, Alkhodair et al., 2023, Kharman et al., 24 Sep 2024, Cano-Benito et al., 3 Jul 2025).

This synthesis integrates critical advances and analytical results from the arXiv literature, capturing the complexity, trade-offs, and emerging best practices for rigorous DLT design, selection, and deployment in contemporary decentralized applications and infrastructures.