Real-Time Monitoring in Blockchain Payment Systems

Conceptual Analysis, Architecture, Algorithms, and Engineering Challenges in Blockchain Payment Systems

Why Real-Time Monitoring Matters

Real-time blockchain monitoring is now a fundamental requirement for modern Blockchain Payment Systems. Finality no longer comes from a central authority; it instead emerges through distributed consensus across thousands of independent nodes. When a transaction enters the mempool, it competes under shifting network conditions and fee markets, and only after stable block inclusion does it achieve economic reliability.

However, this process is inherently probabilistic and often inconsistent across nodes. As a result, no crypto payment gateway can rely on a single RPC response or assume that an on-chain appearance guarantees finality. A robust monitoring layer must therefore observe multiple nodes, reconcile conflicting views, detect forks and reorganizations, and continuously evaluate transaction safety.

A simple scenario illustrates the need. A payment may appear on one node, disappear on another, enter a block that later gets reorganized, and eventually settle on a different branch with different fee dynamics. Consequently, without proper monitoring, the gateway may oscillate between detected, settled, invalid, or reverted states, creating operational and financial risks.

This article therefore introduces a unified scientific and engineering framework for designing such monitoring systems. Moreover, it explores the theoretical foundations of observability, presents a layered architecture with deterministic state machines, and outlines fault-tolerant patterns essential for blockchain payment infrastructure.

1. The Need for Real-Time Blockchain Monitoring

This chapter establishes the theoretical foundation of real-time monitoring engines. Each core concept is defined in a formal scientific format so that later sections can reference it consistently.

1.1 Probabilistic Nature of Blockchain Consensus

Unlike banking systems, where settlement finality is deterministic and enforced by a centralized authority, blockchain finality is inherently probabilistic.

Definition 1 (Probabilistic Finality).

For a transaction tx on a Proof-of-Work or Proof-of-Stake network, the probability that it is final after confirmation depth d can be approximated as:

$P_{final}(tx \mid d) \approx 1 - p_{reorg}(d)$Pfinal​(tx∣d)≈1−preorg​(d)

where $p_{reorg}(d)$preorg​(d) denotes the probability that the chain is reorganized up to depth d. In Bitcoin, as described in the original whitepaper, this probability decreases exponentially as d increases.

Engineering implication. A payment system cannot treat a transaction as final after observing it only once. A real-time monitoring engine must continuously track block changes, forks, and mempool behavior, estimating finality dynamically rather than relying on fixed confirmation thresholds in isolation.

1.2 Transaction Lifecycle and Failure Points

A payment-related transaction typically passes through six high-risk stages:

• Entering the mempool
• Competing for block inclusion
• Risk of being stuck due to low fees
• Being included in a side chain
• Being removed due to a chain reorganization
• Reaching sufficient confirmation depth to be economically final

User Wallet
    │
    ▼
Mempool ──► Block Inclusion ──► Confirmations ──► Economic Finality
   │              │                    │
   │              ├─ fee too low       ├─ stuck / delayed
   │              ├─ conflicting tx    ├─ reorg risk
   │              ├─ wrong network     └─ insufficient depth
   └─ dropped / evicted

Figure 1: Lifecycle of a payment transaction and key failure points.

This lifecycle defines the checkpoints a monitoring engine must observe to make safe decisions in a live payment context.

1.3 Data Heterogeneity and Nodal Divergence

In distributed systems research, a key phenomenon is that different nodes often have different views of the network at the same time.

Definition 2 (Nodal Observation Divergence).

Let Mn(t) denote the mempool contents of node n at time t. For nodes n₁ and n₂, we may have:

‖Mn1(t) − Mn2(t)‖ > δ

because of network latency, different peer sets, local mempool size limits, fee policies, or temporary disconnections.

Engineering implication.
A single listener node is never reliable as the sole source of truth. A robust monitoring engine must:

• Observe multiple independent nodes per chain,
• Reconcile their views through a reconciliation layer,
• Apply weighting and trust models to handle inconsistent or unreliable nodes.

1.4 Latency and Confirmation-Time Modeling

In networks like Bitcoin, average block time is about 10 minutes with high variance. In Ethereum, block times are 2–12 seconds and finality occurs after two epochs (~12.8 minutes). A simple model for expected confirmation time is:

$$\mathbb{E}[T_{\text{confirm}}] = d \cdot \mathbb{E}[T_{\text{block}}] + \varepsilon$$

where $T_{block}$Tblock​ is average block time, d is required confirmation depth, and $ϵ$ϵ captures congestion and network variance.

Why this matters.

• Setting realistic expectations on confirmation time for users,
• Detecting “stuck” transactions,
• Optimizing retry and timeout strategies in payment APIs and POS devices.

1.5 Economic Cost of Misclassification

Misclassifying payment states introduces real financial risk. False positives, false negatives, FX slippage, network congestion, and operational overhead all contribute to the total cost of unreliable settlement decisions.

A simplified cost model can be expressed as:

$$C_{\text{total}} = w_{\text{fp}}C_{\text{fp}} + w_{\text{fn}}C_{\text{fn}} + w_{\text{fx}}C_{\text{fx}} + w_{\text{net}}C_{\text{net}}$$

where C_fp is the cost of false positives, C_fn is the cost of false negatives, C_fx captures FX or slippage losses, C_net represents network-related delays or fees, and each w term is a weighting factor based on business priority.

Why this matters.

• Helps quantify the impact of monitoring errors,
• Guides threshold selection across the monitoring pipeline,
• Enables merchants to balance safety with UX and latency.

1.6 Why a Real-Time Monitoring Engine Is Necessary

A real-time monitoring engine in Blockchain Payment Systems is simultaneously:

• A network observability tool,
• A transaction-behavior analysis engine,
• A payment-state interpretation layer,
• A mechanism for enforcing economic finality and business rules.

Without it:

• No crypto payment can be considered reliably settled,
• No payment API can safely expose “paid” or “failed” states,
• No crypto POS can make instant decisions,
• Advanced features like underpaid detection or network auto-detect cannot exist.

1.7 Layer-2 Dominance in Retail Crypto Payments

Consequently, by late 2025, industry estimates suggest that 15 to 20 percent of Bitcoin merchant payments are handled through the Lightning Network, with an additional 5 to 10 percent flowing through emerging off-chain protocols such as Ark, Liquid, RGB, and ERC-4337 bundles on major L2s. Taken together, these paths introduce instant probabilistic finality, minimal on-chain footprint, and distinct failure modes, which means a modern monitoring engine must analyze both Layer-1 and Layer-2 activity through a unified state machine.

2. Architecture of a Real-Time Blockchain Monitoring Engine

A real-time blockchain monitoring engine is typically composed of three primary layers:

• Listener Layer,
• Confirmation Engine Layer,
• Payment State Machine Layer.

These layers operate on shared infrastructure components such as databases, event buses, caches, and distributed logging systems.

2.1 Listener Layer (Blockchain Listener)

Role. The Listener is the first point of contact with the blockchain network. It must:

• Connect to multiple nodes per network,
• Monitor mempools and new blocks in real time,
• Filter relevant transactions for active payments,
• Forward raw events to the event bus without making business decisions.

          ┌──────────────────────── Blockchain Network ────────────────────────┐
          │                                                                   │
      ┌───▼────┐   ┌───▼────┐   ┌───▼────┐                                   │
      │ Node A │   │ Node B │   │ Node C │   ...                              │
      └───▲────┘   └───▲────┘   └───▲────┘                                   │
          │            │            │                                         │
          │  mempool + new blocks   │                                         │
          ▼            ▼            ▼                                         │
                     Listener Cluster                                         │
          ┌───────────────┬────────────────┬────────────────┐                 │
          │ Listener 1    │ Listener 2     │ Listener 3     │  ...            │
          └──────▲────────┴──────▲─────────┴──────▲─────────┘                 │
                 │ unified payment-related events                             │
                 ▼                                                            │
                            Event Bus (Kafka / NATS / RabbitMQ)

Figure 2: Listener cluster aggregating events from multiple nodes.

Standard Listener Pseudocode.

def match_candidate(tx, p):
    # For UTXO chains like Bitcoin: parse outputs for address and amount
    if not any(output.address == p.target_address for output in tx.outputs):
        return False
    total_amount = sum(output.value for output in tx.outputs if output.address == p.target_address)
    diff = abs(total_amount - p.expected_amount)
    if diff > p.tolerance:
        return False

    # Check for RBF flag (BIP-125 for Bitcoin-like chains)
    if p.network in ["bitcoin", "litecoin"]:  # UTXO chains
        if any(input.nSequence < 0xFFFFFFFE for input in tx.inputs):  # RBF-enabled
            emit_event("TX_RBF_RISK", {"txid": tx.txid, "payment_id": p.id})  # Log risk or reject for zero-conf
            return False  # Or handle as weak candidate based on risk profile

    # For EVM chains, check nonce/sequence to avoid replays
    elif p.network in ["ethereum", "polygon"]:
        if tx.nonce <= p.last_seen_nonce:  # Potential replay or low nonce
            return False

    return True

Pseudocode 1: Standard Listener Matching Function.

Mempool Reconciliation Layer

def reconcile(events):
    grouped = {}

    for ev in events:
        grouped.setdefault(ev["txid"], set()).add(ev["node_id"])

    strong = [txid for txid, nodes in grouped.items()
              if len(nodes) >= MIN_REQUIRED_NODES]

    weak = list(set(grouped.keys()) - set(strong))
    return strong, weak

Pseudocode 2: Mempool Reconciliation Function.

A transaction observed on multiple independent nodes is considered a strong signal; single-node observations remain weak signals until confirmed by others.

2.2 Confirmation Engine Layer

Responsibilities.

• Detect new blocks and best-chain changes,
• Compute transaction confirmation depth,
• Detect chain reorganizations,
• Emit confirmation-related events to the payment state machine.

Depth is computed as:

$$\text{depth}(tx) = \text{best\_height} - \text{block\_height}(tx) + 1$$

          Blockchain Nodes
                 │  (block headers + tx confirmations)
                 ▼
        ┌───────────────────────────────────────────┐
        │        Confirmation Engine Cluster       │
        │ ┌─────────┐  ┌─────────┐  ┌─────────┐    │
        │ │ Conf 1  │  │ Conf 2  │  │ Conf 3  │    │
        │ └────▲────┘  └────▲────┘  └────▲────┘    │
        └────────┴──────────┴──────────┴───────────┘
                        │ unified events
                          ▼
                    Event Bus → Payment State Machine

Figure 3: Confirmation Engine cluster emitting depth and finality events.

Reorg Detection.

def detect_reorg(prev_best, new_best, chain_type):
    # Basic PoW check: parent hash mismatch
    if prev_best.hash != new_best.parent_hash:
        return True

    # For PoS chains: check canonical tip and finality rules
    if chain_type in ["ethereum", "polygon", "bsc", "solana"]:
        canonical_head = get_canonical_tip()        # Consensus-layer canonical head
        finalized = get_finalized_checkpoint()      # Latest finalized block

        # Tip changed unexpectedly → potential reorg
        if canonical_head != prev_best.hash:
            return True

        # PoS chains cannot revert finalized checkpoints
        if new_best.height < finalized.height:
            return True

    return False

Pseudocode 3: Reorg Detection Function.

Confirmation Engine Pseudocode.

def update_confirmations(node, tracked_txs, chain_type):
    best_height = node.get_height()
    best_block = node.get_best_block()  # Get current best block for reorg check

    for tx in tracked_txs:
        if tx.block_height is None:
            continue

        # Recalculate depth if reorg detected
        if detect_reorg(tx.last_seen_block, best_block, chain_type):
            tx.depth = 0  # Reset depth on reorg
            emit_event("REORG_DETECTED", {
                "txid": tx.txid,
                "payment_id": tx.payment_id,
                "old_depth": tx.depth
            })
        else:
            tx.depth = best_height - tx.block_height + 1

        if tx.depth >= tx.required_depth and not tx.settled:
            emit_event("TX_CONFIRMED", {
                "payment_id": tx.payment_id,
                "txid": tx.txid,
                "depth": tx.depth
            })

Pseudocode 4: Confirmation Update Function.

2.3 Payment State Machine Layer

The payment state machine is the “brain” of the payment system. It translates low-level blockchain events into meaningful business states.

Standard States.

• NEW
• AWAITING_PAYMENT
• DETECTED
• UNDERPAID
• OVERPAID
• AWAITING_CONFIRMATIONS
• SETTLED
• EXPIRED
• FAILED

AWAITING_PAYMENT ── TX_DETECTED ──► DETECTED
DETECTED ── amount within tolerance ──► AWAITING_CONFIRMATIONS
DETECTED ── underpaid ──► UNDERPAID
DETECTED ── overpaid  ──► OVERPAID
AWAITING_CONFIRMATIONS ── confirmed ──► SETTLED
AWAITING_PAYMENT ── timeout ──► EXPIRED
ANY STATE ── fatal_error ──► FAILED

Figure 4: ext-based payment state machine for blockchain payments (simplified).

Notes:

• All transitions are idempotent.
• Any state can move to FAILED if a fatal inconsistency is detected.
• Reorgs trigger SETTLED → AWAITING_CONFIRMATIONS rollback.
• REVIEW_* states can be added for manual remediation flows.

class Payment:
    def __init__(self, id, expected_amount, timeout):
        self.id = id
        self.state = "AWAITING_PAYMENT"
        self.expected_amount = expected_amount
        self.timeout = timeout
        self.detected_tx = None

    def on_timeout(self):
        if self.state in ["AWAITING_PAYMENT", "DETECTED"]:
            self.state = "EXPIRED"

    def on_tx_detected(self, tx):
        if self.state != "AWAITING_PAYMENT":
            return

        self.detected_tx = tx
        ratio = tx.amount / self.expected_amount

        if ratio < 0.98:
            self.state = "UNDERPAID"
        elif ratio > 1.02:
            self.state = "OVERPAID"
        else:
            self.state = "AWAITING_CONFIRMATIONS"

    def on_confirmed(self, depth, required):
        if self.state == "AWAITING_CONFIRMATIONS" and depth >= required:
            self.state = "SETTLED"

Pseudocode 5: Payment State Machine Class.

This state machine must be idempotent and audit-friendly: the entire lifecycle of a payment should be reconstructable from logs. A production-grade implementation of a deterministic payment state machine can be seen in real-world gateways such as OxaPay, whose payment lifecycle and webhook-event specifications are documented here: OxaPay Documentation.

2.4 Layer-2 and Channel Monitoring Layer

The architecture is extended with a second, parallel ingestion plane dedicated to off-chain and Layer-2 protocols:

Key components

• Multiple independent watchtowers (typically 3–5 for redundancy, including own + altruistic).
• Real-time invoice subscription via BOLT 12 websockets or LNURL-pay.
• Ark Service Provider (ASP) webhook plus on-chain unilateral exit monitoring (mainnet launched August 2025).
• Liquid Elements and federation watchtowers.
• RGB client-side state validation daemons.
• ERC-4337 bundler and UserOperation mempool listeners.

All Layer-2 events are normalized to the same schema (PAYMENT_DETECTED, PAYMENT_SETTLED, PAYMENT_FAILED, CHANNEL_FORCE_CLOSE, etc.) that is used by Layer-1.

Lightning       Ark        Liquid       RGB        ERC-4337
     ▲             ▲           ▲           ▲            ▲
     │\
     │             │           │           │            │
 ┌────┴────┐   ┌────┴─────┐   ┌─┴─┐   ┌────┴────┐   ┌────┴─────┐
 │ LND /   │   │ Ark       │   │   │   │ RGB      │   │ Bundler /
 │ CLN     │   │ Service   │   │   │   │ Daemon   │   │ UserOp
 │ gRPC +  │   │ Provider  │   │   │   │          │   │ Mempool
 │ BOLT12  │   │ LSP APIs  │   │   │   │          │   │ EntryPoint
 │ subs    │   │           │   │   │   │          │   │
 │ Watch-  │   │           │   │   │   │          │   │
 │ towers  │   │           │   │   │   │          │   │
 └────┬────┘   └────┬─────┘   └───┘   └────┬─────┘   └────┬──────┘
      ▼             ▼                     ▼              ▼
        Layer-2 / Channel Listener Cluster
                     │
            Normalized L2 events
                     ▼
               Unified Event Bus

Figure 5: Layer-2 and channel monitoring layer feeding the unified event bus.

3. Scientific and Engineering Challenges in Real-Time Monitoring

Real-time blockchain monitoring may seem simple at first glance: “connect to a node, detect the transaction, count the confirmations.” In industrial Blockchain Payment Systems, it is one of the most complex topics in distributed systems engineering. The data that the engine observes is neither uniform nor instantly trustworthy.

3.1 Mempool Heterogeneity

No two nodes share the same mempool. Divergence arises from different peer sets, propagation delay, eviction policies, minimum-fee thresholds, and hardware or software differences.

Node A mempool: { tx1, tx2, tx5 }
Node B mempool: { tx2, tx3 }
Node C mempool: { tx1, tx3, tx4 }

→ No single node has the full picture.
→ The monitoring engine must merge and reconcile inconsistent data.

Figure 6: Mempool divergence across nodes (example).

As a result, a real-time monitoring engine is inherently probabilistic rather than purely deterministic.

3.2 Forks and Reorganizations

Forks and reorgs constantly reshape the canonical chain:

• Fork: two or more competing branches exist temporarily.
• Reorg: the network discards one branch and switches to another.

Impact on payments.

• A transaction recorded in the discarded branch disappears,
• A “confirmed” payment may revert to “awaiting confirmations”,
• If reorgs are not detected, the system may incorrectly keep a payment as settled.

def is_reorg(prev_best, new_best):
    # True if the new block does not build on the previous best block
    return new_best.parent_hash != prev_best.hash

Pseudocode 6: Simple Reorg Detection.

Upon reorg detection, confirmation depths must be recalculated and affected payments rolled back to a safe state.

3.3 Dynamic Fee Markets

In fee-based networks such as Bitcoin and Ethereum, a payment system must estimate whether a transaction is likely to be confirmed within an acceptable time or become stuck.

def fee_risk(tx_fee_rate, recent_fee_rates):
    p50 = percentile(recent_fee_rates, 50)
    p80 = percentile(recent_fee_rates, 80)

    if tx_fee_rate >= p80:
        return "low_risk"
    elif tx_fee_rate >= p50:
        return "medium_risk"
    else:
        return "high_risk_of_being_stuck"

Pseudocode 7: Fee Risk Assessment.

This simple model can be extended with machine learning, but even in this form it is suitable for academic treatment in monitoring research.

3.4 Double Spend Attacks

Zero-confirmation payments, especially at POS, are vulnerable to double-spend attempts:

• Replace-By-Fee (RBF) attacks in Bitcoin,
• Broadcasting two transactions that share the same inputs,
• Sending a decoy transaction and invalidating it through a later fork.

def detect_double_spend(tx, mempool):
    for other in mempool:
        if other.txid == tx.txid:
            continue
        if set(other.inputs) & set(tx.inputs):
            return True
    return False

Pseudocode 8: Double Spend Detection.

3.5 Social Engineering Against Payment Workflows

Some attacks target workflow assumptions rather than the blockchain itself:

• Sending near-duplicate amounts such as 0.499 and 0.500,
• Using the correct address with an incorrect memo or tag,
• Sending testnet transactions to confuse misconfigured systems.

Defensive requirements.

• Strict time windows and narrow amount tolerance,
• Wallet or client fingerprinting where possible,
• Cross-checking transactions across multiple nodes and services.

3.6 Lack of Deterministic Finality in PoW

PoW chains do not offer strict deterministic finality. Even six confirmations represent strong economic confidence but not mathematical certainty. PoS networks, such as modern Ethereum, provide stronger finality semantics but still require careful modeling at the monitoring layer.

3.7 Late, Missing, or Incorrect Data

When using public RPC endpoints or third-party services:

• Responses may arrive late or out of order,
• Nodes may be out of sync,
• Some nodes may return incorrect or stale information.

The monitoring engine must therefore rely on multiple independent sources, cross-verify data, and maintain fail-safe modes under inconsistency.

3.8 Cross-Chain Monitoring Complexity (2025)

By 2025, industrial gateways typically support 8–12 chains, each with different models of finality, mempool behavior, and event ordering.

Note: For Ark, offchain preconfirmations provide ~5s settlement, but full Bitcoin anchoring via Batch Swaps includes a 1–2 week covenant expiry window for unilateral exits and disputes.

Chain-Specific Adapter Layer.

[BitcoinAdapter]   ┐
[EthereumAdapter]  ├─► Event Normalizer ──► Unified Event Bus
[SolanaAdapter]    │
[TONAdapter]       │
[TronAdapter]      ┘

Figures 7: Chain-Specific Adapters Feeding Event Bus.

Each adapter implements chain-specific logic and emits normalized events: TX_DETECTED, BLOCK_NEW, REORG, FINALIZED, etc.

      Chain-Specific Adapter Layer
      -----------------------------
      [ Listener Cluster ]
               |
               v
      +---------------------+
      |  Adapter: Bitcoin   |
      |---------------------|
      | mempool → unify()   |
      | confirmations → map()|
      | reorgs → detect()   |
      +---------------------+
               |
               v
      +---------------------+
      |  Adapter: Ethereum  |
      |---------------------|
      | pending → normalize()|
      | receipts → decode() |
      | logs → standardize()|
      +---------------------+
               |
               v
      +---------------------+
      |  Adapter: Solana    |
      |---------------------|
      | slots → map_slot()  |
      | forks → classify() |
      | tps → throttle()    |
      +---------------------+
               |
               v
      Unified Events
      ----------------
      depth, finality, tx_status,
      chain_type, reorg_state

      ↓ sent to →
      Payment State Machine

Figure 8: Chain-Specific Adapter layer for multi-chain monitoring.

3.9 Reorg Behaviour and Finality in Modern PoS Networks

def is_finalized(event, chain_type, payment):
    """
    Unified chain-specific finality detector for industrial monitoring engines
    — December 2025 state of the art —

    Returns True only when a transaction is economically irreversible
    under the defined risk profile. All values reflect real-world gateway practices.
    """
    # ------------------------------------------------------------------
    # Bitcoin & UTXO-based chains (probabilistic finality)
    # ------------------------------------------------------------------
    if chain_type == "bitcoin":
        required_depth = required_depth(payment.chain_risk_profile)
        # 6 confs = medium risk, 12+ = high-value institutional
        return event.depth >= required_depth

    # ------------------------------------------------------------------
    # Ethereum ecosystem – post-Pectra (May 2025)
    # ------------------------------------------------------------------
    elif chain_type in ["ethereum", "polygon", "bsc", "arbitrum", "optimism", "base", "zksync", "starknet"]:
        if payment.is_l1 or chain_type in ["polygon", "bsc"]:
            # L1 and L1-anchored chains remain conservative
            required_epochs = 2
        else:
            # Modern L2s (Base, Arbitrum Orbit, Optimism Bedrock+, zkSync Era, Starknet, etc.)
            # 1 epoch (~6.4 min) or even sub-minute with valid ZK/fraud proofs is sufficient
            required_epochs = 1

        return (event.finalized_epoch is not None and
                event.finalized_epoch >= payment.epoch + required_epochs)

    # ------------------------------------------------------------------
    # Solana – December 2025 exact status (pre-Alpenglow)
    # ------------------------------------------------------------------
    elif chain_type == "solana":
        # Alpenglow (SIMD-0326) approved Sep 2025; testnet Q1 2026 → sub-second finality planned
        # Current: TowerBFT + optimistic confirmation
        return (event.slot - event.first_seen_slot >= 31 and  # Confirmed slots
                event.root_included and
                event.vote_quorum >= 2/3)

    # ------------------------------------------------------------------
    # TON (The Open Network)
    # ------------------------------------------------------------------
    elif chain_type == "ton":
        return (event.masterchain_seqno > payment.mc_seqno and
                event.logical_time > payment.lt)

    # ------------------------------------------------------------------
    # Avalanche subnets & Snow family
    # ------------------------------------------------------------------
    elif chain_type == "avalanche":
        return event.snow_status == "finalized"

    # ------------------------------------------------------------------
    # Cosmos SDK / Tendermint-based chains (Cosmos Hub, Osmosis, dYdX, etc.)
    # ------------------------------------------------------------------
    elif chain_type == "cosmos-sdk" or chain_type.startswith("cosmossdk_"):
        return event.height >= payment.height + 1  # +1 block deterministic finality

    # ------------------------------------------------------------------
    # NEAR Protocol
    # ------------------------------------------------------------------
    elif chain_type == "near":
        return event.height >= payment.height + 2  # 2-epoch finality

    # ------------------------------------------------------------------
    # Algorand
    # ------------------------------------------------------------------
    elif chain_type == "algorand":
        # ~2 seconds deterministic finality after ~500 rounds
        return event.round - payment.round >= 500

    # ------------------------------------------------------------------
    # Fallback for any unsupported or custom chain
    # ------------------------------------------------------------------
    else:
        # Treat as probabilistic with configurable depth (safe default)
        return event.depth >= payment.required_depth

Pseudocode 9: Unified Finality Detector.

Key upgrades reflected:

• Ethereum Pectra (May 2025): Although SSF is not yet deployed, networks continue to operate under the standard two-epoch finality model, which keeps current confirmation assumptions stable.
• Solana Alpenglow (SIMD-0326): Approved in late 2025, and with broader testnet phases expected in early 2026, the network meanwhile continues to rely on TowerBFT for its live consensus.
• Firedancer client: Demonstrated 500k+ TPS in benchmark and testnet environments, while maintaining Solana’s existing finality rules.
• Ark protocol: After being activated on mainnet in 2025, the protocol introduced covenant-based monitoring requirements, thereby expanding the set of off-chain flows that gateways must observe.
• Modern L2s (Base, Arbitrum Orbit, etc.): As these networks operate with one-epoch economic finality under optimistic or ZK-rollup assumptions, confirmation horizons shift toward shorter and more predictable intervals.
• Gateway architecture: Risk models have evolved toward dynamic, context-aware confirmation profiles rather than fixed universal thresholds.

By 2025, fixed confirmation counts are largely obsolete. Each chain requires a chain-specific finality detector that outputs a binary decision: finalized or not finalized.

3.10 Real-Time System Constraints

A payment system cannot simply tell the user “wait and see.” The monitoring engine must guarantee:

• Low latency (typically below 2–3 seconds for updates),
• Regular, deduplicated event emission,
• Resilience to network interruptions and partial failures,
• Scalability under heavy traffic and market events.

4. Transaction Detection and Matching Algorithms

Determining whether a given transaction actually belongs to a specific payment is the most critical and sensitive part of a real-time monitoring engine. Industrial systems combine strict matching, heuristics, probabilistic algorithms, fee-behavior models, and chain-specific logic.

4.1 Strict Matching

Core parameters.

• Destination address,
• Blockchain network,
• Payment amount,
• Valid payment time window.

def strict_match(tx, payment):
    # For UTXO: parse outputs
    if payment.network == "bitcoin":
        outputs = [o for o in tx.outputs if o.address == payment.address]
        if not outputs:
            return False
        tx_amount = sum(o.value for o in outputs)
    else:  # EVM-like
        tx_amount = tx.value
        if tx.to != payment.address:
            return False
    
    if tx.network != payment.network:
        return False
    if tx_amount != payment.expected_amount:
        return False
    if not (payment.valid_from <= tx.timestamp <= payment.valid_until):
        return False
    return True

Pseudocode 10: Strict Matching Function (chain-aware).

In real systems, exact equality rarely holds, so strict matching is only a foundation for more advanced heuristics.

4.2 Tolerance-Based (Heuristic) Matching

Crypto payment gateways typically allow a small tolerance (for example 0.5–3 percent) to account for wallet behavior, fees, and rounding.

def tolerant_match(tx, payment, tolerance_ratio=0.05):
    # Parse amount as in strict_match
    # ... (amount calculation)
    
    if not (payment.valid_from <= tx.timestamp <= payment.valid_until):
        return False

    ratio = tx_amount / payment.expected_amount

    if 1 - tolerance_ratio <= ratio <= 1 + tolerance_ratio:
        return True
    return False

Pseudocode 11: Tolerant Matching Function.

Downstream, the state machine distinguishes UNDERPAID, OVERPAID, or AWAITING_CONFIRMATIONS based on the ratio.

4.3 Probabilistic Matching

At scale, multiple transactions may satisfy the same heuristic filters. A probabilistic model assigns each (transaction, payment) pair a score.

def probabilistic_match(tx, payment):
    score = 0.0

    # Address match
    if tx.to_address == payment.address:
        score += 0.5

    # Amount closeness
    amount_diff = abs(tx.amount - payment.expected_amount)
    score += max(0.0, 0.3 - (amount_diff / payment.expected_amount))

    # Time proximity (5-minute decay)
    time_diff = abs(tx.timestamp - payment.created_at)
    score += max(0.0, 0.2 - (time_diff / 300.0))

    return score  # in [0, 1]

Pseudocode 12: Probabilistic Matching Score.

A gateway can then set an acceptance threshold (for example 0.7) and treat lower scores as ambiguous or suspicious.

4.4 Input-Conflict Detection (Double-Spend)

def detect_double_spend(tx, mempool):
    for other in mempool:
        if tx.txid == other.txid:
            continue
        if set(other.inputs) & set(tx.inputs):
            return True
    return False

Pseudocode 13: Input-Conflict Detection for Double-Spend.

If this function returns true, the payment moves into a risk state, and zero-confirmation acceptance may be blocked.

4.5 Fee Sufficiency Prediction

def estimate_confirmation(tx_fee, recent_fees):
    p50 = percentile(recent_fees, 50)
    p80 = percentile(recent_fees, 80)

    if tx_fee >= p80:
        return "fast"
    elif tx_fee >= p50:
        return "medium"
    else:
        return "slow_or_stuck"

Pseudocode 14: Fee Sufficiency Prediction.

If a payment is labeled as slow_or_stuck, the system may hold the status in a special “awaiting sufficient fee” state instead of moving directly to AWAITING_CONFIRMATIONS.

4.6 Multi-Node Weighted Consensus

def weight_from_nodes(txid, reports):
    unique_nodes = set([r.node_id for r in reports if r.txid == txid])
    return len(unique_nodes)

Pseudocode 15: Multi-Node Weight Calculation.

For example:

• weight ≥ 3 → trusted,
• weight = 2 → medium confidence,
• weight = 1 → weak, to be confirmed or rejected later.

4.7 Multi-Address Matching

If the system does not use unique static addresses per invoice, a single address may be linked to multiple payments. Matching then must combine time window, expected amount, and priority rules to map transactions correctly.

4.8 Near-Duplicate Transaction Filtering

def filter_near_duplicates(txs, epsilon=0.003):
    txs_sorted = sorted(txs, key=lambda x: x.amount)
    filtered = [txs_sorted[0]]

    for tx in txs_sorted[1:]:
        if abs(tx.amount - filtered[-1].amount) > epsilon:
            filtered.append(tx)

    return filtered

Pseudocode 16: Near-Duplicate Filtering.

4.9 Chain-Specific Matching

Matching rules differ across chain families:

• Bitcoin / Litecoin / UTXO: focus on inputs, outputs, and double-spend probability (parse outputs).
• Ethereum / Tron / EVM: require transaction receipt status and may need to inspect internal transfers.
• Solana: often rely on transaction logs and instruction-level analysis rather than simple amount fields.

4.10 Graph-Similarity-Based Matching (Research)

Recent research explores modeling wallet behavior as graphs and comparing new transactions to historical patterns using graph similarity or neural networks. This remains a frontier topic, especially for large PSPs and exchanges.

4.11 Off-Chain and Channel-Based Transaction Detection and Matching

Layer-2 payments are identified through payment_hash / preimage instead of on-chain outputs. Matching is therefore performed at the invoice and hash level, not by scanning UTXOs or account balances.

Lightning / channel-based matching example (conceptual)

def match_lightning_payment(payment_request, preimage, invoice):
    # Check hash-preimage relation
    if sha256(preimage) != invoice.payment_hash:
        return "NO_MATCH"

    # Check amount
    if invoice.amount_msat != payment_request.amount_msat:
        return "NO_MATCH"

    # Check expiry
    if invoice.expiry < now():
        return "NO_MATCH"

    return "EXACT_MATCH"

Pseudocode 17: Lightning Payment Matching.

Industrial engines additionally implement:

• Multi-Path Payments (MPP) and Atomic Multi-Path (AMP) reassembly,
• BOLT 12 blinded paths and offer monitoring,
• HTLC success/failure event streaming via LND/CLN,
• Justice transaction (breach remedy) detection on-chain as rollback trigger.

5. Fault Tolerance in Real-Time Blockchain Monitoring Systems

Real-time monitoring must function exactly when networks are unstable, nodes are offline, connectivity is degraded, or third-party RPC responses become unreliable. Fault tolerance is therefore a first-class design goal.

5.1 Exponential Backoff Retry

def retry_with_backoff(send_func, max_attempts=6):
    for attempt in range(max_attempts):
        try:
            send_func()
            return True
        except Exception:
            delay = min(2 ** attempt, 60)  # cap at 60 seconds
            sleep(delay)
    return False

Pseudocode 18: Exponential Backoff Retry.

Backoff must always be bounded to avoid permanent waiting states.

5.2 Multi-Region Redundancy

Region 1: Node A1, Node B1
Region 2: Node A2, Node B2
Region 3: Node A3, Node B3

                ┌─────────────── Internet ────────────────┐
                │                                          │
       ┌────────▼────────┐                  ┌────────────▼────────────┐
       │   Region eu-west │                  │     Region us-east      │
       └────────┬────────┘                  └─────────┬────────────────┘
                │                                      │
       ┌────────▼────────┐                  ┌─────────▼─────────┐
       │ Bitcoind A1     │                  │ Bitcoind B1       │
       │ (Ireland)       │                  │ (Virginia)        │
       └───────┬─────────┘                  └───────┬───────────┘
               │                                    │
       ┌───────▼─────────┐                  ┌───────▼───────────┐
       │ Listener EU-1   │                  │ Listener US-1     │
       └───────┬─────────┘                  └───────┬───────────┘
               │                                    │
               └──────────────┬─────────────────────┘
                              │
                ┌─────────────▼─────────────┐
                │       Region ap-southeast       │
                └─────────────┬──────────────────┘
                              │
                ┌─────────────▼─────────────┐
                │ Bitcoind C1 (Singapore)   │
                └──────────┬────────────────┘
                           │
                ┌──────────▼──────────┐
                │ Listener ASIA-1      │
                └──────────┬──────────┘
                           │
            ┌──────────────┴──────────────┐
            │       Event Bus (Kafka / NATS)      │
            └──────────────┬──────────────┘
                           │
            ┌──────────────▼──────────────┐
            │   Reconciliation Service    │
            │ (simple majority vote +     │
            │  health-check & de-duplicate)│
            └──────────────┬──────────────┘
                           │
                  ┌────────▼────────┐
                  │ Payment State   │
                  │   Machine      │
                  └─────────────────┘

Figure 9: Production-Grade Multi-Region Listener Architecture.

5.3 Event Debouncing and Idempotency

def handle_event(event):
    if already_processed(event.id):
        return  # idempotent handling

    apply_transition(event)
    mark_processed(event.id)

Pseudocode 19: Event Handling with Idempotency.

This prevents any payment from being updated or settled multiple times due to duplicate events.

5.4 Reliable Webhook Architecture

A professional webhook pipeline includes:

• An event bus where events are queued,
• A dispatcher responsible for delivery with retries,
• A Dead Letter Queue (DLQ) for events that repeatedly fail.

def dispatch(event, endpoint):
    for attempt in range(MAX_RETRIES):
        res = http_post(endpoint, event)
        if res.status_code in (200, 201, 204):
            return True
        sleep(backoff(attempt))

    send_to_dlq(event)
    return False

Pseudocode 20: Webhook Dispatch with Retries.

5.5 Reorg Safety Mechanisms

def handle_reorg(tx):
    if tx.state == "SETTLED":
        tx.state = "AWAITING_CONFIRMATIONS"
    tx.current_depth = 0

Pseudocode 21: Reorg Handling.

Systems that do not handle reorgs cannot be considered industrial-grade.

5.6 RPC Sanitization and Fail-Safe Mode

def verify_block_hash(height, nodes):
    hashes = [node.get_block_hash(height) for node in nodes]
    return len(set(hashes)) == 1

Pseudocode 22: RPC Verification.

If RPC responses diverge, the system should lower the trust weight of abnormal nodes and, in severe cases, enter a fail-safe mode where no new payments are finalized until consistency returns.

5.7 Event Sourcing and CQRS

Industrial gateways that process large volumes increasingly rely on event-sourcing plus CQRS to achieve:

• Full recoverability after reorgs or crashes,
• Horizontal scalability across services,
• Immutable audit trails,
• Multiple independent read models (dashboard, API, analytics, compliance).

Raw Blockchain Events
        │
        ▼
[Listener Cluster] → Event Normalizer
        │
        ▼
Kafka / NATS (append-only topics: blockchain.events.raw)
        │
        ▼
Event Store (EventStoreDB / PostgreSQL WAL / Marten)
        │───────────────────────────────────────────────┐
        ▼                                               ▼
Write Side (Commands)                             Read Side (Projections)
Payment Command Handler                           Multiple Materialized Views
(Validate + Append Event)                         (PostgreSQL, Redis, ClickHouse)

Figure 10: Standard event-sourcing + CQRS architecture for payments.

{
  "event_id": "evt_8f3a1c9e...",
  "event_type": "PaymentTransactionDetected",
  "payment_id": "pay_1732215678",
  "txid": "0xabc123...",
  "chain": "ethereum",
  "amount_sats": 50000000,
  "seen_by_nodes": 7,
  "first_seen_at": "2025-11-30T10:23:11.123Z",
  "event_version": 2,
  "sequence": 1
}

Pseudocode 23: State Rebuild from Events

All events are immutable and strictly ordered. Current payment state is the result of replaying the event stream.

Replay and Snapshotting

def rebuild_payment_state(payment_id, up_to_timestamp=None):
    events = event_store.stream(payment_id, version=0)
    state = PaymentState()  # initial: CREATED

    for event in events:
        if up_to_timestamp and event.timestamp > up_to_timestamp:
            break

        state = state.apply(event)  # pure, idempotent

        if event.sequence % 1000 == 0:
            snapshot_store.save(payment_id, state, event.sequence)

    return state

Pseudocode 24: State Rebuild from Events.

5.8 Load Surge Resistance

During price spikes, airdrops, or major shopping events, the monitoring engine must continue operating under hundreds of events per second, scaling out listeners, event processing, and projections without blocking core payment flows.

5.9 Network Partition Tolerance

If a listener is partially disconnected from the internet, it must enter a degraded but stable mode and resynchronize state once connectivity is restored. Partition tolerance is non-negotiable in a global monitoring system.

5.10 Idempotent Settlements

The fundamental financial rule is simple: a payment must never be settled twice, regardless of reorgs, RPC errors, or duplicated events. Idempotency must be enforced at the state machine and event-handling layers.

5.11 Advanced RPC Optimization Techniques (2024–2025)

High-throughput chains and global volumes require advanced techniques to reduce RPC costs, latency, and bandwidth.

Note: EASL remains experimental/research-only; no widespread adoption.

def next_generation_listener(chain):
    if chain == "bitcoin":
        sources = [
            zmq_rawtx_subscribe(own_bitcoind),
            erlay_relay(peers_via_dht),  # Erlay standard
        ]
    elif chain.family == "evm":
        sources = [
            wss_alchemy_infura_fallback(),
            flashbots_protect_bundle_submit(),
            mev_share_refund_tracking(),
        ]
    elif chain == "solana":
        sources = [
            helius_shredstream_gossip(),
            geyser_grpc_plugin(),
        ]

    return MultiSourceAggregator(
        sources,
        min_confirmations=3,
        strategy="fastest_valid",
    )

Pseudocode 25: Next-Generation Listener Configuration.

5.12 Watchtower Redundancy and Channel State Recovery

Lightning and Ark monitoring engines in 2025 typically maintain 3–5 independent watchtowers (average 4) across ≥ 3 geographic regions and ≥ 2 implementations (LND, Core-Lightning) to achieve robust redundancy and strong collusion resistance. Upon detection of a breach transaction, the system automatically submits the penalty transaction within < 2–5 seconds (P99 < 5 s observed in large-scale deployments in 2025). Static Channel Backups (SCB) and channel.backup files are encrypted and replicated to at least two geographically distributed cloud providers with tested emergency restore pipelines completing in under 5 minutes.

6. Security in Real-Time Blockchain Monitoring Systems

Real-time monitoring engines interact directly with money, nodes, RPCs, and sensitive data. Their security posture must therefore match or exceed that of traditional PSP and banking systems.

6.1 Replay Attacks

If addresses are reused, an attacker can rebroadcast old transactions. A naive engine that checks only address and amount may accept such replays as new payments.

Standard defenses.

• Bind each address to a specific payment identifier,
• Enforce strict validity windows,
• Track nonce/sequence where relevant (EVM, Solana).

def reject_if_replay(tx, payment):
    if tx.timestamp < payment.valid_from:
        return True  # replayed from the past
    if tx.timestamp > payment.valid_until:
        return True  # too late
    return False

Pseudocode 26: Replay Attack Rejection.

6.2 Sanitization of RPC Responses

Public or low-cost RPC endpoints may be slow, inconsistent, or even actively attacked (BGP hijack, TLS MITM).

def verify_block_hash(height, nodes):
    hashes = [node.get_block_hash(height) for node in nodes]
    return len(set(hashes)) == 1

Pseudocode 27: Block Hash Verification.

Nodes that disagree with the majority can have their trust weight reduced or set to zero until they stabilize.

6.3 Memory Safety in Listeners

Listeners are long-running daemons. Memory leaks or unsafe pointer use can lead to crashes, missed detections, and corrupted state.

fn poll_loop() {
    loop {
        if let Ok(mempool) = rpc.get_mempool() {
            process(mempool);
        }
        std::thread::sleep(std::time::Duration::from_millis(500));
    }
}

Pseudocode 28: Listener Poll Loop (Rust Example).

Implementing critical listeners in memory-safe languages (such as Rust or Go), combined with sandboxing and health checks, is now considered best practice.

6.4 API Key Rotation Policies

def rotate_keys(service):
    new_key = service.generate_api_key()
    secret_store.save("service_key", new_key)
    service.revoke_old_keys()

Pseudocode 29: API Key Rotation.

Keys must be stored in secret managers, rotated periodically, logged, and revoked on suspected compromise.

6.5 Event Bus Security

If an attacker can inject, drop, or reorder events on the bus, the payment system becomes untrustworthy.

• Use TLS for all internal communication,
• Sign events and verify payload hashes,
• Use idempotency tokens and hash chains to preserve ordering.

{
  "event_id": "f83a1d",
  "type": "TX_DETECTED",
  "payload_hash": "sha256:74ab2...",
  "signature": "ed25519:9ac4...",
  "timestamp": 173221,
  "payload": {
    "payment_id": 123,
    "txid": "0xabc..."
  }
}

Pseudocode 30: Secure Signed Event Payload

6.6 Zero-Conf Double-Spend Mitigation

def detect_rbf(tx, mempool):
    for other in mempool:
        if other.txid == tx.txid:
            continue
        if tx.inputs == other.inputs and other.fee > tx.fee:
            return True  # likely RBF replacement
    return False

Pseudocode 31: RBF Detection for Zero-Conf.

Zero-conf acceptance without double-spend checks is not reliable, especially in PoW networks.

6.7 Timestamp Spoofing Defense

Transaction timestamps must be taken from authoritative on-chain metadata (block header, logs) instead of client-supplied values. Acceptable clock drift must be tightly bounded at the application layer.

6.8 Rate Limiting and DoS Protection

class TokenBucket:
    def __init__(self, rate, capacity):
        self.rate = rate
        self.capacity = capacity
        self.tokens = capacity
        self.last_refill = now()

    def refill(self):
        delta = now() - self.last_refill
        added = delta * self.rate
        self.tokens = min(self.capacity, self.tokens + added)
        self.last_refill = now()

    def allow(self):
        self.refill()
        if self.tokens > 0:
            self.tokens -= 1
            return True
        return False

Pseudocode 32: Token Bucket Rate Limiting.

Token-bucket rate limiting, backpressure on the event bus, and RPC throttling help protect both internal and external services.

6.9 Tamper-Proof Audit Logs

Audit logs must form a hash chain to ensure immutability. Each event is linked to the previous one through a cryptographic hash, preventing undetected modification of historical entries.

The hash chain is defined as:

$$h_j = H(h_{j-1} \mathbin\Vert \text{event}_j\text{event})$$

where:

• event_j is the j-th logged event,
• h_j is the hash of the log chain after adding this event,
• H is a cryptographic hash function,
• ∥ denotes concatenation.

This structure allows the monitoring system to later prove that no audit record has been removed, altered, or reordered.

6.10 Cross-Chain Security Considerations

Each chain has its own attack surface:

• Bitcoin: RBF and deep forks,
• Ethereum: gas-griefing and spam,
• Solana: noisy and sometimes unstable leader schedules,
• TON: parallel shardchains and complex cross-shard flows,
• Tron: bandwidth and energy abuse.

Security analyses must be chain-specific instead of assuming a single model for all networks.

6.11 Channel-Specific Attack Vectors and Mitigations

7. Open Research Challenges in Real-Time Monitoring

Real-time monitoring in blockchain-based payment systems remains an active research frontier. Although foundational architectures have matured, the operational reality of heterogeneous consensus rules, evolving L2 protocols, and increasingly interconnected settlement layers continually introduces new challenges. Consequently, the research landscape is expanding toward more predictive, cross-layer, and trust-minimized models that directly inform next-generation Blockchain Payment Systems.

7.1 Early Fork Detection and Probabilistic Propagation Modeling

Detecting a fork at the precise moment it emerges is inherently difficult because listeners observe only delayed and incomplete snapshots of network propagation. Accordingly, early detection requires probabilistic reasoning over propagation delays, peer-set diversity, and time-shifted orphan-rate fluctuations. As a result, developing inference models that anticipate fork formation before it becomes globally visible remains an unsolved challenge with immediate implications for payment finality in Blockchain Payment Systems.

7.2 Predictive Zero-Confirmation Risk and Mempool-Level Modeling

A central question is whether double-spend risk can be estimated reliably from mempool data alone. Therefore, models must incorporate wallet-behavior fingerprinting, historical clustering, fee-bump trajectory analysis, and graph-structured neural approaches capable of identifying conflicting spends. Moreover, as payment authorization windows shrink toward sub-second latencies, the need for trustworthy zero-conf prediction models becomes critical for the reliability of real-time Blockchain Payment Systems.

7.3 Monitoring High-Throughput and Non-Linear Execution Chains

Meanwhile, chains such as Solana, Optimism, and Avalanche introduce parallel execution, non-linear block DAGs, sharded propagation, and vote-driven finality. Consequently, advancing real-time monitoring requires new event-ordering algorithms, lightweight distributed indexers, and listener topologies capable of sustaining high throughput without sacrificing determinism. These capabilities are essential as Blockchain Payment Systems increasingly depend on high-frequency settlement flows.

7.4 RPC Authenticity, Trust Scoring, and Webhook Reliability

Reliable monitoring depends not only on on-chain data, but also on off-chain messaging. Therefore, two linked research gaps remain:

• RPC authenticity verification, where no universal trust metric exists for evaluating correctness based on latency, peer deviation, and historical consistency.
• Webhook reliability, where retry semantics, idempotency models, and event schemas remain unstandardized across providers.

Consequently, establishing trust-scored RPC layers and standardized webhook protocols would significantly strengthen infrastructural reliability across payment workflows.

7.5 Cross-Chain Event Correlation and Interoperability

As bridges and cross-chain swaps expand, settlement increasingly relies on the synchronized interpretation of events across chains with divergent timing guarantees. Therefore, research continues on timestamp alignment, universal event schemas, probabilistic cross-chain correlation techniques, and multi-network listener architectures. Ultimately, such capabilities are foundational for Blockchain Payment Systems that rely on secure inter-chain value transfer.

7.6 Trust-Minimized RPC Optimization and Decentralized Data Delivery (2025–2030)

Looking forward, monitoring engines must reduce trust assumptions while supporting sharded, DAG-based, and vote-driven protocols. Accordingly, emerging research directions include:

• generalizing relay-compression protocols like Erlay to PoS and DAG environments,
• using zero-knowledge proofs to attest correct block relay,
• decentralized RPC markets with staking and slashing,
• reinforcement-learning switches between streaming and polling,
• MEV-aware dynamic fee adjustments and routing behavior.

Together, these approaches aim to improve consistency and minimize central dependencies across the broader architecture of Blockchain Payment Systems.

7.7 Lightning Network Reliability, MPP Success Prediction, and Blinded Path Modeling

Off-chain settlement introduces its own challenges. Routing reliability in Lightning payments remains uncertain because liquidity is private, dynamic, and partially obscured. Consequently, research now focuses on:

• constructing real-time liquidity-informed routing graphs,
• Bayesian hop-level success estimation,
• reinforcement-learning strategies for path selection,
• probabilistic modeling of multi-path (MPP/AMP) failures,
• partial reconstruction of blinded-path feasibility.

These models aim to reduce payment failures and improve predictability as Blockchain Payment Systems increasingly rely on fast off-chain channels.

7.8 Cross-Layer Atomic Settlement and Covenant-Based L2 Risk Models

Finally, as Lightning and other L2 systems integrate with on-chain swap mechanisms, correlating events across layers with different timing semantics becomes a core difficulty. Moreover, covenant-based protocols such as Ark or BitVM-style channels introduce deadline risks, challenge windows, and round-dependent failure modes. Therefore, research efforts now target unified cross-layer event schemas, deterministic atomic-settlement rules, and predictive models that anticipate covenant-break conditions under network uncertainty.

8. Conclusion and Future Directions

Real-time blockchain monitoring has shifted from simple event observation to a core reliability layer that must operate correctly under heterogeneous node views, varying mempool states, and different finality mechanisms. As a result, the monitoring stack directly shapes how safely a gateway can evaluate settlement. Moreover, these variations require stricter normalization across networks. Consequently, reliability becomes a function of both data quality and consensus-aware interpretation.

Within this architecture, independent listener nodes capture raw chain data, the confirmation engine normalizes depth and finality across consensus types, and the deterministic payment state machine ensures replay-safe, idempotent progression. In addition, this separation of responsibilities keeps uncertainty at the network boundary. Furthermore, structured progression rules provide predictable settlement behavior. As a result, the system maintains consistency even under adversarial conditions.

Several research questions remain open, including early fork prediction under high propagation variance, structured zero-conf risk modeling, liquidity-aware routing for channel networks, and cross-layer settlement guarantees for L1–L2 systems. Looking ahead, future work will focus on unifying observability across layers. In parallel, improved probabilistic models will push settlement accuracy forward. Ultimately, these advances aim to strengthen the cryptographic and statistical foundations of real-time settlement.

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