Automated Machine to Machine Payments on IoT Networks Start Now
IoT automated machine-to-machine payments let devices pay each other directly, without human approval. A smart car, for example, can automatically settle its own parking or charging fee by communicating with the meter or station. This works because each machine has a digital wallet and a pre-set rule, triggering a transfer the instant a service is used. It eliminates manual wallets and receipts, making everyday transactions invisible and effortless.
The Silent Economy: How Machines Are Becoming Autonomous Payers
In the silent economy, your smart appliances become autonomous payers. A laundry machine detecting low detergent automatically orders and pays for a refill via a machine-to-machine transaction, keeping your routine uninterrupted. Your EV negotiates electricity rates directly with a charging station, settling the fee while you sleep. These IoT automated machine to machine payments eliminate your need to reorder supplies or authorize each micro-purchase. Your refrigerator can pay for groceries it knows you’re low on, and your window blinds could pay for battery swaps. It’s all frictionless background commerce, freeing you from tiny, repetitive decisions.
Defining the Core Mechanism: Triggers, Smart Contracts, and Tokenized Value
The core mechanism relies on pre-set smart contract conditions acting as the transactional backbone. A trigger, such as a sensor detecting inventory depletion or a service completion signal, initiates an automated ledger event. This bypasses human approval, instantly executing a payment in tokenized value—often a stablecoin or utility token. The smart contract verifies the trigger’s validity (e.g., a pump delivering 500 liters of fuel), then authorizes the transfer. The economic relationship is thus embedded directly into the machine’s operational logic, not handled by a separate billing system. Each tokenized transaction creates an immutable record of service rendered versus value exchanged.
Why Traditional Billing Fails When Devices Negotiate in Milliseconds
Traditional billing chokes when devices haggle in milliseconds because it relies on static, pre-set contracts. Machines like a smart car buying energy from a charging station must negotiate price, volume, and delivery in real-time—a process far too fast for human-era invoicing cycles. The core failure is latency: a legacy billing system might take hours or days to process a transaction, but by then the device has already made thousands of micro-deals. This creates reconciliation nightmares, where real-time micro-transaction accounting is impossible. Q: Why can’t old billing handle this speed? A: Because it’s built for monthly batches, not the split-second price agreements autonomous machines need to operate without human oversight. Every millisecond delay in billing could cause a device to miss a critical resource, like a drone losing airspace access mid-route.
Architectural Layers Beneath Autonomous Transactions
Beneath the autonomous transaction, the architectural layers form a silent assembly line. The perception layer begins the payment choreography: a fleet of robotic pallet movers scans RFID tags on loaded inventory, logging each unit’s identity and weight. This data flows immediately to the communication layer, where low-latency MQTT brokers pass a micro-verification request across a private mesh network, confirming the pallet’s journey path. At the execution layer, a smart contract—pre-deployed to an on-premise edge node—checks real-time storage rates and triggers a micro-payment from the warehouse’s operational wallet to the logistics provider’s contract address.
The transaction layer never waits for human approval; it settles the $0.14 fee in a sidechain channel before the pallet even reaches the next zone.
This layered handoff ensures each machine-to-machine payment is a direct, trustless circuit between sensor and settlement, without a centralized ledger bottleneck.
Sensor-Level Event Detection and Payment Initiation Signals
Sensor-level event detection transforms raw environmental data into deterministic payment triggers, forming the bedrock of autonomous machine-to-machine transactions. A pressure sensor in a smart fuel pump, for instance, generates an exact pulse when a vehicle’s tank fills, which is interpreted as a completion event. This signal, parsed at the edge, initiates a cryptographic payment request without human oversight. The system relies on predefined thresholds—such as temperature, weight, or proximity—to validate the event as a billable action. By embedding payment logic directly into sensor firmware, latency drops to milliseconds, ensuring real-time transaction finality between machines.
- Sensor thresholds (e.g., fluid volume or torque) define exact payment trigger conditions
- Edge-based signal parsing converts analog data into a payment protocol payload
- Direct sensor-to-ledger handshakes skip intermediate authorization servers
Edge Computing for Real-Time Verification and Fraud Prevention
Edge computing shifts fraud analysis directly onto IoT gateways, enabling real-time verification of machine-to-machine payments without round trips to the cloud. By processing transaction signatures and device fingerprints locally, edge nodes instantly reject anomalous payment requests, such as those from spoofed sensors or replayed authentication tokens. This architecture eliminates latency windows where fraudulent transactions could slip through centralized checks. Real-time fraud detection at the edge ensures each autonomous payment is cryptographically validated against pre-approved device identities before settlement. The result is a self-contained trust layer that hardens the payment chain against injection attacks and man-in-the-middle exploits.
Edge computing for real-time verification processes telemetry and cryptographic proofs locally, intercepting fraudulent M2M payments before they propagate to settlement networks.
Distributed Ledgers as the Immutable Settlement Backbone
Distributed ledgers function as the immutable settlement backbone by cryptographically sealing each machine-to-machine transaction into a permanent, time-stamped block. This creates a single source of truth that prevents any party—sensor, actuator, or aggregator—from altering past payment records. Every micropayment between IoT devices undergoes consensus verification before being appended, eliminating chargebacks and reconciliation overhead. For autonomous systems, this means settlement finality is guaranteed without a central clearinghouse. The ledger’s append-only architecture allows devices to trust that their balance state is both current and undeniable, enabling real-time value transfer between machines without manual auditing.
| On-Chain Verification | Post-Settlement Immutability |
|---|---|
| Every transaction validated by network consensus before ledger update | No single entity can reverse or modify confirmed blocks |
| IoT devices must include cryptographic proof in each payment request | Historical transaction data remains tamper-proof for audit trails |
High-Impact Use Cases Reshaping Industry Verticals
Specific high-impact use cases reshaping industry verticals through IoT automated machine-to-machine payments include autonomous vehicle refueling, where electric trucks pay charging stations directly via smart contracts, and industrial raw material replenishment, where sensors in silos trigger payment to suppliers when stock hits a threshold. In logistics, smart pallets negotiate and settle tolls or landing fees without human intervention, while in agriculture, irrigation valves autonomously pay for water usage based on real-time soil data. These vertical-specific applications eliminate billing cycles and manual reconciliation, embedding payment logic directly into operational workflows for continuous, trustless asset servicing.
Smart Charging Stations Billing Electric Vehicles per Kilowatt-Hour
Smart charging stations leverage IoT automated machine to machine payments to bill electric vehicles precisely per kilowatt-hour consumed. As a vehicle plugs in, the station’s onboard system authenticates the car’s digital wallet and begins metering energy flow in real time. Upon completion, the M2M payment triggers an instant settlement matching the exact kWh amount, eliminating manual card swipes or subscription tiers. This granular billing ensures drivers pay only for what they use, while station operators avoid revenue loss from flat-rate guesswork. Per-kilowatt-hour M2M settlement transforms EVs into autonomous economic actors, where each charging event executes a microtransaction directly from car to charger without human intervention.
Smart charging stations use IoT M2M payments to bill electric vehicles per kilowatt-hour, enabling precise, automated microtransactions that settle instantly upon charging completion.
Industrial 3D Printers Paying for Raw Material Replenishment
Industrial 3D printers equipped with IoT sensors monitor filament or powder levels in real time. When reserves drop below a programmed threshold, the machine autonomously initiates a direct payment to a pre-approved materials supplier via smart contract. This triggers immediate replenishment shipment without human intervention, ensuring continuous production. The system uses secure machine-to-machine wallets, with each transaction logged for audit. This enables automated raw material replenishment payments that prevent downtime and optimize inventory flow based on actual consumption.
- Printers track material usage via integrated weight sensors and volume scanners.
- Payment triggers are set at customizable stock percentages, like 20% remaining.
- Supplier pricing and delivery terms are pre-negotiated in the linked smart contract.
- Transaction confirmations auto-generate shipping orders and material handling requests.
Autonomous Warehouse Robots Settling Fees for Docking and Power
In high-density logistics hubs, autonomous warehouse robots no longer wait for manual billing. Through IoT automated machine to machine payments, each bot instantly settles fees for docking at a charging station or reserving a floor space for power cycles. The moment a robot locks into a pod, its embedded wallet transmits payment to the station’s controller, deducting a micro-amount based on kilowatt-time consumed. This removes idle negotiation, ensures priority access for high-priority cargo bots, and lets the fleet self-optimize around real-time energy credit balances.
| Function | User-Relevant Benefit |
|---|---|
| Docking fee settlement | No queue disputes; robot pays for reserved slot instantly |
| Power consumption billing | Per-kWh deduction from bot’s IoT wallet |
| Priority override trigger | High-demand bots auto-approve surcharge for faster charging |
Overcoming the Trust Barrier in Unsupervised Financial Handshakes
Overcoming the trust barrier in unsupervised financial handshakes for IoT automated machine-to-machine payments comes down to device identity verification and micro-transaction logic. You need each machine to prove it’s the same device from the last handshake, typically using embedded hardware-backed certificates that can’t be cloned. A practical trick is leveraging session-specific, one-time cryptographic tokens that expire immediately after each payment clears. This stops replay attacks where a rogue device tries to resend a valid payment request. For recurring payments between your smart appliance and a service provider, enforce a hard spending cap per session — the machine cannot authorize a total above this threshold without a fresh human-level authorization. This keeps handshakes fully unsupervised yet financially safe, allowing your IoT devices to settle bills without manual intervention.
Zero-Knowledge Proofs for Privacy Without Sacrificing Audit Trails
In unsupervised machine-to-machine payments, zero-knowledge proofs enable a device to validate a transaction—such as proving sufficient funds or correct meter readings—without exposing the underlying data. This preserves privacy by shielding specific balances or usage patterns, while the cryptographic proof itself is recorded on an immutable audit trail. Regulators or counterparties can later verify that all payments were correctly computed, without ever accessing private details. The practical result is a trustless system where privacy-preserving audit trails ensure both confidentiality and verifiable compliance.
Zero-knowledge proofs separate the fact of a valid transaction from the data used to prove it, allowing full auditability without revealing private device or payment information.
Reputation Scoring Systems for Device-to-Device Creditworthiness
In unsupervised IoT transactions, reputation scoring for device creditworthiness evaluates a device’s historical payment behavior, uptime, and contract fulfillment data. Each machine maintains a tamper-proof ledger of past microtransactions, which peer devices query before authorizing a handshake. Scores update in near-real-time based on settled amounts and defaults, allowing low-reputation devices to rebuild trust through incremental, prepaid engagements. This system eliminates the need for human credit checks, relying instead on collaborative behavioral metrics derived solely from device-to-device interactions.
Reputation scoring systems enable autonomous devices to assess each other’s financial risk using immutable historical transaction data, ensuring trust without oversight.
Escrow Mechanisms That Release Funds Only Upon Task Completion
In IoT machine-to-machine payments, an escrow mechanism holds funds until a service-providing device delivers verifiable proof of task completion. This trust barrier is overcome by programming smart contracts that monitor job completion verification through sensor data or blockchain oracles. A escrow release typically follows a clear sequence:
- The client device deposits payment into a cryptographic escrow account.
- The service device executes the agreed task (e.g., data processing or physical actuation).
- An automated verification script confirms task output against predefined criteria.
- On success, the escrow releases funds to the service device; on failure, funds are returned to the client.
This ensures no party can cheat, as payment is contingent on auditable, machine-validated results.
Protocols and Standards Enabling Interoperable Value Exchange
Protocols like the Interledger Protocol (ILP) and Lightning Network enable interoperable value exchange by creating a standard layer for routing micro-payments across disparate ledgers. For IoT automated machine-to-machine payments, these standards allow a sensor from one manufacturer to pay a charging station from another without pre-agreed contracts. Streaming payments protocols, such as those over WebRTC, facilitate real-time, per-second billing for data or energy usage. A critical detail is the use of Universal Payment Channels (UPC), which standardize state channels to support multiple asset types, ensuring an IoT device can seamlessly transact with any compatible endpoint using a unified settlement mechanism. This removes the need for bilateral integration, enabling truly scalable autonomous economic interactions.
The Role of IOTA, ACN, and Request Network in Micropayment Streams
Micropayment streams for automated machine-to-machine payments rely on IOTA’s Tangle, which eliminates fees and enables zero-value Topio Networks transactions for sensor data streams. ACN (Autonomous Control Network) extends this by routing conditional micropayment triggers between devices, ensuring only verified data exchanges trigger value transfer. Request Network complements these by creating standardized, auditable payment requests that machines can process autonomously, linking each data stream to a corresponding micro-payment without intermediaries. Together, they form a lightweight, scalable protocol layer where IOTA handles settlement, ACN manages stream conditions, and Request Network provides the invoice framework for continuous, trustless device payments.
Why ISO 20022 Adaptation Matters for Industrial Payment Messaging
For industrial IoT machine-to-machine payments, adapting to ISO 20022 matters because it provides a rich, structured data model that industrial machines can parse and act upon autonomously. Unlike flat-file formats, ISO 20022 enables a grinding robot, for example, to receive a payment instruction that includes precise operational context like job ID or maintenance flag, allowing it to reconcile payment with production data without human intervention. This eliminates manual bridging between factory floor and financial systems. Without this standard, automated machines would stall on ambiguous payment messages, breaking the cycle of machine-initiated value exchange. Adoption ensures payment data is as granular and actionable as the telemetry streaming from IoT sensors.
ISO 20022 adaptation ensures industrial machines can process payment messages with the same operational precision they apply to sensor data, enabling fully automated, context-aware value exchange.
How RESTful APIs and Webhooks Replace Human-Initiated Invoices
RESTful APIs replace human-initiated invoices by enabling machines to directly trigger payment requests. When a device logs a service completion, it sends a POST request to the settlement endpoint, bypassing traditional manual invoicing. Webhooks then deliver real-time financial events—such as payment confirmation—back to the machine, eliminating the need for human follow-up. This shift ensures transactions are tied directly to metered usage data, not to a person reviewing or mailing an invoice. The result is an autonomous cycle where the device both initiates and reconciles payment without human intervention.
- Machine sends a REST API request with usage data, auto-generating a payment instead of a human drafting an invoice
- Webhook delivers payment status back to the IoT device, removing the need for manual reconciliation
- Transactions are triggered by sensor events (e.g., completed charge cycles), not by a person reviewing a bill
- Real-time automated settlements replace monthly invoice cycles with per-use micropayments
Security and Resilience When Networks Run Without Human Oversight
For IoT machine-to-machine payments, security without human oversight demands cryptographic attestation at the device level. Each transaction must be signed by a hardware root of trust, and the payment network must validate that signature before settlement. Resilience here means the system autonomously reroutes payments through alternate mesh paths if a primary node fails or is compromised. How does the network detect a compromised device without human intervention? It uses behavioral fingerprinting of transaction patterns—if a sensor suddenly issues erratic payment amounts, the network quarantines that device and triggers a smart-contract-based arbitration hold on its funds, preserving system integrity without a human in the loop.
Mitigating Replay Attacks and Sybil Threats in Autonomous Ledgers
Mitigating replay attacks in autonomous ledgers for IoT machine payments requires embedding unique, time-sensitive nonces into each transaction, ensuring a copied message cannot be re-submitted. Against Sybil threats, a proof-of-stake or reputation-based identity system must validate each participating device, linking its ledger account to a hardware-bound cryptographic key. This dual-layer approach prevents an attacker from both cloning a legitimate payment and creating fraudulent nodes to manipulate consensus weights. Transaction-unique cryptographic nonces thus serve as the primary defense against replay, while Sybil resistance demands continuous validation of device hardware integrity through attestation protocols, not just simple digital signatures.
Fail-Safe Logic for When a Machine Cannot Pay or Overpays
Fail-safe logic for when a machine cannot pay or overpays relies on pre-authorized escrow holds to cap liability. If a machine cannot pay, its connection is severed, and pending transactions revert, preventing service theft. For an overpayment, logic triggers an immediate refund micro-transaction or credits the machine’s ledger against future bills. These actions rely on hardcoded transaction reversal protocols executed offline, ensuring the network avoids cascading debt while maintaining service integrity.
Hardware Security Modules as Trusted Execution Environments
In IoT automated machine-to-machine payments, Hardware Security Modules as Trusted Execution Environments fortify transaction integrity by isolating cryptographic operations from the host system. These tamper-resistant coprocessors ensure that payment keys and signing logic execute within a sealed, auditable enclave, preventing data leakage even if the main IoT firmware is compromised. By performing real-time attestation, an HSM verifies that a connected machine’s trusted execution environment is uncompromised before authorizing a micropayment. Q: Can an HSM safeguard payment keys if a sensor’s OS is breached? A: Yes, because the HSM’s keys never leave its physical boundary—the compromised OS only sees encrypted requests, not the raw private keys.
Economic Shifts Created by Unattended Revenue Streams
When machines pay other machines without human oversight, you unlock unattended revenue streams that fundamentally shift your cash flow dynamics. A vending machine that automatically reorders stock when low, or a smart EV charger that bills a car’s wallet, creates income that flows even while you sleep. This eliminates the lag between service delivery and payment collection, turning sporadic sales into a predictable, always-on income engine. The real economic shift here is the collapse of idle time—equipment that previously waited for manual billing now generates micro-transaction revenue around the clock, transforming underutilized assets into constant profit centers without you lifting a finger.
From CAPEX to OPEX: Devices Leasing Themselves via Microtransactions
The shift from capital expenditure to operational expenditure redefines device acquisition, as machines use IoT-driven microtransactions to pay for their own usage. Under this model, a device leases itself by streaming tiny payments per function—a printer deducts fees per page, a factory sensor pays per data packet. This eliminates upfront hardware costs, enabling businesses to deploy equipment without large capital outlays. Ownership becomes irrelevant when the device autonomously funds its lifecycle through incremental revenue. The result is a fluid, pay-per-use economy where self-leasing IoT assets align cost directly with value generated.
Devices leasing themselves via microtransactions transforms CAPEX-heavy purchases into OPEX-friendly subscriptions, letting machines financially self-sustain through automated, per-action payments.
The Rise of Asset Tokenization and Peer-to-Peer Equipment Sharing
Asset tokenization converts equipment into digital tokens on a ledger, enabling fractional ownership and direct peer-to-peer sharing without intermediaries. An IoT-connected excavator, for instance, can autonomously accept machine-to-machine payments from a builder’s token wallet for a four-hour rental, unlocking its idle hours. The owner earns revenue per second of use, while the renter avoids a full purchase. This shifts value from static assets to dynamic, tokenized access rights, creating programmable revenue streams where the equipment itself negotiates its own uptime and price, governed by smart contracts that settle payments automatically between parties.
Predictive Maintenance Billing Based on Actual Wear, Not Time Passed
Predictive maintenance billing shifts costs from calendar-based schedules to actual component degradation, using IoT sensor data to trigger microtransactions for repairs only when wear thresholds are met. This model aligns payment with real asset health, eliminating wasted spend on premature servicing. A machine might invoice the operator automatically after measuring a specific vibration pattern in its bearing, not simply because six months have elapsed. The payment is executed via machine-to-machine contracts when the system confirms usage-driven decay, ensuring capital is deployed only for value-preserving interventions.
Q: How does billing based on actual wear differ from leasing or subscription fees?
A: It ties each micro-payment directly to a measurable degradation event, like increased friction or cycle count, rather than a flat periodic charge. You pay for the specific repair triggered by verified use, not a general cost of access.
Regulatory and Compliance Hurdles for Non-Human Contracting Parties
Non-human contracting parties in IoT machine-to-machine payments face fundamental legal capacity hurdles. A smart vending machine cannot consent to terms of service, creating binding contract gaps. Compliance automation must therefore pre-validate machine identities against pre-approved contract frameworks. Without clear attribution of liability, a faulty sensor triggering an unauthorized payment exposes all parties to regulatory action. You must design your system so every machine transaction is backed by an immutable, audit-ready trail of regulatory accountability. This means linking each payment to a verified human principal, ensuring your automated agreements don’t become compliance landmines.
Legal Personhood for Autonomous Agents in Payment Agreements
For autonomous agents executing IoT machine-to-machine payments, the core hurdle is that current contract law lacks a framework for digital legal personhood. Without this status, a smart washing machine agreeing to pay a detergent dispenser cannot be held liable for a broken payment agreement, leaving the human owner responsible. This forces users to pre-fund agent wallets or accept full liability for agent actions. To function reliably, each agent must be bound to a specific legal entity via a unique digital identity, with pre-defined spending limits and jurisdictional rules hardcoded into its logic.
- Assign each autonomous agent a verifiable digital identity linked to a legal person or entity.
- Embed explicit spending caps and revocation conditions directly into the agent’s payment contract.
- Configure the agent to only execute payments within a pre-approved legal jurisdiction.
- Record all agent-initiated agreements on a tamper-proof ledger to establish audit trails.
Taxation Challenges When a Sensor Owns a Digital Wallet
Taxation challenges arise when a sensor owns a digital wallet, as the sensor lacks a legal personality to file taxes or report income from IoT automated machine to machine payments. The primary hurdle is assigning tax liability for each micro-transaction, since no human is directly responsible. A clear sequence is required to manage this:
- Identify the sensor’s controlling entity, typically its owner or operator, as the taxable person.
- Automate the tracking of every wallet balance change to calculate taxable event thresholds.
- Configure the sensor’s smart contract to deduct estimated tax and forward it to a revenue account.
This approach addresses sensor-to-sensor tax liability attribution by ensuring that tax obligations are met without manual intervention, but it requires precise coding of tax rules into the wallet’s logic.
Data Sovereignty Rules for Cross-Border Machine Transactions
For IoT automated machine-to-machine payments, cross-border data sovereignty rules mean your smart devices must comply with local laws about where their transaction data is stored and processed. If a sensor in Germany pays a factory in Japan, the payment log might need to stay in Germany’s borders—even if the machine is made in the U.S. This forces you to route payment data through regional servers or use edge computing so data never physically leaves the country. Ignoring this can freeze your machine’s payment flows mid-transaction, so you’ll need to pre-configure each device with a geo-aware data routing plan.
Future Trajectories: Evolving Payment Behaviors in Autonomous Fleets
Within autonomous fleets, future trajectories in payment behaviors will shift from scheduled invoices to immediate, context-aware transactions. Each truck, when it docks for charging or swaps cargo, triggers an IoT automated machine-to-machine payment that settles in seconds. Your fleet manager won’t approve each charge; the vehicle negotiates micro-payments for energy or tolls based on real-time conditions. This means cash flow adapts dynamically—paying more for premium routes when margins allow, or pausing services if budgets tighten. The behavior evolves into a fluid, self-managing economic loop where machines handle the accounting, freeing you from oversight.
Negotiating Dynamic Pricing Based on Energy Grid Load and Demand
Autonomous fleet vehicles negotiate charging rates in real-time by communicating with the grid via IoT. When the grid load spikes, your fleet’s onboard system can accept a higher per-kWh price to charge immediately, or defer and receive a lower rate for off-peak demand. This dynamic grid pricing negotiation happens through automated M2M contracts, where each vehicle evaluates its route schedule and battery status to decide a price threshold. The process follows a clear sequence:
- Grid broadcasts a fluctuating price signal based on current load.
- Fleet vehicle computes its operational need and maximum acceptable cost.
- Machine-to-machine agreement executes at the optimal price point, adjusting in seconds.
This keeps your fleet energy-cost-adaptive without human intervention.
Swarm Economics Where Thousands of Devices Auction Resources
In swarm economics, thousands of devices autonomously auction their compute, storage, or bandwidth in real-time, each bidding for tasks based on current load and power cost. A sensor fleet might outsource data processing to nearby edge nodes with lowest-latency swarm bids, while a delivery drone pays a charging pad for a brief, high-price energy slot during peak demand. This auction mechanism ensures each transaction reflects marginal local scarcity, preventing overpayment for surplus resources and dynamically shifting work to idle units. The system prioritizes efficiency by allowing devices to withdraw bids if their own priority tasks arise, maintaining operational fluidity without centralized negotiation.
Integration with Decentralized Identity for Auditable yet Private Flows
Integration with Decentralized Identity enables autonomous fleet vehicles to authenticate machine-to-machine payments without exposing operator identities. Each vehicle holds a self-sovereign DID, signing transactions that are recorded on a permissioned ledger. The ledger’s cryptographic proofs allow auditors to verify that only authorized units transacted, while zero-knowledge proofs selectively disclose specific attributes (e.g., “vehicle is insured”) without revealing the VIN or owner. This preserves audit compliance for fee settlements and usage logs while keeping sensitive fleet data private from payees and network observers.
What Exactly Are Autonomous Machine-to-Machine Payments?
Defining Smart Transactions Between Connected Devices
How Devices Negotiate and Settle Payments Without Human Intervention
Key Differences From Traditional Digital Payment Systems
Core Components That Enable Automated Device Payments
Embedded Wallets and Cryptocurrency Accounts Inside Machines
Smart Contracts That Trigger Payments When Conditions Are Met
Secure Communication Protocols for Transaction Authorization
