14 CoAP Observe: Efficiency and Lifecycle

coap

protocols

observe

server-push

14.1 Start With the Decision

This section: Start With the Decision — A network address translator (NAT) may let an idle UDP mapping expire before the next change reaches an observer, a client waiting for sensor changes.

A network address translator (NAT) may let an idle UDP mapping expire before the next change reaches an observer, a client waiting for sensor changes. A keep-alive is a small message sent to keep that path open. Weigh its cost against making a new connection.

14.2 Route Overview

This section: Route Overview — This is part 2 of 2.

This is part 2 of 2. Review CoAP Observe: Subscriptions and Updates for the preceding evidence.

14.3 Learning Objectives

This section: Learning Objectives

Calculate a NAT keep-alive interval and its traffic.
Compare Observe traffic, battery wakes, and client restarts.

14.4 Chapter Roadmap

This section: Chapter Roadmap

Interactive Calculator: NAT Keep-Alive Interval
Pitfall: Mismanaging Observe Tokens Across Client Restarts
Bandwidth Savings Calculation
Interactive Calculator: Battery Life Comparison
Interactive: CoAP Observe Pattern Animation
Worked Example: Industrial Vibration Monitoring with Observe
Decision Framework: When to Use CoAP Observe
Common Mistake: Forgetting to Handle Observer Cleanup After Client Crashes
Checkpoint: Failure Modes
Common Pitfalls
1. Using Confirmable Messages for Every CoAP Request
2. Ignoring CoAP Proxy Caching Semantics
3. Forgetting DTLS Session Management
Label the Diagram
Order the Steps
Practice Exercises
Hands-On Practice
Checkpoint: Evidence Before Selection
Concept Relationships
See Also
Match the Concepts
What’s Next
Summary

Interactive Calculator: NAT Keep-Alive Interval

How to use: Enter a measured or assumed NAT mapping lifetime and the number of client mappings. The calculator estimates client-originated refresh traffic under a one-mapping-per-observation assumption; real clients may share mappings. Check the actual network before setting an interval.

14.5 Pitfall: Mismanaging Observe Tokens Across Client Restarts

The Mistake: Clients generate new random tokens after reboot without deregistering previous observations, causing “ghost subscriptions” where the server continues sending notifications to tokens the client no longer recognizes.

Why It Happens: The Observe pattern uses tokens to match notifications to subscriptions. When a client reboots, it loses its token-to-subscription mapping but the server still has the observer registered.

The Fix: Implement proper token lifecycle management:

Re-register after reboot: Rebuild observation state with fresh tokens; persist state only if its endpoint context remains valid
Bind each request safely: Use a fresh observation Token after reboot. For secure CoAP, follow RFC 9175 Section 4.2 to prevent a response being bound to the wrong request; for example, use a per-connection sequence that is not reused until rekeying
Handle orphaned notifications gracefully: When receiving notification with unknown token, send RST
Server-side lease: If needed, configure a separate observer lease; Max-Age only controls representation freshness

14.6 Bandwidth Savings Calculation

This section: Bandwidth Savings Calculation

Example: Temperature sensor with 100 observers

Polling approach (GET every 10 seconds):

Request: 14 bytes (header + token + Uri-Path)
Response: 15 bytes (4-byte header + 2-byte Token + 2-byte Content-Format + 1-byte payload marker + 6-byte JSON string "22.5")
Total: 29 bytes x 100 clients x 6/min x 60 min = 1,044,000 bytes/hour

Observe approach (notify on change, avg 6 changes/hour):

CoAP header: 4 bytes
Token: 2 bytes
Observe option: 3 bytes
Content-Format: 2 bytes
Payload marker: 1 byte
Payload: 6 bytes (JSON string "22.5", including quotes)
Total: 18 bytes x 100 clients x 6 changes = 10.8 KB/hour

Savings: about 99% bandwidth reduction (1,044,000 vs 10,800 bytes/hour)

Interactive Calculator: Battery Life Comparison

How to use: Enter nominal battery capacity and illustrative radio-event costs. The calculator compares polling and Observe energy under those inputs; use a measured full-device power profile to estimate battery life.

14.7 Interactive: CoAP Observe Pattern Animation

CoAP Observe narrated tour

Play silently with captions on provisional storyboard timing; narration audio is pending. Production pacing is not device or protocol time. Choose Reduced motion for a complete still at each beat.

Play the tour to open the workbench. Pause, replay a beat, or read the full transcript below. Calculations or situations this teaching model cannot show keep the existing frame and provide captions only.

Full transcript — CoAP Observe (8 beats)

B1 — Temperature dashboard
You run a temperature dashboard for a sensor with one hundred observers. You need updates when its value changes, without asking for the same reading over and over. Treating an old temperature as current would defeat that job.

Model caption: Your temperature dashboard needs updates without repeated polling, and must avoid treating an old reading as current. The chapter example has 100 observers.

B2 — Register and receive
Observe lets a client register once so the server can send changes. The client sends a get request with the Observe option set to zero. A token, an identifier linking notifications to the subscription, accompanies the request. The server returns the first reading.

Model caption: The client registers with GET, Observe=0, and a token. The server returns the first reading with the matching token and an Observe sequence value.

B3 — Polling byte comparison
Compare an hour of polling exchanges with change notifications. Polling every ten seconds means six exchanges per minute. Multiply six by sixty minutes: three hundred sixty exchanges per observer. Each exchange uses twenty-nine bytes. Multiply by one hundred observers: two thousand nine hundred bytes per round. Multiply by three hundred sixty rounds: one million forty-four thousand bytes. With six changes per hour, each notification uses eighteen bytes. Multiply by one hundred observers: eighteen hundred bytes per change. Multiply by six: ten thousand eight hundred bytes. That notification total excludes registration.

Model caption: Chapter byte comparison: polling every 10 s gives 6 × 60 = 360 exchanges per observer/hour. 29 bytes × 100 observers = 2,900 bytes/round; × 360 = 1,044,000 bytes/hour. Change notifications: 18 bytes × 100 = 1,800 bytes/change; × 6 changes/hour = 10,800 bytes/hour, excluding registration.

B4 — Discard older updates
Each notification has an Observe sequence value to help order updates. Suppose sequence one thousand twelve arrives before a delayed one thousand eleven. The client keeps one thousand twelve in its cache, its saved copy of the reading, and discards the older update.

Model caption: Sequence 1012 arrived first. Delayed sequence 1011 is older, so the client discards it and keeps sequence 1012 in its saved copy.

B5 — NON and CON delivery
Delivery can be lightweight: a nonconfirmable notification expects no acknowledgement. A confirmable notification requires an acknowledgement from the client. That acknowledgement repeats the notification's message identifier, confirming receipt without carrying a new reading.

Model caption: A nonconfirmable notification expects no acknowledgement. A confirmable notification receives an empty acknowledgement with the same message identifier; it confirms receipt and carries no new reading.

B6 — Freshness expires
The misconception is that an active subscription guarantees a current reading. Max-Age sets how long a saved reading stays fresh. With Max-Age at thirty seconds, a reading that is thirty-eight seconds old is stale, even while the observer remains registered. Check with the server for a fresh reading before treating that temperature as current.

Model caption: Age 38 s exceeds Max-Age 30 s. The saved reading is stale while the observer remains registered. Check with the server for a fresh reading before treating that temperature as current.

B7 — End observation
When your dashboard stops observing, it can send a get request with Observe set to one for the same sensor, using its subscription token. The server's response omits Observe, ending the stream. Explicit cancellation removes the observer entry.

Model caption: GET with Observe=1 for the same sensor, using its subscription token requests cancellation. The response omits Observe, the stream ends, and the observer entry is removed.

B8 — Check saved readings
Observe saves repeated questions, but your dashboard must still check whether its saved temperature is fresh.

Model caption: Observe saves repeated questions, but your dashboard must still check whether its saved temperature is fresh.

Worked Example: Industrial Vibration Monitoring with Observe

Scenario: A manufacturing plant monitors vibration levels on 100 motors using ESP32 sensors with accelerometers. Maintenance dashboard needs real-time updates when vibration exceeds thresholds (normal: <2.5 mm/s, warning: 2.5-4.5 mm/s, critical: >4.5 mm/s).

Comparing polling vs CoAP Observe:

Option A: Polling (GET every 5 seconds):

Client polls: GET coap://motor42.local/vibration every 5 seconds
  Request: 16 bytes (CoAP header + token + URI)
  Response: 20 bytes (CoAP header + token + payload "2.3")

Per sensor traffic (24 hours):
  17,280 requests × 16 bytes = 276,480 bytes
  17,280 responses × 20 bytes = 345,600 bytes
  Total: 622,080 bytes/day per sensor

Fleet traffic: 622,080 × 100 = 62.2 MB/day
Energy per sensor: 17,280 × 3.0 mJ (CON request-response) = 51.8 J/day
Radio-event energy-only quotient (nominal 18650, 3.7 V, 3,000 mAh): 40 kJ ÷ 51.8 J/day ≈ 772 days

Option B: CoAP Observe (notify on change, max 1/minute):

Client registers: GET /vibration, Observe: 0 (once at startup)
  Registration: 18 bytes (request) + 22 bytes (response with Observe: 1)

Server notifies only when vibration crosses thresholds:
  Typical motor: 3 threshold crossings/day (normal ↔ warning ↔ critical)
  Notifications: 3 × 20 bytes = 60 bytes/day
  Plus max-rate limit: If vibrating continuously, max 1,440 notifications/day

Per sensor traffic (stable operation, 3 changes/day):
  Registration: 40 bytes (one-time)
  Notifications: 60 bytes/day
  Total: 100 bytes on registration day; 60 bytes on later days

Fleet traffic after startup: 60 × 100 sensors = 6,000 bytes/day (vs 62.2 MB with polling)
Energy: 3 notifications × 1.5 mJ = 4.5 mJ/day
Radio-event energy-only quotient: 40,000 J ÷ 0.0045 J/day ≈ 8.9 million days (not a physical battery-life prediction)

Bandwidth savings after startup: (62,208,000 - 6,000) / 62,208,000 ≈ 99.99%
Transmit-event energy reduction: 51.8 J/day ÷ 0.0045 J/day ≈ 11,500×; actual battery-life gain also depends on sleep, sensing, listening, and battery ageing

Implementation with rate limiting:

class RateLimitedVibrationResource:
    def __init__(self):
        self.min_notify_interval = 60  # seconds (max 1/minute)
        self.threshold_change = 0.5     # mm/s (notify if change ≥ 0.5)
        self.critical_alarm_value = 4.5  # mm/s; alarm crossings bypass routine cap
        self.last_notify_time = {}
        self.last_notified_value = {}

    async def notify_observers(self, new_value):
        now = time.time()
        for observer in self.observers:
            last_time = self.last_notify_time.get(observer.token, 0)
            last_value = self.last_notified_value.get(observer.token, None)

            # Routine changes need both a value delta and the 60 s interval.
            significant_change = (
                last_value is not None and
                abs(new_value - last_value) >= self.threshold_change
            )
            interval_passed = (now - last_time) >= self.min_notify_interval
            urgent_alarm = (
                new_value >= self.critical_alarm_value and
                (last_value is None or last_value < self.critical_alarm_value)
            )

            if last_value is None or urgent_alarm or (significant_change and interval_passed):
                await self.send_notification(observer, new_value)
                self.last_notify_time[observer.token] = now
                self.last_notified_value[observer.token] = new_value

Decision: CoAP Observe

Reasoning:

About 99.99% bandwidth reduction after startup (critical for cellular backhaul)
Transmit-event energy falls from 51.8 J/day to 4.5 mJ/day under these illustrative assumptions; total battery life also depends on sleep and sensing current
Critical alarm crossings bypass the routine 60-second cap, so alerts have no polling delay
Server-side rate limiting prevents notification floods

This section:

Decision Framework: When to Use CoAP Observe

Use this flowchart to decide if Observe is appropriate for your application:

Requirement	Use Observe	Use Polling	Use Neither (MQTT)
Update frequency	Event-driven, infrequent changes	Periodic, consistent intervals	Continuous streaming
Number of observers	1-10 per resource	1-3 per resource	10+ observers (broker scales better)
Change rate	<10% of polling rate	>50% of polling rate	Constant changes
Battery constraints	Critical (coin cell, multi-year)	Moderate (rechargeable)	Mains-powered
Network reliability	Stable (LAN, Wi-Fi)	Lossy (cellular)	Stable with fallback
Observer lifecycle	Long-lived (hours to days)	Short-lived (seconds to minutes)	Permanent subscriptions

Decision tree:

Does the resource change less often than you would poll it? → No: Use polling (Observe overhead not justified) → Yes: Continue
Do you need notifications from more than 10 resources per client? → Yes: Consider MQTT (broker-based pub-sub scales better) → No: Continue
Are clients behind NAT/firewall without port forwarding? → Yes: Problem - server can’t push to client. Options:
- CoAP over TCP (RFC 8323) with connection keep-alives where supported
- Long polling instead of Observe
- MQTT instead → No: Continue
Is battery life critical (>1 year target on coin cell)? → Yes: Use CoAP Observe (eliminate polling overhead) → No: Polling is acceptable, but Observe still beneficial

Example decisions:

Application	Observe?	Reasoning
Temperature sensor (changes every 30 min)	Yes	Polling every 5 min wastes 5× bandwidth
Stock price ticker (changes every second)	No	Polling every second = always changing, no savings
Door sensor (changes 10×/day)	Yes	Massive savings (99% fewer messages)
Accelerometer (100 Hz continuous)	No	Use MQTT or streaming protocol
Smart meter (reads every 15 min)	Depends	If value changes every time, polling OK. If often unchanged, Observe better

This section:

Common Mistake: Forgetting to Handle Observer Cleanup After Client Crashes

The Error: Server registers Observe subscriptions but never removes them when clients crash or restart, leading to “ghost observers” that consume memory and network bandwidth sending notifications to unreachable clients.

Why It Happens: CoAP Observe uses tokens to match notifications to requests. When a client crashes and restarts, it may generate a new token. The server can continue sending notifications to the old token; if the restarted client receives an unrecognized notification, it can reject it with RST.

Illustrative worst case without cleanup: An industrial monitoring system with 500 sensors and 20 dashboard clients observing each sensor, assuming every refresh creates a distinct observation and each stale one persists:

Without observer cleanup:

Each browser refresh creates new Observe subscription (new token)
Each old subscription remains active (server doesn't know browser closed)

After 1 week (browsers refresh ~50 times each):
  Per sensor: 20 clients × 50 refreshes = 1,000 stale observations
  Per sensor: 20 active observations; 1,020 entries total
  Fleet: 500,000 stale + 10,000 active = 510,000 entries
  Ratio per sensor: 1,000 / 20 = 50:1 stale to active

Notification overhead:
  500 sensors × 10 changes/hour × 1,020 observations per sensor = 5.1M notifications/hour
  Wasted: 500 sensors × 10 changes/hour × 1,000 stale observations = 5M/hour (98% waste)
  CPU and network load depend on message size, transport, and how quickly RSTs are processed; they cannot be inferred from these counts.

The Fix:

1. Optional server-side observer lease (application policy, not Max-Age):

# Max-Age describes client-side representation freshness, not a server-side lease.
observer_lease_seconds = 3600  # This application chooses a one-hour lease.

# Application policy: observer must re-register before its lease expires.
def cleanup_expired_observers(self):
    now = time.time()
    for resource_uri, observers in self.observers.items():
        self.observers[resource_uri] = [
            o for o in observers
            if now - o.registered_at < o.timeout
        ]

# Run cleanup periodically (for example, once a minute); defining it alone does nothing.
schedule_periodic(self.cleanup_expired_observers, interval_seconds=60)

2. RST detection (immediate cleanup):

def on_rst_received(self, client_addr, message_id):
    """Remove observer when client sends RST to notification"""
    for resource_uri, observers in self.observers.items():
        self.observers[resource_uri] = [
            o for o in observers
            if not (o.client_addr == client_addr and o.last_mid == message_id)
        ]
    logging.info(f"Removed observer {client_addr} after RST")

3. CON notification failures (retry limit): This is transport pseudocode: send_datagram_once sends exactly one datagram and does not perform library-managed retransmission.

async def send_notification(self, resource_uri, observer, value):
    """Send CON notification, remove observer after 4 failed retries"""
    msg = Message(code=CONTENT, token=observer.token, payload=value)
    msg.mtype = CON  # Confirmable - requires ACK
    msg.mid = allocate_message_id()  # Same MID on every retransmission.
    timeout = random.uniform(2.0, 3.0)  # RFC 7252 initial ACK timeout.

    for attempt in range(5):  # original transmission plus four retransmissions
        try:
            await send_datagram_once(observer.client_addr, msg)
            await wait_for_ack(msg.mid, timeout=timeout)
            return  # Success
        except TimeoutError:
            logging.warning(f"Notification attempt {attempt+1} failed for {observer}")
            timeout *= 2

    # No ACK after original send and four retransmissions: remove observer
    self.remove_observer(resource_uri, observer)
    logging.info(f"Removed unresponsive observer {observer.client_addr}")

Results after implementing cleanup:

With the stated one-hour application lease, stale entries survive at most one hour unless
RST or failed CON delivery removes them earlier. Measure CPU and traffic in the deployment;
these mechanisms alone do not establish percentage savings.

Prevention checklist:

Set Max-Age to state representation freshness (CoAP default: 60 seconds)
If needed, define a separate server-side observer lease as application policy
Implement RST detection and immediate observer removal
Use CON notifications (not NON) so you detect unreachable clients
Use at most four retransmissions after the original CON send (RFC 7252 default)
Log observer registration/removal for debugging
Monitor observer count per resource (alert if >expected)

This section:

Broker Bex, Messaging and broker domain expert

Checkpoint: Failure Modes

You now know unknown tokens after a restart should trigger RST cleanup rather than silent drops.
You now know UDP NAT mapping lifetimes vary, so quiet Observe streams may need client-originated keep-alives or re-registration.
You now know RST and failed CON delivery remove ghost observers; an optional server lease is separate from Max-Age freshness.

This section:

Common Pitfalls

This section: Common Pitfalls

1. Using Confirmable Messages for Every CoAP Request

CON messages require an ACK exchange. With RFC 7252 defaults, four retransmissions occur after randomized waits totaling 30–45 seconds; the final acknowledgement wait can extend failure detection to 62–93 seconds, regardless of any fixed packet-loss percentage. Use NON for periodic telemetry where data freshness matters more than guaranteed delivery; reserve CON for actuation commands.

2. Ignoring CoAP Proxy Caching Semantics

CoAP proxies cache GET responses based on Max-Age option — a sensor returning temperature with Max-Age=60 will serve cached values for 60 seconds even if the physical reading changes. Set Max-Age to match your data freshness requirement, not the default 60 seconds.

3. Forgetting DTLS Session Management

A fresh DTLS handshake can dominate a short CoAP exchange; its round trips and latency depend on DTLS version, authentication, loss, and link delay. Reuse an established session where appropriate, or use the resumption mechanism supported by the deployed DTLS version (RFC 9147 for DTLS 1.3).

Label the Diagram

Order the Steps

14.8 Practice Exercises

This section: Practice Exercises

Hands-On Practice

Exercise 1: Observer Registry Design Design an observer registry that supports:

Maximum 50 observers per resource
Automatic cleanup of stale observers (> 24 hours)
Rate limiting: max 1 notification per second per observer

Exercise 2: Bandwidth Calculation A smart building has 200 temperature sensors, each observed by 3 clients (dashboard, HVAC controller, alarm system). Sensors report every time temperature changes by 0.5°C. Calculate:

Read these points as one connected sequence: start with Estimated notifications per hour (assuming 2 significant changes per sensor per hour); then Bandwidth usage with 20-byte notification payloads; and finish with Savings compared to polling every 30 seconds.

Estimated notifications per hour (assuming 2 significant changes per sensor per hour)
Bandwidth usage with 20-byte notification payloads
Savings compared to polling every 30 seconds

Exercise 3: NAT Keep-Alive Strategy Design a client-originated keep-alive strategy for a CoAP deployment where:

Measured NAT mapping timeout is 45 seconds
1,000 client mappings need refreshing
Aggregate outbound bandwidth is limited to 10 Kbps for keep-alives

What keep-alive interval would you choose and why?

This section:

Checkpoint: Evidence Before Selection

You now know the chapter’s 100-observer example can show 99% bandwidth reduction when changes are infrequent.
You now know the vibration example’s 772-day polling figure is a transmit-only charge quotient, and its Observe quotient is much larger under the same assumptions.
You now know MQTT becomes the better comparison when subscriber counts or continuous streams outgrow direct server push.

This section:

14.9 Concept Relationships

This section: Concept Relationships — Start with message types to see how updates arrive.

How CoAP Observe connects to broader IoT patterns and protocols:

Observe builds on:

Start with message types to see how updates arrive. Then look at the state a server keeps over UDP.

CoAP Message Types - Uses CON/NON for notifications with sequence number tracking
UDP Transport - Stateless protocol requiring application-layer state management

Similar patterns in other protocols:

MQTT Subscriptions - Topic-based pub/sub vs URI-based observe
WebSocket Server Push - Full-duplex vs observe’s asymmetric push
Server-Sent Events - HTTP push mechanism for comparison

Observe enables:

Event-Driven IoT - React to changes instead of polling
Real-Time Monitoring - Dashboard updates without refresh
Smart Home Automation - Light sensors triggering actuators instantly

Implementation challenges:

NAT Traversal - UDP mapping timeouts requiring keepalives
Sequence Number Management - Detecting out-of-order delivery
State Synchronization - Client-server observation lifecycle

Performance considerations:

Energy Optimization - compare CON and NON event charge with the full device duty cycle
Bandwidth Management - Push vs poll bandwidth comparison
Scalability Patterns - Server memory per observer (illustrative 50-100 bytes; measure the chosen stack)

14.10 See Also

This section: See Also — Review CoAP message basics before the GET registration method that starts an Observe subscription.

CoAP Core Topics:

Review CoAP message basics before the GET registration method that starts an Observe subscription.

CoAP Fundamentals - Message types and reliability mechanisms
CoAP Methods - GET with Observe: 0 option for registration
CoAP Security - Securing observe notifications with DTLS

Implementation Guides:

Try the Observe code first. Then study how the server tracks subscribers.

CoAP Observe Implementation - Python and ESP32 code examples
Observer Registry Design - Server-side state management
Rate Limiting Strategies - Preventing notification floods

Protocol Comparisons:

MQTT vs CoAP Observe - Topic subscriptions vs resource observation
Long Polling vs Observe - HTTP alternative to push notifications
gRPC Streaming - Modern RPC with bidirectional streaming

Real-World Applications:

Start with the vibration sensor example. Then compare the farm example.

Industrial IoT Monitoring - Vibration sensors with conditional observe
Smart Agriculture - Soil moisture notifications on threshold changes
Building Automation - Temperature updates to thermostats

Specifications & RFCs:

Read the Observe rules first. Then review the base CoAP exchange.

RFC 7641 - Observe Extension - Official specification
RFC 7252 Section 5.10 - CoAP base protocol observe hooks
CoRE WG Documents - Latest IETF working group updates

Debugging & Tools:

Use the Wireshark reference to inspect notifications, then the client tool to send an Observe request.

Wireshark CoAP Dissector Reference - Analyzing observe sequence numbers
coap-client CLI Tool - Testing observe from command line
Californium Proxy - HTTP-CoAP observe bridging

Match the Concepts

14.11 What’s Next

This section: What’s Next

Now that you understand CoAP server push and the Observe extension, these chapters build directly on your knowledge:

CoAP Observe Registration and Freshness Contracts Focus: Token binding, Max-Age freshness, CON liveness checks, sequence ordering, and stale-notification handling Why read it: Turn Observe from a bandwidth-saving pattern into an auditable notification contract that recovers from loss, reordering, NAT expiry, and client restarts.
CoAP Advanced Features Focus: Block-wise transfer and large payload handling Why read it: Extend your Observe knowledge to handle firmware-update notifications that exceed a single UDP packet.
CoAP API Design Focus: RESTful URI patterns and resource modeling Why read it: Design well-structured observable resources that follow CoAP best practices for naming and content formats.
CoAP Security Applications Focus: Securing CoAP with DTLS and OSCORE Why read it: Protect observer registration and notification streams from eavesdropping and injection attacks.
CoAP Implementation Labs Focus: Python and ESP32 hands-on examples Why read it: Apply the registry and rate-limiting patterns from this chapter in working code on real hardware.
CoAP Message Types Focus: CON vs NON reliability and ACK/RST mechanics Why read it: Understand the reliability layer underpinning Observe notifications and how CON retransmission affects observer cleanup.
MQTT Broker and Topics Focus: Topic-based publish-subscribe with a broker Why read it: Compare Observe’s direct server-push model against MQTT’s broker-mediated fan-out for high-subscriber scenarios.

14.12 Summary

This section: Summary — CoAP Observe turns a resource into a subscription-style stream while keeping RESTful resource semantics.

CoAP Observe turns a resource into a subscription-style stream while keeping RESTful resource semantics. Clients register interest, servers send ordered notifications, and both sides must handle sequence numbers, cancellation, and loss.

14.13 Continue Your Route

This section: Continue Your Route — This is the last part of this chapter.

This is the last part of this chapter. Return to CoAP Observe: Subscriptions and Updates or continue from the coap module index.

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