Antigravity Agent
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10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
179 lines
5.3 KiB
Markdown
179 lines
5.3 KiB
Markdown
---
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id: wiki-2026-0508-iot-and-ai-integration
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title: IoT and AI Integration
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Edge AI, TinyML, IoT AI, AIoT]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [iot, edge-ai, tinyml, embedded, mqtt, sensor-fusion]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: cpp
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framework: tflite-micro
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---
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# IoT and AI Integration
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## 매 한 줄
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> **"매 센서 옆에서 즉시 추론"**. 수 KB-수 MB 모델을 ESP32/Cortex-M/Coral 같은 edge 기기에 올려 latency, privacy, bandwidth 를 동시에 잡는다. 2026 현재 TinyML + MQTT 가 표준.
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## 매 핵심
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### 매 3 layer 아키텍처
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1. **Edge (sensor)**: TinyML 추론, 이상 감지, 게이팅.
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2. **Fog (gateway)**: 다중 센서 통합, heavier 모델, 로컬 의사결정.
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3. **Cloud**: 학습, fleet 모니터링, OTA 모델 업데이트.
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### 매 모델 압축
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- **Quantization**: float32 → int8/int4, 4-8x 작아짐.
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- **Pruning**: weight magnitude 기준 sparsify.
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- **Knowledge distillation**: large teacher → tiny student.
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- **NAS for edge**: MCUNet, MobileNet.
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### 매 통신
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- **MQTT**: pub/sub, QoS 0/1/2.
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- **CoAP**: REST-over-UDP, 더 가벼움.
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- **LoRaWAN**: km 단위, 수백 byte/min.
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- **BLE**: 근거리, 저전력.
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### 매 응용
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1. Predictive maintenance (vibration anomaly).
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2. Vision: door cam person detection, defect inspection.
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3. Voice wakeword (Alexa, "OK Google").
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4. Smart agriculture (soil moisture + weather).
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5. Health wearable (HRV, fall detection).
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## 💻 패턴
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### TFLite Micro 추론 (ESP32, C++)
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```cpp
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#include "tensorflow/lite/micro/micro_interpreter.h"
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const tflite::Model* model = tflite::GetModel(g_model_data);
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static tflite::MicroMutableOpResolver<5> resolver;
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resolver.AddConv2D(); resolver.AddMaxPool2D();
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resolver.AddReshape(); resolver.AddFullyConnected(); resolver.AddSoftmax();
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constexpr int kArena = 60 * 1024;
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uint8_t arena[kArena];
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tflite::MicroInterpreter interp(model, resolver, arena, kArena);
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interp.AllocateTensors();
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TfLiteTensor* in = interp.input(0);
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memcpy(in->data.int8, sensor_buf, in->bytes);
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interp.Invoke();
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int8_t* out = interp.output(0)->data.int8;
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```
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### Post-training quantization (Python)
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```python
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import tensorflow as tf
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conv = tf.lite.TFLiteConverter.from_saved_model("model")
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conv.optimizations = [tf.lite.Optimize.DEFAULT]
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conv.target_spec.supported_types = [tf.int8]
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conv.representative_dataset = lambda: (
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[x.astype("float32")] for x in calib_samples[:200]
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)
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open("model_int8.tflite", "wb").write(conv.convert())
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```
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### MQTT publish 추론 결과 (MicroPython)
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```python
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from umqtt.simple import MQTTClient
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c = MQTTClient("esp32-01", "broker.local")
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c.connect()
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while True:
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feat = read_imu()
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label = tinyml_infer(feat)
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if label != "normal":
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c.publish(b"factory/line1/anomaly",
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ujson.dumps({"label": label, "ts": time.time()}))
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time.sleep(0.1)
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```
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### Sensor fusion (Kalman, complementary)
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```cpp
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// 6DOF IMU complementary filter
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float alpha = 0.98f;
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roll = alpha * (roll + gyro_x * dt) + (1 - alpha) * accel_roll;
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pitch = alpha * (pitch + gyro_y * dt) + (1 - alpha) * accel_pitch;
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```
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### Edge Impulse SDK (anomaly)
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```cpp
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ei_impulse_result_t result;
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signal_t signal;
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numpy::signal_from_buffer(features, EI_FEATURE_COUNT, &signal);
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run_classifier(&signal, &result, false);
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if (result.anomaly > 0.5) trigger_alert();
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```
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### OTA 모델 업데이트
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```cpp
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HTTPClient http;
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http.begin("https://cdn/model_v3.tflite");
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if (http.GET() == 200) {
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File f = SPIFFS.open("/model.tflite", "w");
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http.writeToStream(&f); f.close();
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ESP.restart();
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}
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```
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### Coral Edge TPU (Python)
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```python
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from pycoral.utils.edgetpu import make_interpreter
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from pycoral.adapters import classify
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it = make_interpreter("model_edgetpu.tflite")
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it.allocate_tensors()
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it.set_tensor(it.get_input_details()[0]["index"], img)
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it.invoke()
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print(classify.get_classes(it, top_k=1))
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```
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## 매 결정 기준
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| 제약 | 권장 |
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|---|---|
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| MCU < 1MB RAM | TFLite Micro int8, MCUNet |
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| 배터리 1년+ | LoRaWAN + duty cycle |
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| Vision realtime | Coral / Jetson Nano |
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| Privacy critical | edge inference, no cloud raw |
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| Fleet 수만+ | MQTT broker cluster, OTA |
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**기본값**: ESP32 + TFLite Micro int8 + MQTT QoS 1.
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## 🔗 Graph
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- 부모: [[Edge Computing|Edge-Computing]]
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- 변형: [[TinyML]], [[Federated-Learning]]
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- 응용: [[Predictive-Maintenance]], [[Wearables]]
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- Adjacent: [[LLM_Optimization_and_Deployment_Strategies|Quantization]], [[MQTT]]
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## 🤖 LLM 활용
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**언제**: latency 100ms 이하 필요, 네트워크 불안정, privacy 규제, bandwidth 비싼 환경.
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**언제 X**: 모델 100MB+, 잦은 재학습 필요, 입력이 크고 다양한 multimodal.
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## ❌ 안티패턴
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- **Cloud 의존 edge**: 인터넷 끊기면 동작 안 함 → fallback 필수.
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- **Float32 모델 그대로 배포**: RAM 부족, 발열.
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- **OTA 미고려**: 모델 버그 fix 불가.
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- **Sensor 단일**: 노이즈에 취약, fusion 으로 견고화.
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- **MQTT QoS 0 + 중요 alert**: 패킷 유실 가능.
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## 🧪 검증 / 중복
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- TFLite Micro, Edge Impulse, MQTT 5.0 spec, Coral docs.
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- Warden & Situnayake "TinyML" (O'Reilly).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — 3-layer 구조, TFLite Micro/Edge Impulse/MQTT/OTA 패턴 |
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