2nd/10_Wiki/Topics/Domain_Programming/AI_and_ML/IoT-and-AI-Integration.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-iot-and-ai-integration	IoT and AI Integration	10_Wiki/Topics	verified	self	Edge AI TinyML IoT AI AIoT	none	A	0.9	applied	iot edge-ai tinyml embedded mqtt sensor-fusion		2026-05-10	pending	language framework cpp tflite-micro

IoT and AI Integration

매 한 줄

"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.

매 핵심

매 3 layer 아키텍처

1. Edge (sensor): TinyML 추론, 이상 감지, 게이팅.
2. Fog (gateway): 다중 센서 통합, heavier 모델, 로컬 의사결정.
3. Cloud: 학습, fleet 모니터링, OTA 모델 업데이트.

매 모델 압축

• Quantization: float32 → int8/int4, 4-8x 작아짐.
• Pruning: weight magnitude 기준 sparsify.
• Knowledge distillation: large teacher → tiny student.
• NAS for edge: MCUNet, MobileNet.

매 통신

• MQTT: pub/sub, QoS 0/1/2.
• CoAP: REST-over-UDP, 더 가벼움.
• LoRaWAN: km 단위, 수백 byte/min.
• BLE: 근거리, 저전력.

매 응용

1. Predictive maintenance (vibration anomaly).
2. Vision: door cam person detection, defect inspection.
3. Voice wakeword (Alexa, "OK Google").
4. Smart agriculture (soil moisture + weather).
5. Health wearable (HRV, fall detection).

💻 패턴

TFLite Micro 추론 (ESP32, C++)

#include "tensorflow/lite/micro/micro_interpreter.h"

const tflite::Model* model = tflite::GetModel(g_model_data);
static tflite::MicroMutableOpResolver<5> resolver;
resolver.AddConv2D(); resolver.AddMaxPool2D();
resolver.AddReshape(); resolver.AddFullyConnected(); resolver.AddSoftmax();

constexpr int kArena = 60 * 1024;
uint8_t arena[kArena];
tflite::MicroInterpreter interp(model, resolver, arena, kArena);
interp.AllocateTensors();
TfLiteTensor* in = interp.input(0);
memcpy(in->data.int8, sensor_buf, in->bytes);
interp.Invoke();
int8_t* out = interp.output(0)->data.int8;

Post-training quantization (Python)

import tensorflow as tf

conv = tf.lite.TFLiteConverter.from_saved_model("model")
conv.optimizations = [tf.lite.Optimize.DEFAULT]
conv.target_spec.supported_types = [tf.int8]
conv.representative_dataset = lambda: (
    [x.astype("float32")] for x in calib_samples[:200]
)
open("model_int8.tflite", "wb").write(conv.convert())

MQTT publish 추론 결과 (MicroPython)

from umqtt.simple import MQTTClient

c = MQTTClient("esp32-01", "broker.local")
c.connect()
while True:
    feat = read_imu()
    label = tinyml_infer(feat)
    if label != "normal":
        c.publish(b"factory/line1/anomaly",
                  ujson.dumps({"label": label, "ts": time.time()}))
    time.sleep(0.1)

Sensor fusion (Kalman, complementary)

// 6DOF IMU complementary filter
float alpha = 0.98f;
roll  = alpha * (roll + gyro_x * dt) + (1 - alpha) * accel_roll;
pitch = alpha * (pitch + gyro_y * dt) + (1 - alpha) * accel_pitch;

Edge Impulse SDK (anomaly)

ei_impulse_result_t result;
signal_t signal;
numpy::signal_from_buffer(features, EI_FEATURE_COUNT, &signal);
run_classifier(&signal, &result, false);
if (result.anomaly > 0.5) trigger_alert();

OTA 모델 업데이트

HTTPClient http;
http.begin("https://cdn/model_v3.tflite");
if (http.GET() == 200) {
  File f = SPIFFS.open("/model.tflite", "w");
  http.writeToStream(&f); f.close();
  ESP.restart();
}

Coral Edge TPU (Python)

from pycoral.utils.edgetpu import make_interpreter
from pycoral.adapters import classify

it = make_interpreter("model_edgetpu.tflite")
it.allocate_tensors()
it.set_tensor(it.get_input_details()[0]["index"], img)
it.invoke()
print(classify.get_classes(it, top_k=1))

매 결정 기준

제약	권장
MCU < 1MB RAM	TFLite Micro int8, MCUNet
배터리 1년+	LoRaWAN + duty cycle
Vision realtime	Coral / Jetson Nano
Privacy critical	edge inference, no cloud raw
Fleet 수만+	MQTT broker cluster, OTA

기본값: ESP32 + TFLite Micro int8 + MQTT QoS 1.

🔗 Graph

• 부모: Edge Computing
• 변형: TinyML, Federated-Learning
• 응용: Predictive Maintenance, Wearables
• Adjacent: LLM_Optimization_and_Deployment_Strategies, MQTT

🤖 LLM 활용

언제: latency 100ms 이하 필요, 네트워크 불안정, privacy 규제, bandwidth 비싼 환경. 언제 X: 모델 100MB+, 잦은 재학습 필요, 입력이 크고 다양한 multimodal.

❌ 안티패턴

• Cloud 의존 edge: 인터넷 끊기면 동작 안 함 → fallback 필수.
• Float32 모델 그대로 배포: RAM 부족, 발열.
• OTA 미고려: 모델 버그 fix 불가.
• Sensor 단일: 노이즈에 취약, fusion 으로 견고화.
• MQTT QoS 0 + 중요 alert: 패킷 유실 가능.

🧪 검증 / 중복

• TFLite Micro, Edge Impulse, MQTT 5.0 spec, Coral docs.
• Warden & Situnayake "TinyML" (O'Reilly).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — 3-layer 구조, TFLite Micro/Edge Impulse/MQTT/OTA 패턴