Arcen Research← Back to Home

Proposed Technology Overview

Learn about the cutting-edge technologies powering Arcen Research's adaptive cyber-physical systems.

Core Concepts

Adaptive Cyber-Physical Systems

Cyber-physical systems (CPS) integrate computational and physical processes. Our adaptive CPS go further by:

  • Self-monitoring: Continuous data collection from sensors and actuators
  • Real-time analysis: Processing data at the edge for instant insights
  • Autonomous adaptation: Automatic adjustment based on learned patterns
  • Collaborative learning: Sharing knowledge across systems while preserving privacy

Architecture Overview

┌─────────────────────────────────────────────────┐
│       Orchestration (Flower Server)             │
│       Global FedAvg Aggregation                 │
└─────────────┬───────────────────────────────────┘
              │ Kubernetes (k3s)
    ┌─────────┴─────────┐
    │                   │
┌───▼────────┐    ┌────▼─────-┐    ┌────────────┐
│ Linux PC   │    │ Rasp Pi   │    │ Arduino    │
│ (x86_64)   │    │ (ARM64)   │    │ Uno Q MPU  │
│ flwr Client│    │flwr Client│    │ flwr Client│
└───┬────────┘    └────┬─────-┘    └──────┬─────┘
    │                  │                  │
┌───▼────────┐    ┌───▼────────┐    ┌───▼────────┐
│ Local      │    │ Local      │    │ Local      │
│ Training   │    │ Training   │    │ Training   │
│ (Private)  │    │ (Private)  │    │ (Private)  │
└────────────┘    └────────────┘    └────────────┘

Edge Computing

What is Edge Computing?

Edge computing brings computation and data storage closer to the data sources. This reduces:

  • Latency: Sub-millisecond response times for critical operations
  • Bandwidth: Only relevant data is sent to the cloud
  • Privacy risks: Sensitive data stays local

Edge Node Capabilities

Each Arcen edge node can:

  1. Data Collection: Interface with industrial protocols (Modbus, MQTT)
  2. Local Processing: Run inference models on-device
  3. Decision Making: Execute control logic autonomously
  4. Model Training: Participate in federated learning

Supported Hardware

Tested Devices:

  • Linux PC (x86_64) - Primary compute node
  • Raspberry Pi 4 (ARM64) - Low-power edge processing
  • Arduino Uno Q MPU (ARM64) - Ultra-low power inference
  • NVIDIA Jetson Series (Nano, Xavier, Orin) - GPU acceleration
  • Industrial PCs with compatible GPUs

Multi-Architecture Support:

  • AMD64 (x86_64) images for desktop/server
  • ARM64 images for Raspberry Pi and embedded
  • Docker manifests for automatic architecture selection

Kubernetes Orchestration

We use k3s (lightweight Kubernetes) for edge deployment:

# Master node (Linux PC)
curl -sfL https://get.k3s.io | sh -s - --write-kubeconfig-mode 644

# Worker nodes (RPi, Arduino)
export K3S_URL="https://<MASTER_IP>:6443"
export K3S_TOKEN="<TOKEN>"
curl -sfL https://get.k3s.io | K3S_URL=$K3S_URL K3S_TOKEN=$K3S_TOKEN sh -

Why k3s?

  • Lightweight: < 100MB binary
  • Edge-optimized: Low resource footprint
  • Full Kubernetes API compatibility
  • Perfect for heterogeneous device clusters

Federated Learning

Privacy-Preserving ML with Flower

We use Flower (flwr) framework for federated orchestration. Edge nodes collaboratively train models without sharing raw sensor data:

# Server configuration
from flwr.server import ServerConfig, start_server
from flwr.server.strategy import FedAvg

strategy = FedAvg(
    fraction_fit=0.5,
    fraction_evaluate=0.5,
    min_fit_clients=3,
    min_evaluate_clients=3,
    min_available_clients=3,
)

config = ServerConfig(num_rounds=10)

# Start the SuperLink
start_server(
    server_address="0.0.0.0:9093",
    config=config,
    strategy=strategy,
)

Deployment Architecture

Server (Coordination):

  • MacBook or other PC
  • Runs Flower SuperLink
  • Aggregates model updates (FedAvg)
  • No access to raw data

Clients (Edge Devices):

  • Linux PC, Raspberry Pi, Arduino Uno Q
  • Deploy via Kubernetes (k3s)
  • Train model locally on sensor or other data
  • Send only model weights

How It Works

  1. Client Registration: Edge nodes connect to SuperLink
  2. Local Training: Each node trains desired model on local data (CNN for images)
  3. Weight Upload: Only gradients/weights sent (not data)
  4. Secure Aggregation: Server performs FedAvg aggregation
  5. Model Distribution: Updated global model sent back
  6. Repeat: Process repeats for N rounds

Benefits

  • ✅ Data Privacy: Raw data never leaves the device
  • ✅ Regulatory Compliance: GDPR, HIPAA compatible
  • ✅ Bandwidth Efficiency: Only model updates transmitted
  • ✅ Robustness: System learns from diverse environments

Self-Optimization

Optimization Algorithms

Arcen uses advanced algorithms for autonomous optimization:

  • Reinforcement Learning: Q-learning, Policy Gradients
  • Evolutionary Algorithms: Genetic algorithms for parameter tuning
  • Bayesian Optimization: Efficient hyperparameter search
  • Multi-objective Optimization: Balance multiple KPIs simultaneously

Optimization Targets

Common objectives you can optimize:

  • Throughput maximization
  • Energy consumption minimization
  • Quality improvement (reduce defects)
  • Maintenance cost reduction
  • Response time optimization

Example Configuration

optimization:
  primary_objective: throughput
  constraints:
    - max_energy: 1000W
    - min_quality: 0.95
  algorithm: reinforcement_learning
  update_frequency: 30s

Real-Time Monitoring

  1. Monitor: Collect real-time system metrics
  2. Analyze: Detect anomalies and patterns using adaptive algorithms
  3. Plan: Determine optimal adjustments based on current state
  4. Execute: Apply changes to models
  5. Knowledge: Update models with outcomes for continuous improvement

System Monitoring

Node Health Metrics

Each edge node continuously reports critical system metrics:

  • CPU Usage: Per-core utilization and load averages
  • Memory: RAM usage, available memory, swap utilization
  • Temperature: CPU/GPU temperature sensors for thermal management
  • Disk I/O: Read/write throughput and latency
  • Network: Bandwidth usage, packet loss, connection status

Federated Learning Monitoring

Track training progress across distributed edge nodes:

  • Training Rounds: Monitor current round vs total rounds per node
  • Round Completion: Verify all nodes complete training cycles
  • Model Convergence: Track loss and accuracy across rounds
  • Participation Rate: Ensure minimum clients meet for aggregation
  • Gradient Statistics: Monitor weight updates
  • Node Availability: Real-time status of each federated client

Inference Performance

Monitor production inference in real-time:

  • Latency: Per-request inference time
  • Throughput: Inferences per second across nodes
  • Model Accuracy: Live accuracy metrics on inference results
  • Energy Consumption: Power usage per inference
  • Queue Depth: Pending inference requests per node

Observability Dashboard

Prometheus Metrics Collection

Arcen integrates Prometheus for time-series metrics:

Exposed Metrics:

# Node health
arcen_node_cpu_percent{node_id, architecture}
arcen_node_memory_bytes{node_id, type="used|available"}
arcen_node_temperature_celsius{node_id, sensor}

# Federated learning
arcen_fl_round_number{node_id, job_id}
arcen_fl_round_duration_seconds{node_id, job_id}
arcen_fl_training_loss{node_id, round}
arcen_fl_training_accuracy{node_id, round}
arcen_fl_client_status{node_id, status}

# Inference
arcen_inference_latency_seconds{node_id, model_id}
arcen_inference_throughput{node_id}
arcen_inference_energy_joules{node_id}

Grafana Visualization

Pre-built dashboards for real-time monitoring:

1. Edge Node Overview

  • CPU, memory, temperature heatmaps across all nodes
  • Network traffic and disk I/O per device
  • Alert indicators for threshold violations

2. Federated Training Dashboard

  • Training round progress bars for each client
  • Loss/accuracy curves overlaid per node
  • Client participation timeline
  • Global model convergence graph

3. Inference Performance

  • Latency percentiles (p50, p95, p99) per node
  • Throughput trends over time
  • Energy efficiency metrics
  • Real-time inference queue monitoring

Integration Capabilities

Protocols Supported

  • Industrial: OPC UA, Modbus TCP/RTU, Profinet
  • IoT: MQTT, CoAP, HTTP/REST, gRPC
  • Time-series: InfluxDB, Prometheus
  • Databases: PostgreSQL, MongoDB, TimescaleDB

Security

Built-in Security Features

  • Encryption: TLS 1.3 for all communications
  • Authentication: OAuth 2.0, API keys, certificates
  • Authorization: Role-based access control (RBAC)
  • Audit Logging: Complete audit trails
  • Secure Boot: Verified firmware on edge devices

Compliance

Our platform is designed for:

  • ISO 27001 (Information Security)
  • IEC 62443 (Industrial Cybersecurity)
  • SOC 2 Type II
  • GDPR, CCPA data protection