Coptimizer - Agentic Context-Aware Android Optimizer

A sophisticated Android application that autonomously monitors device context and provides intelligent, ML-powered optimization recommendations. Built with modern Android development practices including Room database, WorkManager, autonomous agent system, and privacy-preserving on-device machine learning.

🌟 Key Features

🤖 Autonomous Agent System

• Self-Planning Agent: Automatically plans and executes optimization tasks every 30 minutes
• Human-in-the-Loop: Requests user approval only for sensitive actions
• Complete Audit Trail: Full visibility into agent goals, tasks, and observations
• Policy-Aware: Respects battery level, network state, and quiet hours
• Tool-Based Architecture: Modular tools for insights, cleanup, and optimization

🧠 Privacy-Preserving Machine Learning

• On-Device ML: Custom online learning models that adapt to your behavior
• Continuous Learning: Models update every optimization cycle (15-30 minutes)
• Personalized Optimization: Learns your brightness, battery, Wi-Fi, and performance preferences
• Predictive Analytics: Predicts battery drain, optimal brightness, and performance issues
• 100% Privacy: All data and ML processing happens on-device (no cloud dependency)

🔍 Intelligent Automation Detection

• Conflict Detection: Identifies third-party automation apps that might conflict
• Multi-Strategy Detection: Static analysis, dynamic monitoring, and known app detection
• Impact Assessment: Estimates battery, data, and CPU impact of detected automations
• Evidence Collection: Shows proof for each detection with confidence scores
• User Controls: Whitelist, mark suspicious, and manage automation conflicts

📊 Comprehensive Analytics Dashboard

• Real-Time Charts: Interactive visualizations using MPAndroidChart
• Multi-Dimensional Monitoring: Battery, brightness, Wi-Fi, performance, and data usage
• Historical Trends: Last 24 hours with predictive insights
• Statistical Analysis: Averages, trends, and pattern recognition
• Export Capability: Export data to CSV/JSON for analysis

⚙️ Optimization Features

• Auto-Brightness Control: ML-powered brightness optimization
• Wi-Fi Saver: Intelligent Wi-Fi management based on signal strength
• Battery Optimizer: Proactive battery management with predictions
• Data Saver: Personalized data usage optimization
• Performance Mode: Thermal-aware performance optimization

📱 User-Defined Rules

• Custom Automation Rules: Create time-based, battery-based, or condition-based rules
• Rule Engine: Flexible trigger-action system
• Enable/Disable: Full control over rule execution
• Multiple Trigger Types: Time windows, battery thresholds, Wi-Fi state, screen off, charging state

🎯 Background Data Collection

• Automatic Monitoring: Collects device context every 15 minutes
• Comprehensive Data: Battery, brightness, Wi-Fi, CPU, memory, network, thermal state
• Persistent Storage: Room database with 7-day retention (configurable)
• Duplicate Prevention: One record per minute with unique constraints
• Smart Cleanup: Automatically removes old data

🏗️ Architecture

Clean Architecture Components

• UI Layer: Activities, Fragments, Adapters with ViewBinding and Material Design
• ViewModels: MVVM pattern with LiveData/Flow for reactive UI updates
• Repository: Data access orchestration layer
• Database: Room database with 15 entities, 7 DAOs, reactive queries
• Workers: ContextWorker, OptimizationWorker, AgentOrchestratorWorker
• Agent System: Planner, Policy Engine, Tool Registry, Approval System
• ML Engine: Online learning models, inference engine, predictive analytics
• Analytics: Real-time system data collection with comprehensive metrics

Data Flow

Device Sensors → Data Collectors → Room Database → Repository → ViewModels → UI
                                    ↓
                            Agent System (Every 30 min)
                                    ↓
                        ML Models (Learn & Predict)
                                    ↓
                        Decision Engine → Optimization Executor

Agent System Flow

AgentOrchestratorWorker
  ├─> RuleBasedPlanner: Plans next tasks
  ├─> PolicyEngine: Checks conditions (battery, network, time)
  ├─> ToolRegistry: Executes tasks
  │   ├─> InsightTool: Analytical tasks
  │   ├─> CleanupTool: Data retention
  │   └─> OptimizerTool: Executes optimizations
  └─> AgentApprovalReceiver: Requests approval for sensitive actions

Key Technologies

• Kotlin: Modern Android development language
• Room: Local database with reactive queries (15 tables, version 10)
• WorkManager: Reliable background task scheduling
• Coroutines & Flow: Asynchronous programming and reactive streams
• MPAndroidChart: Professional charting library
• Material Design: Modern UI components
• Custom ML: Online learning models (Linear/Logistic Regression)
• Gson: JSON serialization for complex types

📱 App Screens

Main Dashboard

• Automation List: Scrollable list of available optimizations
• Real-Time Status: Active/inactive indicators for each automation
• Navigation Drawer: Access to all app features
• Quick Stats: Overview of device health and optimization status

Analytics Dashboard

• Interactive Charts: Battery, brightness, Wi-Fi, performance, data usage trends
• Historical Data: Last 24 hours with zoom and pan capabilities
• Predictive Insights: ML-predicted future behavior
• Statistical Cards: Averages, trends, and pattern summaries

Agent History

• Goals & Tasks: View agent's planning and execution
• Observations: See results of agent actions
• Approval Queue: Pending actions requiring your approval
• Live Updates: Real-time Flow-based updates

Automation Detection

• Detected Automations: List of third-party automation apps
• Evidence Viewer: See proof for each detection
• Event Timeline: Track automation activity over time
• User Controls: Whitelist, mark suspicious, add notes

Rules Management

• Custom Rules: Create and manage automation rules
• Rule Editor: Define triggers and actions
• Enable/Disable: Control rule execution
• Rule History: Track rule execution logs

Optimization History

• Action Logs: Complete history of all optimizations
• Success/Failure Stats: Track optimization effectiveness
• Filter & Search: Find specific optimizations
• Export: Download logs for analysis

🚀 Quick Start

Prerequisites

• Android Studio Hedgehog or later
• Minimum SDK: 26 (Android 8.0)
• Target SDK: 34 (Android 14)
• JDK 11 or later

Installation

1. Clone the repository

   git clone https://github.com/yourusername/coptimizer.git
   cd coptimizer

2. Open in Android Studio

   • File → Open → Select the coptimizer directory

3. Sync Gradle files

   • Android Studio will automatically sync, or click "Sync Now"

4. Build and run

   • Click Run (▶️) or press Shift+F10
   • Select a device/emulator (API 26+)

First Run

1. Grant Permissions: The app will request necessary permissions

   • Usage Stats (for app monitoring)
   • Write Settings (for brightness control)
   • Notifications (for agent approvals)
   • Wi-Fi State (for network optimization)

2. Wait for Learning: The ML models start with zero knowledge

   • Agent runs every 30 minutes automatically
   • Data collection starts every 15 minutes
   • Give it a few days to learn your patterns

3. Review Agent Actions: Check Agent History to see what the agent plans

   • Approve or deny actions as needed
   • Agent learns from your feedback

Required Permissions

• ACCESS_NETWORK_STATE: Monitor network connectivity
• ACCESS_WIFI_STATE: Track Wi-Fi state
• CHANGE_WIFI_STATE: Enable/disable Wi-Fi for optimization
• WRITE_SETTINGS: Modify brightness and system settings
• PACKAGE_USAGE_STATS: Monitor app usage patterns
• FOREGROUND_SERVICE: Background data collection
• RECEIVE_BOOT_COMPLETED: Restart monitoring after reboot
• POST_NOTIFICATIONS: Agent approval notifications
• REQUEST_IGNORE_BATTERY_OPTIMIZATIONS: Ensure reliable background work

📊 Data Collection

What We Monitor

• Battery: Level, temperature, voltage, charging status, health
• Brightness: Current level, auto-brightness status, screen-on time
• Wi-Fi: Connected state, signal strength, link speed, data usage
• Performance: CPU usage, memory usage, thermal state, app launch times
• Data: Wi-Fi usage, mobile data usage, background/foreground split
• Network: Type, signal strength, network switches
• Context: Timestamp, automation type, app foreground time

Collection Frequency

• Interval: Every 15 minutes (via ContextWorker)
• High Frequency: Optional foreground service for detailed analysis
• Persistence: 7 days of historical data (configurable)
• Storage: Local Room database (SQLite)
• Privacy: 100% on-device - no data leaves your device

ML Data Sources

• Real-Time Data: System APIs (BatteryManager, Settings, WifiManager, etc.)
• User Behavior: Brightness preferences, battery patterns, Wi-Fi usage
• Historical Trends: 7-day context data for pattern recognition
• Feedback Loop: User satisfaction ratings improve recommendations

🎯 Current Implementation Status

✅ Fully Implemented Features

• ✅ ML Integration: Online learning models with predictive analytics
• ✅ User Behavior Learning: Learns brightness, battery, Wi-Fi, and performance patterns
• ✅ Predictive Recommendations: Predicts battery drain, optimal brightness, Wi-Fi state
• ✅ Custom Automation Rules: User-defined rules with flexible triggers
• ✅ Export Data: CSV/JSON export functionality
• ✅ Agent System: Autonomous planning and execution
• ✅ Automation Detection: Multi-strategy detection with evidence collection
• ✅ Analytics Dashboard: Comprehensive real-time charts and insights

🔮 Future Enhancements

• TensorFlow Lite: Could add more sophisticated ML models (currently uses lightweight online learning)
• Cloud Sync: Optional encrypted cloud backup (would require user consent)
• Federated Learning: Privacy-preserving multi-device learning
• Advanced Agent: Reinforcement learning for agent optimization
• Automation Marketplace: Community-shared automation patterns

🏛️ Project Structure

app/src/main/java/com/example/coptimizer/
├── MainActivity.kt                     # Dashboard with navigation drawer
├── DetailActivity.kt                   # Analytics dashboard with charts
├── CoptimizerApplication.kt           # Application initialization
├── Automation.kt                       # Automation domain model
├── ContextData.kt                      # Context data entity
├── adapter/                            # RecyclerView adapters
│   ├── AutomationAdapter.kt
│   ├── OptimizationLogAdapter.kt
│   └── DetectedAutomationsAdapter.kt
├── agent/                              # Autonomous agent system
│   ├── AgentModels.kt                  # Agent entities
│   ├── AgentDao.kt                     # Agent data access
│   ├── RuleBasedPlanner.kt             # Task planning
│   ├── PolicyEngine.kt                 # Policy enforcement
│   ├── Tooling.kt                      # Tool interfaces
│   ├── ToolsImpl.kt                    # Tool implementations
│   ├── AgentNotifier.kt                # Approval notifications
│   └── AgentApprovalReceiver.kt         # Approval handling
├── analytics/                          # Real-time data collection
│   ├── RealTimeSystemDataCollector.kt  # System API readers
│   ├── BatteryAnalytics.kt
│   ├── BrightnessAnalytics.kt
│   ├── WifiAnalytics.kt
│   ├── PerformanceAnalytics.kt
│   └── DataAnalytics.kt
├── automationdetector/                 # Automation detection system
│   ├── AutomationDetector.kt           # Detection engine
│   ├── DetectedAutomation.kt           # Detection entity
│   ├── AutomationDetectionDao.kt
│   └── AutomationsViewModel.kt
├── charts/                             # Chart management
│   └── AnalyticsChartManager.kt
├── database/                            # Room database
│   ├── AppDatabase.kt                  # Database definition (15 entities, v10)
│   ├── AutomationDao.kt
│   ├── ContextDataDao.kt
│   └── Converters.kt                   # Type converters
├── ml/                                 # Machine learning
│   ├── OnlineModels.kt                 # Online learning models
│   ├── MlInferenceEngine.kt            # ML inference & training
│   ├── UserBehaviorLearner.kt          # Behavior learning
│   ├── UserBehaviorEntities.kt         # Behavior entities
│   ├── UserBehaviorDao.kt              # Behavior data access
│   ├── PredictiveAnalytics.kt         # Predictive analytics
│   └── ContextAwareDecisionEngine.kt   # ML-assisted decisions
├── optimization/                       # Optimization system
│   ├── OptimizationAction.kt            # Action models
│   ├── OptimizationConfig.kt           # Configuration
│   ├── DecisionEngine.kt               # Decision making
│   ├── OptimizationExecutor.kt        # Action execution
│   ├── OptimizationManager.kt          # Coordination
│   ├── OptimizationLogger.kt          # Logging
│   └── OptimizationLogDao.kt            # Log data access
├── repository/                         # Data orchestration
│   └── AutomationRepository.kt
├── rules/                              # User-defined rules
│   ├── UserRule.kt                     # Rule entity
│   └── UserRuleDao.kt                  # Rule data access
├── service/                            # Foreground services
│   └── HighFrequencySamplingService.kt
├── utils/                              # Utilities
│   ├── DeviceContextUtils.kt           # Device sensors
│   ├── NetworkQualityUtils.kt
│   ├── ThermalMonitor.kt
│   ├── PermissionChecker.kt
│   ├── DataExporter.kt                 # CSV/JSON export
│   └── LogUtils.kt
├── viewmodel/                          # MVVM ViewModels
│   ├── MainViewModel.kt
│   ├── AnalyticsViewModel.kt
│   └── DetailViewModel.kt
└── worker/                             # Background workers
    ├── ContextWorker.kt                # Data collection (15 min)
    ├── OptimizationWorker.kt           # Optimization execution
    └── AgentOrchestratorWorker.kt      # Agent loop (30 min)

🧪 Testing

Unit Tests

• ViewModel logic testing
• Repository operations
• Database operations

Integration Tests

• End-to-end data flow
• UI interactions
• Background worker functionality

📈 Performance & Battery Impact

Optimization Features

• Lazy Loading: Data loaded on-demand
• Efficient Queries: Room database with indexes for fast queries
• Background Processing: Non-blocking UI operations with coroutines
• Memory Management: Automatic cleanup of old data
• Duplicate Prevention: Unique constraints prevent redundant data
• Flow-Based UI: Reactive updates only when data changes

Battery Impact

• Minimal: 15-minute intervals for data collection
• Smart Scheduling: WorkManager respects battery optimization
• Efficient Storage: Room database compression
• Policy-Aware: Agent respects battery level and charging state
• Adaptive: High-frequency sampling only when dashboards are open

Estimated Impact

• Background Collection: ~0.5% battery per day
• Agent System: ~0.3% battery per day
• ML Processing: Negligible (lightweight online learning)
• Total: <1% battery impact for 15-30% battery savings

🔒 Privacy & Security

Privacy-First Design

• 100% On-Device Processing: All ML and data processing happens locally
• No Cloud Dependency: No data sent to external servers
• Local Storage: Data stored in app's private directory
• User Control: Export and delete data anytime
• No Tracking: No behavioral analytics or tracking

Security Features

• Type-Safe Queries: Room prevents SQL injection
• Permission Gating: Proper permission checks before actions
• Policy Enforcement: Agent respects user-defined policies
• Audit Trail: Complete logging of all optimizations

🌟 Unique Features

What Makes Coptimizer Different

1. Autonomous Agent System: First optimization app with self-planning agent
2. Privacy-Preserving ML: On-device learning without cloud dependency
3. Intelligent Conflict Detection: Identifies and manages conflicting automations
4. Predictive Optimization: Acts before problems occur using ML predictions
5. Complete Transparency: Full visibility into agent decisions and actions

Benefits Over Traditional Apps

• ✅ Proactive vs Reactive: Acts automatically vs requiring user initiation
• ✅ Personalized: Learns your behavior vs one-size-fits-all rules
• ✅ Privacy-First: On-device vs cloud-dependent
• ✅ Conflict-Aware: Detects conflicts vs blind optimization
• ✅ Transparent: Full audit trail vs black-box decisions

Development Guidelines

• Follow Kotlin coding standards
• Use MVVM architecture pattern
• Implement proper error handling
• Add comprehensive documentation
• Write unit tests for new features
• Ensure privacy-preserving design

Code Style

• Use meaningful variable names
• Implement proper null safety
• Follow Material Design guidelines
• Use coroutines for async operations
• Document complex algorithms

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Built with ❤️ using modern Android development practices

Coptimizer - Empowering users with intelligent, privacy-preserving device optimization.