International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 11 | Nov 2025
p-ISSN: 2395-0072
www.irjet.net
SYNCHRONIZED INTELLIGENCE AND ORCHESTRATION: A SELFADAPTIVE MOBILE ARCHITECTURE FOR CONTEXT-AWARE SOCIAL COMPUTING WITH COST-EFFICIENT AI INTEGRATION Mayuresh Kulkarni ---------------------------------------------------------------------***--------------------------------------------------------------------2. AI Integration Costs – AI features like image generation Abstract - Mobile social applications face persistent challenges in balancing data synchronization, offline usability, AI integration, and operational costs. Existing solutions often address these concerns individually, resulting in inefficiencies when deployed at scale. This paper presents Synchronized Intelligence, a self-adaptive mobile architecture that unifies these concerns through three innovations: 1. 2. 3.
3.
While previous research addresses these areas independently (offline-first synchronization [1–5], ondevice/cloud AI [6–9], query optimization [10–11]), this paper provides a unified, self-adaptive architecture that balances all three concerns.
Intelligent Hibernation – an offline-first synchronization strategy that prioritizes socially and contextually relevant data. Cost-Efficient AI Orchestration – a service layer that integrates efficient and low-cost AI providers with caching and fallback mechanisms for reliability. Adaptive Firebase Optimization – a middleware that reduces backend reads via context-aware caching and query reduction.
Synchronized Intelligence addresses this gap by combining Intelligent Hibernation, Cost-Efficient AI Orchestration, and Adaptive Firebase Optimization into a single framework.
A prototype implementation in React Native with Firebase Firestore was evaluated across 1,000 user profiles and 500 conversations. Results demonstrate up to 90% faster load times, 95% fewer database reads, and 100% offline functionality for critical features. The AI orchestration layer achieves 3–5 second latency for first-time image generation and near-instant responses for cached requests, reducing projected monthly costs by 80–90%. These findings suggest that integrating synchronization, caching, and service orchestration into a unified architecture provides a scalable, resource-efficient solution for modern social computing.
Figure 1: Schema of E2E Orchestration for Mobile Apps
2. RELATED WORK 2.1 Offline-First Synchronization
Key Words: Mobile Architecture, Social Computing, ContextAware Systems, Offline-First Synchronization, Adaptive Caching, Zero-Cost AI Integration, Firebase Optimization, AI Orchestration, Edge Computing, Cost-Efficient Mobile Systems, Context-Aware Intelligence, Self-Adaptive Systems
Offline-first systems aim to maintain usability under network variability. Techniques such as Conflict-free Replicated Data Types (CRDTs) [1,2] and Operational Transformation (OT) [3] enable eventual consistency across distributed replicas. Frameworks like CouchDB/PouchDB [4] and Realm [5] provide device-local persistence with periodic synchronization.
1.INTRODUCTION
Limitation: These systems generally synchronize all data indiscriminately and do not prioritize the most relevant content, resulting in unnecessary storage and bandwidth usage.
Mobile social applications increasingly demand real-time interactivity, AI-enhanced personalization, and offline resilience. Delivering these features introduces three interconnected challenges: 1. Data Synchronization Complexity – Cloud-first systems rely heavily on network connectivity, while offline-first solutions often replicate all data indiscriminately, causing storage bloat and inefficiencies.
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or recommendations often incur per-call fees ($0.02– $0.10), limiting scalability for smaller applications. Real-Time Communication Overhead – Backend services like Firebase Firestore generate thousands of database reads per session, increasing latency and cost.
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