Self-Adaptive Context Data Distribution with Quality Guarantees in Mobile P2P Networks

Recent advances in wireless technologies and mobile computing are opening brand new opportunities for context-aware services, namely services able to tailor their behavior according to current execution context. Starting from the core assumption that only effective and efficient context distribution can pave the way to the deployment of truly context-aware services, this paper proposes a novel Peer-to-Peer (P2P) model and distributed architecture for context distribution in densely populated mobile systems. The primary guideline is to exploit agreed quality parameters on context distribution (mainly, delivery time and context freshness) and system-level monitoring information dynamically gathered, to take proper management decisions able to foster high scalability and low additional overhead. Our solution monitors both its own local components status and distributed run-time conditions to introduce two main self-adaptive capabilities, namely self-configuration and self-optimization. While the former lets our system automatically select data sending rates with no user intervention, the latter allows to reach better run-time performance by dynamically adjusting system components. The paper reports also several significant results, based on both a simulation-based implementation and a real-world prototype, showing that our solution can grant timely and efficient context delivery and dynamically achieves agreed quality levels with good quality-overhead tradeoff.