Multi-objective query optimization in smartphone social networks

Abstract

The bulk of social network applications for smart phones (e.g., Twitter, Face book, Foursquare, etc.) currently rely on centralized or cloud-like architectures in order to carry out their data sharing and searching tasks. Unfortunately, the given model introduces both data-disclosure concerns (e.g., disclosing all captured media to a central entity) and performance concerns (e.g., consuming precious smart phone battery and bandwidth during content uploads). In this paper, we present a novel framework, coined Smart Opt, for searching objects (e.g., images, videos, etc.) captured by the users in a mobile social community. Our framework, is founded on an in-situ data storage model, where captured objects remain local on their owner's smart phones and searches then take place over a novel lookup structure we compute dynamically, coined the Multi-Objective Query Routing Tree (MO-QRT). Our structure concurrently optimizes several conflicting objectives (i.e., it minimizes energy consumption, minimizes search delay and maximizes query recall), using a Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) that calculates a diverse set of high quality non-dominated solutions in a single run. We assess our ideas with mobility patterns derived by Microsoft's Geolife project and social patterns derived by DBLP. Our study reveals that Smart Opt can yield query recall rates of 95%, with one order of magnitude less time and two orders of magnitude less energy than its competitors.