Combinatorial-Oriented Feedback for Sensor Data Search in Internet of Things

Abstract

Sensor data search is an imminent component for the booming Internet of Things (IoT). Current proposals would refer to either keyword or spatial location, then the matched sensor data should be manually selected by requesters. However, the selection is arduous as there are massive connected devices. To this end, we propose a combinatorial-oriented feedback (COF) mechanism to guarantee the reliability and accessibility of feedback results via enabling an intuitive exhibition of aggregated sensor data. Two critical issues are addressed in this article: 1) sensor data combination and 2) aggregated results ranking. To facilitate the combination process, we first propose two decisive factors for aggregated result evaluation. The multiple sensor-data combinatorial (MSC) problem is converted into a multiobjective optimization model. To balance the tradeoff between competing quality metrics, we introduce Pareto sensor set (PSS) as an optimal solution for MSC problem, and devise the elitist directive breeding (EDB) method to get PSS solutions. In order to speed up the search efficiency and improve the feedback recall, we then develop the fast EDB (FEDB) algorithm, which is able to return top- ${k}$ ranked results according to different searching requirements. To demonstrate the effectiveness of COF mechanism, we conduct extensive simulations based on a virtual mobile sensing scenario. The results show that our proposed COF mechanism outperforms the state-of-the-art solutions significantly in terms of search efficiency and feedback quality, and specially, FEDB responds quite faster than EDB under the command of top- ${k}$ ranking.