SDU: State-Based Dual-Mode Sensor Search Mechanism Toward Internet of Things

Abstract

The wide-scale deployments and interconnections of massive diverse sensors in Internet of Things (IoT) enable the mapping of multimedia entity state data from the physical world into the cyberspace. Sensor search technologies further facilitate the access to multimedia entity state data. However, existing sensor search mechanisms overlook the search requirements for historical state information on entities. Besides, previous current state based sensor search mechanisms realize the low-cost sensor search through predicting the sensor current state based on shallow learning theory, which leads to limited prediction accuracy and higher communication overhead. Aiming at these shortages, an efficient dual-mode sensor search mechanism is firstly designed, including the current state based and historical state based search methods, to fulfill the search needs for current and historical state information on entities. Then based on deep learning theory, a high-accuracy data prediction method towards sensor current state is presented to improve the insufficient accuracy of existing prediction methods. Moreover, a lightweight data representation method is devised to fit the sensor historical state, thereby accomplishing space-saving and low computational overhead historical state based sensor search. Simulation results show that the proposed sensor search mechanism combined with the designed prediction method and approximate representation method can effectively enhance the communication overhead, recall ratio and precision ratio performances.

Keywords

• Internet of Things
• multimedia data
• deep learning
• sensor search
• dual-mode