Understanding human intention and performing different activities automatically is one of the key problems in pervasive computing. In this paper, a new location-based search computing framework (LoSeCo) is proposed to allow one's pervasive device to augment search devices. The objective of our problem is to recognize the real-time user goal through goal inference from traditional Wi-Fi localization techniques. We use accelerometer-based tracking to reduce the effort we need to collect Wi-Fi signals and save battery power consumption effectively. With the help of short-range search, the goal recognition module is enhanced, compared to previous “locationonly” approaches. Therefore, we could augment our mobile devices by automatically analyzing our needs and connecting to corresponding devices. Experimental results on real-world wireless network environments validate the effectiveness of our approach and that even a rough localization accuracy can meet the need of QoS (Quality of Service) in search computing behaviors.
['Yiqiang Chen', 'Zhuo Sun', 'Juan Qi', 'Derek Hao Hu', 'Qiang Yang']
LoSeCo: Location-based search computing for pervasive device augmentation