Abnormal data filtering approach based on collective trust for WSN - Xiao-bin XU; Guang-wei ZHANG; Shang-guang WANG; Qi-bo SUN; Fang-chun YANG (Tongxin xuebao, 2014)
Data security is the major challenge for WSN applications. It's significant in theory and practice to detect and filter false data effectively. Traditional approaches based on symmetric key, public key or polynomial always need large cost in transmission and computation, and could hardly detect the abnormal data caused by hardware of nodes. According to the spatio-temporal correlation of data in WSN, quantitative data can be converted to qualitative knowledge, and col-lective trust of data can be computed based on the comparisons of qualitative knowledge. A real-time outliner filtering approach was proposed to detect and filter abnormal data. Simulation results show that this method cannot only detect and filter the outliner in-time,but also need low cost in transmission and computation.