Abstract-Wireless Sensor Network is an important part of the Internet of Things. Data privacy preservation in wireless sensor networks is extremely urgent and challenging. To address this problem, we propose in this paper a privacy-preserving data aggregation protocol in wireless sensor networks. Compared to the previous research, our protocol protects the actual data from other nodes based on a rotation scheme while reducing communication overhead dramatically. The protocol achieves accurate aggregation results. Finally, theoretical analysis and simulation results confirm the high privacy and efficiency of our proposal.
As wireless sensing has developed, wireless behavior recognition has become a promising research area, in which human motion duration is one of the basic and significant parameters to measure human behavior. At present, however, there is no consideration of the duration estimation of human motion leveraging wireless signals. In this paper, we propose a novel system for robust duration estimation of human motion (R-DEHM) with WiFi in the area of interest. To achieve this, we first collect channel statement information (CSI) measurements on commodity WiFi devices and extract robust features from the CSI amplitude. Then, the back propagation neural network (BPNN) algorithm is introduced for detection by seeking a cutting line of the features for different states, i.e., moving human presence and absence. Instead of directly estimating the duration of human motion, we transform the complex and continuous duration estimation problem into a simple and discrete human motion detection by segmenting the CSI sequences. Furthermore, R-DEHM is implemented and evaluated in detail. The results of our experiments show that R-DEHM achieves the human motion detection and duration estimation with the average detection rate for human motion more than 94% and the average error rate for duration estimation less than 8%, respectively.
Wireless Sensor Networks (WSNs) can be viewed as energy constrained database systems, and join query processing is a very important topic in the field of sensor based systems. Join query for WSNs in a multi-dimensional and continuous way has not been widely explored, as well as is not energy efficient enough. In this paper, we proposed a continuous Multi-attribute Join Query Processing (MJQP) within latest sampling periods for WSNs based applications. We developed a filter-based scheme to discard non-joining tuples, which the center points of filters are identified and updated. Besides, we design an optimized solution to reduce the transmission of non-joining tuples, which is very benefit on energy efficiency. Experiments on real-world data set show that our methods outperform the centralized algorithm.
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