Data fusion is an attractive technology because it allows various trade-offs related to performance metrics, e.g., energy, latency, accuracy, fault-tolerance and security in wireless sensor networks (WSNs). Under a complicated environment, each sensor node must be equipped with more than one type of sensor module to monitor multi-targets, so that the complexity for the fusion process is increased due to the existence of various physical attributes. In this paper, we first investigate the process and performance of multi-attribute fusion in data gathering of WSNs, and then propose a self-adaptive threshold method to balance the different change rates of each attributive data. Furthermore, we present a method to measure the energy-conservation efficiency of multi-attribute fusion. Based on our proposed methods, we design a novel energy equilibrium routing method for WSNs, viz., multi-attribute fusion tree (MAFT). Simulation results demonstrate that MAFT achieves very good performance in terms of the network lifetime.

With the increasing popularity of mobile devices, it has become necessary to protect private information and content in these devices. Face recognition has been favored over conventional passwords or security keys, because it can be easily implemented using a built-in camera, while providing user convenience. However, because mobile devices can be used both indoors and outdoors, there can be many illumination changes, which can reduce the accuracy of face recognition. Therefore, we propose a new face recognition method on a mobile device robust to illumination variations. This research makes the following four original contributions. First, we compared the performance of face recognition with illumination variations on mobile devices for several illumination normalization procedures suitable for mobile devices with low processing power. These include the Retinex filter, histogram equalization and histogram stretching. Second, we compared the performance for global and local methods of face recognition such as PCA (Principal Component Analysis), LNMF (Local Non-negative Matrix Factorization) and LBP (Local Binary Pattern) using an integer-based kernel suitable for mobile devices having low processing power. Third, the characteristics of each method according to the illumination va iations are analyzed. Fourth, we use two matching scores for several methods of illumination normalization, Retinex and histogram stretching, which show the best and $2^{nd}$ best performances, respectively. These are used as the inputs of an SVM (Support Vector Machine) classifier, which can increase the accuracy of face recognition. Experimental results with two databases (data collected by a mobile device and the AR database) showed that the accuracy of face recognition achieved by the proposed method was superior to that of other methods.

In the communication channel, the received signal is affected by many factors that can cause errors. These effects mean that received signal strength (RSS) based methods incur more errors in measuring distance and consequently result in low precision in the location detection process. As one of the approaches to overcome these problems, we propose using direction calibration to improve the performance of the RSS-based method for distance measurement, and sequentially a weighted least squares (WLS) method using reliability factors in conjunction with a conventional RSS weighting matrix is proposed to solve an over-determined localization process. The proposed scheme focuses on the features of the RSS method to improve the performance, and these effects are proved by the simulation results.

Emerging applications with high data rates will need to transport bulk data reliably in wireless sensor networks. ARQ (Automatic Repeat request) or Forward Error Correction (FEC) code schemes can be used to provide reliable transmission in a sensor network. However, the naive ARQ approach drops the whole frame, even though there is a bit error in the frame and the FEC at the bit level scheme may require a highly complex method to adjust the amount of FEC redundancy. We propose a bulk data transmission scheme based on erasure-resilient code in this paper to overcome these inefficiencies. The sender fragments bulk data into many small blocks, encodes the blocks with LT codes and packages several such blocks into a frame. The receiver only drops the corrupted blocks (compared to the entire frame) and the original data can be reconstructed if sufficient error-free blocks are received. An incidental benefit is that the frame error rate (FER) becomes irrelevant to frame size (error recovery). A frame can therefore be sufficiently large to provide high utilization of the wireless channel bandwidth without sacrificing the effectiveness of error recovery. The scheme has been implemented as a new data link layer in TinyOS, and evaluated through experiments in a testbed of Zigbex motes. Results show single hop transmission throughput can be improved by at least 20% under typical wireless channel conditions. It also reduces the transmission time of a reasonable range of size files by more than 30%, compared to a frame ARQ scheme. The total number of bytes sent by all nodes in the multi-hop communication is reduced by more than 60% compared to the frame ARQ scheme.