Search

With the development of network technology, WLAN-based indoor localization plays an increasingly important role. Most current localization methods are based on the comparison between the received signal strength indication (RSSI) and the RSS in the database, whose nearest reference point is the location point. However, since a uniform standard for measuring components of smartphones has not yet been established, the Wi-Fi chipsets on different smartphones may have different sensitivity levels to different Wi-Fi access points (APs) and channels. Even for the same signal, RSSI values obtained by different terminals at the same time and the same location may be different. Therefore, the impact of terminal heterogeneity on localization accuracy can be overlooked. To address this issue, a fusion method based on received signal strength difference and compressive sensing (RSSD-CS) is proposed in this paper, which can reduce the influence caused by the terminal heterogeneity. Besides, a fingerprint database is reconstructed from the existing reference point data. Experiments show that the proposed RSSD-CS algorithm can achieve high localization accuracy in indoor localization, and the accuracy is enhanced by 20.5% and 15.6% compared to SSD and CS algorithm.

Wireless sensor network (WSN) possesses very broad application prospect in many fields, where the node location technology is one of the key technologies of WSN. Distance vector hop (DV-Hop) localization algorithm is a widely used algorithm in this technology, and it uses routing exchange protocol to make unknown nodes obtain beacon node information which will be used for coordinate calculation, therefore there exists certain error for the algorithm itself. Aiming at the disadvantage of large error existing in the traditional wireless sensor network location algorithm based on DV-Hop, an improved DV-Hop algorithm based on hop thinning and distance correction is proposed. The minimum hop is corrected by introducing received signal strength indication (RSSI) ranging technology, and the average hop distance is corrected by weighted average value of hop distance error and estimated distance error. Subsequently, the overall improvement on the location performance of the Hop-DV location algorithm is realized, and the location error is reduced. Under the Matlab simulation environment, the simulation experiment on the improved algorithm is carried out. The experimental results show that the improved algorithm reduces the location error and has higher location accuracy.

Wearable wireless body area network (WBAN) has attracted considerable attention as a means to collect vital data in sports training and urgent life-critical scenarios, which requires high reliability in links from sensor nodes to a central coordinator. Herein, cooperative relaying, which means retransmission not by sensor nodes themselves but through their cooperators, works effectively, since human postures do not suddenly change the states of the links in quite a short duration such as a retransmission interval. In this paper, we propose a cooperator-assisted WBAN for real-time vital data collection. We show that the WBAN is realizable based on two kinds of medium access control (MAC) protocols such as a hybrid-time divison multiple access/carrier sense multiple access (TDMA/CSMA) and a TDMA, both of which are compliant with the IEEE 802.15.4.e standard, and evaluate the packet error rates and power consumptions for a hybrid-TDMA/CSMA-based WBAN and a TDMA-based WBAN. In the evaluation of packet error rates, we use the stored received signal strength indication (RSSI) data, which were obtained from experiments with three subjects in a realistic scenario composed of a series of different actions and postures in a mixed-indoor/outdoor environment.

Received signal strength indication (RSSI)-based localization is emerging in wireless sensor networks (WSNs). Localization algorithms need to include the physical and hardware limitations of RSSI measurements in order to give more accurate results in dynamic real-life indoor environments. In this study, we use the Interdisciplinary Institute for Broadband Technology real-life test bed and present an automated method to optimize and calibrate the experimental data before offering them to a positioning engine. In a preprocessing localization step, we introduce a new method to provide bounds for the range, thereby further improving the accuracy of our simple and fast 2D localization algorithm based on corrected distance circles. A maximum likelihood algorithm with a mean square error cost function has a higher position error median than our algorithm. Our experiments further show that the complete proposed algorithm eliminates outliers and avoids any manual calibration procedure.

We evaluate the performance of realistic wireless sensor networks in indoor scenarios. Most of the considered networks are formed by nodes using the Zigbee communication protocol. For comparison, we also analyze networks based on the proprietary standard Z-Wave. Two main groups of network scenarios are proposed: (i) scenarios with direct transmissions between the remote nodes and the network coordinator, and (ii) scenarios with routers, which relay the packets between the remote nodes and the coordinator. The sensor networks of interest are evaluated considering different performance metrics. In particular, we show how the received signal strength indication (RSSI) behaves in the considered scenarios. Then, the network behavior is characterized in terms of end-to-end delay and throughput. In order to confirm the experiments, analytical and simulation results are also derived. Copyright (c) 2007 G. Ferrari et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.