Comparative Analysis of RSSI-based Indoor Localization when using Multiple Antennas in Wireless Sensor Networks Safa Hamdoun Universit´ e Paris-Est LIGM (UMR 8049), UPEM F-77454, Marne-la-Vall´ ee France Email: hamdoun@univ-mlv.fr Abderrezak Rachedi Universit´ e Paris-Est LIGM (UMR8049), UPEM F-77454, Marne-la-Vall´ ee France Email: rachedi@univ-mlv.fr Abderrahim Benslimane French University of Egypt Informatics Research Center (CRI) Cairo, Egypt Email: abderrahim.benslimane@ufe.edu.eg Abstract—RSSI-based location estimation method in Wireless Sensor Networks (WSNs) present some advantages in terms of complexity and energy consumption. However, the propagation of the radiofrequency signals in indoor environments is subject to fading induced by shadowing effect and multipath propaga- tion. Accurate wireless location estimation can be achieved by employing multiple antennas. In this paper, we make a comparison among three system models in order to show the impact of using multiple antennas on position accuracy at either the transmitter, the receiver side or at the both sides. We use the multilateration as well as the trilateration algorithms to calculate the position error. The obtained results illustrate that the localization performance is improved when using multiple antennas. Specifically, using multiple antennas at the both sides present better performance than using multiple antennas at either the transmitter or the receiver. Index Terms—Indoor localization, Wireless Sensor Networks, multipath fading, shadowing, received signal strength indicator, accuracy, antennas, trilateration, multilateration. I. I NTRODUCTION In recent years, Wireless Sensor Networks (WSNs) have been widely used in several applications such as health care, traffic control and environmental monitoring. Unfortunately, the exact position of sensors is required to make these applications useful. Accurate localization, thus, remains an interesting area of research. Several methods based on the strength of the received signal (RSS) have been proposed in literature [1]. However, RSS measurements in indoor applications are affected by the propagation environment. Spatial diversity achieved by employing multiple antennas improve considerably the reliability and the quality of the wireless link [2]. The basic idea consists in providing different copies of the same signal via different paths having undergone different fading. There has been a wide range of research aiming at developing sensors with multiple antennas. Experimental results have been achieved in [3] to show the system requirements and feasibility [4]. RF based indoor location systems has recently brought attention to multiple antenna technologies in order to combat fading. Compared with the traditional RSS distance measuring, important results in terms of position accuracy have been achieved in [5], [6], [7]. While some of these works exploit receive diversity, others exploit transmit diversity. This classification depends on the node’s role as a receiver, measuring the RSS information, or as a transmitter. As a consequence, multiple antennas are deployed either under reference nodes with known coordinates (called anchors) or under the unknown node called (target node). However, employing multiple antennas on target nodes is not attractive because of their limited space and power consumption constraints. In this paper, we assume that only anchor nodes are equipped with multiple antennas regardless their role as transmitters or receivers. According to this assumption, we propose three system models. The SIMO ”Single Input Multiple Output” system where the receive diversity is exploited. The MISO ”Multiple Input Single Output” system where the transmit diversity is exploited. We also consider the case of the joint receive and transmit diversity called MIMO ”Multiple Input Multiple Output” system. We make a comparison relative to the position accuracy among these three system models when using the trilateration as well as the multilateration algorithm. We ground them with sufficient theoretical foundations. The rest of this paper is organized as follows. In Section II, related work is presented, while in Section III the sys- tem model is proposed. In Section IV, we present a brief description of nodes deployment and the methodology used to estimate the target position. Simulation results are discussed in section V. Finally, we conclude our work in section VI. II. RELATED WORK The localization methods are classified into two categories: range free and range based. The first one depends on