RSSI-based location estimation method in WirelessSensor 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 propagation. 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.
Many localization algorithms in Wireless Sensor Networks (WSNs) are based on received signal strength indication (RSSI). Although they present some advantages in terms of complexity and energy consumption, RSSI values, especially in indoor environments, are very unstable due to fading induced by shadowing effect and multipath propagation. In this paper, we propose a comparative study of RSSI-based localization algorithms using spatial diversity in WSNs. We consider different kinds of single / multiple antenna systems: Single Input Single Output (SISO) system, Single Input Multiple Output (SIMO) system, Multiple Input Single Output (MISO) system and Multiple Input Multiple Output (MIMO) system. We focus on the well known trilateration and multilateration localization algorithms to evaluate and compare different antenna systems. Exploiting spatial diversity by using multiple antenna systems improve significantly the accuracy of the location estimation. We use three diversity combining techniques at the receiver: Maximal Ratio Combiner (MRC), Equal Gain Combining (EGC) and Selection Combining (SC). The obtained results show that the localization performance in terms of position accuracy is improved when using multiple antennas. Specifically, using multiple antennas at the both sides present better performance than using multiple antennas at the transmitter as well as the receiver side. We also conclude that MRC diversity combining technique outperforms EGC that as well outperforms SC.
International audienceMachine to machine (M2M) communications pose significant challenges to the cellular networks due to its unique features such as the massive number of machine type devices (MTDs) as well as the limited data transmission session. Thus, advanced cellular network releases, such as long-term evolution (LTE) and LTE-Advanced (LTE-A), optimally designed to support human to human (H2H) communications, should cater to M2M communications. In this paper, we consider an M2M/H2H coexistence scenario where a simultaneous access to the spectrum is enabled. Taking the opportunity of the new device to device (D2D) communication paradigm offered in LTE-A and at the aim of enabling an efficient resource sharing, we propose to combine M2M and D2D owing to the MTD low transmit power. First, we formulate the resource sharing problem as a maximization of the sum-rate, problem for which the optimal solution has been proved to be non deterministic polynomial time hard (NP-Hard). Then, we formulate the problem as a novel interference-aware bipartite graph to overcome the computational complexity of the optimal solution. Thus, we consider here a two-phase resource allocation approach. In the first phase, H2H radio resource assignment is performed in a conventional way. In the second phase, we introduce two algorithms, one centralized and one semi-distributed to perform the M2M resource allocation. The computational complexity of both introduced algorithms is of polynomial complexity. Simulation results show that the semi-distributed M2M resource allocation algorithm achieves quite good performance in terms of network aggregate sum-rate with markedly lower communication overhead compared to the centralized one
Traditional cellular networks have been considered the most promising candidates to support machine to machine (M2M) communication mainly due to their ubiquitous coverage. Optimally designed to support human to human (H2H) communication, an innovative access to radio resources is required to accommodate M2M unique features such as the massive number of machine type devices (MTDs) as well as the limited data transmission session. In this paper, we consider a simultaneous access to the spectrum in an M2M/H2H coexistence scenario. Taking the advantage of the new device to device (D2D) communication paradigm, enabled in long term evolution-advanced (LTE-A), we propose to combine M2M and D2D owing to the MTD low transmit power and thus enabling efficiently resource sharing. First, we formulate the resource sharing problem as a maximization of the sum-rate, problem for which the optimal solution has been proved to be non deterministic polynomial time hard (NP-Hard). We next model the problem as a novel interference-aware bipartite graph to overcome the computational complexity of the optimal solution. To solve this problem, we consider here a two-phase resource allocation approach. In the first phase, H2H users resource assignment is performed in a conventional way. In the second phase, we introduce two alternative algorithms, one centralized and one semi-distributed to perform the M2M resource allocation. The computational complexity of both introduced algorithms whose aim is to solve the M2M resource allocation, is of polynomial complexity. Simulation results show that the semi-distributed M2M resource allocation algorithm achieves quite good performance in terms of network aggregate sum-rate with markedly lower communication overhead compared to the centralized one. Index Terms-Machine to machine (M2M), human to human (H2H), device to device (D2D), bipartite graph (BP), resource sharing.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.