Outdoor Radio Propagation Model Optimization Using Genetic Algorithm

Path loss which is one of the main issues in wireless communication system and has been studied for long time. With the tremendous increase in demand in wireless technology, this Path loss needs to be optimized. Therefore, it is very important to analyse these different propagation models in order to get some useful information out and develop a system based on it. This is done to get the optimum path loss from different models. These are useful tools which makes the designers capable of designing a wireless system with great efficiency. In pursuit of the same, this paper attempts to optimize free space propagation model and hata model using GA algorithm, and shows a comparison by putting them side by side. This paper gives an insight of comparison between free space and Hata model in wireless communication taking different propagation environments into consideration.

Survey of Radio Propagation Models for Acoustic Applications in Underwater Wireless Sensor Network

Background: Acoustic waves have a large range of applications in UWSNs from underwater monitoring to disaster management, military surveillance to assisted navigation. Acoustic waves are primarily used for wireless communication in water. But radio waves are more suitable than acoustic waves for many underwater applications (e.g. real-time applications, shallow water applications). Objectives: A propagation model is required to effectively design a radio wave based UWSN. Propagation model predicts the average received signal strength at a given distance from the transmitter and the variability of the signal strength in close spatial proximity to a particular location. Various radio propagation models are developed for air. Methods: The performance of RF-EM waves underwater is not the same as that in the air. Many parameters which have real-value in the air becomes complex valued in seawater. Thus, propagation models for air cannot be directly used to calculate propagation loss underwater. Various radio propagation models are developed for water by Al-Shamaa’a et al., Uribe and Grote, Jiang et al., Elrashidi et al., Hattab et al. Each model has some merits and demerits. Path loss model developed by Al-Shamma’a et al. is a simple model based on attenuation only. Results: Uribe and Grote have introduced distance-dependent attenuation coefficient in path loss calculation. Path loss model by Jiang et al. calculates path loss for freshwater. Model by Hattab et al. is specifically designed for UWSN. According to the authors, it is the first path loss model developed for UWSN. Elrashidi et al. have calculated path loss for freshwater and seawater at 2.4 GHz. The model includes the effect of the reflected signals on the received signal by the receiver node. Conclusion: The paper presents a comparative analysis of these various radio propagation models developed for underwater. Among these models, the radio propagation model by Hattab et al. is more realistic and covers both propagation loss and interface loss. According to the authors, it is the first radio propagation model developed for UWSNs.

PLACEMENT OPTIMIZATION OF POSITIONING NODES: MAXIMIZING THE DISTINCTION OF INDOOR ZONES

<p><strong>Abstract.</strong> The performance of an indoor positioning system is highly related to the placement of the transmitting nodes that are used as references for the positioning estimations. In this paper, we propose a methodology that can be used to optimize such a deployment and thus, increase the performance of an indoor positioning system that a) is based on Received Signal Strength (RSS) fingerprinting and b) is orientated towards providing location or zone estimations instead of exact positioning. The optimization process involves 4 fundamental components. Firstly, the modelling of the obstructions in the indoor environment and also the zone modelling. Then, the definition of the performance metric that can be used to evaluate each different deployment scenario, in which case, our proposed metric considers the separation area and distances between the zones in the RSS vector space. The third component is the radio propagation model, required for simulating the RSSs from each node, where a model based on the ray tracing technique is selected. Finally, the last component is the selection of the optimization function that will control and drive the whole optimization process by selecting which deployment schemes to evaluate. For that, the utilization of a Genetic Algorithm is proposed. Although the evaluation of this methodology is outside the paper’s scope, the key factors affecting the optimization performance the most, are expected to be a) the accuracy of the used indoor model and radio propagation model and b) the exact implementation of the optimization function.</p>