Fade Depth Prediction Using Human Presence for Real Life WSN Deployment

Current problem in real life WSN deployment is determining fade depth in indoor propagation scenario for link power budget analysis using (fade margin parameter). Due to the fact that human presence impacts the performance of wireless networks, this paper proposes a statistical approach for shadow fading prediction using various real life parameters. Considered parameters within this paper include statistically mapped human presence and the number of people through time compared to the received signal strength. This paper proposes an empirical model fade depth prediction model derived from a comprehensive set of measured data in indoor propagation scenario. It is shown that the measured fade depth has high correlations with the number of people in non-line-of-sight condition, giving a solid foundation for the fade depth prediction model. In line-of-sight conditions this correlations is significantly lower. By using the proposed model in real life deployment scenarios of WSNs, the data loss and power consumption can be reduced by the means of intelligently planning and designing Wireless Sensor Network

Indoor wireless communications and applications

Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter

Prediction and comparison of downlink electric-field and uplink localised SAR values for realistic indoor wireless planning

In this paper, for the first time a heuristic network calculator for both whole-body exposure due to indoor base station antennas or access points (downlink exposure) and localised exposure due to the mobile device (uplink exposure) in indoor wireless networks is presented. As an application, three phone call scenarios are investigated (Universal Mobile Telecommunications System (UMTS) macrocell, UMTS femtocell andWiFi voice-over-IP) and compared with respect to the electric-field strength and localised specific absorption rate (SAR) distribution. Prediction models are created and successfully validated with an accuracy of 3 dB. The benefits of the UMTS power control mechanisms are demonstrated. However, dependent on the macrocell connection quality and on the user's average phone call connection time, also the macrocell solution might be preferential from an exposure point of view for the considered scenario