Statistical Modeling of FSO Fronthaul Channel for Drone-based Networks

We consider a drone-based communication network, where several drones hover above an area and serve as mobile remote radio heads for a large number of mobile users. We assume that the drones employ free space optical (FSO) links for fronthauling of the users' data to a central unit. The main focus of this paper is to quantify the geometric loss of the FSO channel arising from random fluctuation of the position and orientation of the drones. In particular, we derive upper and lower bounds, corresponding approximate expressions, and a closed-form statistical model for the geometric loss. Simulation results validate our derivations and quantify the FSO channel quality as a function of the drone's instability, i.e., the variation of its position and orientation.Comment: This paper has been submitted to ICC 201

Determination of RF source power in WPSN using modulated backscattering

A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. During RF transmission energy consumed by critically energy-constrained sensor nodes in a WSN is related to the life time system, but the life time of the system is inversely proportional to the energy consumed by sensor nodes. In that regard, modulated backscattering (MB) is a promising design choice, in which sensor nodes send their data just by switching their antenna impedance and reflecting the incident signal coming from an RF source. Hence wireless passive sensor networks (WPSN) designed to operate using MB do not have the lifetime constraints. In this we are going to investigate the system analytically. To obtain interference-free communication connectivity with the WPSN nodes number of RF sources is determined and analyzed in terms of output power and the transmission frequency of RF sources, network size, RF source and WPSN node characteristics. The results of this paper reveal that communication coverage and RF Source Power can be practically maintained in WPSN through careful selection of design parametersComment: 10 pages; International Journal on Soft Computing (IJSC) Vol.3, No.1 (2012). arXiv admin note: text overlap with arXiv:1001.5339 by other author

Doctor of Philosophy

dissertationDevice-free localization (DFL) and tracking services are important components in security, emergency response, home and building automation, and assisted living applications where an action is taken based on a person's location. In this dissertation, we develop new methods and models to enable and improve DFL in a variety of radio frequency sensor network configurations. In the first contribution of this work, we develop a linear regression and line stabbing method which use a history of line crossing measurements to estimate the track of a person walking through a wireless network. Our methods provide an alternative approach to DFL in wireless networks where the number of nodes that can communicate with each other in a wireless network is limited and traditional DFL methods are ill-suited. We then present new methods that enable through-wall DFL when nodes in the network are in motion. We demonstrate that we can detect when a person crosses between ultra-wideband radios in motion based on changes in the energy contained in the first few nanoseconds of a measured channel impulse response. Through experimental testing, we show how our methods can localize a person through walls with transceivers in motion. Next, we develop new algorithms to localize boundary crossings when a person crosses between multiple nodes simultaneously. We experimentally evaluate our algorithms with received signal strength (RSS) measurements collected from a row of radio frequency (RF) nodes placed along a boundary and show that our algorithms achieve orders of magnitude better localization classification than baseline DFL methods. We then present a way to improve the models used in through-wall radio tomographic imaging with E-shaped patch antennas we develop and fabricate which remain tuned even when placed against a dielectric. Through experimentation, we demonstrate the E-shaped patch antennas lower localization error by 44% compared with omnidirectional and microstrip patch antennas. In our final contribution, we develop a new mixture model that relates a link's RSS as a function of a person's location in a wireless network. We develop new localization methods that compute the probabilities of a person occupying a location based on our mixture model. Our methods continuously recalibrate the model to achieve a low localization error even in changing environments

Understanding Traffic Characteristics in a Server to Server Data Center Network

The number of Data Centers and the servers present in them has been on the rise over the last decade with the advent of cloud computing, social networking, Big data analytics etc. This has eventually led to the increase in the power consumption of the Data Center due to the power hungry interconnection fabric which consists of switches and routers. The scalability of the data center has also become a problem due to the interconnect cabling complexity which is also responsible for the increase in the energy used for cooling the data center as these bundles of wires reduce the air flow in the data center. The maintenance costs of the data center is high due to this reason. This brings the challenge of reducing the power consumption as well as improving the scalability of the data center. There is a lot of cost involved in the establishment of a network in a data center and this network is one of the main source of power consumption. Therefore, there is a need to accurately characterize the data center network before its construction which requires the simulation of the data center models. For the simulation of data center models, we require the traffic which is identical to that of an actual data center so that the results will be similar to a real time data center. Traditional data center networks have a wired communication fabric, which is not scalable and contributes largely to the power consumption. This has led to the investigation of other methods. There have been transceivers designed that can support the unlicensed 60 GHz spectrum, supporting high bandwidth similar to the wired network present in traditional data centers. These wireless links have spatial reusability and the data centers can make use of this communication medium to meet the high bandwidth demands and also reduce the use of cable thereby bringing down the cost and the power consumption. This thesis studies the previous traffic models used in the simulation of a data center network. Traffic collected from ten different data centers is then characterized and modelled based on various probability distributions. The implementation of the model tries to generate traffic similar to that of an actual data center. The Data Center Network is then simulated using the traffic generated and the performance of the wired data center is quantified in terms of metrics like throughput, latency and the power consumption of the data center networks