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Title: Problems and Solutions for Backscatter-Based Tag-to-Tag Communication in the Internet of Things
Authors: Djuric, Petar M.
Shen, Zhe
Department of Electrical Engineering.
Bugallo, Monica
Hong, Sangjin
R.Das, Samir.
Issue Date: 1-Dec-2015
Publisher: The Graduate School, Stony Brook University: Stony Brook, NY.
Abstract: The Internet of Things (IoT) has been developing for decades and has been considered as the future of the Internet. The basic idea of the IoT is to enable a wide range of objects (things) around us to interact and cooperate with each other so that certain goals are reached. An important technology that will be part of the IoT is Radio Frequency IDentification (RFID). RFID is based on the concept of backscatter-based communication and the use of inexpensive RFID tags. A very exciting direction of work in the last few years has been the research on backscatter-based tag-to-tag (BBTT) communication systems, that is, systems that do not require the use of expensive RFID readers. In BBTT communication systems, two or more radio-less tags communicate with each other purely by backscattering an external signal. We study several problems of BBTT communication systems. First, we investigate a unique phase cancellation problem that occurs in BBTT systems. The relative phase difference between the backscatter signal and the external excitation signal at the receiving tag often causes a complete cancellation of the baseband information contained in the envelope, which results in a loss of communication between the two tags. We theoretically analyze and experimentally demonstrate this problem. We then present a solution to the problem based on the design of a new backscatter modulator for tags that enables multi-phase backscattering. Second, we address protocols for communication in BBTT systems. We note that the tags of BBTT systems have limitations including memory space and communication ranges. Considering these limitations and simple applications, we choose and modify two existing anti-collision protocols that can be used in BBTT systems. We examine the performance of the proposed protocols through theoretical analysis of linear and complete networks and by computer simulations of general networks. Third, we consider the problem of distributed Bayesian learning in BBTT systems. The BBTT system is composed of tags that can only communicate with their neighbors. These tags are tasked to learn by cooperation with the neighboring tags. More specifically, the objective of the tags is to obtain the global posterior distribution of an unknown parameter of a fictitious fusion center in a distributed way through the use of the Bayesian paradigm. The tags iteratively exchange information with their neighbors, and they update the summary of their information using the signals received from the neighbors. All the tags are assumed to know the topology of the network and keep all the new information in their memories. We propose a method based on a recent work and prove that the distribution of each tag can converge correctly using the proposed method. Furthermore, with the proposed method, convergence is achieved much faster than with the non-Bayesian and consensus-based algorithms. The proposed approach is general and applicable to other types of distributed systems.
Description: 136 pg.
Appears in Collections:Stony Brook Theses and Dissertations Collection

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