Energy Efficiency in Communications and Networks

The topic of "Energy Efficiency in Communications and Networks" attracts growing attention due to economical and environmental reasons. The amount of power consumed by information and communication technologies (ICT) is rapidly increasing, as well as the energy bill of service providers. According to a number of studies, ICT alone is responsible for a percentage which varies from 2% to 10% of the world power consumption. Thus, driving rising cost and sustainability concerns about the energy footprint of the IT infrastructure. Energy-efficiency is an aspect that until recently was only considered for battery driven devices. Today we see energy-efficiency becoming a pervasive issue that will need to be considered in all technology areas from device technology to systems management. This book is seeking to provide a compilation of novel research contributions on hardware design, architectures, protocols and algorithms that will improve the energy efficiency of communication devices and networks and lead to a more energy proportional technology infrastructure

Desenvolupament, proves de camp i anàlisi de resultats en una xarxa de sensors

The objective of this master thesis is to describe the problems of the underwater acoustic sensor network and make some experiments. The experiments carried out try to characterize the communication in underwater environments in order to be able to develop underwater sensor networks. In the first chapter we describe the motivations, features of aquatic environment, the difficulties of underwater acoustic channels, and the open questions in mobile underwater sensor network design. In the second chapter we try to describe the experiments, show the results and try to explain these results. And finally in the third chapter we explain the conclusions and the further works of this master thesis

Design and Implementation of an Embedded System for Software Defined Radio

In this paper, developing high performance software for demanding real-time embedded systems is proposed. This software-based design will enable the software engineers and system architects in emerging technology areas like 5G Wireless and Software Defined Networking (SDN) to build their algorithms. An ADSP-21364 floating point SHARC Digital Signal Processor (DSP) running at 333 MHz is adopted as a platform for an embedded system. To evaluate the proposed embedded system, an implementation of frame, symbol and carrier phase synchronization is presented as an application. Its performance is investigated with an on line Quadrature Phase Shift keying (QPSK) receiver. Obtained results show that the designed software is implemented successfully based on the SHARC DSP which can utilized efficiently for such algorithms. In addition, it is proven that the proposed embedded system is pragmatic and capable of dealing with the memory constraints and critical time issue due to a long length interleaved coded data utilized for channel coding

A Compact Acoustic Communication Module for Remote Control Underwater

This paper describes an end-to-end compact acoustic communication system designed for easy integration into remotely controlled underwater operations. The system supports up to 2048 commands that are encoded as 16 bit words. We present the design, hardware, and supporting algorithms for this system. A pulse-based FSK modulation scheme is presented, along with a method of demodulation requiring minimal processing power that leverages the Goertzel algorithm and dynamic peak detection. We packaged the system together with an intuitive user interface for remotely controlling an autonomous underwater vehicle. We evaluated this system in the pool and in the open ocean. We present the communication data collected during experiments using the system to control an underwater robot.National Science Foundation (U.S.) (NSF 1117178)National Science Foundation (U.S.) (NSF IIS1226883)National Science Foundation (U.S.) (Award 112237

Underwater Localization in a Confined Space Using Acoustic Positioning and Machine Learning

Localization is a critical step in any navigation system. Through localization, the vehicle can estimate its position in the surrounding environment and plan how to reach its goal without any collision. This thesis focuses on underwater source localization, using sound signals for position estimation. We propose a novel underwater localization method based on machine learning techniques in which source position is directly estimated from collected acoustic data. The position of the sound source is estimated by training Random Forest (RF), Support Vector Machine (SVM), Feedforward Neural Network (FNN), and Convolutional Neural Network (CNN). To train these data-driven methods, data are collected inside a confined test tank with dimensions of 6m x 4.5m x 1.7m. The transmission unit, which includes Xilinx LX45 FPGA and transducer, generates acoustic signal. The receiver unit collects and prepares propagated sound signals and transmit them to a computer. It consists of 4 hydrophones, Red Pitay analog front-end board, and NI 9234 data acquisition board. We used MATLAB 2018 to extract pitch, Mel-Frequency Cepstrum Coefficients (MFCC), and spectrogram from the sound signals. These features are used by MATLAB Toolboxes to train RF, SVM, FNN, and CNN. Experimental results show that CNN archives 4% of Mean Absolute Percentage Error (MAPE) in the test tank. The finding of this research can pave the way for Autonomous Underwater Vehicle (AUV) and Remotely Operated Vehicle (ROV) navigation in underwater open spaces

Latency-Optimized and Energy-Efficient MAC Protocol for Underwater Acoustic Sensor Networks: A Cross-Layer Approach

Considering the energy constraint for fixed sensor nodes and the unacceptable long propagation delay, especially for latency sensitive applications of underwater acoustic sensor networks, we propose a MAC protocol that is latency-optimized and energy-efficient scheme and combines the physical layer and the MAC layer to shorten transmission delay. On physical layer, we apply convolution coding and interleaver for transmitted information. Moreover, dynamic code rate is exploited at the receiver side to accelerate data reception rate. On MAC layer, unfixed frame length scheme is applied to reduce transmission delay, and to ensure the data successful transmission rate at the same time. Furthermore, we propose a network topology: an underwater acoustic sensor network with mobile agent. Through fully utilizing the supper capabilities on computation and mobility of autonomous underwater vehicles, the energy consumption for fixed sensor nodes can be extremely reduced, so that the lifetime of networks is extended

TEMPORAL CODING OF SPEECH IN HUMAN AUDITORY CORTEX

Human listeners can reliably recognize speech in complex listening environments. The underlying neural mechanisms, however, remain unclear and cannot yet be emulated by any artificial system. In this dissertation, we study how speech is represented in the human auditory cortex and how the neural representation contributes to reliable speech recognition. Cortical activity from normal hearing human subjects is noninvasively recorded using magnetoencephalography, during natural speech listening. It is first demonstrated that neural activity from auditory cortex is precisely synchronized to the slow temporal modulations of speech, when the speech signal is presented in a quiet listening environment. How this neural representation is affected by acoustic interference is then investigated. Acoustic interference degrades speech perception via two mechanisms, informational masking and energetic masking, which are addressed respectively by using a competing speech stream and a stationary noise as the interfering sound. When two speech streams are presented simultaneously, cortical activity is predominantly synchronized to the speech stream the listener attends to, even if the unattended, competing speech stream is 8 dB more intense. When speech is presented together with spectrally matched stationary noise, cortical activity remains precisely synchronized to the temporal modulations of speech until the noise is 9 dB more intense. Critically, the accuracy of neural synchronization to speech predicts how well individual listeners can understand speech in noise. Further analysis reveals that two neural sources contribute to speech synchronized cortical activity, one with a shorter response latency of about 50 ms and the other with a longer response latency of about 100 ms. The longer-latency component, but not the shorter-latency component, shows selectivity to the attended speech and invariance to background noise, indicating a transition from encoding the acoustic scene to encoding the behaviorally important auditory object, in auditory cortex. Taken together, we have demonstrated that during natural speech comprehension, neural activity in the human auditory cortex is precisely synchronized to the slow temporal modulations of speech. This neural synchronization is robust to acoustic interference, whether speech or noise, and therefore provides a strong candidate for the neural basis of acoustic background invariant speech recognition

Feasibility and Security Analysis of Wideband Ultrasonic Radio for Smart Home Applications

Smart home Internet-of-Things (IoT) accompanied by smart home apps has witnessed tremendous growth in the past few years. Yet, the security and privacy of the smart home IoT devices and apps have raised serious concerns, as they are getting increasingly complicated each day, expected to store and exchange extremely sensitive personal data, always on and connected, and commonly exposed to any users in a sensitive environment. Nowadays wireless smart home IoT devices rely on electromagnetic wave-based radio-frequency (RF) technology to establish fast and reliable quality network connections. However, RF has its limitations that can negatively affect the smart home user experience and even cause serious security issue, such as crowded spectrum resources and RF waves leakage. To overcome those limitations, people have to use technology with sophisticated time and frequency division management and rely on the assumptions that the attackers have limited computational power. In this thesis we propose URadio, a wideband ultrasonic communication system, using electrostatic ultrasonic transducers. We design and develop two different types of transducer membranes using two types of extremely thin materials, Aluminized Mylar Film (AMF) and reduced Graphene Oxide (rGO), for assembling transducers, which achieve at least 45 times more bandwidth than commercial transducers. Equipped with the new wideband transducers, an OFDM communication system is designed to better utilize the available 600 kHz wide bandwidth. Our experiments show that URadio can achieve an unprecedentedly 4.8 Mbps data rate with a communication range of 17 cm. The attainable communication range is increased to 31 cm and 35 cm with data rates of 1.2 Mbps and 0.6 Mbps using QPSK and BPSK, respectively. Although the current wideband system only supports short-range communication, it is expected to extend the transmission range with better acoustic engineering. Also, by conducting experiments to measure the ultrasonic adversaries\u27 eavesdropping and jamming performance, we prove that our system is physically secure even when exchanging plaintext data. Adviser: Qiben Ya

Performance evaluation of a prototyped wireless ground sensor networks

This thesis investigated the suitability of wireless, unattended ground sensor networks for military applications. The unattended aspect requires the network to self-organize and adapt to dynamic changes. A wireless, unattended ground sensor network was prototyped using commercial off-the-shelf technology and three to four networked nodes. Device and network performance were measured under indoor and outdoor scenarios. The measured communication range of a node varied between three and nineteen meters depending on the scenario. The sensors evaluated were an acoustic sensor, a magnetic sensor, and an acceleration sensor. The measured sensing range varied by the type of sensor. Node discovery durations observed were between forty seconds and over five minutes. Node density calculations indicated that the prototype was scalable to five hundred nodes. This thesis substantiated the feasibility of interconnecting, self-organizing sensor nodes in military applications. Tests and evaluations demonstrated that the network was capable of dynamic adaptation to failure and degradation.http://archive.org/details/performanceevalu109452263Approved for public release; distribution is unlimited

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig