Green, Energy-Efficient and Sustainable Networks

Edited by
January 2020
382 pages
  • ISBN978-3-03928-038-4 (Paperback)
  • ISBN978-3-03928-039-1 (PDF)

This book is a reprint of the Special Issue Green, Energy-Efficient and Sustainable Networks that was published in

Chemistry & Materials Science
Environmental & Earth Sciences

The book Green, Energy-Efficient and Sustainable Networks provides insights and solutions for a range of problems in the field of obtaining greener, energy-efficient, and sustainable networks. The book contains the outcomes of the Special Issue on “Green, Energy-Efficient and Sustainable Networks” of the Sensors journal. Seventeen high-quality papers published in the Special Issue have been collected and reproduced in this book, demonstrating significant achievements in the field. Among the published papers, one paper is an editorial and one is a review, while the remaining 15 works are research articles. The published papers are self-contained peer-reviewed scientific works that are authored by more than 75 different contributors with both academic and industry backgrounds. The editorial paper gives an introduction to the problem of information and communication technology (ICT) energy consumption and greenhouse gas emissions, presenting the state of the art and future trends in terms of improving the energy-efficiency of wireless networks and data centers, as the major energy consumers in the ICT sector. In addition, the published articles aim to improve energy efficiency in the fields of software-defined networking, Internet of things, machine learning, authentication, energy harvesting, wireless relay systems, routing metrics, wireless sensor networks, device-to-device communications, heterogeneous wireless networks, and image sensing. The last paper is a review that gives a detailed overview of energy-efficiency improvements and methods for the implementation of fifth-generation networks and beyond. This book can serve as a source of information in industrial, teaching, and/or research and development activities. The book is a valuable source of information, since it presents recent advances in different fields related to greening and improving the energy-efficiency and sustainability of those ICTs particularly addressed in this book

  • Paperback
© 2020 by the authors; CC BY licence
internet-of-things; opportunistic networks; wireless power transfer; inter-meeting time; Markov chain; node speed; battery capacity; node density; energy-efficient Ethernet; QoS; SDN; real-time traffic; ONOS; image compressive sensing (CS); green internet of things (IoT); measurement structure; random structural matrices; linear recovery; Internet of Things; malware detection; adversarial samples; machine learning; edge computing; clustering; physical-layer authentication; lightweight cipher; channel state information; lightweight authentication; HetNets; interference coordination; energy efficiency; stochastic geometry; Device-to-Device (D2D); peer discovery; energy harvesting; social awareness; PHY-layer; light-weight authentication; neural network; WSN; industrial; wireless power transfer; directional charging vehicle; charging efficiency; RWSN; green networking; energy aware routing; carbon footprint; adaptive link rate; control and data plane; 5G; energy-efficiency; sustainability; NOMA; energy harvesting; amplify-and-forward; imperfect CSI; successive interference cancellation (SIC); machine learning; LTE-A; energy efficiency; resource block allocation; bisection based optimal power allocation; water filling algorithm; proportional rate constraint; mobile edge computing; IoT; RF Fingerprinting; authentication; cooperative smart community; scheduling algorithm; consumer preferences; renewables; software defined networking (SDN); data center; optimization; traffic engineering; energy awareness; energy-efficiency; wireless; green; sustainable; data centre; networks; ICT; 5G; power; wired access; IoT; spatial modulation; multiple-input multiple-output; full-duplex; self-interference cancellation; symbol error probability