3,615 research outputs found
Energy Consumption of Wireless Network Access Points
2nd International Conference on Green Communications and Networking, GreeNets 2012; Gandia; Spain; 25 October 2012 through 26 October 2012The development of low cost technology based on IEEE 802.11 standard permits to build telecommunication networks at low cost, allowing providing Internet access in rural areas in developing countries. The lack of access to the electrical grid is a problem when the network is being developed in rural areas, so that wireless access points should operate using solar panels and batteries. Many cases can be found where the energy consumption becomes a key point in wireless network design. In this paper we present a comparative study of the energy consumption of several wireless network access points. We will compare the energy consumption of different brands and models, for several operation scenarios and operating modes. Obtained results allow us to achieve the objective of this article, that is, promote the development of wireless communication networks energetically efficient.Andrade Morelli, S.; Ruiz Sanchez, E.; Granell Romero, E.; Lloret, J. (2013). Energy Consumption of Wireless Network Access Points. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST. 113:81-91. doi:10.1007/978-3-642-37977-2_8S8191113Khoa Nguyen, K., Jaumard, B.: Routing Engine Architecture for Next Generation Routers: Evolutional Trends. Network Protocols and Algorithms 1(1), 62–85 (2009)IEEE Std 802.11: IEEE Standard for Information technology -Telecommunications and information exchange between systems -Local and metropolitan area networks - Specific requirements – Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. Institute of Electrical and Electronics Engineers, New York, USA, pp.1–1184 (2007)Lloret, J., Garcia, M., Bri, D., Sendra, S.: A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification. Sensors 9(11), 8722–8747 (2009)Tapia, A., Maitland, C., Stone, M.: Making IT work for Municipalities: Building municipal wireless networks. Government Information Quarterly 23(3), 359–380 (2006)van Drunen, R., Koolhaas, J., Schuurmans, H., Vijn, M.: Building a Wireless Community Network in the Netherland. In: USENIX 2003 / Freenix Annual Technical Conference Proceedings, San Antonio, Texas, USA, June 9-14, pp. 219–230 (2003)Powell, A., Shade, L.R.: Going Wi-Fi in Canada: Municipal and Community Initiatives. Canadian Research Alliance for Community Innovation and Networking (2005)Sendra, S., Fernández, P.A., Quilez, M.A., Lloret, J.: Study and Performance of Interior Gateway IP routing Protocols. Network Protocols and Algorithms 2(4), 88–117 (2010)Galperin, H.: Wireless Networks and Rural Development: Opportunities for Latin America. Information Technologies and International Development 2(3), 47–56 (2005)Segal, M.: Improving lifetime of wireless sensor networks. Network Protocols and Algorithms 1(2), 48–60 (2009)Momani, A.A.E., Yassein, M.B., Darwish, O., Manaseer, S., Mardini, W.: Intelligent Paging Backoff Algorithm for IEEE 802.11 MAC Protocol. Network Protocols and Algorithms 4(2), 108–123 (2012)Mohsin, A.H., Bakar, K.A., Adekiigbe, A., Ghafoor, K.Z.: A Survey of Energy-aware Routing protocols in Mobile Ad-hoc Networks: Trends and Challenges. Network Protocols and Algorithms 4(2), 82–107 (2012)Feeney, L.M., Nilsson, M.: Investigating the Energy Consumption of a Wireless Network Interface in an Ad Hoc Networking Environment. In: Proceedings of the Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2001, Anchorage, Alaska, April 22-26, vol. 3, pp. 1548–1557. IEEE (2001)Barbancho, J., León, C., Molina, F.J., Barbancho, A.: Using artificial intelligence in routing schemes for wireless networks. Computer Communications 30(14-15), 2802–2811 (2007)Tao, C., Yang, Y., Honggang, Z., Haesik, K., Horneman, K.: Network energy saving technologies for green wireless access networks. IEEE Wireless Communications 18(5), 30–38 (2011)Sendra, S., Lloret, J., Garcia, M., Toledo, J.F.: Power saving and energy optimization techniques for Wireless Sensor Networks. Journal of Communications 6(6), 439–459 (2011
Nano-scale reservoir computing
This work describes preliminary steps towards nano-scale reservoir computing
using quantum dots. Our research has focused on the development of an
accumulator-based sensing system that reacts to changes in the environment, as
well as the development of a software simulation. The investigated systems
generate nonlinear responses to inputs that make them suitable for a physical
implementation of a neural network. This development will enable
miniaturisation of the neurons to the molecular level, leading to a range of
applications including monitoring of changes in materials or structures. The
system is based around the optical properties of quantum dots. The paper will
report on experimental work on systems using Cadmium Selenide (CdSe) quantum
dots and on the various methods to render the systems sensitive to pH, redox
potential or specific ion concentration. Once the quantum dot-based systems are
rendered sensitive to these triggers they can provide a distributed array that
can monitor and transmit information on changes within the material.Comment: 8 pages, 9 figures, accepted for publication in Nano Communication
Networks, http://www.journals.elsevier.com/nano-communication-networks/. An
earlier version was presented at the 3rd IEEE International Workshop on
Molecular and Nanoscale Communications (IEEE MoNaCom 2013
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Implantation modified deep echo state neural networks and improve harmony clustering algorithm for optimal and energy efficient path in mobile sink
Wireless network sensors based on the mobile sink are regarded to be a common network and used in various fields in the last few years, they are thought to be easy to use, but contain the problem of energy loss and are affected by an energy hole problem, as it depends on batteries. This paper proposes a solution to this problem by using an innovative objective function for a consistent distributing of cluster heads, the enhanced harmony search based routing protocols based on energy equilibrated node clustering protocol. In order to route the data packet among the sink and cluster heads, an enhanced modified deep echo state neural network is suggested. The efficiency of a projected integrated clustering and routing protocol has been investigated at 500 nodes, and the 96 per cent success data for the proposed algorithm is given using the average energy consumption, send and receive packaged and optimum numbers of CH
DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout
The paper presents a novel, principled approach to train recurrent neural
networks from the Reservoir Computing family that are robust to missing part of
the input features at prediction time. By building on the ensembling properties
of Dropout regularization, we propose a methodology, named DropIn, which
efficiently trains a neural model as a committee machine of subnetworks, each
capable of predicting with a subset of the original input features. We discuss
the application of the DropIn methodology in the context of Reservoir Computing
models and targeting applications characterized by input sources that are
unreliable or prone to be disconnected, such as in pervasive wireless sensor
networks and ambient intelligence. We provide an experimental assessment using
real-world data from such application domains, showing how the Dropin
methodology allows to maintain predictive performances comparable to those of a
model without missing features, even when 20\%-50\% of the inputs are not
available
Robotic ubiquitous cognitive ecology for smart homes
Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work
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