An eco-friendly hybrid urban computing network combining community-based wireless LAN access and wireless sensor networking

Computer-enhanced smart environments, distributed environmental monitoring, wireless communication, energy conservation and sustainable technologies, ubiquitous access to Internet-located data and services, user mobility and innovation as a tool for service differentiation are all significant contemporary research subjects and societal developments. This position paper presents the design of a hybrid municipal network infrastructure that, to a lesser or greater degree, incorporates aspects from each of these topics by integrating a community-based Wi-Fi access network with Wireless Sensor Network (WSN) functionality. The former component provides free wireless Internet connectivity by harvesting the Internet subscriptions of city inhabitants. To minimize session interruptions for mobile clients, this subsystem incorporates technology that achieves (near-)seamless handover between Wi-Fi access points. The WSN component on the other hand renders it feasible to sense physical properties and to realize the Internet of Things (IoT) paradigm. This in turn scaffolds the development of value-added end-user applications that are consumable through the community-powered access network. The WSN subsystem invests substantially in ecological considerations by means of a green distributed reasoning framework and sensor middleware that collaboratively aim to minimize the network's global energy consumption. Via the discussion of two illustrative applications that are currently being developed as part of a concrete smart city deployment, we offer a taste of the myriad of innovative digital services in an extensive spectrum of application domains that is unlocked by the proposed platform

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

Comparison of two approaches for web-based 3D visualization of smart building sensor data

Abstract. This thesis presents a comparative study on two different approaches for visualizing sensor data collected from smart buildings on the web using 3D virtual environments. The sensor data is provided by sensors that are deployed in real buildings to measure several environmental parameters including temperature, humidity, air quality and air pressure. The first approach uses the three.js WebGL framework to create the 3D model of a smart apartment where sensor data is illustrated with point and wall visualizations. Point visualizations show sensor values at the real locations of the sensors using text, icons or a mixture of the two. Wall visualizations display sensor values inside panels placed on the interior walls of the apartment. The second approach uses the Unity game engine to create the 3D model of a 4-floored hospice where sensor data is illustrated with aforementioned point visualizations and floor visualizations, where the sensor values are shown on the floor around the location of the sensors in form of color or other effects. The two approaches are compared with respect to their technical performance in terms of rendering speed, model size and request size, and with respect to the relative advantages and disadvantages of the two development environments as experienced in this thesis

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Understanding the Cognitive Impact of Emerging Web Technologies: A Research Focus Area for Embodied, Extended and Distributed Approaches to Cognition

Alongside existing research into the social, political and economic impacts of the Web, there is also a need to explore the effects of the Web on our cognitive profile. This is particularly so as the range of interactive opportunities we have with the Web expands under the influence of a range of emerging technologies. Embodied, extended and distributed approaches to cognition are relevant to understanding the potential cognitive impact of these new technologies because of the emphasis they place on extra-neural and extra-corporeal factors in the shaping of our cognitive capabilities at both an individual and collective level. The current paper outlines a number of areas where embodied, extended and distributed approaches to cognition are useful in understanding the impact of emerging Web technologies on future forms of both human and machine intelligence

The role of communication systems in smart grids: Architectures, technical solutions and research challenges

The purpose of this survey is to present a critical overview of smart grid concepts, with a special focus on the role that communication, networking and middleware technologies will have in the transformation of existing electric power systems into smart grids. First of all we elaborate on the key technological, economical and societal drivers for the development of smart grids. By adopting a data-centric perspective we present a conceptual model of communication systems for smart grids, and we identify functional components, technologies, network topologies and communication services that are needed to support smart grid communications. Then, we introduce the fundamental research challenges in this field including communication reliability and timeliness, QoS support, data management services, and autonomic behaviors. Finally, we discuss the main solutions proposed in the literature for each of them, and we identify possible future research directions