Special section on smart grids: A hub of interdisciplinary research : IEEE ACCESS Special section editorial smart grids: A hub of interdisciplinary research

International audienceThe smart grid is an important hub of interdisciplinary research where researchers from different areas of science and technology combine their efforts to enhance the traditional electrical power grid. Due to these efforts, the traditional electrical grid is now evolving. The envisioned smart grid will bring social, environmental, ethical, legal and economic benefits. Smart grid systems increasingly involve machine-to-machine communication as well as human-to-human, or simple information retrieval. Thus, the dimensionality of the system is massive. The smart grid is the combination of different technologies, including control system theory, communication networks, pervasive computing , embedded sensing devices, electric vehicles, smart cities, renewable energy sources, Internet of Things, wireless sensor networks, cyber physical systems, and green communication. Due to these diverse activities and significant attention from researchers, education activities in the smart grid area are also growing. The smart grid is designed to replace the traditional electrical power grid. The envisioned smart grid typically consists of three networks: Home Area Networks (HANs), Neighborhood Area Networks (NANs), and Wide Area Networks (WANs). HANs connect the devices within the premises of the consumer and connect smart meters, Plug-in Electric Vehicles (PEVs), and distributed renewable energy sources. NANs connect multiple HANs and communicate the collected information to a network gateway. WANs serve as the communication backbone. Communication technologies play a vital role in the successful operation of smart grid. These communication technologies can be adopted based upon the specific features required by HANs, NANs, and WANs. Both wired and the wireless communication technologies can be used in the smart grid [1]. However, wireless communication technologies are suitable for many smart grid applications due to the continuous development in the wireless research domain. One drawback of wireless communication technologies is the limited availability of radio spectrum. The use of cognitive radio in smart grid communication will be helpful to break the spectrum gridlock through advanced radio design and operating in multiple settings, such as underlay, overlay, and interweave [2]. The smart grid is the combination of diverse sets of facilities and technologies. Thus, the monitoring and control of transmission lines, distribution facilities, energy generation plants, and as well as video monitoring of consumer premises can be conducted through the use of wireless sensor networks [3]–[6]. In remote sites and places where human intervention is not possible, wireless sensor and actuator networks can be useful for the successful smart grid operation [7], [8]. Since wireless sensor networks operate on the Industrial, Scientific, and Medical (ISM) band, the spectrum might get congested due to overlaid deployment of wireless sensor networks in the same premises. Thus, to deal with this spectrum congestion challenge, cognitive radio sensor networks can be used in smart grid environments [9], [10]. The objective of this Special Section in IEEE ACCESS is to showcase the most recent advances in the interdisciplinary research areas encompassing the smart grid. This Special Section brings together researchers from diverse fields and specializations, such as communications engineering, computer science, electrical and electronics engineering, educators, mathematicians and specialists in areas related to smart grids. In this Special Section, we invited researchers from academia, industry, and government to discuss challenging ideas, novel research contributions, demonstration results, and standardization efforts on the smart grid and related areas. This Special Section is a collection of eleven articles. These articles are grouped into the following four areas: (a) Reliability, security, and privacy for smart grid, (b), Demand response management, understanding customer behavior, and social networking applications for smart grid, (c) Smart cities, renewable energy, and green smart grid, and (d) Communication technologies, control and management for the smart grid

Fulcrum: Flexible Network Coding for Heterogeneous Devices

Producción CientíficaWe introduce Fulcrum, a network coding framework that achieves three seemingly conflicting objectives: 1) to reduce the coding coefficient overhead down to nearly n bits per packet in a generation of n packets; 2) to conduct the network coding using only Galois field GF(2) operations at intermediate nodes if necessary, dramatically reducing computing complexity in the network; and 3) to deliver an end-to-end performance that is close to that of a high-field network coding system for high-end receivers, while simultaneously catering to low-end receivers that decode in GF(2). As a consequence of 1) and 3), Fulcrum has a unique trait missing so far in the network coding literature: providing the network with the flexibility to distribute computational complexity over different devices depending on their current load, network conditions, or energy constraints. At the core of our framework lies the idea of precoding at the sources using an expansion field GF(2 h ), h > 1, to increase the number of dimensions seen by the network. Fulcrum can use any high-field linear code for precoding, e.g., Reed-Solomon or Random Linear Network Coding (RLNC). Our analysis shows that the number of additional dimensions created during precoding controls the trade-off between delay, overhead, and computing complexity. Our implementation and measurements show that Fulcrum achieves similar decoding probabilities as high field RLNC but with encoders and decoders that are an order of magnitude faster.Green Mobile Cloud project (grant DFF-0602-01372B)Colorcast project (grant DFF-0602-02661B)TuneSCode project (grant DFF - 1335-00125)Danish Council for Independent Research (grant DFF-4002-00367)Ministerio de Economía, Industria y Competitividad - Fondo Europeo de Desarrollo Regional (grants MTM2012-36917-C03-03 / MTM2015-65764-C3-2-P / MTM2015-69138-REDT)Agencia Estatal de Investigación - Fondo Social Europeo (grant RYC-2016-20208)Aarhus Universitets Forskningsfond Starting (grant AUFF-2017-FLS-7-1