Reservoir Computing Meets Smart Grids: Attack Detection Using Delayed Feedback Networks

130

In order to satisfy the power thirsty of communication devices in the imminent 5G era, wireless charging techniques have attracted much attention both from the academic and industrial communities. Although the inductive coupling and magnetic resonance based charging techniques are indeed capable of supplying energy in a wireless manner, they tend to restrict the freedom of movement. By contrast, RF signals are capable of supplying energy over distances, which are gradually inclining closer to our ultimate goal -charging anytime and anywhere. Furthermore, transmitters capable of emitting RF signals have been widely deployed, such as TV towers, cellular base stations and Wi-Fi access points. This communication infrastructure may indeed be employed also for wireless energy transfer (WET). Therefore, no extra investment in dedicated WET infrastructure is required. However, allowing RF signal based WET may impair the wireless information transfer (WIT) operating in the same spectrum. Hence, it is crucial to coordinate and balance WET and WIT for simultaneous wireless information and power transfer (SWIPT), which evolves to Integrated Data and Energy communication Networks (IDENs). To this end, a ubiquitous IDEN architecture is introduced by summarising its natural heterogeneity and by synthesising a diverse range of integrated WET and WIT scenarios. Then the inherent relationship between WET and WIT is revealed from an information theoretical perspective, which is followed by the critical appraisal of the hardware enabling techniques extracting energy from RF signals. Furthermore, the transceiver design, resource allocation and user scheduling as well as networking aspects are elaborated on. In a nutshell, this treatise can be used as a handbook for researchers and engineers, who are interested in enriching their knowledge base of IDENs and in putting this vision into practice.Index Terms-RF signals, wireless energy transfer (WET), wireless information transfer (WIT), simultaneous wireless information and power transfer (SWIPT), wireless powered communication networks (WPCNs), integrated data and energy communication networks (IDENs).an increasing research interest from both the electronic and communication engineering communities. C. Near-field Wireless Energy TransferAt the time of writing, resonant inductive coupling [35] and magnetic resonance coupling [36] emerge as flexible wireless charging options for electronic devices in the nearfield. Resonant inductive coupling based wireless charging relies on the magnetic coupling that delivers electrical energy between two coils tuned to resonate at the same frequency. This technique has already been commercialised for small electronic appliances [37], such as mobile phones, electric toothbrushes and smart watches etc. However, the coupling coils only support near-field wireless power transfer over a distance spanning from a few millimetres to a few centimetres [38], while achieving a power transfer efficiency as high as 56.7%, when operating at a frequency of 508 kHz [...

Section: B Harvesting Energy From the Ambient Environmentmentioning

confidence: 99%

“…The design of efficient power splitters at the circuit level has attracted enormous interest from the electronics engineering community. For example, Lee 24. Time switching based receiver architecture.…”

Section: ) Power Splitting Based Receivermentioning

confidence: 99%

Integrated Data and Energy Communication Network: A Comprehensive Survey

Yang

Wen

et al. 2018

130

“…Evaluations on the well-known NSL-KDD dataset [499] demonstrate that their model achieves remarkable accuracy in identifying denial of service, probing, remote to user and user to root attacks, outperforming traditional ML methods by 0.18 in terms of F1 score. Hamedani et al employ MLPs to detect malicious attacks in delayed feedback networks [411]. The proposal achieves more than 99% accuracy over 10,000 simulations.…”

Section: Infrastructure Level Securitymentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

1,335

838

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

“…Recently, electric vehicles (EVs) and plug-in hybrid EVs have been considered as natural components of future electricity power systems, due to their efficient integration, cost savings, and environmental advantages. This is challenging the design of power systems in: scheduling energy charging plans for connected EVs, guaranteeing energy demands of EVs during peak hours, and exchanging information among EVs and the grid (or aggregators [246].…”

Section: Other Scenariosmentioning

confidence: 99%

Game Theory for Multi-Access Edge Computing: Survey, Use Cases, and Future Trends

Moura

Hutchison

2019

177

108

Game Theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming Multi-Access Edge Computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as Quality of Experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely: rational vs. evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse trade-offs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios.