Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

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Table Of Integrals Series And Products

Cooperative spectrum sensing in cognitive radio networks: A survey

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.

/pdf/federated-learning-in-mobile-edge-networks-a-comprehensive-5dgelc5m6h.pdf

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever-growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.

/pdf/a-survey-on-iot-security-application-areas-security-threats-1gb31j78px.pdf

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end-users. MEC provides seamless integration of multiple application service providers and vendors toward mobile subscribers, enterprises, and other vertical segments. It is an important component in the 5G architecture which supports variety of innovative applications and services where ultralow latency is required. This paper is aimed to present a comprehensive survey of relevant research and technological developments in the area of MEC. It provides the definition of MEC, its advantages, architectures, and application areas; where we in particular highlight related research and future directions. Finally, security and privacy issues and related existing solutions are also discussed.

/pdf/mobile-edge-computing-a-survey-3eu2fzhuxk.pdf

Mobile Edge Computing: A Survey

Substation automation for power distribution systems poses challenging problems for communication networks. This article examines whether state-of-the-art Ethernet technology can satisfy the real-time performance requirements of this application.

Ethernet in substation automation

Computers get faster every year, but the demand for computing resources seems to grow at an even faster rate. Depending on the problem domain, this demand for more power can be satisfied by either, massively parallel computers, or clusters of computers. Common for both approaches is the dependence on high performance interconnect networks such as Myrinet, Infiniband, or 10 Gigabit Ethernet. While high throughput and low latency are key features of interconnection networks, the issue of fault-tolerance is now becoming increasingly important. As the number of network components grows so does the probability for failure, thus it becomes important to also consider the fault-tolerance mechanism of interconnection networks. The main challenge then lies in combining performance and fault-tolerance, while still keeping cost and complexity low. This paper proposes a new deterministic routing methodology for tori and meshes, which achieves high performance without the use of virtual channels. Furthermore, it is topology agnostic in nature, meaning it can handle any topology derived from any combination of faults when combined with static reconfiguration. The algorithm, referred to as segment-based routing (SR), works by partitioning a topology into subnets, and subnets into segments. This allows us to place bidirectional turn restrictions locally within a segment. As segments are independent, we gain the freedom to place turn restrictions within a segment independently from other segments. This results in a larger degree of freedom when placing turn restrictions compared to other routing strategies. In this paper a way to compute segment-based routing tables is presented and applied to meshes and tori. Evaluation results show that SR increases performance by a factor of 1.8 over FX and up*/down* routing.

/pdf/segment-based-routing-an-efficient-fault-tolerant-routing-45mco5nvyg.pdf

Segment-based routing: an efficient fault-tolerant routing algorithm for meshes and tori

Femtocell is envisioned as a highly promising solution for indoor wireless communications. The spectrum allocated to femtocells is traditionally from the same licensed spectrum bands of macrocells. In this case, the capacity of femtocell networks is highly limited due to the finite number of licensed spectrum bands and also the interference with macrocells and other femtocells. In this paper, we propose a radically new communication paradigm by incorporating cognitive radio in femtocell networks. The cognitive radio enabled femtocells are able to access spectrum bands not only from macrocells but also from other licensed systems (e.g. TV systems) provided the interference from femtocells to the existing systems is not harmful. It results in more channel opportunities for femtocells. Thus, the co-channel interference in femtocells can be greatly reduced and the network capacity can be significantly improved. Because of the difference from other traditional wireless networks, we argue the traditional spectrum sharing schemes such as coloring methods are not efficient to femtocell networks especially for dense deployment scenarios. We formulate the downlink spectrum sharing problem in cognitive radio femtocell networks, and employ decomposition theories to solve the problem. Simulation results indicate that cognitive radio enabled femtocells could achieve much higher capacity than the femtocell networks which do not employ agile spectrum access. Simulation results also show that our proposed scheme without any iteration can achieve almost twice of the average capacity by coloring method when the number of available channels is less than five. Moreover, our proposed scheme can converge very fast with a typical value of only five iterations, and it can achieve around two percent extra average capacity than the fixed power control scheme.

Downlink Spectrum Sharing for Cognitive Radio Femtocell Networks

Through several giant evolutionary steps, Ethernet has become an almost ubiquitous technology for communication. Being versatile enough to be employed in new and various fields of application, it is now making inroads in factories. However, automated systems are different from many other applications of Ethernet,first and foremost because they require the network technology to deliver real-time performance. In the present study, a number of critical aspects of Ethernet, usually referred to as an Industrial Ethernet, are examined. More specifically, there is a focus on the application-to-application delay and jitter characteristics of such networks, when using Internet protocols such as UDP and TCP. It is demonstrated how important it is to take control of the latency in the station nodes, since the main communication delays occur inside the nodes, and different solutions are presented for controlling these delays. In particular, a priority-based protocol stack is assessed. The results show that real-time, Ethernet-based IP communication is now adequate even for demanding automated applications. In this paper, substation automation (power distribution) is used as an example of a demanding automation system.

http://www.kode.no/Svein/EthernetSimulations.pdf

Tor Skeie

Papers

Mobile Edge Computing: A Survey

Ethernet in substation automation

Segment-based routing: an efficient fault-tolerant routing algorithm for meshes and tori

Downlink Spectrum Sharing for Cognitive Radio Femtocell Networks

Timeliness of real-time IP communication in switched industrial Ethernet networks