scholarly journals A Temporally Hierarchical Deployment Architecture for an Enhanced Name Resolution System

2019 ◽  
Vol 9 (14) ◽  
pp. 2891 ◽  
Author(s):  
Yi Liao ◽  
Yiqiang Sheng ◽  
Jinlin Wang
Keyword(s):  
Smart Home ◽  
Network Architectures ◽  
Use Case ◽  
Integrated Network ◽  
Fifth Generation ◽  
Delay Sensitive ◽  
Spatial Features ◽  
Resolution System ◽  
Name Binding ◽  
Monitoring Service

The challenging requirements of the fifth generation (5G) cellular networks motivate the need to explore the feasibility of delivering services over new network architectures. Information-Centric Networking (ICN) is an emerging and promising network to satisfy 5G requirements. The name resolution is at the heart of ICN. We focus on satisfying the delay-sensitive requirement of the Name Resolution Service (NRS) in a 5G-ICN integrated network. We aim to design a local NRS that provides a deterministic low latency name resolution service. In this paper, we propose a temporally hierarchical deployment architecture for an Enhanced Name Resolution System (ENRS) to realize deterministic latency. The ENRS quantifiably organizes the nodes into hierarchical and nested domains by latency constraints. We design demand-aware name registration and resolution schemes to achieve constant forwarding hops in order to realize local resolution and forwarding locality. We introduce a tolerable latency-based peer resolver forwarding algorithm to improve the query hit ratio. We present a proactive name binding replicas distribution approach based on temporal–spatial features to reduce the resolution latency and query traffic. The video streaming monitoring service in Smart Home is used as a typical use case to show the continuity of service guaranteed by ENRS. Analysis demonstrates that ENRS can achieve deterministic latency. Evaluation results show that the average query hit ratio of ENRS outperforms the K-NearestNeighbor-Distributed Name Resolution System (KNN-DNRS) and Random Name Resolution System (Random-NRS) with 23.2% and 18.1%, respectively. The query traffic overhead of ENRS is up to 33.3 times smaller than KNN-DNRS. ENRS can process up to 21 GB/s name resolution traffic when the user nodes are in the magnitude order of 106.

scholarly journals Energy performance of 5G backhaul wireless network utilizing hybrid centric-distributed architecture

2019 ◽  
Vol 16 (3) ◽  
pp. 1343
Author(s):  
Hamza Mohammed Ridha Al-Khafaji ◽  
Hasan Shakir Majdi
Keyword(s):  
Energy Efficiency ◽  
Wireless Network ◽  
Network Architecture ◽  
Energy Performance ◽  
Small Cells ◽  
Network Architectures ◽  
Distributed Architecture ◽  
Wireless Backhaul ◽  
Fifth Generation ◽  
5G Network

<p>This paper scrutinizes the influence of deployment scenarios on the energy performance of fifth-generation (5G) network at various backhaul wireless frequency bands. An innovative network architecture, the hybrid centric-distributed, is employed and its energy efficiency (EE) model is analyzed. The obtained results confirm that the EE of the 5G network increases with an increasing number of small cells and degrades with an increasing frequency of wireless backhaul and radius of small cells regardless of the network architectures. Moreover, the hybrid centric-distributed architecture augments the EE when compared with the distributed architecture.</p>

scholarly journals Network architectures supporting learnability

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190323 ◽  
Author(s):  
Perry Zurn ◽  
Danielle S. Bassett
Keyword(s):  
Biological Networks ◽  
Statistical Physics ◽  
Knowledge Network ◽  
Network Architectures ◽  
Philosophical Discussion ◽  
Hierarchical Networks ◽  
Integrated Network ◽  
Physical Constraints ◽  
Philosophical Foundations ◽  
Relational Knowledge

Human learners acquire complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on two factors: the architecture (or informational structure) of the knowledge network itself and the architecture of the computational unit—the brain—that encodes and processes the information. That is, learning is reliant on integrated network architectures at two levels: the epistemic and the computational, or the conceptual and the neural. Motivated by a wish to understand conventional human knowledge, here, we discuss emerging work assessing network constraints on the learnability of relational knowledge, and theories from statistical physics that instantiate the principles of thermodynamics and information theory to offer an explanatory model for such constraints. We then highlight similarities between those constraints on the learnability of relational networks, at one level, and the physical constraints on the development of interconnected patterns in neural systems, at another level, both leading to hierarchically modular networks. To support our discussion of these similarities, we employ an operational distinction between the modeller (e.g. the human brain), the model (e.g. a single human’s knowledge) and the modelled (e.g. the information present in our experiences). We then turn to a philosophical discussion of whether and how we can extend our observations to a claim regarding explanation and mechanism for knowledge acquisition. What relation between hierarchical networks, at the conceptual and neural levels, best facilitate learning? Are the architectures of optimally learnable networks a topological reflection of the architectures of comparably developed neural networks? Finally, we contribute to a unified approach to hierarchies and levels in biological networks by proposing several epistemological norms for analysing the computational brain and social epistemes, and for developing pedagogical principles conducive to curious thought. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

Surface EMG-Based Hand Gesture Recognition via Hybrid and Dilated Deep Neural Network Architectures for Neurorobotic Prostheses

2020 ◽  
Vol 05 (01n02) ◽  
pp. 2041001 ◽  
Author(s):  
Elahe Rahimian ◽  
Soheil Zabihi ◽  
Seyed Farokh Atashzar ◽  
Amir Asif ◽  
Arash Mohammadi
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Surface Emg ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Network Architectures ◽  
Spatial Features ◽  
Deep Model ◽  
Machine Interface ◽  
Fully Connected

Motivated by the potentials of deep learning models in significantly improving myoelectric control of neuroprosthetic robotic limbs, this paper proposes two novel deep learning architectures, namely the [Formula: see text] ([Formula: see text]) and the [Formula: see text] ([Formula: see text]), for performing Hand Gesture Recognition (HGR) via multi-channel surface Electromyography (sEMG) signals. The work is aimed at enhancing the accuracy of myoelectric systems, which can be used for realizing an accurate and resilient man–machine interface for myocontrol of neurorobotic systems. The HRM is developed based on an innovative, unconventional, and particular hybridization of two parallel paths (one convolutional and one recurrent) coupled via a fully-connected multilayer network acting as the fusion center providing robustness across different scenarios. The hybrid design is specifically proposed to treat temporal and spatial features in two parallel processing pipelines and to augment the discriminative power of the model to reduce the required computational complexity and construct a compact HGR model. We designed a second architecture, the [Formula: see text], as a compact architecture. It is worth mentioning that efficiency of a designed deep model, especially its memory usage and number of parameters, is as important as its achievable accuracy in practice. The [Formula: see text] has significantly less memory requirement in training when compared to the HRM due to implementation of novel dilated causal convolutions that gradually increase the receptive field of the network and utilize shared filter parameters. The NinaPro DB2 dataset is utilized for evaluation purposes. The proposed [Formula: see text] significantly outperforms its counterparts achieving an exceptionally-high HGR performance of [Formula: see text]%. The TCNM with the accuracy of [Formula: see text]% also outperforms existing solutions while maintaining low computational requirements.

scholarly journals Techno-Economic Evaluation of 5G Technology for Automated Guided Vehicles in Production

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 192
Author(s):  
Raphael Kiesel ◽  
Leonhard Henke ◽  
Alexander Mann ◽  
Florian Renneberg ◽  
Volker Stich ◽  
...  
Keyword(s):  
Control Systems ◽  
Net Present Value ◽  
Evaluation Model ◽  
Network Control ◽  
Automated Guided Vehicles ◽  
Automated Guided Vehicle ◽  
Use Case ◽  
Fifth Generation ◽  
Noticeable Improvement ◽  
Network Control Systems

The fifth generation of mobile communication (5G) is expected to bring immense benefits to automated guided vehicles by improving existing respectively enabling 5G-distinctive network control systems, leading to higher productivity and safety. However, only 1% of production companies have fully deployed 5G yet. Most companies currently lack an understanding of return on investment and of technical use-case benefits. Therefore, this paper analyses the influence of 5G on an automated guided vehicle use case based on a five-step evaluation model. The analysis is conducted with a use case in the Digital Experience Factory in Aachen. It shows a difference of net present value between 4G and 5G of 1.3 M€ after 10 years and a difference of return of investment of 66%. Furthermore, analysis shows an increase of mobility (13%), productivity (20%) and safety (136%). This indicates a noticeable improvement of a 5G-controlled automated guided vehicle compared to a 4G-controlled automated guided vehicle.

Energy Optimizations in Broadband Access Networks

2013 ◽  
pp. 237-255
Author(s):  
Mamoun Guenach ◽  
Koen Hooghe ◽  
Michael Timmers ◽  
Jochen Maes ◽  
Dominique Singy ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Impact Energy ◽  
Network Architecture ◽  
Access Network ◽  
Access Networks ◽  
Network Architectures ◽  
Use Case ◽  
Broadband Access ◽  
Broadband Access Networks ◽  
Energy Optimizations

The second part of this chapter focuses on deployment practices and describes how different access network architectures can improve the energy consumption, when considering both the telecom equipment and its supporting functions. The authors show that introducing an access network architecture that distributes more functions in the outside plant does not negatively impact energy consumption of the access network. A use case for the Benelux is worked out and a related innovation in the Swisscom access network shows that also in the more centralized architectures further optimizations are possible.1

Sign in / Sign up

Export Citation Format

Share Document