scholarly journals Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3726
Author(s):  
Runzhi Li ◽  
Jian Wang ◽  
Jiongyi Chen
Keyword(s):  
Network Topology ◽  
Large Scale ◽  
Time Synchronization ◽  
Geographic Routing ◽  
High Energy ◽  
Large Scale Networks ◽  
Node Routing ◽  
Cost Parameters ◽  
Partial Connection ◽  
Geological Position

With the increasing adoption of the Internet-of-Things (IoT), the wireless sensors network (WSN), as an underlying application of IoT, has attracted increasing attention. Topology, the working structure used to observe WSN, is the most instinctive form in troubleshooting and has great significance to WSN management and safety. To this end, it is imperative to recover WSN topology for the purpose of network management and non-cooperative network detection. Traditional network topology recovery mainly relies on the monitoring modules installed in nodes, or an extra network attached. However, these two approaches have several limitations, such as high energy consumption for monitoring nodes, time synchronization problems, reuse failure, limitation to specific targeted networks and high cost. In this paper, we present a new approach to recover the topology of WSN that adopts location-based routing protocols, based on movable platforms. Our observation is that the network topology is consistent with the node routing, as the nodes choose the next hop according to the geological position of neighbor nodes. Hence, we calculate the cost parameters of choosing routing nodes for the targeted network according to the partial connection of the nodes. Based on those cost parameters, we can determine the topology of the whole network. More specifically, by collecting the geological position and data packets of the nodes from movable platforms, we are able to infer the topology of the WSN according to the recovered partial connection of nodes. Our approach can be easily adopted to many scenarios, especially for non-cooperative large-scale networks. The evaluation of 30 simulations shows that the accuracy of recovery is above 90%.

A Time Synchronization Algorithm Based on Cluster-Tree

2014 ◽  
Vol 1046 ◽  
pp. 348-351
Author(s):  
Hao Gang ◽  
Yi Zhuang
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Time Synchronization ◽  
High Energy ◽  
Cluster Tree ◽  
Network Synchronization ◽  
Hop Count ◽  
Synchronization Algorithm ◽  
Reduce Energy Consumption ◽  
High Energy Consumption

Concerning the problem that classical time synchronization algorithms applied to large-scale Wireless Sensor Network have low precision and high energy consumption, this paper proposes a time synchronization algorithm based on cluster-tree. The algorithm can decrease the synchronization hop count by constructing a spanning tree, and uses two-way SRS in inter-cluster and one-way ROS in intra-cluster to reduce the number of messages required for the network synchronization. The experimental results show that the algorithm can keep the network synchronization precision at a higher level and effectively reduce energy consumption of nodes compared with the RBS and TPSN.

scholarly journals A Design of a Time Synchronization Protocol Based on Dynamic Route and Forwarding Certification

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5061
Author(s):  
Dejing Zhang ◽  
Yuan Yuan ◽  
Yanqing Bi
Keyword(s):  
Network Topology ◽  
Large Scale ◽  
Time Synchronization ◽  
Dynamic Routing ◽  
Synchronization Error ◽  
Wireless Sensor ◽  
Clock Frequency ◽  
Reference Node ◽  
Error Accumulation ◽  
Synchronization Protocol

Time synchronization is a key technique in large-scale wireless sensor network applications. In order to tackle the problems of multi-hop synchronization error accumulation, clock frequency skew swinging, and network topology changes, a time synchronization protocol based on dynamic routing and forwarding certification (DRFC-TSP) is proposed in this paper. During the time synchronization process, a reference node with fewer synchronization hops and a more stable clock frequency is selected for every single hop, in order to obtain the best synchronization route. In this way, synchronization error accumulation can be restrained and the impact of clock frequency skew swinging on the time synchronization precision can be reduced. Furthermore, changes of the network topology can be well adapted by dynamic routing, in which the reference node is updated in every synchronization round. In the forwarding certification process, the status of nodes forwarding synchronous information outwards is authored by information exchange between neighboring nodes. Only synchronous information of the certificated nodes with a better performance can be forwarded. The network traffic can be decreased and the time synchronization precision can also be ensured, even with less energy consumption. Feasibility testing in large-scale wireless sensor networks is verified on NS2 simulation and more performances are evaluated on an embedded Linux platform.

A Drug Target Interaction Prediction Based on LINE-RF Learning

2020 ◽  
Vol 15 (7) ◽  
pp. 750-757
Author(s):  
Jihong Wang ◽  
Yue Shi ◽  
Xiaodan Wang ◽  
Huiyou Chang
Keyword(s):  
Network Topology ◽  
Drug Target ◽  
Large Scale ◽  
Representation Learning ◽  
New Drugs ◽  
Combination Method ◽  
Learning Methods ◽  
Network Representation ◽  
On Line ◽  
Clinical Experiments

Background: At present, using computer methods to predict drug-target interactions (DTIs) is a very important step in the discovery of new drugs and drug relocation processes. The potential DTIs identified by machine learning methods can provide guidance in biochemical or clinical experiments. Objective: The goal of this article is to combine the latest network representation learning methods for drug-target prediction research, improve model prediction capabilities, and promote new drug development. Methods: We use large-scale information network embedding (LINE) method to extract network topology features of drugs, targets, diseases, etc., integrate features obtained from heterogeneous networks, construct binary classification samples, and use random forest (RF) method to predict DTIs. Results: The experiments in this paper compare the common classifiers of RF, LR, and SVM, as well as the typical network representation learning methods of LINE, Node2Vec, and DeepWalk. It can be seen that the combined method LINE-RF achieves the best results, reaching an AUC of 0.9349 and an AUPR of 0.9016. Conclusion: The learning method based on LINE network can effectively learn drugs, targets, diseases and other hidden features from the network topology. The combination of features learned through multiple networks can enhance the expression ability. RF is an effective method of supervised learning. Therefore, the Line-RF combination method is a widely applicable method.

Organic solvent-assisted free-standing Li2MnO3·LiNi1/3Co1/3Mn1/3O2 on 3D graphene as a high energy density cathode

2015 ◽  
Vol 51 (91) ◽  
pp. 16381-16384 ◽  
Author(s):  
Yuelong Xin ◽  
Liya Qi ◽  
Yiwei Zhang ◽  
Zicheng Zuo ◽  
Henghui Zhou ◽  
...  
Keyword(s):  
Energy Density ◽  
Organic Solvent ◽  
Freeze Drying ◽  
Large Scale ◽  
High Energy ◽  
High Energy Density ◽  
Free Standing ◽  
3D Graphene ◽  
Active Particles

A novel organic solvent-assisted freeze-drying pathway, which can effectively protect and uniformly distribute active particles, is developed to fabricate a free-standing Li2MnO3·LiNi1/3Co1/3Mn1/3O2 (LR)/rGO electrode on a large scale.

scholarly journals Contribution of Ionotropic Glutamatergic Receptors to Excitability and Attentional Signals in Macaque Frontal Eye Field

2021 ◽  
Author(s):  
Miguel Dasilva ◽  
Christian Brandt ◽  
Marc Alwin Gieselmann ◽  
Claudia Distler ◽  
Alexander Thiele
Keyword(s):  
Attentional Control ◽  
Large Scale ◽  
Neuronal Excitability ◽  
Cell Types ◽  
Receptor Activation ◽  
Frontal Eye Field ◽  
Control Signals ◽  
Cognitive Operations ◽  
Eye Field ◽  
Large Scale Networks

Abstract Top-down attention, controlled by frontal cortical areas, is a key component of cognitive operations. How different neurotransmitters and neuromodulators flexibly change the cellular and network interactions with attention demands remains poorly understood. While acetylcholine and dopamine are critically involved, glutamatergic receptors have been proposed to play important roles. To understand their contribution to attentional signals, we investigated how ionotropic glutamatergic receptors in the frontal eye field (FEF) of male macaques contribute to neuronal excitability and attentional control signals in different cell types. Broad-spiking and narrow-spiking cells both required N-methyl-D-aspartic acid and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor activation for normal excitability, thereby affecting ongoing or stimulus-driven activity. However, attentional control signals were not dependent on either glutamatergic receptor type in broad- or narrow-spiking cells. A further subdivision of cell types into different functional types using cluster-analysis based on spike waveforms and spiking characteristics did not change the conclusions. This can be explained by a model where local blockade of specific ionotropic receptors is compensated by cell embedding in large-scale networks. It sets the glutamatergic system apart from the cholinergic system in FEF and demonstrates that a reduction in excitability is not sufficient to induce a reduction in attentional control signals.

High energy density aqueous zinc–benzoquinone batteries enabled by carbon cloth with multiple anchoring effects

2021 ◽  
Author(s):  
Zhiqiang Luo ◽  
Silin Zheng ◽  
Shuo Zhao ◽  
Xin Jiao ◽  
Zongshuai Gong ◽  
...  
Keyword(s):  
Energy Storage ◽  
Energy Density ◽  
Porous Carbon ◽  
Large Scale ◽  
High Energy ◽  
High Energy Density ◽  
Carbon Cloth ◽  
Anchoring Effects ◽  
High Theoretical Capacity ◽  
Energy Storage Applications

Benzoquinone with high theoretical capacity is anchored on N-plasma engraved porous carbon as a desirable cathode for rechargeable aqueous Zn-ion batteries. Such batteries display tremendous potential in large-scale energy storage applications.

scholarly journals 1.5 °C degrowth scenarios suggest the need for new mitigation pathways

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lorenz T. Keyßer ◽  
Manfred Lenzen
Keyword(s):  
Technological Change ◽  
High Speed ◽  
Large Scale ◽  
High Energy ◽  
Climate Mitigation ◽  
Intergovernmental Panel ◽  
Economic Output ◽  
Integrated Assessment Modelling ◽  
Negative Emissions ◽  
Energy Emissions

Abstract1.5  °C scenarios reported by the Intergovernmental Panel on Climate Change (IPCC) rely on combinations of controversial negative emissions and unprecedented technological change, while assuming continued growth in gross domestic product (GDP). Thus far, the integrated assessment modelling community and the IPCC have neglected to consider degrowth scenarios, where economic output declines due to stringent climate mitigation. Hence, their potential to avoid reliance on negative emissions and speculative rates of technological change remains unexplored. As a first step to address this gap, this paper compares 1.5  °C degrowth scenarios with IPCC archetype scenarios, using a simplified quantitative representation of the fuel-energy-emissions nexus. Here we find that the degrowth scenarios minimize many key risks for feasibility and sustainability compared to technology-driven pathways, such as the reliance on high energy-GDP decoupling, large-scale carbon dioxide removal and large-scale and high-speed renewable energy transformation. However, substantial challenges remain regarding political feasibility. Nevertheless, degrowth pathways should be thoroughly considered.

scholarly journals How does the position of firms in the supply chain affect their performance? An empirical study

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Siddharth Arora ◽  
Alexandra Brintrup
Keyword(s):  
Supply Chain ◽  
Empirical Data ◽  
Large Scale ◽  
Individual Performance ◽  
Supply Network ◽  
Supply Chain Networks ◽  
Focal Firm ◽  
Cooperative Competition ◽  
Large Scale Networks ◽  
The Relationship

AbstractThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

Sign in / Sign up

Export Citation Format

Share Document