scholarly journals Self-configuring asynchronous sleeping in heterogeneous networks

2021 ◽  
Vol 2 (7) ◽  
pp. 51-62
Author(s):  
Ali Medlej ◽  
Eugen Dedu ◽  
Kamal Beydoun ◽  
Dominique Dhoutaut
Keyword(s):  
Density Estimation ◽  
Heterogeneous Networks ◽  
Network Topology ◽  
Estimation Algorithm ◽  
Limited Resources ◽  
Destination Node ◽  
Packet Routing ◽  
Duty Cycling ◽  
Fine Grained ◽  
Sleeping Mechanism

Nowadays, the heterogeneous wireless nano-network topology becomes a need for diverse applications based on heterogeneous networks composed of regions of different node densities. In Wireless Nano-networks (WNNs), nodes are of nano-metric size and can be potentially dense in terms of neighbouring nodes. Nano-nodes have limited resources in terms of processing, energy and memory capabilities. In nano-network(s), even in a communication range limited to tens of centimeters, thousands of neighbours can be found. We proposed a fine-grained duty-cycling method (sleeping mechanism), appropriate to nanonodes, which aims to reduce the number of receptions seen by a node during data packet routing. The present study reveals the usefulness of implementing the sleeping mechanism in heterogeneous networks, as well as configuring a dynamic awaken duration for nodes based on a density estimation algorithm. We also proposed an algorithm that helps in increasing the reliability of the packet received by the destination node.

scholarly journals Exploiting Fine-Grained Subcarrier Information for Device-Free Localization in Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3110 ◽  
Author(s):  
Yan Guo ◽  
Dongping Yu ◽  
Ning Li
Keyword(s):  
Compressive Sensing ◽  
Estimation Algorithm ◽  
Surface Model ◽  
Frequency Diversity ◽  
Fine Grained ◽  
Area Of Interest ◽  
Radio Signals ◽  
Device Free ◽  
Target Locations ◽  
Spatial Sparsity

Device-free localization (DFL) that aims to localize targets without carrying any electronic devices is addressed as an emerging and promising research topic. DFL techniques estimate the locations of transceiver-free targets by analyzing their shadowing effects on the radio signals that travel through the area of interest. Recently, compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements by exploiting the inherent spatial sparsity of target locations. In this paper, we propose a novel CS-based multi-target DFL method to leverage the frequency diversity of fine-grained subcarrier information. Specifically, we build the dictionaries of multiple channels based on the saddle surface model and formulate the multi-target DFL as a joint sparse recovery problem. To estimate the location vector, an iterative location vector estimation algorithm is developed under the multitask Bayesian compressive sensing (MBCS) framework. Compared with the state-of-the-art CS-based multi-target DFL approaches, simulation results validate the superiority of the proposed algorithm.

scholarly journals Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm

2010 ◽  
Vol 11 (Suppl 1) ◽  
pp. S52 ◽  
Author(s):  
Chih-Hung Hsieh ◽  
Darby Chang ◽  
Cheng-Hao Hsueh ◽  
Chi-Yeh Wu ◽  
Yen-Jen Oyang
Keyword(s):  
Density Estimation ◽  
Estimation Algorithm

A new method for calculating bulk density in pulsed neutron-gamma density logging

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. D219-D232
Author(s):  
Hu Wang ◽  
Wensheng Wu ◽  
Tianzhi Tang ◽  
Ruigang Wang ◽  
Aizhong Yue ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Pair Production ◽  
Bulk Density ◽  
Density Estimation ◽  
Measurement Accuracy ◽  
Gamma Ray ◽  
Estimation Algorithm ◽  
Gamma Density ◽  
Pulsed Neutron

Formation density is one of the most important parameters in formation evaluation. Radioisotope chemical sources are used widely in conventional gamma-gamma density (GGD) logging. Considering security and environmental risks, there has been growing interest in pulsed neutron generators in place of the radioactive-chemical source in using bulk-density measurements. However, there still is the requirement of high accuracy of the neutron-gamma density (NGD) calculation. Pair production is one of the factors influencing the accuracy of the results, which should be considered. We have adopted a method, based on the difference between the inelastic gamma-ray response of high- and low-energy windows, to reduce the impact of pair production upon calculating the bulk density. A new density estimation algorithm is derived based on the coupled-field theory and gamma-ray attenuation law in NGD logging. We analyze the NGD measurement accuracy with different mineral types, porosity, and pore fluid and determine the influence of the borehole environment on NGD logging. The Monte Carlo simulation results indicate that the improved processing algorithm limits the influence of the mineral type, porosity, or pore fluid. The NGD measurement accuracy is ±0.025 g/cm3 in shale-free formations, which is close to the GGD measurement (±0.015 g/cm3). Our results also show that the borehole environment has a significant impact on NGD measurement. Therefore, it is necessary to take the influence of the borehole parameters into account in NGD measurements. Combined with Monte Carlo simulation cases, we evaluate the application results of the new density estimation algorithm in various model wells.

The Feature Description Method of Radar Signal Based on Improved Kernel Density Estimation

2013 ◽  
Vol 380-384 ◽  
pp. 3501-3504
Author(s):  
Fei Ye ◽  
Jie Zhou ◽  
Jun Luo ◽  
Xing Rong Gao
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Estimation Algorithm ◽  
Radar Signal ◽  
Sample Distribution ◽  
Feature Description ◽  
Feature Based ◽  
Selection Of ◽  
Probability Density Curve

According to the problem that the existing radar signal feature cannot effectively express and analysis its characteristic, a description method of radar emitter signal feature based on improved kernel density estimation is proposed. This improved kernel density estimation algorithm combine the selection of fixed window and variable window's width to achieve the window's width automatic adjustment value between the different estimation points based on the sample distribution. Then the probability density curve using kernel density estimation algorithm as radar emitter signal parameters characteristics stored into database.

FINDING SHORTEST ROUTING SOLUTION IN MOBILE AD HOC NETWORKS USING FIREFLY ALGORITHM AND QUEUING NETWORK ANALYSIS

2015 ◽  
Vol 77 (18) ◽  
Author(s):  
Aznida Hayati Zakaria ◽  
Md. Yazid Mohd Saman ◽  
Ahmad Shukri M Noor ◽  
Hasni Hassan
Keyword(s):  
Network Analysis ◽  
Network Topology ◽  
Routing Protocol ◽  
Firefly Algorithm ◽  
Ad Hoc ◽  
Dynamic Network ◽  
Destination Node ◽  
Queuing Network ◽  
Shortest Route ◽  
Mobile Ad Hoc

Mobile ad hoc network (MANET) is a group of mobile nodes establishing a wireless network without using centralized and fixed infrastructure.  In MANET, nodes may function as hosts and routers. The nodes can move freely and in arbitrary ways. The network topology in MANET is dynamic because of the frequent mobility of nodes, thus routing is challenging aspects in MANET.  Routing protocol plays a role in choosing and selecting the optimal route for transferring the packets of data from the source node to the destination node efficiently. Mostly the previous routing protocols are not practical to this dynamic network topology. Therefore designing an efficient routing protocol for this dynamic network is vital issue. In this paper, the author has proposed an approach, which selects shortest route for transferring the packets of data from source node to the destination node combining firefly algorithm and queuing network analysis. Firefly algorithm can be applied to find the shortest route in this routing problem. The response times taken to send packets of data can be calculated using the suggested queuing model. The result reveals that attractiveness of node in MANET decreases with the increasing value of response time.

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