Diameter-Aggregation Delay Tradeoff for Data Gathering Trees in Wireless Sensor Networks

We define the aggregation delay as the minimum number of time slots it takes for the data to be aggregated in a Data Gathering tree (DG tree) spanning all the nodes of the sensor network; the diameter of a DG tree is the maximum distance (number of hops) from a leaf node to the root node of the tree. We assume that intermediate nodes at the same level or different levels of a DG tree could simultaneously aggregate data from their respective child nodes using different CDMA (Code Division Multiple Access) codes; but, an intermediate node has to schedule non-overlapping time slots (one for each of its child nodes) to aggregate data from its own child nodes. We employ an algorithm to determine the minimum aggregation delay at every intermediate node of the Bottleneck Node Weight (BNW) and Bottleneck Link Weight (BLW)-based DG trees. We observe the BNW-DG trees to incur a smaller tree diameter, but a significantly larger aggregation delay; on the other hand, the BLW-DG trees incur a larger tree diameter and a relatively lower aggregation delay, especially with increase in node density.

A Benchmarking Algorithm for Maximum Bottleneck Node Trust Score-based Data Gathering Trees in Wireless Sensor Networks

International Journal of Interdisciplinary Telecommunications and Networking ◽

10.4018/ijitn.2015070102 ◽

2015 ◽

Vol 7 (3) ◽

pp. 13-35

Author(s):

Natarajan Meghanathan

Keyword(s):

Sensor Networks ◽

Data Gathering ◽

Residual Energy ◽

Wireless Sensor ◽

Intermediate Node ◽

Root Node ◽

Link Weight ◽

Bottleneck Link ◽

Bottleneck Node

The author proposes a benchmarking algorithm to determine maximum bottleneck node trust score-based data gathering trees (MaxBNT-DG trees) for wireless sensor networks (WSNs) wherein the bottleneck node trust score of a path (minimum trust score for any node on the path, including those of the end nodes) from any node to the root node of the DG tree is the maximum. He compares the performance of the MaxBNT-DG trees with that of the maximum bottleneck link weight-based data gathering trees (MaxBLT-DG trees) for which the bottleneck link trust score (minimum trust score for constituent links) of a path from any node to the root node is the maximum. The author observes the MaxBNT-DG trees to incur a smaller tree diameter, a larger percentage of nodes as leaf nodes and a larger trust score per intermediate node; whereas, the MaxBLT-DG trees incur a lower aggregation delay, indicating a trust-aggregation delay tradeoff in WSNs. The MaxBNT-DG algorithm is also generic and can be extended to any other node criterion like residual energy, wake-up frequency, etc

Advances in Wireless Technologies and Telecommunication - Handbook of Research on Advanced Wireless Sensor Network Applications, Protocols, and Architectures ◽

Impact of the Structure of the Data Gathering Trees on Node Lifetime and Network Lifetime in Wireless Sensor Networks

10.4018/978-1-5225-0486-3.ch007 ◽

2017 ◽

pp. 184-196

Author(s):

Natarajan Meghanathan

Keyword(s):

Sensor Networks ◽

Network Lifetime ◽

Data Gathering ◽

Wireless Sensor ◽

Node Failure ◽

Minimum Number ◽

Bottleneck Link ◽

Bottleneck Node ◽

The Impact

We analyze the impact of the structure of the Data Gathering (DG) trees on node lifetime (round of first node failure) and network lifetime (minimum number of rounds by which the network gets either disconnected due to node failures or the fraction of coverage loss reaches a threshold) in wireless sensor networks through extensive simulations. The two categories of DG trees studied are: the Bottleneck Node Weight-Based (BNW-DG) trees and Bottleneck Link Weight-Based (BLW-DG) trees. The BNW-DG trees incur a smaller diameter and a significantly larger fraction of nodes as leaf nodes: thus, protecting a majority of the nodes in the network from simultaneously being exhausted of the energy resources (contributing to a significantly larger network lifetime); nevertheless the nodes that serve as intermediate nodes in the first few instances of the BNW-DG trees are bound to lose their energy more quickly than the other nodes, leading to a smaller node lifetime compared to that of the BLW-DG trees (that incur a larger diameter and a relatively lower fraction of nodes as leaf nodes).

A Generic Algorithm to Determine Maximum Bottleneck Node Weight-based Data Gathering Trees for Wireless Sensor Networks

Network Protocols and Algorithms ◽

10.5296/npa.v7i3.7961 ◽

2015 ◽

Vol 7 (3) ◽

pp. 18 ◽

Cited By ~ 7

Author(s):

Natarajan Meghanathan

Keyword(s):

Sensor Networks ◽

Data Gathering ◽

Leaf Node ◽

Wireless Sensor ◽

Generic Algorithm ◽

Intermediate Node ◽

Root Node ◽

Bottleneck Node ◽

Node Weight

We propose a generic algorithm to determine maximum bottleneck node weight-based data gathering (MaxBNW-DG) trees for wireless sensor networks (WSNs) and compare the performance of the MaxBNW-DG trees with those of maximum and minimum link weight-based data gathering trees (MaxLW-DG and MinLW-DG trees). Assuming each node in a WSN graph has a weight, the bottleneck weight for the path from a node u to the root node of the DG tree is the minimum of the node weights on the path (inclusive of the weights of the end nodes). The MaxBNW-DG tree algorithm determines a DG tree such that each node has a path of the largest bottleneck weight to the root node. We observe the MaxBNW-DG trees to incur lower height, larger percentage of nodes as leaf nodes and a larger weight per intermediate node compared to the leaf node; the tradeoff being a larger a network-wide data aggregation delay due to larger number of child nodes per intermediate node. The MaxBNW-DG algorithm could be used to determine DG trees with larger trust score, larger energy (and other such criterion for node weight) per intermediate node compared to the leaf node.

Constructing Maximum-Lifetime Data Gathering Tree without Data Aggregation for Sensor Networks

Studies in Computational Intelligence - Computer and Information Science 2011 ◽

10.1007/978-3-642-21378-6_4 ◽

2011 ◽

pp. 47-57 ◽

Cited By ~ 1

Author(s):

Jinhui Yuan ◽

Hongwei Zhou ◽

Hong Chen

Keyword(s):

Sensor Networks ◽

Data Aggregation ◽

Data Gathering ◽

Lifetime Data ◽

Maximum Lifetime ◽

Advances in Wireless Technologies and Telecommunication - Handbook of Research on Progressive Trends in Wireless Communications and Networking ◽

Graph Intersection-Based Benchmarking Algorithm for Maximum Stability Data Gathering Trees in Wireless Mobile Sensor Networks

10.4018/978-1-4666-5170-8.ch017 ◽

2014 ◽

pp. 433-458

Author(s):

Natarajan Meghanathan ◽

Philip Mumford

Keyword(s):

Sensor Networks ◽

Spanning Trees ◽

Data Gathering ◽

Mobile Sensor Networks ◽

Sensor Nodes ◽

Mobile Sensor ◽

Topology Changes ◽

Maximum Stability ◽

Wireless Mobile ◽

The authors propose a graph intersection-based benchmarking algorithm to determine the sequence of longest-living stable data gathering trees for wireless mobile sensor networks whose topology changes dynamically with time due to the random movement of the sensor nodes. Referred to as the Maximum Stability-based Data Gathering (Max.Stable-DG) algorithm, the algorithm assumes the availability of complete knowledge of future topology changes and is based on the following greedy principle coupled with the idea of graph intersections: Whenever a new data gathering tree is required at time instant t corresponding to a round of data aggregation, choose the longest-living data gathering tree from time t. The above strategy is repeated for subsequent rounds over the lifetime of the sensor network to obtain the sequence of longest-living stable data gathering trees spanning all the live sensor nodes in the network such that the number of tree discoveries is the global minimum. In addition to theoretically proving the correctness of the Max.Stable-DG algorithm (that it yields the lower bound for the number of discoveries for any network-wide communication topology like spanning trees), the authors also conduct exhaustive simulations to evaluate the performance of the Max.Stable-DG trees and compare to that of the minimum-distance spanning tree-based data gathering trees with respect to metrics such as tree lifetime, delay per round, node lifetime and network lifetime, under both sufficient-energy and energy-constrained scenarios.

Optimizing design to estimate genetic correlations between environments with common environmental effects

Journal of Animal Science ◽

10.1093/jas/skaa034 ◽

2020 ◽

Vol 98 (2) ◽

Author(s):

Maria Lozano-Jaramillo ◽

Hans Komen ◽

Yvonne C J Wientjes ◽

Han A Mulder ◽

John W M Bastiaansen

Keyword(s):

Genetic Correlation ◽

Environmental Effects ◽

Genetic Correlations ◽

Improve Performance ◽

Breeding Programs ◽

Genotype By Environment ◽

Optimizing Design ◽

Minimum Number ◽

Optimal Population ◽

Different Levels

Abstract Breeding programs for different species aim to improve performance by testing members of full-sib (FS) and half-sib (HS) families in different environments. When genotypes respond differently to changes in the environment, this is defined as genotype by environment (G × E) interaction. The presence of common environmental effects within families generates covariance between siblings, and these effects should be taken into account when estimating a genetic correlation. Therefore, an optimal design should be established to accurately estimate the genetic correlation between environments in the presence of common environmental effects. We used stochastic simulation to find the optimal population structure using a combination of FS and HS groups with different levels of common environmental effects. Results show that in a population with a constant population size of 2,000 individuals per environment, ignoring common environmental effects when they are present in the population will lead to an upward bias in the estimated genetic correlation of on average 0.3 when the true genetic correlation is 0.5. When no common environmental effects are present in the population, the lowest standard error (SE) of the estimated genetic correlation was observed with a mating ratio of one dam per sire, and 10 offspring per sire per environment. When common environmental effects are present in the population and are included in the model, the lowest SE is obtained with mating ratios of at least 5 dams per sire and with a minimum number of 10 offspring per sire per environment. We recommend that studies that aim to estimate the magnitude of G × E in pigs, chicken, and fish should acknowledge the potential presence of common environmental effects and adjust the mating ratio accordingly.

The tradeoff between transmission cost and network lifetime of data gathering tree in Wireless Sensor Networks

2013 IEEE International Conference on Communications (ICC) ◽

10.1109/icc.2013.6654779 ◽

2013 ◽

Cited By ~ 2

Author(s):

Xi Chen ◽

Chen He ◽

Lingge Jiang

Keyword(s):

Sensor Networks ◽

Network Lifetime ◽

Data Gathering ◽

Wireless Sensor ◽

Transmission Cost ◽

Emotional Intelligence and Organizational Commitment between the Hotel Staff in Tehran, Iran

American Journal of Business and Management ◽

10.11634/21679606170693 ◽

2012 ◽

Vol 1 (1) ◽

pp. 54 ◽

Cited By ~ 1

Author(s):

K Mohamadkhani ◽

M Nasiri Lalardi

Keyword(s):

Emotional Intelligence ◽

Organizational Commitment ◽

Significant Relationship ◽

Data Gathering ◽

Descriptive Survey ◽

The Relationship ◽

Emotionally Intelligent ◽

Different Levels

The aim of this paper is to find out the relationship between emotional intelligence and organizational commitment of the hotel staff in 5-Star hotels of Tehran, Iran. The research enjoys an applied, descriptive, survey-based, and correlational framework. The population of the study was comprised of 423 employees (N =423) of public 5- star hotels in Tehran including Esteghlal, Laleh, and Homa. The sample was randomly selected based on Kerjesi- Morgan table and included 142 (n=142) individuals. The data gathering instruments were two standard questionnaires measuring emotional intelligence and organizational commitment. To analyze the data, Pierson correlation, ANOVA, and Qi-square were employed and results revealed that there was a significant relationship between the two main variables of the study, namely; emotional intelligence and organizational commitment and some of the components of these variables. This signifies the necessity of attracting and employing highly emotionally intelligent individuals, training them in different levels and leading them towards the application of the skills required. Paving the ground for the development and continuation of emotional intelligence within managers and the staff of the hotels and residential centers are also inevitable factors to be followed.

RPAS COMMUNICATION CHANNELS BASED ON WCDMA 3GPP STANDARD

Aviation ◽

10.3846/aviation.2020.12166 ◽

2020 ◽

Vol 24 (1) ◽

pp. 42-49 ◽

Cited By ~ 2

Author(s):

Andrii Grekhov ◽

Vasyl Kondratiuk ◽

Svitlana Ilnytska

Keyword(s):

Communication Channels ◽

Base Station ◽

Critical Conditions ◽

Line Of Sight ◽

Signal Noise ◽

Radio Line ◽

Channel Characteristics ◽

Noise Ratio ◽

Code Division Multiple ◽

Different Levels

First built models of Remotely Piloted Air System (RPAS) communication channels based on Wideband Code Division Multiple Access (WCDMA) 3GPP Standard were designed. Base Station (BS) transmission within the Radio Line of Sight (RLoS) and through the satellite using Beyond Radio Line of Sight (BRLoS) was considered. The dependencies of the Bit Error Rate (BER) on the signal-noise ratio for different RPAS velocities and WCDMA сhannel models were obtained. The dependences of the BER on the signal-noise ratio for different levels of satellite transponder nonlinearity were studied. The dependence of the BER on the BS antenna diameter in case of the transponder nonlinearity was analysed. The dependencies for satellite channel characteristics, first obtained taking into account the motion of the RPAS, make it possible to predict the behavior of the communication system in critical conditions.