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

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

Download Full-text

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):

Wireless Sensor Networks ◽

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.

Download Full-text

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

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

10.4018/978-1-5225-0486-3.ch007 ◽

2017 ◽

pp. 184-196

Author(s):

Natarajan Meghanathan

Keyword(s):

Wireless Sensor Networks ◽

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).

Download Full-text

An energy minimizing score based optimal data gathering in wireless sensor networks

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.31.13431 ◽

2018 ◽

Vol 7 (2.31) ◽

pp. 161

Author(s):

P Balamurugan ◽

M Shyamala Devi ◽

V Sharmila

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Data Gathering ◽

Residual Energy ◽

Base Station ◽

Sensor Nodes ◽

Wireless Sensor ◽

Efficient Manner ◽

High Level ◽

The Impact

In wireless sensor networks, Sensor nodes are arranged randomly in unkind physical surroundings to collect data and distribute the data to the remote base station. However the sensor nodes have to preserve the power source that has restricted estimation competence. The sensed information is difficult to be transmitted over the sensor network for a long period of time in an energy efficient manner. In this paper, it finds the problem of communication data between sink nodes and remote data sources via intermediate nodes in sensor field. So this paper proposes a score based data gathering algorithm in wireless sensor networks. The high-level contribution of this study is the enhancement of a score- based data gathering algorithm and the impact of energy entity for Wireless Sensor Networks. Then the energy and delay of data gathering are evaluated. Unlike PEGASIS and LEACH, the delay for every process of data gathering is considerably lower when SBDG is employed. The energy consumed per round of data gathering for both SBDG and EE-SBDG is less than half of that incurred with PEGASIS and LEACH. Compared with LEACH and PEGASIS, SBDG and EE-SBDG are fair with node usage because of the scoring system and residual energy respectively. Overall, the Score-based data gathering algorithm provides a significant solution to maximize the network lifetime as well as minimum delay per round of data gathering.

Download Full-text

Data Gathering Algorithms and Sink Mobility Models for Wireless Sensor Networks

Advances in Wireless Technologies and Telecommunication - Multidisciplinary Perspectives on Telecommunications, Wireless Systems, and Mobile Computing ◽

10.4018/978-1-4666-4715-2.ch008 ◽

2014 ◽

pp. 123-151

Author(s):

Natarajan Meghanathan

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Sensor Node ◽

Data Gathering ◽

Residual Energy ◽

Performance Comparison ◽

Wireless Sensor ◽

Mobility Models ◽

Energy Aware ◽

Static Wireless Sensor Networks

In the first half of the chapter, the authors provide a comprehensive description of two broad categories of data gathering algorithms for wireless sensor networks: the classical energy-unaware algorithms and the modern energy-aware algorithms, as well as presented an exhaustive performance comparison of representative algorithms from both these categories. While the first half of the chapter focuses on static sink (that is located outside on the network boundary), the second half of the chapter explores the use of mobile sinks that gather data by stopping at the vicinity of the sensor nodes. As a first step, the authors investigate the performance of three different strategies to develop sink mobility models for delay and energy-efficient data gathering in static wireless sensor networks. The three strategies differ on the approach to take to determine the next stop for data gathering: randomly choosing a sensor node that is yet to be covered (Random), choose the sensor node that has the maximum number of uncovered neighbor nodes (Max-Density), and choose the sensor node that has the largest value for the product of the maximum number of uncovered neighbor nodes and the residual energy (Max-Density-Energy). Based on the simulation results, the authors recommend incorporating the random node selection-based strategy to be a better strategy for sink mobility models (with minimal deployment overhead) rather than keeping track of the number of uncovered neighbor nodes per node and the residual energy available at the nodes.

Download Full-text