Distributed Data Gathering Algorithms for Mobile Sensor Networks .................... NATARAJAN MEGHANATHAN

2016 ◽  
pp. 354-385
Keyword(s):  
Sensor Networks ◽  
Data Gathering ◽  
Mobile Sensor Networks ◽  
Distributed Data ◽  
Mobile Sensor

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

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 ◽  
Data Gathering Tree

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.

scholarly journals Adaptive Data Gathering in Mobile Sensor Networks Using Speedy Mobile Elements

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 23218-23248 ◽  
Author(s):  
Yongxuan Lai ◽  
Jinshan Xie ◽  
Ziyu Lin ◽  
Tian Wang ◽  
Minghong Liao
Keyword(s):  
Sensor Networks ◽  
Data Gathering ◽  
Mobile Sensor Networks ◽  
Mobile Elements ◽  
Mobile Sensor

Edge Centrality Metrics to Quantify the Stability of Links for Mobile Sensor Networks

2018 ◽  
pp. 98-110 ◽  
Keyword(s):  
Sensor Networks ◽  
Data Gathering ◽  
Mobile Sensor Networks ◽  
Algebraic Connectivity ◽  
Mobile Sensor ◽  
Centrality Metrics ◽  
Expiration Time ◽  
Egocentric Network ◽  
Link Expiration Time ◽  
The Stability

In this chapter, we explore the use of neighborhood overlap (NOVER), bipartivity index (BPI) and algebraic connectivity (ALGC) as edge centrality metrics to quantify the stability of links for mobile sensor networks. In this pursuit, we employ the notion of the egocentric network of an edge (comprising of the end vertices of the edge and their neighbors as nodes, and the edges incident on the end vertices as links) on which the above three edge centrality metrics are computed. Unlike the existing approach of using the predicted link expiration time (LET), the computations of the above three edge centrality metrics do not require the location and mobility information of the nodes. For various scenarios of node density and mobility, we observe the stability of the network-wide data gathering trees (lifetime) determined using the proposed three edge centrality metrics to be significantly larger than the stability of the LET-based data gathering trees.

Sign in / Sign up

Export Citation Format

Share Document