A Benchmarking Algorithm to Determine Maximum Stability Data Gathering Trees for Wireless Mobile Sensor Networks

Meghanathan, Natarajan; Mumford, Philip

doi:10.1109/itng.2013.83

Cited by 11 publications

(20 citation statements)

References 14 publications

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“…The overall average percentage difference in height between the two trees (when averaged over all the scenarios) is 6.2%. With the significant gains in the lifetime of the MLSPTs and an inconsequential increase in the height of the trees, we could infer that there is no stability-hop count tradeoff in CRAHNs, unlike the MANETs and mobile sensor networks wherein we have observed a stability-hop count tradeoff (Meghanathan, 2008) and stability-data gathering delay tradeoff (Meghanathan & Mumford, 2013) respectively. Overall, we observe a slight increase in the percentage difference in the height between the two trees with increase in the PU-SU ratio and/or increase in the availability time of the PU channels.…”

Section: Average Tree Heightmentioning

confidence: 90%

“…In earlier works, separate benchmarking algorithms have been proposed based on the idea of taking graph intersections to determine stable sequence of unicast paths, multicast Steiner trees and broadcast connected dominating sets (Meghanathan, 2008) for mobile ad hoc networks (MANETs) and to determine stable sequence of data gathering trees (Meghanathan & Mumford, 2013) for wireless mobile sensor networks (WMSNs). The characteristic of both MANETs and WMSNs is that the nodes are mobile and it is the mobility of the nodes that triggers the topology changes.…”

Section: Related Work and Our Contributionsmentioning

confidence: 99%

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Maximum Lifetime Communication Topologies of Secondary User Nodes in Cognitive Radio Ad hoc Networks

Meghanathan¹

2015

CIS

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A Cognitive radio ad hoc network (CRAHN) is an ad hoc network of primary user (PU) nodes and secondary user (SU) nodes, wherein the SU nodes use the licensed channels of the PU nodes when not in use. As the availability of the PU channels fluctuates with time, communication topologies spanning the SU nodes have to be dynamically and frequently reconfigured. Given the complete knowledge of the availability of the PU channels, we propose a generic benchmarking algorithm that determines a sequence of stable communication topologies spanning all of the SU nodes such that the number of transitions from one instance of the topology to another is the global minimum. At any time instant t when we need a stable communication topology spanning the entire network, we look for the largest value of k such that the intersection of the static SU graphs from time instants t to t+k, defined as the mobile graph, is connected and that the mobile graph G t...t+k+1 (SU) is not connected. We repeat the above procedure for the entire network session to determine a sequence of longest-living instances of the mobile graphs and the corresponding instances of the communication topology of interest (say a shortest path tree rooted at a source SU node) such that the number of topology transitions is the minimum. We prove the theoretical correctness of the algorithm and evaluate its effectiveness by implementing it to determine a sequence of shortest path trees of the maximum lifetime.

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Section: Average Tree Heightmentioning

confidence: 90%

Section: Related Work and Our Contributionsmentioning

confidence: 99%

Maximum Lifetime Communication Topologies of Secondary User Nodes in Cognitive Radio Ad hoc Networks

Meghanathan¹

2015

CIS

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“…This is because the leaf nodes tend to lose relatively less energy compared to the intermediate nodes [16]. For every data aggregation cycle, the leaf nodes lose energy for transmitting the data only once to their upstream intermediate node and there is no energy lost due to reception.…”

Section: Performance Tradeoffsmentioning

confidence: 99%

“…We run the proposed algorithm on four broad categories of DG trees typically used for data aggregation in WSNs [5,[15][16][17][18][19][20]: The Maximum Bottleneck Node Weight-based Data Gathering trees (MaxBNW-DG trees) are the ones for which the bottleneck node weight (minimum node weight) of the path from any node to the root node is the maximum; the Minimum Bottleneck Node Weight-based Data Gathering trees (MinBNW-DG trees) are the ones for which the bottleneck node weight (maximum node weight) of the path from any node to the root node is the minimum. The Maximum Bottleneck Link Weight-based Data Gathering trees (MaxBLW-DG trees) are the ones for which the bottleneck link weight (minimum link weight) of the constituent links of a path from any node to the root node is the maximum; the Minimum Bottleneck Link Weight-based Data Gathering trees (MinBLW-DG trees) are the ones for which the bottleneck link weight (maximum link weight) of the constituent links of a path from any node to the root node is the minimum.…”

Section: Introductionmentioning

confidence: 99%

“…The Maximum Bottleneck Link Weight-based Data Gathering trees (MaxBLW-DG trees) are the ones for which the bottleneck link weight (minimum link weight) of the constituent links of a path from any node to the root node is the maximum; the Minimum Bottleneck Link Weight-based Data Gathering trees (MinBLW-DG trees) are the ones for which the bottleneck link weight (maximum link weight) of the constituent links of a path from any node to the root node is the minimum. MaxBNW-DG trees could be used to determine DG trees with a larger residual energy per intermediate node such that the minimum node energy of the path from any node to the root node is the maximum (e.g., [15,16]). The MinBNW-DG trees could be used to determine DG trees in mobile sensor networks with lower velocity for the intermediate nodes such that the maximum node velocity of the path from any node to the root node is the minimum (e.g., [17]).…”

Section: Introductionmentioning

confidence: 99%

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A Benchmarking Algorithm to Determine Minimum Aggregation Delay for Data Gathering Trees and an Analysis of the Diameter-Aggregation Delay Tradeoff

Meghanathan

2015

Algorithms

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Aggregation delay is the minimum number of time slots required to aggregate data along the edges of a data gathering tree (DG tree) spanning all the nodes in a wireless sensor network (WSN). We propose a benchmarking algorithm to determine the minimum possible aggregation delay for DG trees in a WSN. We assume the availability of a sufficient number of unique CDMA (Code Division Multiple Access) codes for the intermediate nodes to simultaneously aggregate data from their child nodes if the latter are ready with the data. An intermediate node has to still schedule non-overlapping time slots to sequentially aggregate data from its own child nodes (one time slot per child node). We show that the minimum aggregation delay for a DG tree depends on the underlying design choices (bottleneck node-weight based or bottleneck link-weight based) behind its construction. We observe the bottleneck node-weight based DG trees incur a smaller diameter and a larger number of child nodes per intermediate node; whereas, the bottleneck link-weight based DG trees incur a larger diameter and a much lower number of child nodes per intermediate node. As a result, we observe a complex diameter-aggregation delay tradeoff for data gathering trees in WSNs.

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

Balamurugan¹,

devi²,

Sharmila³

2018

IJET

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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.

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A Benchmarking Algorithm to Determine Maximum Stability Data Gathering Trees for Wireless Mobile Sensor Networks

Cited by 11 publications

References 14 publications

Maximum Lifetime Communication Topologies of Secondary User Nodes in Cognitive Radio Ad hoc Networks

Maximum Lifetime Communication Topologies of Secondary User Nodes in Cognitive Radio Ad hoc Networks

A Benchmarking Algorithm to Determine Minimum Aggregation Delay for Data Gathering Trees and an Analysis of the Diameter-Aggregation Delay Tradeoff

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

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