Performance Comparison of Minimum Hop and Minimum Edge Based Multicast Routing Under Different Mobility Models for Mobile Ad Hoc Networks

Meghanathan, Natarajan

doi:10.5121/ijwmn.2011.3301

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…The only treebased data gathering algorithm we have come across for WMSNs is a shortest path-based spanning tree algorithm [9] wherein each sensor node can have at most a certain number of child nodes. Based on our experiences from mobile ad hoc networks, minimum hop-based shortest paths and trees in mobile network topologies are quite unstable and need to be frequently reconfigured [10] [11].…”

Section: Introductionmentioning

confidence: 99%

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

Meghanathan

Mumford

2013

2013 10th International Conference on Information Technology: New Generations

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The high-level contribution of this paper is the design of a benchmarking algorithm to determine a sequence of the longest-living stable data gathering trees for wireless mobile sensor networks (MSNs) such that the number of tree discoveries is the theoretical global minimum. Referred to as the Max.Stability-DG algorithm, the algorithm assumes the availability of the complete knowledge of future topology changes, and operates according to a greedy strategy: Whenever a new data gathering tree is needed at time instant t, determine a spanning tree that will exist for the longest time since t and derive a data gathering tree by conducting a Breadth First Search on the spanning tree. We prove the correctness of the Max.Stability-DG algorithm that it indeed determines the sequence of longestliving stable data gathering trees. Since the Max.Stability-DG trees are based on spanning trees covering the entire network of live sensor nodes, the average lifetime and the number of tree discoveries incurred for the Max.Stability-DG trees will serve respectively as the upper bound and lower bound for any network-wide communication topology determined using any other algorithm for mobile sensor networks.

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Section: Introductionmentioning

confidence: 99%

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

Meghanathan

Mumford

2013

2013 10th International Conference on Information Technology: New Generations

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“…The only tree-based data gathering algorithm we have come across for WMSNs is a shortest path-based spanning tree algorithm [9] wherein each sensor node is constrained to have at most a certain number of child nodes. Based on the results from the literature of mobile ad hoc networks (e.g., [10] [11]), minimum hop shortest paths and trees in mobile network topologies are quite unstable and need to be frequently reconfigured. We could not find any other related work on tree-based data gathering for WMSNs.…”

Section: Introductionmentioning

confidence: 99%

Link Expiration Time and Minimum Distance Spanning Trees based Distributed Data Gathering Algorithms for Wireless Mobile Sensor Networks

Meghanathan¹

2022

Int. j. commun. netw. inf. secur.

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The high-level contributions of this paper are the design and development of two distributed spanning tree-based data gathering algorithms for wireless mobile sensor networks and their exhaustive simulation study to investigate a complex stability vs. node-network lifetime tradeoff that has been hitherto not explored in the literature. The topology of the mobile sensor networks changes dynamically with time due to random movement of the sensor nodes. Our first data gathering algorithm is stability-oriented and it is based on the idea of finding a maximum spanning tree on a network graph whose edge weights are predicted link expiration times (LET). Referred to as the LET-DG tree, the data gathering tree has been observed to be more stable in the presence of node mobility. However, stability-based data gathering coupled with more leaf nodes has been observed to result in unfair use of certain nodes (the intermediate nodes spend more energy compared to leaf nodes), triggering pre-mature node failures eventually leading to network failure (disconnection of the network of live nodes). As an alternative, we propose an algorithm to determine a minimum-distance spanning tree (MST) based data gathering tree that is more energy-efficient and prolongs the node and network lifetimes, at the cost frequent tree reconfigurations.

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“…target delay and delay probability. The trade-offs between two tree based routing strategies with respect to hop count and number of edges are described in [11,12]. In short, several trade-off analyses of various QoS metrics on different aspects have been carried out.…”

Section: Introductionmentioning

confidence: 99%

A Trade-off Analysis of Quality of Service (QoS) Metrics Towards Routing in Mobile Networks: MOGA Based Approach

Rudra

Thakurta

Poddar

2015

Advances in Intelligent Systems and Computing

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The trade-off analysis between two QoS metrics towards efficient routing in mobile networks is proposed in this paper. For such analysis the behavioural natures of end-to-end transmission cost and hop-count as QoS metrics are accounted in discrete domain and subsequently, the probability density functions (pdf) of those important factors are determined. The pdf of these are transformed into continuous domain to perform mathematical operation and subsequent analysis is presented to obtain the optimal routing path(s). In this context, a multi objective optimization (MOO) on these parameters is proposed and more refined results among all possible solutions are obtained accordingly. The diverse set of possible solutions for such analysis is explored through a Multi Objective Genetic Algorithm (MOGA) based approach.

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Performance Comparison of Minimum Hop and Minimum Edge Based Multicast Routing Under Different Mobility Models for Mobile Ad Hoc Networks

Abstract: ABSTRACT

Cited by 7 publications

References 16 publications

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

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

Link Expiration Time and Minimum Distance Spanning Trees based Distributed Data Gathering Algorithms for Wireless Mobile Sensor Networks

A Trade-off Analysis of Quality of Service (QoS) Metrics Towards Routing in Mobile Networks: MOGA Based Approach

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