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Proceedings ArticleDOI

Max-min d-cluster formation in wireless ad hoc networks

TLDR
A heuristic to form d-clusters in a wireless ad hoc network that tends to re-elect existing clusterheads even when the network configuration changes and has a tendency to evenly distribute the mobile nodes among the clusterheads, and evently distribute the responsibility of acting as clusterheads among all nodes.
Abstract
An ad hoc network may be logically represented as a set of clusters. The clusterheads form a d-hop dominating set. Each node is at most d hops from a clusterhead. Clusterheads form a virtual backbone and may be used to route packets for nodes in their cluster. Previous heuristics restricted themselves to 1-hop clusters. We show that the minimum d-hop dominating set problem is NP-complete. Then we present a heuristic to form d-clusters in a wireless ad hoc network. Nodes are assumed to have a non-deterministic mobility pattern. Clusters are formed by diffusing node identities along the wireless links. When the heuristic terminates, a node either becomes a clusterhead, or is at most d wireless hops away from its clusterhead. The value of d is a parameter of the heuristic. The heuristic can be run either at regular intervals, or whenever the network configuration changes. One of the features of the heuristic is that it tends to re-elect existing clusterheads even when the network configuration changes. This helps to reduce the communication overheads during transition from old clusterheads to new clusterheads. Also, there is a tendency to evenly distribute the mobile nodes among the clusterheads, and evently distribute the responsibility of acting as clusterheads among all nodes. Thus, the heuristic is fair and stable. Simulation experiments demonstrate that the proposed heuristic is better than the two earlier heuristics, namely the LCA and degree-based solutions.

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Citations
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Journal ArticleDOI

Two algorithms for minimum 2-connected r-hop dominating set

TL;DR: Two approximation algorithms to compute minimum 2-connected r-hop dominating set are presented, one of which is a greedy algorithm using ear decomposition of 2- connected graphs and the other is a three-phase algorithm which is only applicable to unit disk graphs.
Book ChapterDOI

An energy-efficient K-hop clustering framework for wireless sensor networks

TL;DR: Constrained by the maximum cluster size K, EDC distributes clusters uniformly, and minimizes the number of clusters by introducing a relative synchronization technique, which helps to improve the successful transmission rate from nodes to their cluster heads.
Journal ArticleDOI

Energy Efficient Approach in Wireless Sensor Networks Using Game Theoretic Approach and Ant Colony Optimization

TL;DR: The simulation results obtained in the proposed work confirm their superiority over the LEACH and the D-SEP protocols.
Book ChapterDOI

Distributed node location in clustered multi-hop wireless networks

TL;DR: This paper describes a localization scheme based on Distributed Hashed Tables and Interval Routing which takes advantage of the underlying clustering structure and only requires O(1) memory space size on each node.
Proceedings ArticleDOI

Zone based ant colony routing in mobile ad-hoc network

TL;DR: A new routing algorithm for mobile ad hoc network is being proposed by combining the concept of Ant Colony approach and Zone based routing approach using clustering to get shortest path with small number of control messages to minimize the overhead.
References
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Proceedings ArticleDOI

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Journal ArticleDOI

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Journal ArticleDOI

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