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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

A distributed approach to node clustering in decentralized peer-to-peer networks

TL;DR: This scheme presents a scalable and efficient solution for discovering connectivity-based clusters in peer networks, and mechanisms to allow new nodes to be incorporated into appropriate existing clusters and to gracefully handle the departure of nodes in the clusters are provided.
Proceedings ArticleDOI

Clustering algorithms for wireless ad hoc networks

TL;DR: A communication model that is derived directly from that of Bluetooth, an emerging technology for pervasive computing, is described and a completely deterministic O(N) distributed algorithm for clustering in wireless ad hoc networks is proposed.
Journal ArticleDOI

A survey on data aggregation techniques in IoT sensor networks

TL;DR: Major techniques of data integration in wireless sensor networks covering ground, underground and underwater sensor networks are presented in this paper and the applications, advantages and disadvantages of using each technique are described.
Journal ArticleDOI

Three power-aware routing algorithms for sensor networks

TL;DR: It is shown that online power-aware routing does not have a constant competitive ratio to the off-line optimal algorithm, so an approximation algorithm is developed called max –min zPmin that has a good empirical competitive ratio.
Proceedings ArticleDOI

Discrete mobile centers

TL;DR: A new randomized algorithm for maintaining a set of c lusters among moving nodes in the plane that can be implemented without exact knowledge of the node positions, if each node is able to sense its distance to other nodes up to the cluster radius.
References
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