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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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Dissertation

Efficient Data Dissemination in Wireless Ad Hoc Networks

TL;DR: Two centralized approximation algorithms are proposed to construct a D(2,k,r) to act as a virtual backbone or a clustering hierarchy in unit disk graphs and in general graphs, and upper bounds are given on the number of forwarding nodes returned by HBLF in a network with or without holes.

Energy-efficient, Reliable, and Flexible Data Transmission in Wireless Personal Area Networks

Daeyoung Kim
TL;DR: A clustering and routing method for hierarchical sensor networks is proposed which provides the optimal ratio of cluster-heads and a clustering scheme which expands the range of clusters until d-hop calculated by the ratio of Cluster-head.
Journal ArticleDOI

A Distributed Agent-based Approach to Manage Industrial Wireless Sensor Networks

TL;DR: An overview of a wireless sensor network management approach, the simulator platform developed using Java Agent Development Environment (JADE) that will be used to test this design, as well as preliminary experimental results using this simulator are proposed.
Proceedings ArticleDOI

A study on data aggregation scheme over wireless sensor network

TL;DR: A detailed study has been made about the available data aggregation techniques in the form of taxonomy to design a data aggregation scheme with optimal energy consumption across the sensing area to enhance the network lifetime.
Journal ArticleDOI

Coverage and connectivity-aware clustering within k hops in wireless sensor and actuator networks

TL;DR: A coverage and connectivity aware clustering within k-hops (CCAC-k), where sensors pick a cluster head before the actuators have been placed, and there are no orphan sensors in the monitored area, even when deployed randomly.
References
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Proceedings ArticleDOI

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Proceedings ArticleDOI

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Journal ArticleDOI

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Journal ArticleDOI

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