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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

Design and analysis of mobility-aware clustering algorithms for wireless mesh networks

TL;DR: This work identifies the cases where clustering is helpful and proposes two clustering schemes that take into consideration the mobility properties of the users in order to improve the WMN performance and proves that both schemes can achieve significant gains in terms of radio resource utilization.
Proceedings ArticleDOI

Analysis of Clustering and Routing Overhead for Clustered Mobile Ad Hoc Networks

TL;DR: It is observed that the control overhead in a clustered network is closely related to different network parameters, e.g. node mobility, node transmission range, network size, and network density, which facilitates the design of efficient clustering algorithms in order to minimize theControl overhead.
Proceedings ArticleDOI

Hierarchical Character Oriented Wildlife Species Recognition Through Heterogeneous Wireless Sensor Networks

TL;DR: A new tiered heterogeneous wireless image sensor network for real-time unobtrusive species detection and video cataloguing based on a new hierarchically scalarized-character oriented detection algorithm and architecture is proposed.
Proceedings ArticleDOI

Minimum-cost gateway deployment in cellular Wi-Fi networks

R. Prasad, +1 more
TL;DR: Simulation results show that the proposed approaches can effectively identify a set of gateways at optimal locations in a cellular Wi-Fi network, resulting in an overall cost reduction of up to 50%.
Proceedings ArticleDOI

Competitive Self-Stabilizing k-Clustering

TL;DR: This paper proposes a silent self-stabilizing asynchronous distributed algorithm for constructing a k-clustering of any connected network with unique IDs, and shows that the distributed MIS tree construction is a P-complete problem.
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