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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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Proceedings ArticleDOI

Heterogeneous distributed clustering in sensor networks

TL;DR: This paper proposes a general network clustering approach which is able to operate in heterogeneous environments and clusters network nodes by considering multi-task and multi-parameter characteristics and formalizes this solution and introduces a problem-unrelated distributed algorithm.
Book ChapterDOI

A Centralized Cluster Head Selection Scheme for Reducing Discrepancy among Clusters over WSN

TL;DR: A cluster head selection scheme that considers residual energy of a node and local node density to maintain cluster size and the number of clusters, which dynamically adjusts cluster size to a recommended threshold with the ever changing network dynamics of sensor network is presented.
Book ChapterDOI

Performance Comparison of Clustering Schemes in Sensor Networks

TL;DR: Clustering can reduce the need for global coordination and restrict most of the sensing, data processing and communication activities within clusters, thus can improve resource efficiency and prolong network lifetime and provide load balancing if appropriately configured.
Proceedings ArticleDOI

Cognitive radio systems clustering

TL;DR: The clustering program, based on the maximum energy efficiency model, minimizes communication loses in cognitive radio systems and can be recommended for the rational using of the radio frequency resource.

A refinement for secure datagathering in wireless sensor networks

J. Gayathri
TL;DR: The intend data-gathering algorithm can greatly shorten the moving distance of the Investor compared with the shortest path algorithm and is close to the optimal algorithm for small networks and the results demonstrate that MoteSec-Aware consumes much less power, but get higher security than some state-of-the-art methods.
References
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Proceedings ArticleDOI

Highly dynamic Destination-Sequenced Distance-Vector routing (DSDV) for mobile computers

TL;DR: The modifications address some of the previous objections to the use of Bellman-Ford, related to the poor looping properties of such algorithms in the face of broken links and the resulting time dependent nature of the interconnection topology describing the links between the Mobile hosts.
Journal ArticleDOI

Approximation algorithms for combinatorial problems

TL;DR: For the problem of finding the maximum clique in a graph, no algorithm has been found for which the ratio does not grow at least as fast as n^@e, where n is the problem size and @e>0 depends on the algorithm.
Proceedings ArticleDOI

A highly adaptive distributed routing algorithm for mobile wireless networks

TL;DR: The proposed protocol is a new distributed routing protocol for mobile, multihop, wireless networks that is highly adaptive, efficient and scalable; being best-suited for use in large, dense, mobile networks.
Journal ArticleDOI

Multicluster, mobile, multimedia radio network

TL;DR: A multi-cluster, multi-hop packet radio network architecture for wireless adaptive mobile information systems is presented that supports multimedia traffic and relies on both time division and code division access schemes.
Journal ArticleDOI

A design concept for reliable mobile radio networks with frequency hopping signaling

TL;DR: This paper outlines those features that distinguish the High Frequency (HF) Intra Task Force (ITF) Network from other packet radio networks, and presents a design concept for this network that encompasses organizational structure, waveform design, and channel access.
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