A mobility-based framework for adaptive clustering in wireless ad hoc networks

McDonald, A.B.; Znati, Taieb

doi:10.1109/49.780353

Cited by 552 publications

(357 citation statements)

References 22 publications

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“…2 We try to rewrite this equation and values in another way. In [25] it was shown that the joint mobility problem can be transformed into an equivalent problem involving the movement of a single node by fixing the frame reference of one node to the other. For each movement of this node, the other node is translated an equal distance in the opposite direction.…”

Section: Packet Hint Calculationmentioning

confidence: 99%

Probabilistic proactive routing with active route trace-back for MANETs

et al. 2010

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a b s t r a c tMobile Ad Hoc network (MANET) is a very attractive networking technology for providing flexible communication in an anytime and anywhere fashion. However, MANET is infrastructure-less and highly dynamic, so the design of an efficient routing strategy for reliable end-to-end communication in such a network has been a challenging issue. The available routing protocols and their embedded information retrieval methods for MAMETs work well only for networks with certain limited assumptions of number of nodes, density of nodes and mobility. With the continuous expansion of the MANETs in real applications, it is now imperative to develop a new routing protocol for MAMETs that is more scalable and topology independent. In this paper, we introduce such a routing scheme for MANETs, which works well under a wide range of network topologies, nodes-density, coverage area size and nodes-mobility. The proposed scheme is based on a novel enhancement of the hint-based probabilistic protocol. Instead of broadcasting extensive control packets for network topology information retrieval as that of conventional routing schemes, the proposed scheme carefully reuses the feedback information carried in unicast packets for this purpose without introducing any extra overhead. The efficiency of the proposed scheme is demonstrated through both mathematical analysis and an extensive simulations study.

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Section: Packet Hint Calculationmentioning

confidence: 99%

Probabilistic proactive routing with active route trace-back for MANETs

et al. 2010

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“…Clustering techniques (e.g., [1,12,24,23,8,33,2,39]) can increase scalability, but existing active clustering mechanisms require periodic refreshing of neighborhood information and introduce significant maintenance overhead due to global query flooding. Another class of protocols use the idea of routing via dedicated fixed anchors (e.g., [4,5]), but such techniques depend on deployment of fixed anchor nodes.…”

Section: Related Workmentioning

confidence: 99%

Safari: A self-organizing, hierarchical architecture for scalable ad hoc networking

Khan

PalChaudhuri

et al. 2008

Ad Hoc Networks

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As wireless devices become more pervasive, mobile ad hoc networks are gaining importance, motivating the development of highly scalable ad hoc networking techniques. In this paper, we give an overview of the Safari architecture for highly scalable ad hoc network routing, and we present the design and evaluation of a specific realization of the Safari architecture, which we call Masai. We focus in this work on the scalability of learning and maintaining the routing state necessary for a large ad hoc network. The Safari architecture provides scalable ad hoc network routing, the seamless integration of infrastructure networks when and where they are available, and the support of self-organizing, decentralized network applications. Safari's architecture is based on (1) a self-organizing network hierarchy that recursively groups participating nodes into an adaptive, locality-based hierarchy of cells; (2) a routing protocol that uses a hybrid of proactive and reactive routing information in the cells and scales to much larger numbers of nodes than previous ad hoc network routing protocols; and (3) a distributed hash table grounded in the network hierarchy, which supports decentralized network services on top of Safari. We evaluate the Masai realization of the Safari architecture through analysis and simulations, under varying network sizes, fraction of mobile nodes, and offered traffic loads. Compared to both the DSR and the L+ routing protocols, our results show that the Masai realization of the Safari architecture is significantly more scalable, with much higher packet delivery ratio and lower overhead.

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“…In addition, mobility is a prominent characteristic of MANETs, and is the main factor affecting topology change and route invalidation [12] [16]. MHs that exhibit high mobility are inadequate for serving as CHs since their movement is likely to trigger frequent re-clustering, therefore increasing control traffic volume.…”

Section: Cluster Formation Algorithmmentioning

confidence: 99%

“…This process is commonly referred to as clustering and the substructures that are collapsed in higher levels are called clusters [3]. Clustering not only makes a large MANET appear smaller, but more importantly, it makes a highly dynamic topology to appear less dynamic [12].…”

Section: Introductionmentioning

confidence: 99%

Stable and Energy Efficient Clustering of Wireless Ad-Hoc Networks with LIDAR Algorithm

Gavalas

Pantziou

Konstantopoulos

et al. 2006

Personal Wireless Communications

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Abstract. The main objective of clustering in mobile ad-hoc network environments is to identify suitable node representatives, i.e. cluster heads (CHs) to store routing and topology information; CHs should be elected so as to maximize clusters stability, that is to prevent frequent cluster re-structuring. A popular clustering algorithm (LID) suggests CH election based on node IDs (nodes with locally lowest ID value become CHs). Although fast and simple, this method is biased against nodes with low IDs, which are likely to serve as CHs for long periods and are therefore prone to rapid battery exhaustion. Herein, we propose LIDAR, a novel clustering method which represents a major improvement over traditional LID algorithm: node IDs are periodically re-assigned so that nodes with low mobility rate and high energy capacity are assigned low ID values and, therefore, are likely to serve as CHs. Our protocol also greatly reduces control traffic volume of existing algorithms during clustering maintenance phase, while not risking the energy availability of CHs. Simulation results demonstrate the efficiency, scalability and stability of our protocol against alternative approaches.

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A mobility-based framework for adaptive clustering in wireless ad hoc networks

Cited by 552 publications

References 22 publications

Probabilistic proactive routing with active route trace-back for MANETs

Probabilistic proactive routing with active route trace-back for MANETs

Safari: A self-organizing, hierarchical architecture for scalable ad hoc networking

Stable and Energy Efficient Clustering of Wireless Ad-Hoc Networks with LIDAR Algorithm

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