Performance analysis of mobility-based d-hop (MobDHop) clustering algorithm for mobile ad hoc networks

Er, Inn Inn; Seah, Winston K.G.

doi:10.1016/j.comnet.2005.12.013

Cited by 29 publications

(13 citation statements)

References 23 publications

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“…However, MAPLE does not take into consideration the host mobility in the course of the cluster-head selection phase. Er and Seah [9] proposed a mobility-based dhop (MobDHop) clustering algorithm that partitions the mobile ad-hoc networks into variablediameter clusters based only on the mobility information each host receives from its one-hop neighbors. To improve the cluster stability, MobDHop groups the hosts with the similar mobility pattern together.…”

Section: Related Workmentioning

confidence: 99%

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A mobility-based cluster formation algorithm for wireless mobile ad-hoc networks

Torkestani

Meybodi

2011

Cluster Comput

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In the last decade, numerous efforts have been devoted to design efficient algorithms for clustering the wireless mobile ad-hoc networks (MANET) considering the network mobility characteristics. However, in existing algorithms, it is assumed that the mobility parameters of the networks are fixed, while they are stochastic and vary with time indeed. Therefore, the proposed clustering algorithms do not scale well in realistic MANETs, where the mobility parameters of the hosts freely and randomly change at any time. Finding the optimal solution to the cluster formation problem is incredibly difficult, if we assume that the movement direction and mobility speed of the hosts are random variables. This becomes harder when the probability distribution function of these random variables is assumed to be unknown. In this paper, we propose a learning automata-based weighted cluster formation algorithm called MCFA in which the mobility parameters of the hosts are assumed to be random variables with unknown distributions. In the proposed clustering algorithm, the expected relative mobility of each host with respect to all its neighbors is estimated by sampling its mobility parameters in various epochs. MCFA is a fully distributed algorithm in which each mobile independently chooses the neighboring host with the minimum expected relative mobility as its cluster-head. This is done based solely on the local information each host receives from its neighbors and the hosts need not to be synchronized. The experimental results show the superiority of MCFA over the best existing mobilitybased clustering algorithms in terms of the number of clusters, cluster lifetime, reaffiliation rate, and control message overhead.

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Section: Related Workmentioning

confidence: 99%

“…The best, but very difficult, solution is to construct the resistant-to-mobility clusters [30,31,32]. To achieve this, recently many studies [4,5,6,7,8,9,10] have been conducted on mobility-based network clustering techniques. Generally, these works aim to form the clusters with the maximum stability against the host mobility.…”

Section: Introductionmentioning

confidence: 99%

A mobility-based cluster formation algorithm for wireless mobile ad-hoc networks

Torkestani

Meybodi

2011

Cluster Comput

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“…The clusterheads shoulder greater responsibilities which deplete their batteries faster than the other nodes in the network [6]. So every effort should be made to increase the lifespan of the clusterheads and to reduce the amount of communication overheads to avoid dropping out of nodes prematurely from the network.…”

Section: Introductionmentioning

confidence: 99%

( k , r )-Dominating set-based, weighted and adaptive clustering algorithms for mobile ad hoc networks

Anitha¹,

Sebastian

2011

IET Commun.

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k, r)-Dominating set-based, weighted and adaptive clustering algorithms for mobile ad hoc networks Abstract: The creation of stable, scalable and adaptive clusters with good performance, faster convergence rate and with minimal overhead is a challenging task in mobile ad hoc networks (MANETs). This study proposes two clustering techniques for MANETs, which are (k, r)-dominating set-based, weighted and adaptive to changes in the network topology. The set of dominating nodes functions as the clusterheads. The scenario-based clustering algorithm for MANETs (SCAM) is a greedy approximation algorithm, whereas the distributed-SCAM (DSCAM) selects the (k, r)-dominating set through a distributed election mechanism. These algorithms achieve variable degree of clusterhead redundancy through the parameter k which contributes to reliability. Similarly, flexibility in creating variable diameter clusters is achieved with the parameter r. To improve the stability of the created clusters, the affiliation of other nodes with the clusterhead is decided based on the quality of the clusterhead, which is a function of connectivity, stability, residual battery power and transmission range. Mechanisms are available for accounting the group mobility and load balancing. The performance of these algorithms are evaluated through simulation and the results show that these algorithms create stable, scalable and load-balanced clusters with relatively less control overhead in comparison with the existing popular algorithms.

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“…In the mobility-based d-hop (MobDHop) [6] clustering algorithm, clusters of variable diameters are created based on the mobility patterns. Unlike existing clustering algorithms, the diameter of clusters is not restricted by any preset value.…”

Section: Clusteringmentioning

confidence: 99%

“…It first generate a pseudo-random hop sequence based on its master clock and address, and then maps it into a desired hopping frequency so that each packet is transmitted through a different channel. However, recent works have shown that the performance of the Bluetooth system using pseudorandom FH technique can be heavily degraded due to unavoidable co-existing interferences (e.g., interferences caused by collocated Bluetooth devices or IEEE 802.11x based wireless local area network (WLAN)) in the unlicensed band [2][3][4][5][6].…”

Section: Bluetoothmentioning

confidence: 99%

Interference Avoidance Mechanisms in Bluetooth - A Comparative Study

Sughasiny¹,

Dhanapal²

2013

IJCA

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Bluetooth is a short range operating technology that helps in the exchange of data. Faster and efficient communications using a Bluetooth device is mandatory, since it has resource limitations. Service discovery delays pose a major problem when considering the Bluetooth devices. Our paper provides a comparative study that discusses efficient mechanisms to reduce these delays by providing a better service discovery process. Further, we also discuss mechanisms that help improve the power consumption. KEYWORDS BLUETOOTHBluetooth is a short range wireless system operating in 2.4 GHz industry, society and medical (ISM) unlicensed band, where a number of radio systems share the same resource. The Bluetooth rapidly hops over all the 79 channels to mitigate interferences using a pseudo-random frequency hopping (FH) technique [1]. It first generate a pseudo-random hop sequence based on its master clock and address, and then maps it into a desired hopping frequency so that each packet is transmitted through a different channel. However, recent works have shown that the performance of the Bluetooth system using pseudorandom FH technique can be heavily degraded due to unavoidable co-existing interferences (e.g., interferences caused by collocated Bluetooth devices or IEEE 802.11x based wireless local area network (WLAN)) in the unlicensed band [2-6].To alleviate this problem, a number of non-collaborative schemes working regardless of any other systems have been considered [7][8][9][10][11][12]. Most of them consider the use of two fundamental processes; channel classification and adaptive control action. Channel classification estimates channel conditions to detect the presence of any interference source nearby. Adaptive control action mitigates interference sources by exploiting the channel classification information.

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Performance analysis of mobility-based d-hop (MobDHop) clustering algorithm for mobile ad hoc networks

Cited by 29 publications

References 23 publications

A mobility-based cluster formation algorithm for wireless mobile ad-hoc networks

A mobility-based cluster formation algorithm for wireless mobile ad-hoc networks

( k , r )-Dominating set-based, weighted and adaptive clustering algorithms for mobile ad hoc networks

Interference Avoidance Mechanisms in Bluetooth - A Comparative Study

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