Cluster Density of Dependent Thinning Distributed Clustering Class of Algorithms in Ad Hoc Deployed Wireless Networks

Gamwarige, Sankalpa; Kulasekere, E.C.

doi:10.1155/2012/781275

Cited by 1 publication

(2 citation statements)

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“…To minimise the channel contention amongst n nodes, the upper bound limit on the transmission range r shall be selected such that the number of nodes in the overlap region n o should be no more than n /2. In , the authors have shown that cluster area A c is at its smallest when

A_{c} = \frac{\sqrt{3 r^{2}}}{2}

, which occurs when seven cluster heads are on the perimeter of its neighbours broadcasting range r [Figure (a)]. In the case of Figure (a), A o can be approximated to

\begin{matrix} A_{o} = π r^{2} & + 6 (r^{2} (2 π / 3 - \frac{1}{2} \sqrt{3}) \\ - (\frac{π r^{2}}{6} + 2 (\frac{π r^{2}}{6} - \frac{r^{2}}{2} \sqrt{3 / 4}))) \end{matrix}

A_{o} = 2 π r^{2}

…”

Section: Transmission Range Assignment Problemmentioning

confidence: 99%

“…Using , it is possible to derive the combined number of the expected cluster heads E [ n c h ], and the number of unconnected nodes n u taken into account the boundary layer for a rectangular region with dimensions l 1 × l 2 . According to Gamwarige and Kulasekere , the expected number of nodes n d within the transmission range of size r with n nodes uniformly distributed across an infinite area size but with a boundary layer ( l 1 × l 2 ) is given by

n_{d} = \frac{nπ r^{2}}{l_{1} l_{2}} ((), 1 - \frac{4 r}{3 π l_{1} l_{2}} ((), l_{1} + l_{2}) + \frac{r^{2}}{2 π l_{1} l_{2}})

…”

Section: Expected Number Of Clustersmentioning

confidence: 99%

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Reinforcement learning‐based clustering protocols for a self‐organising cognitive radio network

Ramli

Grace

2015

Trans. Emerging Tel. Tech.

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This paper presents a methodology on how cognitive radio networks can form clusters by exploiting reinforcement learning-based principles. Each node repeatedly senses the received signal strength indicator beacons given off by other nodes in the network. This information can be used by the nodes to learn about its positioning significance within the network and whether to become cluster heads thus forming a cluster. Extensive simulation results are presented, and it is shown that on average, the clustering performance (packing efficiency) can be improved when nodes have the ability to learn. The results are compared with that of a k-means and node degree-based approach for the formation of clusters. It is found that the node degree scheme can result in the clustering performance deteriorating, which is undesirable as it can lead to an increase in the total energy consumption of the network over time. It is shown that in a shadowing environment, that clusters formed via learning through received signal strength indicator can reduce their transmission power by up to 2 dBW (achieving a potential power saving of 37 per cent) while achieving the same signal-to-noise ratio as that of the no learning and node degree schemes. Further energy conservation can be obtained by restricting beacon transmissions to immediate neighbouring nodes. In certain geographical node distributions, a no learning scheme is preferred for the formation of clusters due to its lower overhead.

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A_{c} = \frac{\sqrt{3 r^{2}}}{2}

, which occurs when seven cluster heads are on the perimeter of its neighbours broadcasting range r [Figure (a)]. In the case of Figure (a), A o can be approximated to