Low-Energy Adaptive Clustering Hierarchy Using Affinity Propagation for Wireless Sensor Networks

Sohn, Illsoo; Lee, Jong-Ho; Lee, Sang Hyun

doi:10.1109/lcomm.2016.2517017

Cited by 76 publications

(50 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm speed mainly depends on the p update strategy. The p update strategy of B is as follows: ⎧ ⎪ ⎨ ⎪ ⎩ q = 0.1 * √ K + 50 p step = 0.01 × pm q p = p + b × p step (12) q is changed dynamically with K . p step is the change step value of the p. p m is median value of sim.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive affinity propagation algorithm based on new strategy of dynamic damping factor and preference

Liu

Yan

2018

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

The affinity propagation algorithm has been extensively applied in various fields. However, it is still faces two severe challenges in actual applications: one is that the algorithm may be nonconvergent; and the other is that the convergence speed is low. Aiming at solving these two problems, an adaptive affinity propagation algorithm based on a new strategy of dynamic damping factor and preference is proposed in this paper. On one hand, the dynamic damping factor changes the factor value according to the check state of oscillation to eliminate and escape from the oscillation. On the other hand, dynamic preference adjusts the value of the preference based on the bisection and memory tuple to reduce the search scope of the target preferences continuously. Simulation results show that the proposed algorithms can solve the potential nonconvergence problem effectively and reduce the time consumed significantly. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

show abstract

Section: Discussionmentioning

confidence: 99%

“…b is same value with our OSFn. According to (12), p changes fast when K is larger, and it changes very slowly when K is smaller. This limits the convergence speed of B algorithm.…”

Section: Discussionmentioning

confidence: 99%

Adaptive affinity propagation algorithm based on new strategy of dynamic damping factor and preference

Liu

Yan

2018

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…Therefore, their sustenance to continue with maximum operational rounds of data aggregation can never be guaranteed by such approaches. Approaches such as in previous studies 25,28,[30][31][32][33][34][35]38,39 have claimed of optimizing clustering performance by introducing a cluster head selection process; however, such selection process does not prove the longevity of cluster head operation in case of random dispersal of cluster head. • Few studies towards topology control: Any new and unique process introduced in clustering also affects the topology.…”

Section: Research Problemmentioning

confidence: 99%

Interstellar‐based topology control scheme for optimal clustering performance in WSN

Jyothi

Usha

2020

Int J Communication

View full text Add to dashboard Cite

Summary Clustering is one of the essential operations in wireless sensor network (WSN) to ensure organized data aggregation followed by energy efficiency. However, obtaining optimal clustering performance is yet an unsolved problem in WSN. Review of existing approaches towards cluster optimization shows that effective balance between energy efficiency and topology control is still missing. Therefore, the proposed system presents a unique topology control mechanism using a novel concept of interstellar orientation toward optimizing the clustering performance in WSN. Adopting an analytical research methodology, the proposed system introduces two interstellar‐based topology control system, which targets the maximum saving of resource consumption of the cluster head. The simulated outcome of the study shows that the proposed topology control system offers significant energy conservation performance in comparison to the existing hierarchical clustering scheme in WSN.

show abstract

“…In [19]P-LEACH, it combines the features of LEACH and PEGASIS [20] to improve energy efficiency in routing. In [21], each node broadcasts a Hello message which contains its identity and energy status with a predefined transmit power. With the received power of Hello messages and the prior knowledge of the transmit power, nodes can estimate the distance among them.…”

Section: Related Workmentioning

confidence: 99%

Connected Dominating Set based Optimized Routing Protocol for Wireless Sensor Networks

Faheem¹,

Ilyas²,

Muneer³

et al. 2016

ijacsa

View full text Add to dashboard Cite

Abstract-Wireless Sensor Networks(WSNs) have problem of energy starvation in their operations. This constraint demands that the topology of communicating nodes should be limited. One of the ways of restraining the communicating nodes is by creating a Connected Dominating Set(CDS) of nodes out of them. In this paper, an Optimized Region Based Efficient Data(AORED) routing protocol for WSNs has been proposed. CDS has been employed in AORED to create a virtual backbone of communicating nodes in the network. The empirical study involving extensive simulations show that the proposed routing protocol outperforms the legacy DEEC and SEP protocols. AORED has increased number of transmission rounds, increased number of clusterheads and reduced number of packets sent to the basestation as compared to DEEC and SEP protocols.

show abstract

Low-Energy Adaptive Clustering Hierarchy Using Affinity Propagation for Wireless Sensor Networks

Cited by 76 publications

References 13 publications

Adaptive affinity propagation algorithm based on new strategy of dynamic damping factor and preference

Adaptive affinity propagation algorithm based on new strategy of dynamic damping factor and preference

Interstellar‐based topology control scheme for optimal clustering performance in WSN

Connected Dominating Set based Optimized Routing Protocol for Wireless Sensor Networks

Contact Info

Product

Resources

About