A SURVEY ON VARIED DISTRIBUTED CLUSTERING ALGORITHMS FOR WIRELESS SENSOR NETWORKS

2018 ◽  
Vol 7 (1) ◽  
pp. 35
Author(s):  
PRADHAN SWAGATIKA ◽  
PATNAIK PAWAN ◽  
S.D. MISHRA ◽  
◽  
◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Clustering Algorithms ◽  
Wireless Sensor ◽  
Distributed Clustering

scholarly journals A Distributed Clustering Algorithm Guided by the Base Station to Extend the Lifetime of Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2312 ◽  
Author(s):  
Antonio-Jesus Yuste-Delgado ◽  
Juan-Carlos Cuevas-Martinez ◽  
Alicia Triviño-Cabrera
Keyword(s):  
Wireless Sensor Networks ◽  
Fuzzy Logic ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Base Station ◽  
Wireless Sensor ◽  
P Value ◽  
Distributed Clustering

Clustering algorithms are necessary in Wireless Sensor Networks to reduce the energy consumption of the overall nodes. The decision of which nodes are the cluster heads (CHs) greatly affects the network performance. The centralized clustering algorithms rely on a sink or Base Station (BS) to select the CHs. To do so, the BS requires extensive data from the nodes, which sometimes need complex hardware inside each node or a significant number of control messages. Alternatively, the nodes in distributed clustering algorithms decide about which the CHs are by exchanging information among themselves. Both centralized and distributed clustering algorithms usually alternate the nodes playing the role of the CHs to dynamically balance the energy consumption among all the nodes in the network. This paper presents a distributed approach to form the clusters dynamically, but it is occasionally supported by the Base Station. In particular, the Base Station sends three messages during the network lifetime to reconfigure the s k i p value of the network. The s k i p , which stands out as the number of rounds in which the same CHs are kept, is adapted to the network status in this way. At the beginning of each group of rounds, the nodes decide about their convenience to become a CH according to a fuzzy-logic system. As a novelty, the fuzzy controller is as a Tagaki–Sugeno–Kang model and not a Mandami-one as other previous proposals. The clustering algorithm has been tested in a wide set of scenarios, and it has been compared with other representative centralized and distributed fuzzy-logic based algorithms. The simulation results demonstrate that the proposed clustering method is able to extend the network operability.

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