scholarly journals An Energy Aware Adaptive Kernel Density Estimation Approach to Unequal Clustering in Wireless Sensor Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 40569-40580 ◽  
Author(s):  
Fagui Liu ◽  
Yufei Chang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Wireless Sensor ◽  
Unequal Clustering ◽  
Energy Aware ◽  
Adaptive Kernel

scholarly journals Fuzzy Logic Based Zone Head Selection Algorithm for Wireless Sensor Networks

2021 ◽  
Vol 23 (07) ◽  
pp. 1210-1215
Author(s):  
Anchal Garg ◽  
◽  
Gurjinder Kaur ◽  
Keyword(s):  
Wireless Sensor Networks ◽  
Fuzzy Logic ◽  
Sensor Networks ◽  
Sensor Network ◽  
Hot Spots ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Unequal Clustering ◽  
Energy Aware ◽  
Efficient Data

Hot-spots are a problem that comes in the cluster-based routing protocol that employs multi-hop communication due to this problem the energy among the sensor nodes is not balanced. The hot-spots issue requires high overhead and is prone to connectivity issues in the sensor network this can be only possible because of unequal clustering. In this method, we have to act on all the nodes of the sensor network for communication. This process consumes high system energy if the numbers of nodes are very high. To offer guaranteed connectivity, decrease high usage and complexity, a fuzzy logic-based zone divisional method has been proposed in this paper. Use fuzzy logic to create clusters and assign nodes to them to decrease the consumption of energy and the age of the network prolongation. The simulation and results section shows the outperformance of the proposed protocol, where the (LEACH) low-energy adaptive clustering hierarchy, (EAUCF) energy-aware unequal clustering fuzzy,(EAMMH) energy-aware multi-hop multi-path hierarchical, and (TTDFP) two-tier distributed fuzzy logic-based protocol for efficient data aggregation in multi-hop wireless sensor networks algorithms. The proposed algorithm has better results in terms of energy consumption minimization, and prolongation of the network lifetime.

Near-optimal energy-aware approach through INSTANT-OFF and NEVER-OFF clustering by fuzzy logic for wireless sensor networks

2021 ◽  
pp. 1-15
Author(s):  
Abid Hussain ◽  
Saima Munawar ◽  
Nasir Naveed
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Base Station ◽  
Wireless Sensor ◽  
Minor Amount ◽  
Main Concern ◽  
Unequal Clustering ◽  
Energy Aware ◽  
Inference System ◽  
Clustering Approach

Wireless Sensor Networks (WSNs) consist of various low-cost devices with limited battery power for surveillance of certain vicinity. The main concern was to prolong the network lifetime to save energy. The heterogeneous nodes are deployed in the given setting divided into two INSTANT-OFF and NEVER-OFF states. Then each one is further subdivided by a Fuzzy Inference System (FIS). The INSTANT-OFF (Good, Better, and Best) has three states active, idle, sleep, and always worked as Cluster Members (CMs) to sense the physical environment. The NEVER-OFF (Good, Better, and Best) has active and idle states. The first two most optimum NEVER-OFF selected as Cluster Head (CH) and Data Collector (DC), and the remaining belonged to CMs. The cluster boundary was defined by parameter Distance from Base Station (DisBS) to meet the unequal clustering approach. The energy consumes during sensing, processing, and transmission phases by its appropriate nodes. The CMs worked reactively and saved energy by idle and sleep states, while the CH and DC worked in a proactive mode and saved energy in an idle state. The sensing job was done by CMs that consumed a minor amount of energy and transmitted packets of 200 bits length to DC. The DC received packets of 200 bits length from CMs and aggregated them into 6400 bits length packets, then delivered them to CH. The reactive and proactive mechanisms saved the energy as 85.1033% in 2000 rounds; increased lifetime up to 774 rounds, re-clustering setup took place after 1912 rounds, and enhanced the throughput as 100% and latency time 0.001123 by experiment evaluation. The result shows that most energy consumption job were communicated with BS performed by CH hop by hop through other CH. The unequal clustering approach maintained the consumption of energy levels throughout WSNs processing.

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