scholarly journals Energy-Efficient Collaborative Communication for Optimization Cluster Heads Selection Based on Genetic Algorithms in Wireless Sensor Networks

2015 ◽  
Vol 11 (6) ◽  
pp. 396121 ◽  
Author(s):  
Wei-gang Ma ◽  
Yuan Cao ◽  
Wei Wei ◽  
Xin-hong Hei ◽  
Jian-feng Ma
Keyword(s):  
Wireless Sensor Networks ◽  
Genetic Algorithms ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Collaborative Communication ◽  
Cluster Heads

Energy Efficient Sensor Configuration by Fishing Spider Inspired Mechanism

2013 ◽  
Vol 284-287 ◽  
pp. 2049-2055
Author(s):  
Kyu Hong Lee ◽  
Hee Sang Lee
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Sensor Node ◽  
Wireless Sensor ◽  
Cluster Heads ◽  
Self Organized ◽  
Clustering Model ◽  
Dolomedes Triton ◽  
Fishing Spider

Wireless sensor networks have inherent characteristics that differ from other wireless networks. Therefore, topology configuration and routing methods in WSNs must address these characteristics. In this paper, we propose an energy efficient clustering model. This model was inspired by the behaviors and capabilities of the six-spotted fishing spider, Dolomedes triton. The suggested model performs cluster-heads selection and clustering in self-organized ways. In order to determine the cluster-heads and the cluster-members, each sensor node uses the local information and simple rules that have been inspired by the Dolomedes triton. We compared our model with a well-known cluster-based routing protocol that uses random fairness for the selection of sensor node cluster-heads. In our computational experiments, we have showed that the energy efficiency and lifetimes of our bio-inspired model exceeds those of the comparison protocol by only using simple bio-inspired mechanism. We also demonstrate our model’s good performance in terms of scalability, which is one of the important indicators of performance for self-organized wireless sensor networks.

scholarly journals Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks Based on Yellow Saddle Goatfish Algorithm

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1515 ◽  
Author(s):  
Alma Rodríguez ◽  
Carolina Del-Valle-Soto ◽  
Ramiro Velázquez
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Cluster Structure ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Heads ◽  
Energy Efficient Clustering

The usage of wireless sensor devices in many applications, such as in the Internet of Things and monitoring in dangerous geographical spaces, has increased in recent years. However, sensor nodes have limited power, and battery replacement is not viable in most cases. Thus, energy savings in Wireless Sensor Networks (WSNs) is the primary concern in the design of efficient communication protocols. Therefore, a novel energy-efficient clustering routing protocol for WSNs based on Yellow Saddle Goatfish Algorithm (YSGA) is proposed. The protocol is intended to intensify the network lifetime by reducing energy consumption. The network considers a base station and a set of cluster heads in its cluster structure. The number of cluster heads and the selection of optimal cluster heads is determined by the YSGA algorithm, while sensor nodes are assigned to its nearest cluster head. The cluster structure of the network is reconfigured by YSGA to ensure an optimal distribution of cluster heads and reduce the transmission distance. Experiments show competitive results and demonstrate that the proposed routing protocol minimizes the energy consumption, improves the lifetime, and prolongs the stability period of the network in comparison with the stated of the art clustering routing protocols.

scholarly journals Energy-Efficient Cluster-Head Selection for Wireless Sensor Networks Using Sampling-Based Spider Monkey Optimization

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5281 ◽  
Author(s):  
Jin-Gu Lee ◽  
Seyha Chim ◽  
Ho-Hyun Park
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Cluster Head ◽  
Spider Monkey ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Adaptive Clustering ◽  
Cluster Heads ◽  
Spider Monkey Optimization

Extending the lifetime and stability of wireless sensor networks (WSNs) through efficient energy consumption remains challenging. Though clustering has improved energy efficiency through cluster-head selection, its application is still complicated. In existing cluster-head selection methods, the locations where cluster-heads are desirable are first searched. Next, the nodes closest to these locations are selected as the cluster-heads. This location-based approach causes problems such as increased computation, poor selection accuracy, and the selection of duplicate nodes. To solve these problems, we propose the sampling-based spider monkey optimization (SMO) method. If the sampling population consists of nodes to select cluster-heads, the cluster-heads are selected among the nodes. Thus, the problems caused by different locations of nodes and cluster-heads are resolved. Consequently, we improve lifetime and stability of WSNs through sampling-based spider monkey optimization and energy-efficient cluster head selection (SSMOECHS). This study describes how the sampling method is used in basic SMO and how to select cluster-heads using sampling-based SMO. The experimental results are compared to similar protocols, namely low-energy adaptive clustering hierarchy centralized (LEACH-C), particle swarm optimization clustering protocol (PSO-C), and SMO based threshold-sensitive energy-efficient delay-aware routing protocol (SMOTECP), and the results are shown in both homogeneous and heterogeneous setups. In these setups, SSMOECHS improves network lifetime and stability periods by averages of 13.4%, 7.1%, 34.6%, and 1.8%, respectively.

Distributed Energy-Efficient Unequal Clustering Mechanism for Wireless Sensor Networks

2011 ◽  
Vol 186 ◽  
pp. 225-229
Author(s):  
Chang Jiang Jiang ◽  
Wei Ren Shi ◽  
Min Xiang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Base Station ◽  
Wireless Sensor ◽  
Distributed Energy ◽  
Unequal Clustering ◽  
Multihop Routing ◽  
Cluster Heads

Unequal clustering mechanism, in combination with inter-cluster multihop routing, provides a new effective way to balance the energy dissipation among nodes and prolong the lifetime of wireless sensor networks. In this paper, a distributed energy-efficient unequal clustering mechanism (DEEUC) is proposed and evaluated. By a time based competitive clustering algorithm, DEEUC partitions all nodes into clusters of unequal size, in which the clusters closer to the base station have smaller size. The cluster heads of these clusters can preserve some more energy for the inter-cluster relay traffic and the “hot-spots” problem can be avoided. For inter-cluster communication, DEEUC adopts an energy-aware multihop routing to reduce the energy consumption of the cluster heads. Simulation results demonstrate that the protocol can efficiently decrease the dead speed of the nodes and prolong the network lifetime

scholarly journals A Decentralized Fuzzy C-Means-Based Energy-Efficient Routing Protocol for Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Osama Moh’d Alia
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Data Transmission ◽  
Energy Efficient ◽  
Base Station ◽  
Wireless Sensor ◽  
Fuzzy C Means ◽  
Cluster Heads ◽  
Transmission Phase

Energy conservation in wireless sensor networks (WSNs) is a vital consideration when designing wireless networking protocols. In this paper, we propose a Decentralized Fuzzy Clustering Protocol, named DCFP, which minimizes total network energy dissipation to promote maximum network lifetime. The process of constructing the infrastructure for a given WSN is performed only once at the beginning of the protocol at a base station, which remains unchanged throughout the network’s lifetime. In this initial construction step, a fuzzy C-means algorithm is adopted to allocate sensor nodes into their most appropriate clusters. Subsequently, the protocol runs its rounds where each round is divided into a CH-Election phase and a Data Transmission phase. In the CH-Election phase, the election of new cluster heads is done locally in each cluster where a new multicriteria objective function is proposed to enhance the quality of elected cluster heads. In the Data Transmission phase, the sensing and data transmission from each sensor node to their respective cluster head is performed and cluster heads in turn aggregate and send the sensed data to the base station. Simulation results demonstrate that the proposed protocol improves network lifetime, data delivery, and energy consumption compared to other well-known energy-efficient protocols.

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