scholarly journals Combinatorial Optimization-Based Clustering Algorithm for Wireless Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yuxiao Cao ◽  
Zhen Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Clustering Algorithm ◽  
Fitness Function ◽  
Clustering Algorithms ◽  
Wireless Sensor ◽  
Binary Particle Swarm Optimization ◽  
Conflicting Objectives

As node energy of wireless sensor networks (WSN) is limited and cannot be supplemented after exhaustion, clustering algorithm is frequently taken as an effective method to prolong the lifetime of WSN. However, the existing clustering algorithms have some drawbacks, either consuming excessive energy as a result of exchanging too much controlling information between nodes, or lacking a comprehensive perspective in terms of the balance among several conflicting objectives. In order to overcome these shortcomings, a novel combinatorial optimization-based clustering algorithm (COCA) for WSN is proposed in this paper. Different from the above mentioned algorithms which take clustering as a continuous optimization problem, COCA solves the clustering problem from the perspective of combinatorial optimization. Firstly, the clustering of WSN is abstracted into a combinatorial optimization problem. Then, the binary particle coding scheme of cluster head is proposed, which is based on the corresponding relationship between nodes and particle position vectors, and the fitness function is designed according to the parameters used in the process of cluster formation. Finally, the binary particle swarm optimization algorithm is applied to implement the clustering. COCA is validated under different scenarios compared with three other clustering algorithms. The simulation results show that COCA has better performance than its comparable algorithms.

An Energy-Efficient Layered Clustering Algorithm for Routing in Wireless Sensor Networks

2017 ◽  
Vol 8 (3) ◽  
pp. 43-66 ◽  
Author(s):  
Alphonse PJA ◽  
Sivaraj C ◽  
Janakiraman T N.
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Conservation ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Transmission Rate ◽  
Clustering Algorithms ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Simulation Experiments

Efficient energy management is a key issue in battery equipped wireless sensor networks (WSNs). The cluster based routing in WSNs is a prominent approach for energy conservation of the network which provides a hierarchical data collection mechanism. In order to maximize the energy conservation of sensor nodes, this paper proposes an Energy-efficient Layered Clustering Algorithm (ELCA) for routing in wireless sensor networks. ELCA constructs two layers of clusters to reduce the transmission rate and to balance the energy consumption of sensors. As early energy depletion of clusterheads (CHs) is a major limitation in clustering, this algorithm provides local remedy for energy suffering CHs through efficient CH substitution scheme. The performance of the proposed algorithm is analysed through extensive simulation experiments and verified by compared results with existing clustering algorithms.

New Fault-Tolerant Hierarchical Routing Protocol for WSNs

2012 ◽  
Vol 433-440 ◽  
pp. 3223-3229
Author(s):  
Davood Keykhosravi ◽  
Ali Hosseinalipour
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
High Performance ◽  
Clustering Algorithm ◽  
Fault Tolerant ◽  
Clustering Algorithms ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Geographical Position

Clustering in wireless sensor networks is one of the crucial methods for increasing of network lifetime. There are many algorithms for clustering. One of the cluster based algorithm in wireless sensor networks is LEACH algorithm. In this paper we proposed a new clustering method for increasing of network lifetime. In proposed method Clustering is done symmetrically and the best node with respect to remained energy and distance of other nodes in comparing with each that selected as a cluster head. Although in this protocol we didn’t use GPS but we could find geographical position nodes so easily. However, failures in higher level of hierarchy e.g. cluster-head cause more damage to the system because they also limit accessibility to the nodes that are under their supervision. In this paper we propose an efficient mechanism to recover sensors from a failed cluster. In this performance of the proposed algorithm via computer simulation was evaluated and compared with other clustering algorithms. The simulation results show the high performance of the proposed clustering algorithm.

A Clustering Algorithm for Wireless Sensor Networks Basing on Distance and Distribution

2011 ◽  
Vol 474-476 ◽  
pp. 1221-1227
Author(s):  
Ying Liao ◽  
Wei Xu Hao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Topology ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Hierarchical Network ◽  
The Common

Wireless sensor networks (WSNs) detect and monitor the outside physical state by the sensor nodes organizing automatically. Utilizing clustering algorithm to form hierarchical network topology is the common method which implements managing network and aggregating data in WSNs. Different from the previous clustering algorithms, this article proposes a clustering algorithm for WSNs based on distance and distribution to generate clusters considering residual energy of nods in WSNs with inhomogeneous distribution. The simulation result indicates that the algorithm can establish more balanceable clustering structure effectively and enhance the network life cycle obviously.<b></b>

scholarly journals EDCH: A Novel Clustering Algorithm for Wireless Sensor Networks

2019 ◽  
Vol 4 (3) ◽  
pp. 45-51
Author(s):  
Raj Kumar Pyage ◽  
H. G. Chandrakanth
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Main Role ◽  
Considerable Decrease

In wireless sensor networks, sensor nodes play the most important role. These sensor nodes are mainly un-chargeable, so it an issue regarding lifetime of the network.  The main objective of this research is concerning clustering algorithms to minimize the energy utilization of each sensor node, and maximize the sensor network lifetime of WSNs. In this paper, we propose a novel clustering algorithm for wireless sensor networks (WSN) that decrease the networks energy consumption and significantly prolongs its lifetime. Here main role play distribution of CHs ( Cluster Heads) across the network. Our simulation result shows considerable decrease in network energy utilization and therefore increase the network lifetime.  

scholarly journals DHCFL: Dual Head Clustering Based on Fuzzy Logic for Wireless Sensor Networks

2020 ◽  
Vol 8 (5) ◽  
pp. 1049-1054
Keyword(s):  
Wireless Sensor Networks ◽  
Fuzzy Logic ◽  
Sensor Networks ◽  
Data Transmission ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Research Work ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor

Wireless Sensor Networks (WSN) are constructed by interconnecting miniature sensor nodes for monitoring the environment uninterrupted. These miniature nodes are having the sensing, processing and communication capability in a smaller scale powered by a battery unit. Proper energy conservation is required for the entire system. Clustering mechanism in WSN advances the lifetime and stability in the network. It achieves data aggregation and reduces the number of data transmission to the Base station (BS). But the Cluster Head (CH) nodes are affected by rapid energy depletion problem due to overload. A CH node spends its energy for receiving data from its member nodes, aggregation and transmission to the BS. In CH election, multiple overlapping factors makes it difficult and inefficient which costs the lifetime of the network. In recent years, Fuzzy Logic is widely used for CH election mechanism for WSN. But the underlying problem of the CHs node continues. In this research work, a new clustering algorithm DHCFL is proposed which elects two CHs for a cluster which shares the load of a conventional CH node. Data reception and aggregation will be done by CH aggregator (CH-A) node and data transmission to the BS will be carried over by CH relay (CH-R) node. Both CH-A and CH-R nodes are elected through fuzzy logic which addresses the uncertainty in the network too. The proposed algorithm DHCFL is compared and tested in different network scenarios with existing clustering algorithms and it is observed that DHCFL outperforms other algorithms in all the network scenarios.

An Energy-Efficient Layered Clustering Algorithm for Routing in Wireless Sensor Networks

2020 ◽  
pp. 238-262
Author(s):  
P. J. A. Alphonse ◽  
C. Sivaraj ◽  
T. N. Janakiraman
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Conservation ◽  
Energy Management ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Transmission Rate ◽  
Clustering Algorithms ◽  
Sensor Nodes ◽  
Wireless Sensor

Efficient energy management is a key issue in battery equipped wireless sensor networks (WSNs). The cluster based routing in WSNs is a prominent approach for energy conservation of the network which provides a hierarchical data collection mechanism. In order to maximize the energy conservation of sensor nodes, this paper proposes an Energy-efficient Layered Clustering Algorithm (ELCA) for routing in wireless sensor networks. ELCA constructs two layers of clusters to reduce the transmission rate and to balance the energy consumption of sensors. As early energy depletion of clusterheads (CHs) is a major limitation in clustering, this algorithm provides local remedy for energy suffering CHs through efficient CH substitution scheme. The performance of the proposed algorithm is analysed through extensive simulation experiments and verified by compared results with existing clustering algorithms.

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.

scholarly journals An Innovative Hyperheuristic, Gaussian Clustering Scheme for Energy-Efficient Optimization in Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Oluwasegun Julius Aroba ◽  
Nalindren Naicker ◽  
Timothy Adeliyi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Gaussian Elimination ◽  
Clustering Algorithms ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fast Time

Energy stability on sensor nodes in wireless sensor networks (WSNs) is always an important challenge, especially during data capturing and transmission of packets. The recent advancement in distributed clustering algorithms in the extant literature proposed for energy efficiency showed refinements in deployment of sensor nodes, network duration stability, and throughput of information data that are channelled to the base station. However, much scope still exists for energy improvements in a heterogeneous WSN environment. This research study uses the Gaussian elimination method merged with distributed energy efficient clustering (referred to as DEEC-Gauss) to ensure energy efficient optimization in the wireless environment. The rationale behind the use of the novel DEEC-Gauss clustering algorithm is that it fills the gap in the literature as researchers have not been able to use this scheme before to carry out energy-efficient optimization in WSNs with 100 nodes, between 1,000 and 5000 rounds and still achieve a fast time output. In this study, using simulation, the performance of highly developed clustering algorithms, namely, DEEC, EDEEC_E, and DDEEC, was compared to the proposed Gaussian Elimination Clustering Algorithm (DEEC-Gauss). The results show that the proposed DEEC-Gauss Algorithm gives an average percentage of 4.2% improvement for the first node dead (FND), a further 2.8% improvement for the tenth node dead (TND), and the overall time of delivery was increased and optimized when compared with other contemporary algorithms.

scholarly journals Intelligent energy optimization for advanced IoT analytics edge computing on wireless sensor networks

2020 ◽  
Vol 16 (7) ◽  
pp. 155014772090877
Author(s):  
Israel Edem Agbehadji ◽  
Samuel Ofori Frimpong ◽  
Richard C Millham ◽  
Simon James Fong ◽  
Jason J Jung
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Network ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Energy Optimization ◽  
Base Station ◽  
Wireless Sensor ◽  
Optimal Time ◽  
Local Optimum

The current dispensation of big data analytics requires innovative ways of data capturing and transmission. One of the innovative approaches is the use of a sensor device. However, the challenge with a sensor network is how to balance the energy load of wireless sensor networks, which can be achieved by selecting sensor nodes with an adequate amount of energy from a cluster. The clustering technique is one of the approaches to solve this challenge because it optimizes energy in order to increase the lifetime of the sensor network. In this article, a novel bio-inspired clustering algorithm was proposed for a heterogeneous energy environment. The proposed algorithm (referred to as DEEC-KSA) was integrated with a distributed energy-efficient clustering algorithm to ensure efficient energy optimization and was evaluated through simulation and compared with benchmarked clustering algorithms. During the simulation, the dynamic nature of the proposed DEEC-KSA was observed using different parameters, which were expressed in percentages as 0.1%, 4.5%, 11.3%, and 34% while the percentage of the parameter for comparative algorithms was 10%. The simulation result showed that the performance of DEEC-KSA is efficient among the comparative clustering algorithms for energy optimization in terms of stability period, network lifetime, and network throughput. In addition, the proposed DEEC-KSA has the optimal time (in seconds) to send a higher number of packets to the base station successfully. The advantage of the proposed bio-inspired technique is that it utilizes random encircling and half-life period to quickly adapt to different rounds of iteration and jumps out of any local optimum that might not lead to an ideal cluster formation and better network performance.

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