Energy Efficient Clustering using Modified Multi-Hop Clustering

2019 ◽  
Vol 8 (2) ◽  
pp. 18-30
Author(s):  
Vimala M. ◽  
Rajeev Ranjan
Keyword(s):  
Energy Consumption ◽  
Power Supply ◽  
Energy Efficient ◽  
Second Step ◽  
Communication Overhead ◽  
The Third ◽  
The Past ◽  
Real World Problem ◽  
Limited Power ◽  
Energy Efficient Clustering

In recent years, WSN has been widely used for building the decision support system (DSS) for solving the real-world problem. Moreover, out of several fields, one of the interesting fields that requires DSS is the monitoring of agriculture environment. Nowadays, monitoring an agricultural environment has become one of the essential fields. IoT has been one of the eminent technologies in recent years and WSN model has played the parallel role into it. These sensors have a limited power supply and this affects the efficiency of the algorithm. In the past, several methods have been proposed for efficient clustering, however, these methods fail to provide satisfactory accuracy. Hence, this article proposes an energy efficient methodology named modified multi-hop clustering (MMHC). This methodology takes three steps. The first step is assembling the nodes where the nodes are assembled, the second step couples the nodes, and the third step detects the redundant nodes and discard them. The main advantage of MMHC is that it can select multiple redundant nodes at one time and discard, and this makes the algorithm more fast and efficient. Moreover, in order to evaluate the algorithm several parameters have been considered, such as energy consumption, number of failed nodes, number of active nodes, and communication overhead.

scholarly journals Energy efficient clustering and routing optimization model for maximizing lifetime of wireless sensor network

2020 ◽  
Vol 10 (5) ◽  
pp. 4798
Author(s):  
Savitha S. ◽  
S. C. Lingareddy ◽  
Sanjay Chitnis
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Sensor Network ◽  
Heterogeneous Networks ◽  
Energy Efficient ◽  
Intelligent Transportation ◽  
Wireless Sensor ◽  
Communication Overhead ◽  
Routing Optimization ◽  
Energy Efficient Clustering

Recently, the wide adoption of WSNs (Wireless-Sensor-Networks) is been seen for provision non-real time and real-time application services such as intelligent transportation and health care monitoring, intelligent transportation etc. Provisioning these services requires energy-efficient WSN. The clustering technique is an efficient mechanism that plays a main role in reducing the energy consumption of WSN. However, the existing model is designed considering reducing energy- consumption of the sensor-device for the homogenous network. However, it incurs energy-overhead (EO) between cluster-head (CH). Further, maximizing coverage time is not considered by the existing clustering approach considering heterogeneous networks affecting lifetime performance. In order to overcome these research challenges, this work presents an energy efficient clustering and routing optimization (EECRO) model adopting cross-layer design for heterogeneous networks. The EECRO uses channel gain information from the physical layer and TDMA based communication is adopted for communication among both intra-cluster and inter-cluster communication. Further, clustering and routing optimization are presented to bring a good trade-off among minimizing the energy of CH, enhancing coverage time and maximizing the lifetime of sensor-network (SN). The experiments are conducted to estimate the performance of EECRO over the existing model. The significant-performance is attained by EECRO over the existing model in terms of minimizing routing and communication overhead and maximizing the lifetime of WSNs.

Energy Efficient Clustering and Routing Algorithm for WSN

2019 ◽  
Vol 12 ◽  
Author(s):  
Mohit Kumar ◽  
Sonu Mittal ◽  
Md. Amir Khusru Akhtar
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Hot Spot ◽  
Routing Algorithm ◽  
Relay Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Clustering ◽  
Hot Spot Problem

Background: This paper presents a novel Energy Efficient Clustering and Routing Algorithm (EECRA) for WSN. It is a clustering-based algorithm that minimizes energy dissipation in wireless sensor networks. The proposed algorithm takes into consideration energy conservation of the nodes through its inherent architecture and load balancing technique. In the proposed algorithm the role of inter-cluster transmission is not performed by gateways instead a chosen member node of respective cluster is responsible for data forwarding to another cluster or directly to the sink. Our algorithm eases out the load of the gateways by distributing the transmission load among chosen sensor node which acts as a relay node for inter-cluster communication for that round. Grievous simulations show that EECRA is better than PBCA and other algorithms in terms of energy consumption per round and network lifetime. Objective: The objective of this research lies in its inherent architecture and load balancing technique. The sole purpose of this clustering-based algorithm is that it minimizes energy dissipation in wireless sensor networks. Method: This algorithm is tested with 100 sensor nodes and 10 gateways deployed in the target area of 300m × 300m. The round assumed in this simulation is same as in LEACH. The performance metrics used for comparisons are (a) network lifetime of gateways and (b) energy consumption per round by gateways. Our algorithm gives superior result compared to LBC, EELBCA and PBCA. Fig 6 and Fig 7 shows the comparison between the algorithms. Results: The simulation was performed on MATLAB version R2012b. The performance of EECRA is compared with some existing algorithms like PBCA, EELBCA and LBCA. The comparative analysis shows that the proposed algorithm outperforms the other existing algorithms in terms of network lifetime and energy consumption. Conclusion: The novelty of this algorithm lies in the fact that the gateways are not responsible for inter-cluster forwarding, instead some sensor nodes are chosen in every cluster based on some cost function and they act as a relay node for data forwarding. Note the algorithm does not address the hot-spot problem. Our next endeavor will be to design an algorithm with consideration of hot-spot problem.

EDMC: An Energy-Efficient Distributed Multi-Hop Clustering Approach for Wireless Sensor Networks

2012 ◽  
Vol 198-199 ◽  
pp. 1668-1671
Author(s):  
Zhu Guo Li ◽  
Bing Wen Wang ◽  
Li Zhu Feng
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Clustering Method ◽  
The Past ◽  
Decrease Energy Consumption ◽  
Potential Applications ◽  
Clustering Approach

The past few years have witnessed increasing focus on the potential applications of wireless sensor networks. Sensors in these networks are expected to be remotely dispersed in large number and to operate autonomously and unattended. Clustering is a widely used technique that can enhance scalability and decrease energy consumption over sensor networks. We present an energy-efficient distributed multi-hop clustering approach for sensor networks, which combined multi-hop transmission with clustering method, aiming to balance the energy dissipation and prolong the whole network lifetime. Simulations showed that the protocol proposed worked nearly 100% more efficient compared with LEACH and HEED.

An Energy-Efficient Clustering Algorithm Using Fuzzy C-Means and Genetic Fuzzy System for Wireless Sensor Network

2016 ◽  
Vol 26 (01) ◽  
pp. 1750004 ◽  
Author(s):  
Ayub Shokrollahi ◽  
Babak Mazloom-Nezhad Maybodi
Keyword(s):  
Energy Consumption ◽  
Fuzzy System ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Wireless Sensor ◽  
Cluster Center ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Genetic Fuzzy System ◽  
Energy Efficient Clustering

The energy efficiency in wireless sensor networks (WSNs) is a fundamental challenge. Cluster-based routing is an energy saving method in this type of networks. This paper presents an energy-efficient clustering algorithm based on fuzzy c-means algorithm and genetic fuzzy system (ECAFG). By using FCM algorithm, the clusters are formed, and then cluster heads (CHs) are selected utilizing GFS. The formed clusters will be remaining static but CHs are selected at the beginning of each round. FCM algorithm forms balanced clusters and distributes the consumed energy among them. Using static clusters also reduces the data overhead and consequently the energy consumption. In GFS, nodes energy, the distance from nodes to the base station and the distance from each node to its corresponding cluster center are considered as determining factors in CHs selection. Then, genetic algorithm is also used to obtain fuzzy if–then rules of GFS. Consequently, the system performance is improved and appropriate CHs can be selected, hence energy dissipation is reduced. The simulation results show that ECAFG, compared with the existing methods, significantly reduces the energy consumption of the sensor nodes, and prolongs the network lifetime.

scholarly journals WSN Clustering Based on EECI (Energy Efficient Clustering using Interconnection) Method

2019 ◽  
Vol 9 (1) ◽  
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Sensor Data ◽  
Main Technique ◽  
Energy Efficient Clustering ◽  
Selection Of

in WSN, clustering gives an effective way to enhance the network lifetime. Moreover It has been observed that the clustering algorithm utilizes the two main technique first is selection of cluster head and cycling it periodically in order to distribute the energy among the clusters and this in terms increases the lifetime of network. Another challenge comes with this is minimize the energy consumption. In past several algorithm has been proposed to increase the lifetime of the network and energy consumption, however these methodologies lacks from efficiency. In this paper, we have proposed a methodologies named as EE-CI (Energy Efficient Clustering using Interconnection), along with the random updation. Here the networks are parted into different clusters, the cluster updation are done based on the CHC scheme. Moreover, in proposed methodology cluster updation and data sample is determined through the change in sensor data. Here we propose a method for sampling sensor and CHC for selecting the cluster head to balance the energy and improvise the energy efficiency. Moreover, the proposed methodology is evaluated and the result is demonstrated by considering the Leach as existing methodology, experiments results shows that the proposed methodology outperforms the existing methodology.

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 Threshold Balanced Sampled DEEC Model for Heterogeneous Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Sercan Vançin ◽  
Ebubekir Erdem
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Distributed Energy ◽  
Clustering Method ◽  
Clustering Protocol ◽  
Energy Efficient Clustering

Due to the restricted hardware resources of the sensor nodes, modelling and designing energy efficient routing methods to increase the overall network lifetime have become one of the most significant strategies in wireless sensor networks (WSNs). Cluster-based heterogeneous routing protocols, a popular part of routing technology, have proven effective in management of topology, energy consumption, data collection or fusion, reliability, or stability in a distributed sensor network. In this article, an energy efficient three-level heterogeneous clustering method (DEEC) based distributed energy efficient clustering protocol named TBSDEEC (Threshold balanced sampled DEEC) is proposed. Contrary to most other studies, this study considers the effect of the threshold balanced sampled in the energy consumption model. Our model is compared with the DEEC, EDEEC (Enhanced Distributed Energy Efficient Clustering Protocol), and EDDEEC (Enhanced Developed Distributed Energy Efficient Clustering Protocol) using MATLAB as two different scenarios based on quality metrics, including living nodes on the network, network efficiency, energy consumption, number of packets received by base station (BS), and average latency. After, our new method is compared with artificial bee colony optimization (ABCO) algorithm and energy harvesting WSN (EH-WSN) clustering method. Simulation results demonstrate that the proposed model is more efficient than the other protocols and significantly increases the sensor network lifetime.

scholarly journals Implementation of Energy HEED (ER-HEED) Protocol Using Super Cluster Head for WSN

2018 ◽  
Vol 7 (S1) ◽  
pp. 119-122
Author(s):  
G. Pattabirani ◽  
K. Selvakumar
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Head ◽  
Hybrid Energy ◽  
Almost All ◽  
Energy Efficient Clustering ◽  
Super Cluster Head

Wireless Sensor Network (WSN) is used in almost all applications in developing environment. This is due to their ability and easy implementation through several applications. The most important criteria in WSN are to minimize the energy consumption and improve the network lifetime. Clustering algorithms are considered as one of the effective way to improve the network lifetime in WSN. Hybrid, Energy-Efficient and Distributed (HEED) clustering approach uses energy-efficient clustering algorithm. This paper proposes an Enhanced Rotational HEED (ER-HEED) protocol using super cluster head for minimizing energy consumption and to improve the network lifetime. The proposed work is carried out in two stages, first stage, super cluster head is introduced. In second stage, the node with maximum threshold is chosen as a cluster head on rotation within in the cluster. The results show that the ER-HEED performs well when compared with HEED and LEACH.

scholarly journals CMOS: Efficient Clustered Data Monitoring in Sensor Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jun-Ki Min
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Data Transmission ◽  
Energy Efficient ◽  
Data Gathering ◽  
Synthetic Data ◽  
Sensor Nodes ◽  
Smart Sensors ◽  
User Query ◽  
Energy Efficient Clustering

Tiny and smart sensors enable applications that access a network of hundreds or thousands of sensors. Thus, recently, many researchers have paid attention to wireless sensor networks (WSNs). The limitation of energy is critical since most sensors are battery-powered and it is very difficult to replace batteries in cases that sensor networks are utilized outdoors. Data transmission between sensor nodes needs more energy than computation in a sensor node. In order to reduce the energy consumption of sensors, we present an approximate data gathering technique, called CMOS, based on the Kalman filter. The goal of CMOS is to efficiently obtain the sensor readings within a certain error bound. In our approach, spatially close sensors are grouped as a cluster. Since a cluster header generates approximate readings of member nodes, a user query can be answered efficiently using the cluster headers. In addition, we suggest an energy efficient clustering method to distribute the energy consumption of cluster headers. Our simulation results with synthetic data demonstrate the efficiency and accuracy of our proposed technique.

scholarly journals Energy-Efficient Clustering Multi-Hop Routing Protocol in a UWSN

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 627
Author(s):  
Nhat-Tien Nguyen ◽  
Thien T. T. Le ◽  
Huy-Hung Nguyen ◽  
Miroslav Voznak
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Depth Level ◽  
Large Numbers ◽  
Energy Efficient Clustering

Underwater wireless sensor networks are currently seeing broad research in various applications for human benefits. Large numbers of sensor nodes are being deployed in rivers and oceans to monitor the underwater environment. In the paper, we propose an energy-efficient clustering multi-hop routing protocol (EECMR) which can balance the energy consumption of these nodes and increase their network lifetime. The network area is divided into layers with regard to the depth level. The data sensed by the nodes are transmitted to a sink via a multi-hop routing path. The cluster head is selected according to the depth of the node and its residual energy. To transmit data from the node to the sink, the cluster head aggregates the data packet of all cluster members and then forwards them to the upper layer of the sink node. The simulation results show that EECMR is effective in terms of network lifetime and the nodes’ energy consumption.

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