Energy-efficient Clustering Method Research Articles

EELEACH Clustering Approach to Improve Energy Efficiency in WSN

Background: Wireless sensor network is self-organizing which consists of a large number of sensor nodes and one sink node according to recent patents. The most important characteristics of such a network are the restricted resources like battery power, consumption capacity and consumption range. Energy consumption is one of the important issues in the wireless sensor network and the challenge is to prolong the network lifespan. Objective: The objective of the proposed approach is to balance a consumption of energy at member node as well as head node of cluster during the data transmission stage and to improve energy efficiency and lifespan of the network. Methods: The aim of an energy efficient clustering method to deal with the homogenous distribution and deployment of tree structure is performed. The performance of network is enhanced by electing head node with data to the node with greater cluster rate and having lowest distance from sink node. The member node sends their data to the head node which forwards their data to the node with greater weight rate which is sent to the sink node in an energy balancing way. Results: A performance analysis of existing approach as LEACH and proposed approach as EELEACH is undertaken by considering different metrics such as energy consumption successful data delivery, throughput, routing overhead, packet delivery fraction and delay ratio. Conclusion: From result analysis, the proposed system as EELEACH shows successful data delivery, throughput, routing overhead, packet delivery fraction and delay ratio. Hence, the low energy consumption improved lifespan of the network and better data transfer rate.

Energy-efficient clustering method for wireless sensor networks using modified gravitational search algorithm

Past decades have witnessed the advancement of wireless sensor networks (WSNs) in both academic and industrial communities. Clustering is one of the most popular methods to increase the lifespan of WSNs. The optimal number of cluster heads and how to organize the clusters are the most important issues to be addressed in the clustering methods. In this paper, we proposed a novel user-independent and dynamical method to calculate the optimal number of clusters, organize the clusters, and determine the best cluster heads in each round. In this method, efficient energy consumption and link quality were considered to compute the optimal number of clusters. Then, the algorithm began to organize the compact clusters with high energy level cluster heads. We investigated a new fitness function in order to achieve these objectives. A new version of gravitational search algorithm (GSA) was used to solve this optimization problem. In this algorithm, the power distance sums scaling method was applied to calculate the mass values. Then, a fuzzy logic controller is employed to identify the parameter of this algorithm to control the exploitation and exploration abilities of the method during the computational process of the algorithm. Then, the novel version of GSA was applied to reach an appropriate solution for the fitness function, find the optimal number of clusters, and properly organize these clusters. To evaluate the effectiveness of the proposed method, several experiments were performed and the obtained results were compared with the results of other popular clustering methods. The simulation results revealed that the performance of the modified GSA was better than other state-of-the-art meta-heuristic optimization algorithms. Moreover, the proposed method for the clustering problem in WSNs outperformed other popular clustering methods and increased the lifetime of WSNs.

Dynamicity of the scout bee phase for an Artificial Bee Colony for optimized cluster head and network parameters for energy efficient sensor routing

Data transmitted to the base station from the sensor node by selecting an optimal cluster head is a massive challenge subjected to the routing protocol, in the case of the wireless sensor network. An energy efficient clustering method, depending on the Artificial Bee Colony (ABC) algorithm and the Fractional Artificial Bee Colony (FABC) algorithm, has a propensity to maximize the energy of the network and life time of nodes by the optimal cluster head selection (CHS). When conveying data, an ABC-Dynamic Scout bee algorithm is presented that multiplies the scout bee production to enlarge the number of alive nodes and the of CH energy. An assessment among the performances of the implemented ABC-based Dynamic Scout bee algorithm routing mechanism in opposition to that of ABC-based CHS and FABC-based CHS routing is done. The experimental outcome demonstrates that the implemented method increases the quantity of alive nodes with 25% of maximum energy for the normalized network compared with conventional protocols.

Novel PEECR-based clustering routing approach

To solve the problem of unreasonable distribution of cluster heads in wireless sensor network (WSN) clustering routing methods, as well as to optimize data transferring between clusters, the novel predictive efficient energy consumption reclaim (PEECR)-based clustering routing approach for wireless sensor network has been presented in this paper. On how to select cluster head, a new energy-efficient clustering method based on degree, relative distance and residual energy of nodes is designed, which guarantees that distribution of cluster heads is uniform, scale of clusters is balanced, and nodes with high energy act as cluster head preferentially. By using swarm colony optimization idea, we design PEECR strategy for data transferring. A predictive data transferring strategy between clusters is designed. Taking expected energy consumption on a road, hops and propagation delay into account, definition of good ratio of a road is given, according to which we choose optimal road. In reception mode, energy consumption of transceivers from source node to sink node of wireless sensor network is similar to energy consumption from sink node to source node, and both can be minimized by our approach. Our experimental tests show that novel optimized clustering routing approach can improve quality of clusters and whole performance of network, reduce and balance energy consumption of the whole network, and prolong lifetime of WSN as well.

Energy-Efficient Clustering Using Correlation and Random Update Based on Data Change Rate for Wireless Sensor Networks

This paper presents an energy-efficient clustering method using random update (EECRU) for wireless sensor networks. We divide the network into preliminary clusters depending on the temporal and spatial correlation of sensor data. These clusters are then updated by applying a dynamic update policy and a cluster head rotation scheme, where the change of sensor data and the energy residue of sensors are taken into consideration to achieve high energy efficiency and balance. The larger the change rate of the sensor data in a cluster, the higher its update frequency. A sensor node that has a high data frequency is more likely to be selected as the head of its cluster. In the data transmission phase, a sampling rate control method is adopted to improve the efficiency of data sampling. The experimental results indicate that the proposed EECRU method achieves high energy efficiency and energy balance.

CMOS: Efficient Clustered Data Monitoring in Sensor Networks

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.

WSNs clustering based on semantic neighborhood relationships

We propose a semantic clustering model based on a fuzzy inference system to find out the semantic neighborhood relationships in wireless sensor networks in order to both reduce energy consumption and improve the data accuracy. As a case study we describe a structural health monitoring application which was used to illustrate and assess the proposed model. We conduct experiments in order to evaluate the proposal in two different scenarios of damage with different data aggregation methods. We also compared our proposal, using the same data set, with a deterministic clustering method and with the LEACH algorithm. The results indicate that our approach is an energy-efficient clustering method for WSNs, outperforming both the deterministic clustering and LEACH algorithms in about 70% and 47% of energy savings respectively. The energy saving comes from the fact that we have a more efficient in-network data aggregation process since by exploiting the semantic relation between sensor nodes we can potentially aggregate more similar data and consequently, decrease the data redundancy (thus minimizing transmissions). Nodes that are semantically unrelated can operate in low-duty cycle, further reducing the energy consumption. Moreover, our proposal has the potential to improve the data accuracy provided for the application where accuracy is a QoS requirement in typical WSN applications.