Dynamic Cluster Head Selection Research Articles

Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks

With the rapid growth of Internet-of-things (IoT) devices, IoT communication has become an increasingly crucial part of 5G wireless communication systems. The large-scale IoT devices access results in system overload and low utilization of energy efficiency under the existing network framework. In this paper, the cluster head uses the LTE-M protocol, and the intra-cluster uses the low-power wide-area network (LPWAN) self-networking protocol in the wireless sensor network. By a detailed analysis of the messages exchanged between the device and the base station, we describe the causes of overload and the steps of data aggregate combined with the physical channel. Then, we explore the cluster head quantity and the optimal scale in the intra-cluster under the traditional K-mean algorithm. When K is 30 under specific resources, the simulation results show that the system’s access success probability and resource utilization are optimal. Also, we propose a distributed dynamic cluster-head selection and clustering scheme based on an improved K-means algorithm. Simulation results show that the proposed scheme can reach 88.07% on the access success probability. The throughput and resource utilization are 3.5 times high than that of the optimal K-means.

Energy-Efficient Harvested-Aware Clustering and Cooperative Routing Protocol for WBAN (E-HARP)

Wireless body area network (WBAN) is an interconnection of small bio-sensor nodes (BSNs) that are deployed in/on different parts of human body. It is used to sense health-related data, such as rate of heart beat, blood pressure, blood glucose level, electro-cardiogram (ECG), and electro-myography (EMG), of human body and pass these readings to real-time health monitoring systems. WBANs are the important research area and are used in different applications, such as medical field, sports, entertainment, and social welfare. BSNs or simply called sensor nodes (SNs) are the main backbone of WBANs. SNs normally have very limited resources due to its smaller size. Therefore, minimum consumption of energy is an essential design requirement of the WBAN schemes. In the proposed work, Energy-efficient Harvested-Aware clustering and cooperative Routing Protocol for WBAN (E-HARP) are presented. The presented protocol mainly proposes a novel multi-attribute-based technique for dynamic cluster head (CH) selection and cooperative routing. In the first phase of this two-phased technique, optimum CH is selected among the cluster members, based on calculated cost factor (CF). The parameters used for calculation of CF are residual energy of SN, required transmission power, communication link signal-to-noise ratio (SNR), and total network energy loss. In order to distribute load on one CH, E-HARP selects new CH in each data transmission round. In the second phase of E-HARP, data are routed with cooperative effort of the SN, which saves the node energy by prohibiting the transmission of redundant data packets. To evaluate the performance of the proposed technique, comprehensive experimentations using the NS-2 simulation tool has been conducted. The results are compared with some latest techniques, named EH-RCB, ELR-W, Co-LAEEBA, and EECBSR. The acquired results show a significant enhancement of E-HARP in terms of network stability, network life time, throughput, end-to-end delay, and packet delivery ratio.

Dynamic Cluster Head Selection Mechanism with Wireless Sensor Networks for Irrigation Application

Dynamic Cluster Head Selection Mechanism with Wireless Sensor Networks for Irrigation Application

Energy Efficient Reservation-Based Cluster Head Selection in WSNs

Due to the type of applications, wireless sensor nodes must always be inexpensive and small. Hence, the presence of constraints such as the limitation of energy resource is inevitable. So far, several studies have been carried out in order to present solutions for the reduction of energy consumption. In the meantime, clustering is given prime significance as an efficient method, which means partitioning network into distinct areas and is a way for managing nodes communication. In clustering algorithms, although the continuous execution of clustering phase and dynamic cluster head selection lead to energy consumption parity, they cause considerable energy dissipation due to the need for message transmitting to set new clusters and cluster heads. In this paper, the effect of using reservation to reduce message transmitting and energy dissipation has been studied. Reservation is the mechanism by the aid of which the number of communicated messages for the regular execution of clustering phase and cluster head selection can be reduced. The results of analysis and simulation show that the proposed method has significant impact on energy dissipation reduction.

Dynamic Clustering and Cluster Head Selection for Energy Optimization under Wireless Sensor Network

Wireless sensor network is a field of networking that has been used for sensing information from environment. In WSN the sensor nodes are attached to a battery for sensing information. Each node utilizes three types of energy during its lifetime over the network. These energies are sensing energy, transmission or receiving energy and idle energy. During the sensing information the nodes consumes energy and transmission energy is used to transmit a data over a distance. Idle energy is that when node is not working but remains in on state. Due to deployment of WSN in unreachable area energy is main constraint for network to be cost effective.The major issue is network lifetime that must be increase so that network performs for long duration of time and provide cost effective for an n organization. To overcome this issue various methods had been proposed, chaining, pegasis, clustering and chain head selection are one of these methods.

Energy-Efficient Data Collection Scheme for Environmental Quality Management in Buildings

How to efficiently collect sensory data for supporting energy-efficient operation of buildings has become a great challenge, especially for large-scale networks in buildings. In this paper, a spatio-temporal compression-based optimized clustering scheme is proposed for energy-efficient environmental data collection in buildings. In the scheme, first, according to the establishment of a cluster, an adaptive dynamic cluster head selection method for prolonging the lifetime of sensory nodes in buildings is developed. Meanwhile, to further reduce energy consumption, we construct the dynamic optimal quantity of the cluster heads solution method to achieve minimum energy consumption. Moreover, the spatio-temporal correlations of sensory data are explored in the data sampling and transmission processes, which can decrease the amount of data transmission and further extend the lifetime of the entire data collection network. The proposed scheme provides sensing data with high quality for environmental quality management in buildings and supports energy-efficient building operation. Finally, the simulation results show that the proposed scheme obtains obvious advantages in energy consumption compared with other related schemes, and the lifetime of the proposed scheme is also longer than others when it maintains high reconstruction precision.

Achieving energy efficient wireless sensor network by choosing effective cluster head

A wireless sensor network encompass of tiny, constrained battery powered sensor nodes. Every node is able to observe the substantial surroundings and send it to the sink either in single hop or multihop fashion. In the network, the nodes which is nearer to the sink will lose their liveliness soon. Clustering is a key method for reducing the power utilization and also the life time of the network is increased at the same time. In this method, entire network is separated as a set of clusters. Each cluster consists of one cluster head (CH) and non cluster member nodes from each cluster will transfer the sensed data to the CH. This will take care of forwarding the information to the end user. Enhanced dynamic cluster head selection method (EDCHSM) are advanced to take care of the issue of unreasonable cluster head selection that leads to the overlapping coverage and unbalanced energy consumption inside the cluster communication. Experimental outputs clearly shows the proposed method balance the sensor node energy in two phases as compared with the existing algorithms like stable election protocol (SEP), distributed energy efficient clustering (DEEC), dynamic cluster head selection method (DCHSM).

Comparative Analysis of Route Information Based Enhanced Divide and Rule Strategy in WSNs

In wireless sensor network, routing data efficiently to the base station is a big issue and for this purpose, a number of routing algorithms are invented by researchers. Clustering plays a very important role in the design and as well as development of wireless sensor networks for well distribution of network and also to route data efficiently. In this paper, we had done the enhancement of divide and rule strategy that is basically route information protocol based upon static clustering and dynamic cluster head selection. Simulation results show that our technique outperforms DR, LEACH, and AODV on the basis of packet loss, delay, and throughput.

Comparative Analysis of Route Information Based Enhanced Divide and Rule Strategy in WSNs

In wireless sensor network, routing data efficiently to the base station is a big issue and for this purpose, a number of routing algorithms are invented by researchers. Clustering plays a very important role in the design and as well as development of wireless sensor networks for well distribution of network and also to route data efficiently. In this paper, we had done the enhancement of divide and rule strategy that is basically route information protocol based upon static clustering and dynamic cluster head selection. Simulation results show that our technique outperforms DR, LEACH, and AODV on the basis of packet loss, delay, and throughput.

Dynamic Cluster Head Selection Method for Wireless Sensor Network

In view of the wireless sensor network clustering algorithm at home and abroad, the dynamic cluster head selection methods for a wireless sensor network are put forward in order to solve the problem of the unreasonable cluster head selection that may lead to the overlapping coverage and unbalanced energy consumption in the cluster communication. Experimental results show that this method balances the network node energy in two phases compared with the existed algorithms. The network lifetime is increased by 50%, higher than that of low energy adaptive clustering hierarchy (LEACH), and increased by 30%, higher than that of distribute energy-efficient clustering algorithm (DEEC); also the survival time of the network is longer than that of energy-balanced deterministic clustering algorithm and adaptive energy optimized clustering algorithm, achieving the effectiveness of the network energy consumption, and it has the longest network lifetime.

Lifetime Enhancement of WSN using Energy-Balanced Distributed Clustering Algorithm with Honey Bee Optimization

Wireless sensor network consists of sensor nodes with sensing and communication capabilities. Typical communication model of wireless sensor network consists of several sensor nodes as group members, sensor head and sinks. Past few years have witnessed increased in the potential use of Wireless Sensor Network (WSN) such as military surveillance, tracking and monitoring, disaster management and combat field reconnaissance. Many issues in WSN are formulated as multidimensional optimization problem and solved through energy based distributed clustering techniques [1]. The main problem in WSN is the lifetime of network. To support scalability, nodes are often grouped in clusters having a leader, often referred as Sensor Head (SH). A SH is responsible for not only sending data to base station but also assists the general nodes to send sensed data to target nodes. The energy consumption of SH is greater than general nodes. Therefore SH selection will affect the lifetime of WSN and also support packet loss. This proposed approach is based on Energy-Balanced Distributed Clustering (EBDC) [2–3] algorithm. Honey bee optimization with some parameters is employed over EBDC algorithm for effective dynamic cluster head selection. This approach helps in increasing the energy consumption. This proposed technique works in three stages: Cluster nodes send data to SH, SH sends data directly to leader and leader sends data to Base Station (BS). Sensor Head (SH) has responsibility to select neighbor node as a Sub Sensor Head (SSH) to reduce overload. Simulations results demonstrate that EBDC-HBO algorithm improves the life time of network.

A Novel Approach for Cluster Head Selection in Wireless Sensor Network

Wireless Sensor Network (WSN) is one of the emerging technological fields due to recent advancements and area of research. The WSN is collection of small sensor nodes, which run on battery and have very less power. WSN requires various protocols to transmit information from one node to other. This paper focuses on dynamic Cluster Head (CH) selection process in Low Energy Adaptive Cluster Hierarchy (LEAH) protocol. This paper also discusses problems in LEACH protocol and giving a possible solution for it.

Dynamic Cluster Head Selection Using Fuzzy Logic on Cloud in Wireless Sensor Networks

One of the most vital activities to reduce energy consumption in wireless sensor networks is clustering. In clustering, one node from a group of nodes is selected to be a cluster head, which handles majority of the computation and processing for the nodes in the cluster. This paper proposes an algorithm for fuzzy based dynamic cluster head selection on cloud in wireless sensor networks. The proposed algorithm calculates a Potential value for each node and selects cluster heads with high potential. The proposed algorithm minimizes cluster overlapping by spatial distribution of cluster heads and discards malicious nodes i.e. never allows malicious nodes to be cluster heads.

Secure and Fault Tolerant Dynamic Cluster Head Selection Method for Wireless Sensor Networks

In this paper we propose a dynamic cluster head selection technique, Secure and Fault Tolerant Dynamic Cluster Head (SFDCH) Selection algorithm. The proposed algorithm selects the nodes having the threshold value above the average. From the selected nodes, the node with maximum available energy, at a minimum distance and having maximum throughput is selected as the cluster head .The proposed method is dynamic in nature as selection process is refreshed periodically. The Proposed SFDCH is compared with existing K-means and K-sep methods using netsim simulator. The results prove the efficiency of our SFDCH in terms of accuracy, energy efficiency and enhancement of network lifetime over the existing methods.

Divide-and-Rule Scheme for Energy Efficient Routing in Wireless Sensor Networks

From energy conservation perspective in Wireless Sensor Networks (WSNs), clustering of sensor nodes is a challenging task. Clustering technique in routing protocols play a key role to prolong the stability period and lifetime of the network. In this paper, we propose and evaluate a new routing protocol for WSNs. Our protocol; Divide-and-Rule (DR) is based upon static clustering and dynamic Cluster Head (CH) selection technique. This technique selects fixed number of CHs in each round instead of probabilistic selection of CH. Simulation results show that DR protocol outperform its counterpart routing protocols.