Energy Load Balancing for Fixed Clustering in Wireless Sensor Networks (original) (raw)
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A cluster based energy balancing strategy to improve Wireless Sensor Networks lifetime
2007 International Conference on Industrial and Information Systems, 2007
Wireless Sensor Networks (WSNs) have been of interest due to its many areas of application. The lifetime of the sensor bed is of crucial importance for the successful deployment of such networks. Many algorithms have been proposed based on clustering and cluster head (CH) rotation to improve the lifetime of WSNs. EDCR [1] and EDCR-MH [2] are two such algorithms that have shown promising results. The lifetime of the entire sensor bed can be significantly improved if the energy associated with a sensor node is depleted at the same rate irrespective of its location with respect to the Base Station (BS). This research proposes a new algorithm for CH selection and rotation that incorporates the desirable features of both EDCR and EDCR-MH together with a modified minimum distance communication between CH and BS. The algorithm is capable of balancing the energy depletion rate of sensor nodes across the entire sensor bed irrespective of their node locations. Unlike previous algorithms, the proposed algorithm can be applied to significantly larger sensor beds while maintaining the lifetime performance. Analytical models are presented to identify salient parameters of the proposed algorithm. Simulation results are provided to illustrate the applicability of the algorithm. The results indicate that the lifetime of the entire sensor bed is improved over existing algorithms.
The objective of this paper is to develop a mechanism to increase the lifetime of homogeneous wireless sensor networks (WSNs) through minimizing long range communication, efficient data delivery and energy balancing. Energy efficiency is a very important issue for sensor nodes which affects the lifetime of sensor networks. To achieve energy balancing and maximizing network lifetime we divided the whole network into different clusters. In cluster based architecture, the role of aggregator node is very crucial because of extra processing and long range communication. Once the aggregator node becomes non functional, it affects the whole cluster. We introduced a candidate cluster head node on the basis of node density. We proposed a modified cluster based WSN architecture by introducing a server node (SN) that is rich in terms of resources. This server node (SN) takes the responsibility of transmitting data to the base station over longer distances from the cluster head. We proposed clu...
2007
This paper proposes a fixed clustering algorithm (FCA) to improve energy efficiency for wireless sensor networks (WSNs). In order to reduce the consuming energy of sending data at each sensor, the proposed algorithm uniformly divides the sensing area into clusters where the cluster head is deployed in the center of the cluster area. Simulation results show that the proposed algorithm definitely reduces the energy consumption of the sensors and extends the lifetime of the networks nearly more 80% compared to the random clustering (RC).
A Framework for Energy-Efficient Clustering With Utilizing Wireless Energy Balancer
IEEE Access, 2021
Clustering has been accepted as one of the most efficient techniques for conserving the energy of wireless sensor networks (WSNs). In cluster-based WSN, the cluster heads (CH) consume more energy compared with normal nodes, which results in rising their energy consumption and reducing the network lifetime. There are several energy-efficient routing schemes which have been presented to reduce the energy consumption of CHs. In such schemes, further clustering of CHs will result in reducing the energy consumption of CHs. However, this rule has not been considered in previous related works. Likewise, unbalanced energy consumption of CHs is one the main design challenges which leads to increase the wasted energy and premature network death. A number of recent studies have sought to improve the balanced energy via utilizing devices equipped with large and expensive energy harvesters, which leads to extra cost Therefore, a feasible solution is needed to achieve balanced energy consumption of CHs. Consequently, to overcome the aforementioned limitations, we proposed a framework for energy-efficient clustering by utilizing a wireless energy balancer. Firstly, an n-level clustering is presented which results in fully leveraging and reducing the energy consumption of CHs. Secondly, an energy balancer is utilized in order to reduce the wasted energy to the fullest possible extent by zeroing the variance between the remaining energy of CHs. The performance of the proposed scheme has been compared against CMS2TO and DGOB mechanisms. Simulation results show that the proposed scheme achieved improved performance in terms of network lifetime by 20%, total energy consumption by 52%, network overhead by 20%, computation time by 46%, and wasted energy by 86% compared to existing schemes. In conclusion, the proposed framework proves to be a viable solution for enhancing the network lifetime and energy efficiency. INDEX TERMS Wireless sensor network, clustering, energy balancer, energy efficiency.
IEEE Communications Letters, 2015
A suitable clustering algorithm for grouping sensor nodes can increase the energy efficiency of WSNs. However, clustering requires additional overhead, such as cluster head selection and assignment, and cluster construction. This paper proposes a new regional energy aware clustering method using isolated nodes for WSNs, called Regional Energy Aware Clustering with Isolated Nodes (REAC-IN). In REAC-IN, CHs are selected based on weight. Weight is determined according to the residual energy of each sensor and the regional average energy of all sensors in each cluster. Improperly designed distributed clustering algorithms can cause nodes to become isolated from CHs. Such isolated nodes communicate with the sink by consuming excess amount of energy. To prolong network lifetime, the regional average energy and the distance between sensors and the sink are used to determine whether the isolated node sends its data to a CH node in the previous round or to the sink. The simulation results of the current study revealed that REAC-IN outperforms other clustering algorithms.
Energy efficient clustering algorithm for maximizing lifetime of wireless sensor networks
AEU - International Journal of Electronics and Communications, 2010
The sensor nodes deployed in wireless sensor networks are extremely power constrained, so maximizing the lifetime of the entire networks is mainly considered in the design. An energy efficient clustering algorithm with optimum parameters is designed for reducing the energy consumption and prolonging the system lifetime. An analytical clustering model with one-hop distance and clustering angle is given. The optimum one-hop distance and clustering angle are formulated by minimizing the energy consumption between inter-cluster and intra-cluster. Furthermore, the continuous working mechanism of each cluster head which acts as the local control center and will not be replaced by the candidate cluster head until its continuous working times reach the optimum values is given, and the optimum continuous working times of each cluster head can be obtained through the optimum one-hop distance and the clustering angle. With the mechanism, the frequency of updating cluster head and the energy consumption for establishing new cluster head can be reduced. The simulation results demonstrate that the clustering algorithm can effectively reduce the energy consumption and increase the system lifetime.
An Energy Efficient Clustering Scheme in Wireless Sensor Networks
Ad Hoc & Sensor Wireless Networks, 2007
Data gathering is a common but critical operation in many applications of wireless sensor networks. Innovative techniques that improve energy efficiency to prolong the network lifetime are highly required. Clustering is an effective topology control approach in wireless sensor networks, which can increase network scalability and lifetime. In this paper, we propose a novel energy efficient clustering scheme (EECS) for single-hop wireless sensor networks, which better suits the periodical data gathering applications. Our approach elects cluster heads with more residual energy in an autonomous manner through local radio communication with no iteration while achieving good cluster head distribution; further more, it introduces a novel distance-based method to balance the load among the cluster heads. Simulation results show that EECS prolongs the network lifetime significantly against the other clustering protocols such as LEACH and HEED.
We propose details of a novel clustering scheme which combines distributed clustering considering local node density and centralized clustering considering residual energy of sensor nodes. Our scheme dynamically adjusts to a recommended numbers of clusters and cluster size. Differing to existing clustering schemes, our scheme deploys a base station that selects proper cluster heads based on their residual energy. Our scheme greatly extends network lifetime as compare to existing schemes.
International Journal of Research and Analytical Reviews (IJRAR), 2018
The field of Wireless sensor networks is the main attraction of various researchers due to its practical applications in daily life. It comprises of sensor nodes geographically distributed over an area, which sense the environmental conditions like temperature, humidity etc and pass this information to the sink .As these nodes are non-rechargeable means energy constrained, so main focus of the researches in this field is to make it energy efficient as par as possible and prolong its network lifetime .Clustering is one of the hierarchical routing technique which is used to make the network energy efficient. It can be further improved by adopting non-uniform cluster size techniques and sub-clustering in farther as well as larger clusters. Cluster heads of distant clusters will need to communicate with only sub-cluster heads which further covers the entire cluster members. This proposed model effectively improves lifetime over LEACH. Keywords-Wireless Sensor Networks, effective lifetime, sub-clustering, cluster head and residual energy. I. INTRODUCTION As sensor nodes have limited and non-rechargeable energy resources, energy is a very scarce resource and has to be managed carefully in order to extend the lifetime of the sensor networks [1]. In recent years, researchers have done a lot of studies and proved that clustering is an effective scheme in increasing the scalability and lifetime of wireless sensor networks [2-5]. In clustering schemes, there are two kinds of nodes in one cluster, one cluster head (CH) and several cluster members (CMs). Cluster members gather data from the environment periodically and send the data to cluster heads. Cluster heads aggregate the data from their cluster members, and send the aggregated data to the base station (BS). There are two kinds of communications between cluster heads and the BS, single-hop communication and multi-hop communication. In multi-hop communication clustering algorithms, the energy consumption of cluster heads consists of the energy for receiving, aggregating and sending the data from their cluster members (intra-cluster energy consumption) and the energy for forwarding data for their neighbor cluster heads (inter-cluster energy consumption) [1]. Cluster-based communication protocols have significant savings in total energy consumption of a sensor network. In these protocols, creation of clusters and assigning special tasks to cluster heads can greatly contribute to overall system scalability, lifetime, and bandwidth efficiency [6].Clustering reduces the energy dissipation in the network by aggregating the messages of cluster members ,thus reducing the number of messages being transferred to the sink. Moreover, the size of the clusters must be optimum as exploiting both small and large clusters would make the sensor networks energy inefficient. When the cluster size is very large (e.g. one cluster of whole network), the nodes have to transmit data very far to the cluster head, consuming more energy. And when the cluster size is very small (e.g. one node in each cluster) the number of messages to be transferred to the sink increases, so the energy saving by aggregation would be reduced, thus resulting in more dissipation of network energy. [6] Clustering is grouping of sensor nodes. Here, each cluster is managed by a special node or leader, called cluster head (CH), which is responsible for coordinating the data transmission activities of all sensors in its group.CH is decided with a different probability [7,8].The selection of cluster head in the clusters contribute a lot to the overall efficiency. In clustering networks, the imbalanced energy consumption among nodes is the key factor affecting the network lifetime. In order to balance the energy consumption among nodes, clustering algorithms for networks with uniform node distribution tend to construct uniformly distributed cluster heads, so that the clusters have the approximate number of members and coverage areas. Thus, the intra-cluster energy consumption of cluster heads is approximate and the energy consumption of cluster heads can be balanced. For cluster members, the maximum communicate distances of cluster members are approximate, because of the uniform cluster sizes. Thus, the energy consumption of cluster members can be balanced too. Therefore, the uniformly distributed cluster head set can balance the energy consumption among nodes and finally prolong the network lifetime [1]. Leach is the very basic protocol used for uniformly distributed nodes. It is simple and does not require a large communication overhead. But its performance in heterogeneous networks is not very well; because it elects cluster heads without considering the residual energy of the nodes .To solve this problem. Researchers improved LEACH and proposed new algorithms [1, 9-11]. Also, LEACH has a disadvantage of non-uniform energy consumption by the cluster heads, so they dissipate their energy quickly. Its proposed solution is non uniform clustering.