An Energy-Balanced Cluster-Based Protocol for Wireless Sensor Networks

An Energy-Balanced Cluster-Based Protocol for Wireless Sensor Networks

Eyad Taqieddin (Department of Network Engineering and Security, Jordan University of Science and Technology, Irbid, Jordan), Moad Mowafi (Department of Network Engineering and Security, Jordan University of Science and Technology, Irbid, Jordan), Fahed Awad (Department of Network Engineering and Security, Jordan University of Science and Technology, Irbid, Jordan), Omar Banimelhem (Department of Network Engineering and Security, Jordan University of Science and Technology, Irbid, Jordan) and Hani Maher (Department of Computer Engineering, Jordan University of Science and Technology, Irbid, Jordan)
DOI: 10.4018/ijitwe.2013070104


This paper proposes a novel energy-efficient clustering protocol for wireless sensor networks. It combines the benefits of using the k-means clustering algorithm with the, recently developed, LEACH with virtual forces (LEACH-VF) protocol. In this work, the k-means algorithm is employed to determine k centroids around which the clusters will be formed. After that, the virtual field force method is applied to these clusters to determine the most suitable positions for each node. The main target of such an approach is to improve the energy balance in the network and to extend the network lifetime. Simulation results show that the proposed protocol extends the time before the first node death, minimizes the variance of the average node energy, and reduces the distance that the sensor nodes travel within their respective clusters.
Article Preview

1. Introduction

The numerous advances in the field of Wireless Sensor Networks (WSN) have opened the door for their use in daily tasks. The main target applications of WSNs were, initially, in the fields of military surveillance and tracking, habitat monitoring, and collecting environmental readings, such as temperature, humidity, and pressure. However, this is no longer the case since the use of WSNs is expanding into different fields such as search and rescue operations, and land mine detection/extraction using robots. Moreover, the use of single or multiple sensors connected to smart phones is being investigated in different areas such as telemedicine applications to monitor a patient’s vital signs (Petr, 2012).

Regardless of the application, efficient use of the limited available energy reserves in the sensor nodes is essential. Due to its energy-efficiency and scalability, the hierarchical architecture has proven to be the most efficient for WSNs (Abbasi & Younis, 2007; Boyinbode et al., 2010). Hence, node clustering is the most commonly used approach, in which each subset of nodes are grouped together in a cluster and only one of them, called the Cluster Head (CH), is responsible of communicating with the Base Station (BS). All other nodes in the cluster need only to communicate with the CH. This leads to an improvement in the energy management since the distance between the sensor nodes and the CH is less than that towards the BS. An example of a clustering protocol for static WSNs is the LEACH protocol (Heinzelman et al., 2000).

LEACH organizes the network into clusters, such that each cluster is managed by a CH in charge of collecting/aggregating the data from the nodes and then relaying them to the BS. Each node in the cluster assumes the role of the CH for a certain period of time, called a round, before switching back to a non-CH role. This is done to guarantee a balanced consumption of the nodes’ energy. It has been shown through simulation that LEACH results in a better network lifetime when compared with direct transmission and minimum path transmission ‎(Heinzelman et al., 2000).

Network lifetime is either defined as the period of time from the beginning of the network operation until the first node dies or until the last node consumes all its available energy (Ali & Sevgi, 2012). The use of either definition depends on the application at hand. For example, a border surveillance application would require all the nodes to be operational and the system would be rendered useless if at least one node was out. On the other hand, the loss of a single node in a temperature sensing application would not be harmful since the reading may be approximated using spatial correlation.

Ali and Sevgi (2012) made the case that losing at least one node in the network will cause network instability and loss of coverage. Thus, by adopting the first definition of network lifetime, it becomes essential to reach a reasonable level of load balancing between all the nodes, especially that the energy consumption levels will vary between CH and non-CH nodes. Such load balancing should guarantee that the nodes would die around the same time. Figure 1 depicts the network lifetime for the ideal case of balanced energy consumption versus the non-balanced case. The ideal case is not achievable but it is desirable to get a lifetime behavior as close as possible to it.

Figure 1.

Lifetime curves for the balanced (ideal) and non-balanced network lifetime

The work of Awad et al. (2012) and Seyam et al. (2012) highlighted several deficiencies in the LEACH protocol and proposed a new protocol, LEACH with Virtual Force (LEACH-VF), to improve the network lifetime by better positioning the nodes within the cluster. The main idea in LEACH-VF is to use mobile sensor nodes that can be redistributed within the cluster such that their distance to the CH is minimal while maintaining the sensing coverage of the network (i.e.; reducing the overlap between the sensing coverage of the individual nodes). LEACH-VF was shown to be superior to the LEACH protocol in terms of the network lifetime and the average remaining energy per node per round.

Complete Article List

Search this Journal:
Open Access Articles: Forthcoming
Volume 14: 4 Issues (2019): Forthcoming, Available for Pre-Order
Volume 13: 4 Issues (2018): 2 Released, 2 Forthcoming
Volume 12: 4 Issues (2017)
Volume 11: 4 Issues (2016)
Volume 10: 4 Issues (2015)
Volume 9: 4 Issues (2014)
Volume 8: 4 Issues (2013)
Volume 7: 4 Issues (2012)
Volume 6: 4 Issues (2011)
Volume 5: 4 Issues (2010)
Volume 4: 4 Issues (2009)
Volume 3: 4 Issues (2008)
Volume 2: 4 Issues (2007)
Volume 1: 4 Issues (2006)
View Complete Journal Contents Listing