A survey on clustering algorithms for wireless sensor networks
Introduction
Recent advances in miniaturization and low-power design have led to the development of small-sized battery-operated sensors that are capable of detecting ambient conditions such as temperature and sound. Sensors are generally equipped with data processing and communication capabilities. The sensing circuitry measures parameters from the environment surrounding the sensor and transforms them into an electric signal. Processing such a signal reveals some properties about objects located and/or events happening in the vicinity of the sensor. Each sensor has an onboard radio that can be used to send the collected data to interested parties. Such technological development has encouraged practitioners to envision aggregating the limited capabilities of the individual sensors in a large scale network that can operate unattended [1], [2], [3], [4], [5], [6], [7]. Numerous civil and military applications can be leveraged by networked sensors. A network of sensors can be employed to gather meteorological variables such as temperature and pressure. These measurements can be then used in preparing forecasts or detecting harsh natural phenomena. In disaster management situations such as earthquakes, sensor networks can be used to selectively map the affected regions directing emergency response units to survivors. In military situations (Fig. 1), sensor networks can be used in surveillance missions and can be used to detect moving targets, chemical gases, or the presence of micro-agents.
One of the advantages of wireless sensors networks (WSNs) is their ability to operate unattended in harsh environments in which contemporary human-in-the-loop monitoring schemes are risky, inefficient and sometimes infeasible. Therefore, sensors are expected to be deployed randomly in the area of interest by a relatively uncontrolled means, e.g. dropped by a helicopter, and to collectively form a network in an ad-hoc manner [8], [9]. Given the vast area to be covered, the short lifespan of the battery-operated sensors and the possibility of having damaged nodes during deployment, large population of sensors are expected in most WSNs applications. It is envisioned that hundreds or even thousands of sensor nodes will be involved. Designing and operating such large size network would require scalable architectural and management strategies. In addition, sensors in such environments are energy constrained and their batteries cannot be recharged. Therefore, designing energy-aware algorithms becomes an important factor for extending the lifetime of sensors. Other application centric design objectives, e.g. high fidelity target detection and classification, are also considered [10].
Grouping sensor nodes into clusters has been widely pursued by the research community in order to achieve the network scalability objective. Every cluster would have a leader, often referred to as the cluster-head (CH). Although many clustering algorithms have been proposed in the literature for ad-hoc networks [11], [12], [13], [14], [15], the objective was mainly to generate stable clusters in environments with mobile nodes. Many of such techniques care mostly about node reachability and route stability, without much concern about critical design goals of WSNs such as network longevity and coverage. Recently, a number of clustering algorithms have been specifically designed for WSNs [16], [17], [18], [19], [20]. These proposed clustering techniques widely vary depending on the node deployment and bootstrapping schemes, the pursued network architecture, the characteristics of the CH nodes and the network operation model. A CH may be elected by the sensors in a cluster or pre-assigned by the network designer. A CH may also be just one of the sensors or a node that is richer in resources. The cluster membership may be fixed or variable. CHs may form a second tier network or may just ship the data to interested parties, e.g. a base-station or a command center.
In addition to supporting network scalability, clustering has numerous advantages. It can localize the route set up within the cluster and thus reduce the size of the routing table stored at the individual node [21]. Clustering can also conserve communication bandwidth since it limits the scope of inter-cluster interactions to CHs and avoids redundant exchange of messages among sensor nodes [22]. Moreover, clustering can stabilize the network topology at the level of sensors and thus cuts on topology maintenance overhead. Sensors would care only for connecting with their CHs and would not be affected by changes at the level of inter-CH tier [23]. The CH can also implement optimized management strategies to further enhance the network operation and prolong the battery life of the individual sensors and the network lifetime [22]. A CH can schedule activities in the cluster so that nodes can switch to the low-power sleep mode most of the time and reduce the rate of energy consumption. Sensors can be engaged in a round-robin order and the time for their transmission and reception can be determined so that the sensors reties are avoided, redundancy in coverage can be limited and medium access collision is prevented [24], [25], [26], [27]. Furthermore, a CH can aggregate the data collected by the sensors in its cluster and thus decrease the number of relayed packets [28].
In this paper, we opt to categorize clustering algorithms proposed in the literature for WSNs. We report on the state of the research and summarize a collection of published schemes stating their features and shortcomings. We also compare the different approaches and analyze their applicability. In the next section, we discuss the different classifications of clustering techniques and enumerate a set of attributes for categorizing published algorithms. In Section 3, we summarize a collection of clustering algorithms for WSNs and present classification of the various approaches pursued. Finally, Section 4 concludes the paper.
Section snippets
Taxonomy of clustering attributes
Clustering techniques for WSNs proposed in the literature can be generally classified based on the overall network architectural and operation model and the objective of the node grouping process including the desired count and properties of the generated clusters. In this section we discuss the different classifications and present taxonomy of a clustering attributes. We later use such attributes to categorize and compare the surveyed clustering algorithms.
Clustering algorithms for WSNs
Generally, WSNs involve a large number of sensors ranging in the hundreds or even thousands. Clustering is an effective mean for managing such high population of nodes. In this section we present a literature survey of published distributed algorithms for clustering WSNs. Given that scalability is regarded as the main advantage of network clustering, the surveyed algorithms are grouped according to their convergence rate into two subsections for variable and constant convergence time
Conclusion
Wireless sensor networks (WSNs) have attracted significant attention over the past few years. A growing list of civil and military applications can employ WSNs for increased effectiveness; especially in hostile and remote areas. Examples include disaster management, border protection, combat field surveillance. In these applications a large number of sensors are expected, requiring careful architecture and management of the network. Grouping nodes into clusters has been the most popular
Ameer Ahmed Abbasi was born in Karachi, Pakistan. He went to the Sheikh Zayed Research Centre, University of Karachi, where he studied IS with computer technology and obtained his Master’s degree in 2000. He worked one and half years for the Usman Business Solution Inc. Karachi, Pakistan as a software engineer. In 2001 he moved to the Al-Hussan Institute of Management & Computer Science, Dammam, Saudi Arabia. He is teaching computer science courses and participating in research in the fields of
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Ameer Ahmed Abbasi was born in Karachi, Pakistan. He went to the Sheikh Zayed Research Centre, University of Karachi, where he studied IS with computer technology and obtained his Master’s degree in 2000. He worked one and half years for the Usman Business Solution Inc. Karachi, Pakistan as a software engineer. In 2001 he moved to the Al-Hussan Institute of Management & Computer Science, Dammam, Saudi Arabia. He is teaching computer science courses and participating in research in the fields of mobile computing, ad-hoc and wireless sensor networks. His e-mail address is: [email protected].
Mohamed Younis received B.S. degree in computer science and M.S. in engineering mathematics from Alexandria University in Egypt in 1987 and 1992, respectively. In 1996, he received his Ph.D. in computer science from New Jersey Institute of Technology. He is currently an associate professor in the department of computer science and electrical engineering at the university of Maryland Baltimore County (UMBC). Before joining UMBC, he was with the Advanced Systems Technology Group, an Aerospace Electronic Systems R&D organization of Honeywell International Inc. While at Honeywell he led multiple projects for building integrated fault tolerant avionics, in which a novel architecture and an operating system were developed. This new technology has been incorporated by Honeywell in multiple products and has received worldwide recognition by both the research and the engineering communities. He also participated in the development the Redundancy Management System, which is a key component of the Vehicle and Mission Computer for NASA’s X-33 space launch vehicle. Dr. Younis’ technical interest includes network architectures and protocols, embedded systems, fault tolerant computing and distributed real-time systems. Dr. Younis has four granted and three pending patents. He served on multiple technical committees and published over 85 technical papers in refereed conferences and journals.