Analytical Modelling of the A-ANCH Clustering Algorithm for WSNs (original) (raw)

Analytical Modelling of ANCH Clustering Algorithm for WSNs

Wireless sensor networks are a popular choice in a vast number of applications, despite their energy constraints, due to their distributed nature, low cost infrastructure deployment and administration. One of the main approaches for addressing the energy consumption and network congestion issues is to organise the sensors in clusters. The number of clusters and also distribution of Cluster Heads are essential for energy efficiency and adaptability of clustering approaches. ANCH is a new energy-efficient clustering algorithm proposed recently for wireless sensor networks to prolong network lifetime by uniformly distributing of Cluster Heads across the network. In this paper, we propose an analytical method to model the energy consumption of the ANCH algorithm. The results of our extensive simulation study show a reasonable accuracy of the proposed analytical model to predict the energy consumption under different operational conditions. The proposed analytical model reveals a number of implications regarding the effects of different parameters on the energy consumption pattern of the ANCH clustering algorithm.

Energy Based Analytical Modelling of ANCH Clustering Algorithm for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have had remarkable advances in the past couple of decades due to their fast growth and flexibility. In order to supervise an area, hundreds or thousands of sensors can be established and collaborate with each other in the environment. The sensors’ sensed and collected data can be delivered to the base station. Energy optimisation is crucial in WSN’s efficiency. Organising sensor nodes into small clusters helps save their initial energy and thus increases their lifetime. Also, the number and distribution of Cluster Heads (CHs) are fundamental for energy saving and flexibility of clustering methods. Avoid Near Cluster Heads (ANCH) is one of the most recent energy-efficient clustering algorithms proposed for WSNs in order to extend their lifetime by uniform distributing of CHs through the network area. In this manuscript, we suggest an analytical approach to model the energy consumption of the ANCH algorithm. The results of our comprehensive research show a 95.4% to 98.6% accuracy in energy consumption estimation using the proposed analytical model under different practical situations. The suggested analytical model gives a number of indications concerning the impact of different factors on the energy depletion pattern of the ANCH clustering algorithm.

ANCH: A New Clustering Algorithm for Wireless Sensor Networks

The adaptable and distributed nature of wireless sensor networks has made them popular in a broad range of applications. Clustering is a widely accepted approach for organising nodes in sensor networks to address the network congestion and energy efficiency concerns. In clustering, the number and uniform distribution of the cluster heads are crucial for the effectiveness of an algorithm. In this paper, we propose a new clustering algorithm for wireless sensor networks that reduces the networks energy consumption and significantly prolongs its lifetime. This is achieved by optimising the distribution of cluster heads across the network. The results of our extensive simulation study show considerable reduction in network energy consumption and therefore prolonging network lifetime.

Activity-aware Clustering Algorithm for Wireless Sensor Networks

In a Wireless Sensor Network (WSN) environment, there are many application cases that make use of unequally importance of the nature of distributed sensors. This paper proposes a clustering algorithm for these kinds of environments: prioritising the prolonging of active sensors by way of monitoring the important regions in a network environment. The results of our extensive study for this type of environment shows a considerable increase in the number of sensed events on average by 104.28%, 73.14%, and 50.97%, compared with those of Low Energy Adaptive Clustering Hierarchy (LEACH), Hybrid Energy-Efficient Distributed (HEED), and Avoid Near Cluster Head (ANCH) algorithms, respectively.

Some Issues in Clustering Algorithms for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) present new generation of real time embedded systems with limited computation, energy and memory resources that are being used in wide variety of applications where traditional networking infrastructure is practically infeasible. In recent years many approaches and techniques have been proposed for optimization of energy usage in Wireless Sensor Networks. In order to gather information more efficiently, wireless sensor networks are partitioned into clusters. However, these methods are not without problems. The most of the proposed clustering algorithms do not consider the location of the base station. This situation causes hot spots problem in multi-hop wireless sensor networks.

A Survey on Clustering Techniques for Wireless Sensor Network

International Journal of Research in Computer Science, 2012

Wireless sensor networks have been used in various fields like battle feilds, surveillance, schools, colleges, etc. It has been used in our day-today life. Its growth increases day by day. Sensor node normally senses the physical event from the environment such as temperature, sound, vibration, pressure etc. Sensor nodes are connected with each other through wireless medium such as infrared or radio waves it depends on applications. Each node has its internal memory to store the information regarding the event packets. In this paper we will come to know the various algorithms in clustering techniques for wireless sensor networks and discuss them. Clustering is a key technique used to extend the lifetime of a sensor network by reducing energy consumption .It can also increase network scalability. Sensor nodes are considered to be homogeneous since the researches in the feild of WSNs have been evolved but in reality homogeneous sensor networks hardly exist. Here we will discuss some of the impact of heterogeneous sensor networks on WSN and various clustering algorithms used in HWSN.

Applying hierarchical agglomerative clustering to wireless sensor network

… and Algorithmic Aspects of Sensor and …, 2007

Wireless Sensor Networks (WSNs) have a wide range of applications that base on the collaborative effort of a number of sensor nodes. Cluster-based network architecture can enhance network self-control capability and resource efficiency, and prolong the whole network lifetime. Thus, finding an effective and efficient way to generate clusters is an important topic in WSNs. Existing clustering approaches may not be flexible enough to cope with various factors or have higher communication overhead. To achieve the goal, we tailor the HAC (Hierarchical Agglomerative Clustering) algorithm for WSNs. HAC is a well-known approach and has been successfully applied to many disciplines. HAC uses simple numerical methods to make clustering decisions. In addition, HAC provides flexibility with respect to input data type (e.g., location data or connectivity information) and weight assignment to different factors (e.g., connections or power strength). This paper demonstrates our preliminary work in applying several well-understood HAC methods to WSNs. Initial results look promising. We are investigating other specific factors of WSNs, such as degree of connectivity, power level, and reliability, and are incorporating them into the HAC approaches. Many clustering approaches have been proposed for WSNs. The existing approaches typically first select a set of CHs among the nodes in the network by considering one or multiple factors, and then gather the rest of the nodes under these CHs. LEACH [7, 8] is an important clustering protocol for WSNs as there are many approaches that are based on it. LEACH is fully distributed through randomly selecting CHs and rotating the CH task among nodes. Thus, the approach can uniformly distribute the energy consumption in the whole network. PEGASIS [9, 10] is based on LEACH and uses the greedy algorithm to organize all sensor nodes into a chain and then periodically promote the first node on the chain to be the CH. HEED [13] extends LEACH by initializing a probability for each node to be a tentative CH depending on its residual energy and making the decision according to the cost based on the connectivity degree of the node. These approaches have two main disadvantages. The first one is the random selection of the CHs, which may cause higher communication overhead for: (i) the ordinary member nodes in communicating with their corresponding CH, (ii) CHs in establishing the communication among them, or (iii) between a CH and a base station (BS) or other sinks. Another issue is the periodic CH rotation or election which needs extra energy to rebuild clusters. To avoid the problem of random CH selection, there are many other approaches focusing on how to select appropriate CHs to achieve efficient communications. Stojmenovic, et al. [11] proposed a dominating set algorithm which focuses on the efficiency of broadcasting to all the nodes. The approach divides all the nodes into four types: Gateway, Inter-Gateway, Intermediate and Member. The selected Gateway nodes which form a View publication stats View publication stats

A Generalized Clustering Algorithm for Dynamic Wireless Sensor Networks

2008 IEEE International Symposium on Parallel and Distributed Processing with Applications, 2008

We propose a general clustering algorithm for dynamic sensor networks, that makes localized decisions (1-hop neighbourhood) and produces disjoint clusters. The purpose is to extract and emphasise the essential clustering mechanisms common for a set of state-ofthe-art algorithms, which allows for a better understanding of these algorithms and facilitates the definition and demonstration of common properties.

Survey on Recent Clustering Algorithms in Wireless Sensor Networks

2013

The use of wireless sensor networks (WSNs) has grown enormously in the last decade, pointing out the crucial need for scalable and energy-efficient routing and data gathering and aggregation protocols in corresponding large-scale environments. To maximize network lifetime in Wireless Sensor Networks (WSNs) the paths for data transfer are selected in such a way that the total energy consumed along the path is minimized. To support high scalability and better data aggregation, sensor nodes are often grouped into disjoint, non overlapping subsets called clusters. Clusters create hierarchical WSNs which incorporate efficient utilization of limited resources of sensor nodes and thus extends network lifetime. The objective of this paper is to present a survey on clustering algorithms reported in the literature of WSNs. This paper presents taxonomy of energy efficient clustering algorithms in WSNs.

An Integrated Distributed Clustering Algorithm for Large Scale WSN

Latest researches in wireless communications and electronics has imposed the progress of low-cost wireless sensor nodes. Clustering is a thriving topology control approach, which can prolong the lifetime and increase scalability for wireless sensor networks. The admired criteria for clustering methodology are to select cluster heads with more residual energy and to rotate them periodically. Sensors at heavy traffic locations quickly deplete their energy resources and die much earlier, leaving behind energy hole and network partition. In this paper, a model of distributed layer-based clustering algorithm is proposed based on three concepts. First, the aggregated data is forwarded from cluster head to the base station through cluster head of the next higher layer with shortest distance between the cluster heads. Second, cluster head is elected based on the clustering factor, which is the combination of residual energy and the number of neighbors of a particular node within a cluster. Third, each cluster has a crisis hindrance node, which does the function of cluster head when the cluster head fails to carry out its work in some critical conditions. The key aim of the proposed algorithm is to accomplish energy efficiency and to prolong the network lifetime. The proposed distributed clustering algorithm is contrasted with the existing clustering algorithm LEACH.