A Self-Adaptive Clustering Based Algorithm for Increased Energy-Efficiency and Scalability In Wireless Sensor Networks (original) (raw)
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Efficient energy consumption is a critical factor for the deployment and operation of wireless sensor networks (WSNs). In general, WSNs perform clustering and routing using localized neighbor information only. Therefore, some studies have used self-organized systems and smart mechanisms as research methods. In this paper, we propose a self-organized and smart-adaptive clustering (SOSAC) and routing method, which performs clustering in WSNs, operates the formed clusters in a smart-adaptive way, and performs cluster-based routing. SOSAC is comprised of three mechanisms, which are used to change the fitness value over time, to back up routing information in preparation for any potential breakdown in WSNs, and to adapt to the changes of the number of sensor nodes for a WSN. We compared the performance of the proposed SOSAC with that of a well-known clustering and routing protocol for WSNs. Our computational experiments demonstrate that the network lifetime, energy consumption, and scalability of SOSAC are better than those of the compared method.
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Scalability is an important and crucial issue which in routing protocols for Wireless Sensor Networks (WSNs). In this paper, we present an approach to achieving a balanced energy consumption rate using dynamic clustering to provide scalability in WSN. The proposed work in this paper is based on the dynamic clustering using kmeans compared to LEACH (LowEnergy Adaptive Clustering Hierarchy), which is one of the most simple and effective clustering solutions widely deployed for WSN. The simulation results show that our proposed algorithm significantly improves high network scalability compared to LEACH.
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Wireless sensor networks are powered by limited battery resources. Thus energy efficiency is one of the most important issues and designing power-efficient is critical for prolonging the lifetime. Clustering techniques can reduce energy consumption and prolong sensor network lifetime. We proposed a Modified load balanced clustering algorithm for wireless sensor nodes on the basis of residual energy. The algorithm not only realizes load balance among sensor node, but also achieves selection of cluster head based on residual energy. Simulation results indicate our algorithm is more energy-efficient compared to previous algorithms. Our algorithm is to exploit efficiently the network energy, by reducing the energy consumed for cluster formation and also to avoid the formation of forced cluster head.
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Stability is one of the major concerns in advancement of Wireless Sensor Networks (WSN). A number of applications of WSN require guaranteed sensing, coverage and connectivity throughout its operational period. Death of the first node might cause instability in the network. Therefore, all of the sensor nodes in the network must be alive to achieve the goal during that period. One of the major obstacles to ensure these phenomena is unbalanced energy consumption rate. Different techniques have already been proposed to improve energy consumption rate such as clustering, efficient routing, and data aggregation. However, most of them do not consider the balanced energy consumption rate which is required to improve network stability. In this paper, we present a novel technique, Stable Sensor Network (SSN) to achieve balanced energy consumption rate using dynamic clustering to guarantee stability in WSN. Our technique is based on LEACH (Low-Energy Adaptive Clustering Hierarchy), which is one of the most widely deployed simple and effective clustering solutions for WSN. We present three heuristics to increase the time before the death of first sensor node in the network. We devise the algorithm of SSN based on those heuristics and also formulate its complete mathematical model. We verify the efficiency of SSN and correctness of the mathematical model by simulation results. Our simulation results show that SSN significantly improves network stability period compared to LEACH and its best variant.
A novel cluster-based self-organization algorithm for wireless sensor networks
2008 International Symposium on Collaborative Technologies and Systems, 2008
Wireless sensor networks (WSNs) consist of a large number of tiny sensor nodes. Hence, a cluster-based architecture can be used to deal the self-organization issues of large networks. This cluster-based organization can prolong network lifetime and reduce broadcast overhead. In this paper, we propose an efficient self- organization algorithm for clustering (ESAC), which uses a weight-based criterion for cluster-head's election.
scirp.org
Stability is one of the major concerns in advancement of Wireless Sensor Networks (WSN). A number of applications of WSN require guaranteed sensing, coverage and connectivity throughout its operational period. Death of the first node might cause instability in the network. Therefore, all of the sensor nodes in the network must be alive to achieve the goal during that period. One of the major obstacles to ensure these phenomena is unbalanced energy consumption rate. Different techniques have already been proposed to improve energy consumption rate such as clustering, efficient routing, and data aggregation. However, most of them do not consider the balanced energy consumption rate which is required to improve network stability. In this paper, we present a novel technique, Stable Sensor Network (SSN) to achieve balanced energy consumption rate using dynamic clustering to guarantee stability in WSN. Our technique is based on LEACH (Low-Energy Adaptive Clustering Hierarchy), which is one of the most widely deployed simple and effective clustering solutions for WSN. We present three heuristics to increase the time before the death of first sensor node in the network. We devise the algorithm of SSN based on those heuristics and also formulate its complete mathematical model. We verify the efficiency of SSN and correctness of the mathematical model by simulation results. Our simulation results show that SSN significantly improves network stability period compared to LEACH and its best variant.
A stable energy efficient clustering protocol for wireless sensor networks
Sensor networks comprise of sensor nodes with limited battery power that are deployed at different geographical locations to monitor physical events. Information gathering is a typical but an important operation in many applications of wireless sensor networks (WSNs). It is necessary to operate the sensor network for longer period of time in an energy efficient manner for gathering information. One of the popular WSN protocol, named low energy adaptive clustering hierarchy (LEACH) and its variants, aim to prolong the network lifetime using energy efficient clustering approach. These protocols increase the network lifetime at the expense of reduced stability period (the time span before the first node dies). The reduction in stability period is because of the high energy variance of nodes. Stability period is an essential aspect to preserve coverage properties of the network. Higher is the stability period, more reliable is the network. Higher energy variance of nodes leads to load unbalancing among nodes and therefore lowers the stability period. Hence, it is perpetually attractive to design clustering algorithms that provides higher stability, lower energy variance and are energy efficient. In this paper to overcome the shortcomings of existing clustering protocols, a protocol named stable energy efficient clustering protocol is proposed. It balances the load among nodes using energy-aware heuristics and hence ensures higher stability period. The results demonstrate that the proposed protocol significantly outperforms LEACH and its variants in terms of energy variance and stability period.
Effect Of Clustering On Energy Efficiency And Network Lifetime In Wireless Sensor Networks
2009
Wireless Sensor Network is Multi hop Self-configuring Wireless Network consisting of sensor nodes. The deployment of wireless sensor networks in many application areas, e.g., aggregation services, requires self-organization of the network nodes into clusters. Efficient way to enhance the lifetime of the system is to partition the network into distinct clusters with a high energy node as cluster head. The different methods of node clustering techniques have appeared in the literature, and roughly fall into two families; those based on the construction of a dominating set and those which are based solely on energy considerations. Energy optimized cluster formation for a set of randomly scattered wireless sensors is presented. Sensors within a cluster are expected to be communicating with cluster head only. The energy constraint and limited computing resources of the sensor nodes present the major challenges in gathering the data. In this paper we propose a framework to study how parti...