A dynamic clustering construction for wireless sensor networks

IJRCAR, 2014

Wireless sensor network consists of many tiny sensor nodes. Energy, bandwidth, processing power and memory nodes are limited. Hence reducing power consumption, increasing the network lifetime and scalability are the main challenges in sensor networks. Cluster based routing protocols are the most useful schemes for extending Wireless Sensor Networks lifetime through dividing the nodes into several clusters and electing of a local cluster head for aggregating of data from cluster nodes and transmitting a packet to Base Station. However, there are several energy efficient cluster-based methods in the literature. In this paper, we will review clustering in wireless sensor networks and LEACH algorithm

International Journal of Electrical and Computer Engineering (IJECE), 2022

The rapid development of connected devices and wireless communication has enabled several researchers to study wireless sensor networks and propose methods and algorithms to improve their performance. Wireless sensor networks (WSN) are composed of several sensor nodes deployed to collect and transfer data to base station (BS). Sensor node is considered as the main element in this field, characterized by minimal capacities of storage, energy, and computing. In consequence of the important impact of the energy on network lifetime, several researches are interested to propose different mechanisms to minimize energy consumption. In this work, we propose a new enhancement of low-energy adaptive clustering hierarchy (LEACH) protocol, named clustering location-based LEACH (CLOC-LEACH), which represents a continuity of our previous published work location-based LEACH (LOC-LEACH). The proposed protocol organizes sensor nodes into four regions, using clustering mechanism. In addition, an efficient concept is adopted to choose cluster head. CLOC-LEACH considers the energy as the principal metric to choose cluster heads and uses a gateway node to ensure the inter-cluster communication. The simulation with MATLAB shows that our contribution offers better performance than LEACH and LOC-LEACH, in terms of stability, energy consumption and network lifetime.

2008

TOP-DOWN CLUSTERING BASED SELF-ORGANIZATION OF COLLABORATIVE WIRELESS SENSOR NETWORKS Recent advances in Wireless Sensor Network (WSN) technology are enabling the deployment of large-scale and collaborative sensor networks. Energy efficient operation, channel contention, latency, management, and security of such networks are complex and critical issues that have to be addressed with large-scale WSN deployments. Collaborative sensor networks further require dynamic grouping of nodes observing similar events and communication within such groups or across different groups. Cluster based organization of large sensor networks is the key for many techniques that addresses these issues. A backbone network in the form of a cluster tree can further enhance upper layer functions such as routing, broadcasting, and in-network query processing. A configurable cluster and cluster tree formation algorithm is presented that is independent of network topology and does not require a-priori neighborhood information, location awareness, or time synchronization. Configurable parameters of the algorithm can be used to form cluster trees with desirable properties such as controlled breadth and depth, uniform cluster size, and more circular clusters. Message complexity of the algorithm grows linearly with the number of nodes in the network, therefore algorithm scales well into large networks. Two-step, post cluster optimization phase is proposed to iv increase the connectivity of the network and to further reduce the depth of the cluster tree. Simulation based analysis shows that the algorithm forms more circular and uniform clusters, cluster tree with lower depth, and more importantly forms a more ordered structure in the network. Closeness of clusters to hexagonal packing is evaluated. The structure imposed by the algorithm makes it applicable to broad classes of applications. The proposed cluster tree based routing strategy facilitates both node-to-sink and node-to-node communication. Hierarchical addresses that reflect the parent-child relationship of cluster heads is used to route data along the cluster tree. Utilization of cross-links among neighboring cluster heads and a circular path within the network approximately doubles the capacity of the network. Under ideal conditions, this approach guarantees delivery of events/queries and has a lower overhead compared to routing strategies such as rumor routing and ant routing. The cluster tree formed by our algorithm is used to identify and form Virtual Sensor networks (VSNs), an emerging concept that supports resource efficient collaborative WSNs. Our implementation of VSN is able to deliver unicast, multicast, and broadcast traffic among nodes observing similar events, efficiently. Efficacy of the VSN based approach is evaluated by simulating a subsurface chemical plume monitoring system. The algorithm is further extended to support the formation of a secure backbone that can enable secure upper layer functions and dynamic distribution of cryptographic keys, among nodes and users of collaborative sensor networks.

International Journal of Sensor Networks, 2010

... we propose a new weight-based clustering algorithm that consists of grouping sensors into a set of disjoint clusters, hence giving at the network a hierarchical organisation. Each cluster has a cluster-head that is elected among its 2-hop neighbourhood based on nodes ...

… and Networks, 2010. …, 2010

Why to build clusters in sensor networks ? Agregating nodes in clusters allows to reduce the complexity of the routing algorithms, to optimize the medium resource by letting it to be locally managed by a cluster head, to make easy the data fusion, to simplify the network management and particularly the address allocation, to optimize the energy consumption, and at last to make the network more scalable. Using clusters allows also to stabilize the topology if the cluster size is large in comparison to the speed of the nodes. This chapter is dedicated to clustering in sensor networks. First, the state of the art is presented, followed by the detailed presentation of one of the best and most cited cluster formation method with its validation and correction. Then, the next parts of the chapter are dedicated to some considerations on cluster modelling. In the last part, a method to assign addresses to the nodes within a cluster is presented.

… 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 sensor may be deployed in an open space; on a battle field in front of, or beyond, enemy lines; in the interior of industrial machinery; at the bottom of water Body; in a biologically and chemically contaminated field; in a commercial building; in a home; or in on a human body. It is not possible to recharge the batteries in all environments and hence lifetime of a sensor is very crucial. So to increase the lifetime, the sensors are grouped into clusters and information is exchanged with base station via cluster head. An energy efficient cluster head selection technique is required to increase the lifetime of WSN. The traditional LEACH protocol selects the cluster head by random time which may select low energy sensors as cluster head. In this paper, the cluster head selection is based on energy variable and mean distance of neighboring nodes. This methodology is implemented using both CDMA and TDMA approaches and results show that using the CDMA approach reduces the energy consumption and improves the network lifetime compared to TDMA approach.

International Journal of Information Technology and Computer Science, 2014

Generally, grouping sensor nodes into clusters has been widely adopted by the research community to satisfy the above scalability objective and generally achieve high energy efficiency and prolong network lifetime in large scale WSN environments. The corresponding hierarchical routing and data gathering protocols imply cluster based organization of the sensor nodes in order that data fusion and aggregation are possible, thus leading to significant energy savings. We propose a clustering approach which organizes the whole network into a connected hierarchy and discuss the design rationale of the different clustering approaches and design principles. Further, we propose several key issues that affect the practical deployment of clustering techniques in wireless sensor network applications. Index Terms-WSN (Wireless Sensor Network), Sensor Node (SN), Base Station (BS), Cluster Head (CH), Mobile ad hoc network (MANET).

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.

2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), 2019

Mobile wireless sensor network (MWSN) is an emerging technology, which is an extended form of wireless sensor network (WSN). It is employed in diverse real time applications due to its unique characteristics of self conFigure and mobility. We initiate a novel clustering technique by integrating super cluster head (SCH) concept with MTE protocolin this paper. The SCH are static and highly efficient sensor nodes which are placed inside the network to gather data from CHs. The use of SCH will reduce the distance of data transmission from CHs to BS and thereby increases the energy efficiency. The CHs transmit data to SCH and it will forward the data to BS. The incorporation of SCH with the MTE protocol will further increase the networks performance. The projected method is implemented and the results depicted the superiority of the projected method over another methods.

International Journal of Computer Applications, 2012

In numerous applications, self-organizing property of Wireless Sensor Networks (WSN) is an important characteristic. It calls for decompositions of the network into clusters of desirable bound. Cluster based WSN can enhance the whole networks lifetime. In every cluster, the cluster head (CH) plays an important role in aggregating and forwarding data sensed by other non-leader nodes. A major issue in the cluster based approach in WSN is the selection of proper cluster head and attainment of desirable cluster size by maximum number of clusters formed, keeping into consideration the inherent constraints such as limited battery energy, failure of nodes, selfish behavior of nodes, limited bandwidth etc, which inhibits superior message efficiency. This research paper presents a clustering approach termed as Sequential Multi-Clustering Protocol (SMCP) incorporating node deployment, which enhances the lifetime of the network. This Protocol is applied on some popular clustering algorithms like 'Expanding Ring', 'Rapid' and 'Persistent' along with our own clustering algorithm 'Message Based Memory Efficient Clustering Algorithm' (MMEC) to cluster an entire topology of the network. Simulation results mainly in MATLAB interpreter shows the effectiveness of clustering using SMCP protocol.

HELIX

The field of Wireless Sensor Network (WSN) is striving for devising ways to minimize energy consumption. Clustering reduces energy consumption and increases scalability along with network lifetime. There is a need to identify appropriate number of clusters to balance traffic in network which is a challenging task for energy efficient WSN. Manually it is difficult to decide number of clusters. Finding optimum number of clusters to minimize energy consumption is the major issue in WSN. Existing algorithms find optimum number of clusters but not optimum transmission range. This paper contributes towards the aforesaid issue by proposing a novel method to find optimum number of clusters and a first attempt to find optimum transmission range. We report a new algorithm, where the number of clusters obtained from proposed method is compared with state-of-the-art methods. Extensive experiments are carried out and result comparison with state-of-the-art approaches demonstrate that our method shows significantly better performance. The analysis reveals that optimum number of clusters obtained by proposed method is less than state-of-the-art method. It is especially suitable for clustering in WSN.

—A wireless sensor network comprises a number of small sensors that communicate with each other. Each sensor collects the data and communicates through the network to a single processing center that is a base station. The communication of node and process of message passing consumes energy. This energy consumption by the nodes to transmit data decreases the network lifetime significantly. Clustering is by far the best solution to save the energy consumption in the context of such network. Clustering divides the sensors into groups, so that sensors communicate information only to cluster heads and then the cluster heads communicate the aggregated information to the processing center so as to save energy. This paper studies and discusses various dimensions and approaches of some broadly discovered algorithms for clustering. It also presents a comparative study of various clustering algorithms and discussion about the potential research areas and the challenges of clustering 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.