Energy Efficient Clustering in Sensor Networks Using Cluster Manager K.Sathya
D.RajeshKumar
Bannari Amman Institute of Technology, Sathyamangalam
[email protected]
Bannari Amman Institute of Technology, Sathyamangalam
[email protected]
Abstract – In order to organize the sensor nodes in a wireless communication network and to route the sensed information from the field sensors to a remote base station various techniques are available. The major problem of the sensor network is to increase the lifetime of the sensor nodes. For this reason many protocols have been established. In this paper we presents a new energy efficient clustering technique for large-scale sensor networks. By monitoring the received signal about power from its neighboring nodes, each node estimates the number of active nodes in real time and computes its optimal probability of becoming a cluster head. The goal is to select cluster heads that minimize transmission costs and energy usage. Based on the clustered architecture, we propose a new Energy Efficient Clustering Algorithm for the efficient Cluster Head selection by using a Cluster Manager (CMR). When compare to existing work the proposed technique work efficiently and reduces the energy consumption of sensor nodes. Keywords- Energy Efficient clustering; Wireless Sensor Networks; Cluster Hea; Cluster Membe; Cluster Manager
I.
clusters), more energy is drained from CH due to message transmission over long distances (CH to the base Station) compared to other sensor nodes in the cluster. Periodic selection of CH within clusters based on their residual energy is a possible solution to balance the power consumption of each cluster. Clustering increases the efficiency of data transmission by reducing the number of sensors attempting to transmit data to the base station. By energy-efficient, we mean that the energy spent on delivering packets from a source to a destination is minimized. By power- awareness, we mean that a route with nodes currently having higher remaining battery power should be selected, although it may not be the shortest one. The main objective of this work is to reduce the energy consumption by the sensor node by using a cluster manager (CMR). It is organized as follows: in part 2 related works is explained and in part 3, the design of cluster communication is explained. In part 4, the simulation environment is explained and in part 5, t he conclusion is stated.
INTRODUCTION
Wireless Sensor Networks (WSNs) have given rise to many applications including environmental monitoring and military surveillance. In these applications sensors are usually remotely deployed in large numbers and operated autonomously. In these unattended environments, the sensors cannot be charged, so energy constraints is the most critical problem that must be considered. In large WSNs sensors are often grouped into clusters. Clustering is essential for sensor network applications where a large number of ad-hoc sensors are deployed for sensing purposes. If each and every sensor starts to communicate and engage in data transmission in the network, great data congestion and collisions will be experienced. This will drain energy quickly from the sensor network. Clustering is a method used to overcome these issues. In clustered networks, some sensors are elected as cluster heads (CH) for each cluster created. Sensor nodes in each cluster transmit their data to the respective CH and the CH aggregates data and forwards them to a central base station. Clustering facilitates efficient utilization of limited energy of sensor nodes and hence extends network lifetime. Although sensor nodes in clusters transmit messages over a short distance (within
II.
RELATED WORK
The extensive work related to this paper can be categorized into energy-efficient clustering methods. A. Related work in clustering methods The clustering methods in sensor networks can be categorized into and dynamic types. The clustering methods aim at minimizing the total energy spent during the formation of the clusters for a set of given network parameters, such as the number of nodes in the network [2]. A problem that is closely related to the static clustering is the localized topology control, which maintains energy-efficient network connectivity by controlling the transmission power at each node, or selecting a small subset of the local links of a node. One way is to minimize the total power levels in all nodes and search for a connected topology. Another way is to select a minimum set of sensors that form a connected communication graph to cover the entire network region, by iteratively searching for one path at a time and adding the nodes of the path to a set of already selected sensors [13].
The dynamic clustering methods deal with the same energy efficiency problem as the static ones but target for a set of changing network parameters, such as the number of active nodes or the available energy levels in a network. In Lowenergy Adaptive Clustering Hierarchy (LEACH), the position of a Cluster Head (CH) was rotated among the nodes within a cluster depending on their remaining energy levels. It was assumed that the number of active nodes in the network and the optimal number of clusters are the parameters for dynamic clustering. Two-Level LEACH (TL-LEACH) is a proposed extension to the LEACH algorithm. It utilizes two levels of cluster heads (primary and secondary) in addition to the other simple sensing nodes. In this algorithm, the primary cluster head in each cluster communicates with the secondary, and the corresponding secondary communicate with the nodes in their sub-cluster. Data-fusion can also be performed as in LEACH. In addition, communication within a cluster is still scheduled using TDMA time-slots. The organization of a round will consist of first selecting the primary and secondary cluster heads using the same mechanism as LEACH, with the a priori probability of being elevated to a primary cluster head less than that of a secondary node. The two-level structure of TLLEACH reduces the number of nodes that need to transmit to the base station, effectively reducing the total energy usage. It might not be effective if the CH is far from the base station [7]. Energy Efficient Clustering Scheme (EECS) is a clustering algorithm in which cluster head candidates compete for the ability to elevate to cluster head for a given round. This competition involves candidates broadcasting their residual energy to neighboring candidates. If a given node does not find a node with more residual energy, it becomes a cluster head. Cluster formation is different than that of LEACH. LEACH forms clusters based on the minimum distance of nodes to their corresponding cluster head. EECS extends this algorithm by dynamic sizing of clusters based on cluster distance from the base station. The result is an algorithm that addresses the problem that clusters at a greater range from the base station requires more energy for transmission than those that are closer. Ultimately, this improves the distribution of energy throughout the network, resulting in better resource usage and extended network lifetime. However clusters closer to the base station may become congested which may result in early CH death [7]. Hybrid Energy-Efficient Distributed clustering (HEED) periodically selects cluster heads according to a hybrid of their residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED does not make any assumptions about the distribution or density of nodes, or about node capabilities. The clustering process terminates in O (1) iterations, and does not depend on the network topology or size [9]. III.
PROPOSED WORK
This work proposes a Multi level hierarchical approach in the evaluation of the clustered network and dynamically
changes the sensor nodes based on their energy depletion. Through the energy efficient algorithm the cluster head is selected from the set of sensor nodes by a cluster manager (CMR) and aggregated data is transferred to the Base station through next hop CMR. The main goal of implementing the hierarchical structure is to prolong the sensor’s lifetime and to improve the scalability issues. A. Design of Network model All the sensors in the network area are clustered into different clusters. The cluster managers are deployed in a sensor network in a fixed position (static). During cluster formation, each node tries to become a CH with a certain probability by winning a competition with its neighbors. While data collection, each Cluster Member (CM) communicates to its CH directly by using a MAC layer protocol. The cluster manager (CMR) maintains the nodes ID along with their residual energy. When the residual energy of CH goes below the threshold level then the CMR reelect the CH based on High residual energy. For data delivery, the CMR in the hotspot area aggregates the data received from its CH and then delivers the aggregated data to the CMR (cluster manager) in the next hop. So the data is transferred to the Base Station (BS) by using a CMR. BIntra-Cluster and Inter Cluster Communication In a cluster, one node acts as a Cluster Head and rest of the nodes act as a Cluster Member. In the set of sensor nodes, Cluster Head is selected using Energy efficient Algorithm that is based on the energy consumption. When the CH’s energy level goes to lower then the threshold energy the new CH selection will be perform by a cluster manager (CMR). A node which holds higher energy level will be given higher priority.
-CM (Cluster Member) -CH (Cluster Head) Figure 1. Architecture of the clustered network
In Inter-Cluster Communication, the communication between the cluster managers CMR in the network is framed which will save the transmitting power of the node while sending any information to the Base Station from sensors. The overall clustered architecture is shown in figure 3.1. C.Dynamic and Multilevel Hierarchical Clustering The proposed multi level hierarchy approach reduces the Routing complexity and energy is greatly saved. A CM that has not been selected as a CH during its previous round can also monitor the total signal power it has received. In the next round of cluster updating, the node can join the competition and may become a CH by winning the competition. Optimal Parameters for the Algorithm To determine the optimal parameters for the algorithm, we make the following assumptions: a) The sensors distributed in the wireless sensor network are homogeneous. b) All sensors transmit at the same power level and hence have the same radio range r. c) Each sensor uses 1 unit of energy to transmit or receive 1 unit of data. The data processing cost is negligible. d) The communication environment is free of contention and error; hence, sensors never need to retransmit data. The dynamic clustering algorithm is outlined as follows: Algorithm Description: Let the value of threshold energy E threshold =20J While (E (current CH) < E threshold) { The CMR broadcasts a message to poll the residual energy level of all cluster members of a particular cluster to choose a CH among the nodes When a sensor receives this message, it will report the current residual energy to CMR; The CMR selects the child with the maximum residual energy as the new CH and broadcasts the message to all children node of same cluster All of children in old cluster change its previous hop to the NEW CH in its primary path. The CMR define TDMA schedule for Cluster to collect the aggregated data from CH Then the CMR transmits the aggregated data to the next hop CMR which transmit it to the BS } For all nodes, When (E threshold
