An efficient cluster-based power saving scheme for wireless sensor networks
© Chang and Ju; licensee Springer. 2012
Received: 13 February 2012
Accepted: 16 May 2012
Published: 16 May 2012
In this article, efficient power saving scheme and corresponding algorithm must be developed and designed in order to provide reasonable energy consumption and to improve the network lifetime for wireless sensor network systems. The cluster-based technique is one of the approaches to reduce energy consumption in wireless sensor networks. In this article, we propose a saving energy clustering algorithm to provide efficient energy consumption in such networks. The main idea of this article is to reduce data transmission distance of sensor nodes in wireless sensor networks by using the uniform cluster concepts. In order to make an ideal distribution for sensor node clusters, we calculate the average distance between the sensor nodes and take into account the residual energy for selecting the appropriate cluster head nodes. The lifetime of wireless sensor networks is extended by using the uniform cluster location and balancing the network loading among the clusters. Simulation results indicate the superior performance of our proposed algorithm to strike the appropriate performance in the energy consumption and network lifetime for the wireless sensor networks.
In this article, we propose a saving energy clustering algorithm (SECA) to provide efficient energy consumption in wireless sensor networks. In order to make an ideal distribution for sensor node clusters, we calculate the average distance between the sensor nodes and take into account the residual energy for selecting the appropriate cluster head nodes. The lifetime of wireless sensor networks is extended by using the uniform cluster location and balancing the network loading among the clusters. The main benefits of proposed scheme are that the energy consumption is reduced and better network lifetime can be carried out.
The rest of this article is organized as follow. In Section 2, we present the system model of wireless sensor networks. In Section 3, we illustrate the proposed scheme in detail. In Section 4, we present our simulation model and analyze the comparative evaluation results of the proposed scheme through simulations. Finally, some conclusions are given in Section 5.
2. System model
The system infrastructure is composed of a BS and some sensor nodes. We classify all sensor nodes into non-cluster head nodes and cluster head nodes. The non-cluster head nodes operate in sensing mode to monitor the environment information and transmit data to the cluster head node. Also, the sensor node becomes a cluster head to gather data, compresses it and forwards to the BS in cluster head mode. The system framework of this article is shown in Figure 1.
where d0 is a threshold value. If the distance d is less than d0, the free-space propagation model is used. Otherwise, the multipath fading channel model is used. ε fs and ε mp are communication energy parameters. Using the previously described in the literature [5, 6], the ε fs is set as 10 pJ/bit/m2 and ε mp is set as 0.0013 pJ/bit/m4. Also, the energy for data aggregation of a cluster head node is set as E DA = 5 nJ/bit/signal and the initial energy of a sensor node is set as E init = 2 J. Suppose that a non-cluster head node N transmits L N bits to the BS. Let dN, CHbe the distance between the non-cluster head node N and its cluster head node CH. Let dCH, BSbe the distance between the cluster head node CH and the BS. Due to the multi-hop communication, a non-cluster head node only sends data to its cluster head node. The residual energy of the non-cluster head node N is equal to E init - E Tx (L N , dN, CH). In addition, the residual energy of the cluster head node CH is equal to E init - E Rx (L N ) - E DA - E Tx (L N , dCH, BS), because the cluster head node must collect and process the information of non-cluster head nodes in the cluster, and then send data to the BS.
It is obvious that the data transmission between sensor nodes takes most of the energy consumption in the wireless sensor networks. Taking into account the energy consumption of sensor nodes, the data transmission distance must be reduced and the packets delay should be avoided. Hence, the energy consumption and routing design become an important issue in the wireless sensor networks.
3. Proposed methods: SECA
In order to increase energy efficiency and extend the lifetime of the sensor nodes in wireless sensor networks, efficient power saving algorithm must be developed and designed. Based on the centralized clustering architecture, we propose a SECA to provide efficient energy consumption and better network lifetime in the wireless sensor networks. In the proposed scheme, we assume that the BS receives the information of location and residual energy for each sensor node and the average residual energy can be calculated. When the residual energy of sensor node is higher than the average residual energy, the sensor node becomes a candidate of cluster head. We modify k-means algorithm to make an ideal distribution for sensor node clusters by using the information of location and residual energy for all sensor nodes [12, 13]. In this algorithm, the operation includes two phases: set-up and steady-state phases.
3.1. Set-up phase
where X i is the coordinate of sensor node i.
where M is the side of the given square field. The d to BS is the average distance from the cluster head nodes to the BS which is defined in LEACH-C. However, the cluster head nodes are selected by creating some clusters in our proposed algorithm. Hence, we re-define d to BS which is the average distance from the all sensor nodes to the BS.
where S i is the cluster i, X j is coordinate of sensor node j and m i is the coordinate of mean of point. The main reason for this expression is to obtain the minimum average distance between the means of points and the sensor nodes for all clusters.
where each sensor node joins exactly one cluster. The main goal of this expression is to decide which cluster the sensor node j belongs to in the t th execution.
3.2. Steady-state phase
Once the clusters are created and the TDMA schedule is fixed, data transmission can begin. The non-cluster head nodes send data to cluster head node during their allocated transmission time. When all the data have been received, the cluster head node performs signal processing to compress the data into a single signal. Then, this signal is sent to the BS. The amount of information is reduced due to the data aggregation done at the cluster head node. This round is done and the next round begins with set-up and steady-state phases repeatedly.
To avoid unnecessary nodes control messages transmission and control overhead of the BS, the clusters are re-created only when the sensor node cannot work in a certain round. So, the calculating overhead is only cluster head selecting in the most set-up phase.
4. Performance analysis
In this section, we evaluate the performance of our proposed SECA using a simulation model. We describe our simulation model and illustrate the simulation results, and compare our scheme with the LEACH, HEED, and LEACH-C. We design a simulation environment by using C#. The assumptions for our simulation study are as follows.
The simulation environment is composed of a BS and some sensor nodes.
The BS is fixed and located far from the sensor nodes.
The location of each sensor node is randomly distributed in the sensing area.
The non-cluster head node can monitor the environment and send data to the cluster head node.
The cluster head node can gather data, compress it, and forward to the BS.
Parameters used in simulation mode
Electronics energy (E elec )
Energy for data aggregation (E DA )
Initial energy of node (E init )
Number of nodes (n)
50, 100, 150
Position of BS (X, Y)
Sensing area (M × M)
100 × 100, 200 × 200
The energy saving is a challenging issue in the wireless sensor networks. To increase energy efficiency and extend the lifetime of sensor node, new and efficient energy saving schemes must be developed. In the proposed scheme, we calculate the average distance between the sensor nodes and take into account the residual energy for selecting the appropriate cluster head nodes. The lifetime of wireless sensor networks is extended by using the uniform cluster location and balancing the network loading among the clusters. Simulation results indicate our proposed algorithm achieves the low energy consumption and better network lifetime in the wireless sensor networks.
- Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E: A survey on sensor networks. IEEE Commun Mag 2002, 40(8):102-114. 10.1109/MCOM.2002.1024422View ArticleGoogle Scholar
- Tubaishat M, Madria S: Sensor networks: an overview. IEEE Potentials 2003, 22(2):20-23.View ArticleGoogle Scholar
- Al-Karaki JN, Kamal AE: Routing techniques in wireless sensor networks: a survey. IEEE Wirel Commun 2004, 11(6):6-28. 10.1109/MWC.2004.1368893View ArticleGoogle Scholar
- Chamam A, Pierre S: On the planning of wireless sensor networks: energy-efficient clustering under the joint routing and coverage constraint. IEEE Trans Mob Comput 2009, 8(8):1078-1086.View ArticleGoogle Scholar
- Heinzelman WR, Chandrakasan A, Balakrishnan H: Energy-efficient communication protocol for wireless microsensor networks. Proc 33rd Hawaii International Conference on System Sciences 2000, 1-10.Google Scholar
- Heinzelman WB, Chandrakasan P, Balakrishnan H: An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 2002, 1(4):660-670. 10.1109/TWC.2002.804190View ArticleGoogle Scholar
- Younis O, Fahmy S: HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 2004, 3(4):366-379. 10.1109/TMC.2004.41View ArticleGoogle Scholar
- Babaie S, Zadeh AK, Amiri MG: The new clustering algorithm with cluster members bounds for energy dissipation avoidance in wireless sensor network. Proc Computer Design and Applications (ICCDA) 2010, 613-617.Google Scholar
- Xuegong Q, Yan C: A control algorithm based on double cluster-head for heterogeneous wireless sensor network. Proc Industrial and Information Systems (IIS) 2010, 541-544.Google Scholar
- Yun Y-U, Choi J-K, Hao N, Yoo S-J: Location-based spiral clustering for transmission scheduling in wireless sensor networks. Proc Advanced Communication Technology (ICACT) 2010, 470-475.Google Scholar
- Tarigh HD, Sabaei M: A new clustering method to prolong the lifetime of WSN. Proc Computer Research and Development (ICCRD) 2011, 143-148.Google Scholar
- Kanungo T, Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu AY: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 2002, 24(7):881-892. 10.1109/TPAMI.2002.1017616View ArticleMATHGoogle Scholar
- Zhu J, Wang H: An improved K-means clustering algorithm. Proc 2nd IEEE International Conference on Information Management and Engineering (ICIME) 2010, 190-192.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.