Efficient Clustering technique for Improving Network Performance in Wireless Sensor Networks
Volumn 2

Efficient Clustering technique for Improving Network Performance in Wireless Sensor Networks

Punam Dandare1, Vikrant Chole2, Shruti Kolte3
Department of Computer Science and Engineering
G. H. Raisoni Academy of Engineering and Technology, Nagpur, India
1punam170592@gmail.com
2vikrant.chole@raisoni.net
3shruti.kolte@raisoni.net

Abstract:

Wireless sensor networks (WSNs) consist of sensor nodes. It is a collection of wild number of low cost device constraint sensor nodes that communicates using wireless medium and they are small in size, low battery power and limited processing capability. This restraint of low electricity power of a sensor node and limited energy capability makes the wireless sensor network failure. Clustering is one of the important methods for prolonging the network lifetime in wireless sensor network. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group called as cluster are more similar to each other than to those in other clusters. Cluster heads collect the data from respective clusters nodes and forward the aggregated data to base station. A major challenge in WSNs is to select appropriate cluster head. In this paper, we present a cluster head i.e. every sensor node send the data to the closet cluster head. Using cluster head we reduce the noise in the spectrum and balanced the load of the network. The simulation results validate that this way is more effective in prolonging the network lifetime than the distributed hierarchical agglomerative clustering (DHAC) protocol. Thus, reducing noise by using cluster head is an important issue to be considered in WSNs.

Keywords – Data Aggregation, Wireless sensor network, Cluster head, Privacy, HEED, LEACH

I. INTRODUCTION

Wireless Sensor Network (WSN) sometimes called as Wireless Sensor and Actuator networks (WSAN), are spatially handed out autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to simultaneously pass their data through the network to a base station. The more networks are bidirectional, also sanctioning control of sensor exertion. The development of wireless sensor networks was motivated by military applications such as field direction; today such networks are used in many industrial and consumer applications, such as industrial process supervise and control, machine health monitoring and so on. An event based Wireless Sensor Network (WSN) [1] consists of many numbers of resource compelled wireless sensor nodes, it mutually collects data from the open area and pass data to the Base Station (BS), through a wireless medium in a multi hop manner. The WSN’s are mainly used for security operation such as field direction. However, it is used in civilian application areas, including site monitoring, home computerization, health care applications, traffic control etc. Wireless Sensor Network consists of a number of hundreds or thousands sensor nodes that sense the data from environment process it and communicate the refined data to the base station. In WSNs, sensor nodes are really energy constrained. If the entire sensor node transmits their information to the base station then precious energy of WSNs is wasted and life time of WSNs is diminish. Wireless sensor network is single-purpose design; serving one specific application. Energy is the main compulsion in the design of all node and network components. Moreover, in WSNs, neighboring sensor nodes usually sense same or co-related data which is seeming likely superfluous and hence does not need all to be communicated to the base station. Hence, on behalf of communicating the superfluous data, a better alternative is to identify and pre-process such data into an aggregated form and communicate it.

II. RELATED WORKS

Y. Yu, K. Li, W. Zhou, and P. Li, et. al. [2], proposed a Trust and reputation systems in wireless sensor network, they play critical role in WSNs as a method of analyzing a number of crucial problems, such as secure routing, fault tolerance, false data or information detection, compromised sensor node detection, secure data aggregation process, cluster head election, outlier detection of the system, etc.

O. Younis and S. Fahmy et. Al. [10], proposed hybrid energy efficient distributed clustering approach for ad-hoc sensor networks. In hybrid energy efficient distributed clustering sensor quasi-stationary Links are symmetric in a network and energy consumption is non-uniform for all sensor nodes. In clustering, Nodes-location unaware and processing and communication capability is similar. This algorithm balanced clusters in the network and used low message overhead. Uniform & non-uniform node distribution in a wireless sensor network and inter cluster communication explained.

H.-S. Lim, G. Ghinita, E. Bertino, and M. Kantarcioglu et. al. [3], proposed a sensors which are deployed in hostile remote area and unattended environments are highly susceptible to sensor node compromising attacks in the WSNs. While offering better protection than the simple averaging method, our simulation results demonstrate that indeed current Iterative Filtering (IF) algorithms are vulnerable to such new attack strategy in WSN.

Z. Fan and Z. Jin et. Al. [11], proposed Mobility resistant efficient clustering approach for ad-hoc sensor networks. Sensor quasi-stationary of the network and nodes-location unaware in the network of mobility resistant efficient clustering. Every node as source and server and every node is grouped to form a cluster. Every node has a one message and those messages send to a cluster it saves the energy. It can be used for environmental monitoring and battlefield applications

He, W., Liu, X., Nguyen, H. V., Nahrstedt, K. et. al. [5], they present one privacy -preserving data aggregation scheme for data aggregation functions, i.e. secure data aggregation, secure preserving data aggregation and so on, which can be extensive to approximate MAX/MIN aggregation function. The first method Cluster-based Private Data Aggregation (CPDA) -leverages clustering protocol and algebraic properties of polynomials. The cluster leaders carry out aggregation of the data received from the cluster member nodes. The data communication is assured by using a mutual key between each pair of communicating nodes for the purpose of encryption. The aggregate function grease algebraic properties of the polynomials to compute the desired aggregate value in a cluster. While the aggregation is pushed out at the aggregator node in each cluster, it is guaranteed that no individual node gets to know the delicate private values of other nodes in the cluster. It has the advantage of draw less communication overhead. The second scheme Slice –Mix -AggRegaTe(SMART) builds on slicing techniques and the associative property of addition.

Carlos R. Perez-Toro, Rajesh K. Panta, Saurabh Bagchi et.al. [6], proposed RDAS, a strong data aggregation protocol in WSNs that use a reputation-based advance to recognize and cut off cruel nodes in a sensor network. RDAS is based on an ordered cluster form of nodes that has the creation of cluster in the network, where a cluster head illustrate data from the cluster nodes to find out the location of an event. It uses the repetition of multiple nodes sense an event to decide what data need to have been reported by each node. RDAS is able to execute accurate data aggregation in the residence of individually hateful and collude nodes, as well as nodes that try to accord the integrity of the reputation system by lying about other nodes performance.

S. Ganeriwal, L. K. Balzano, and M. B. Srivastava, et. al. [7], Our work is also firmly related to the trust and reputation systems in WSNs, proposed a general reputation framework for wireless sensor networks in which each sensor node develops a reputation estimation for other nodes by penetrating its neighbors which make a trust community for sensor nodes in the network.

Suat Ozdemir ,Yang Xiao et. al. [8] presents Data aggregation is the process of analyzing and combining sensor data in order to shorten the large amount of data transmission in the network. As wireless sensor networks are rarely deployed in remote and unfavorable environments to transmit sensitive messages, sensor nodes are likely to node compromise attacks and security concern such as data confidentiality and integrity are very relevant. Hence, wireless sensor system protocols, e.g., data aggregation protocol, must be programmed with security in mind. This paper investigates the relationship between security and data aggregation process in wireless sensor networks.

III. NETWORK MODEL

A WSN consists of small-sized sensor devices, which are assembled with limited battery power and are capable of wireless communications. Wireless sensor network is single-purpose design; serving one specific application. Energy is the main constraint in the design of all node and network Components. WSNs are deployment, network structure, and resource uses are often ad-hoc. When a WSN is set up in a sensing field, these sensor nodes will be important for sensing unusual events or for collecting the sensed data of the environment and sensor networks often operate in environments with harsh conditions. In the case of a sensor node detecting an unusual event or being set to periodically report the sensed data, it will send the message hop-by-hop to a special node, called a base station. Physical access to sensor nodes is often difficult or even impossible. The sink node will then notify the supervisor over the Internet. Component failure is expected and addressed in the design of the network. The sensors nodes are divided into disarticulate clusters, and every cluster has a cluster head which acts as an aggregator. Data are annually together and aggregated by the aggregator.

Fig.1 : Network model of a WSN

IV. PROPOSED WORK

In clustering, Cluster head (CH) selection norm and the number of CH selected strongly control the network behavior in terms of communication, latency, inter-clustering and intra-clustering communication. The Cluster Head performs aggregation of the packets received from all the nodes present in their cluster. The Cluster head are formed by using LEACH and RDAS method. LEACH is the first cluster based routing protocol that select CHs based on threshold criteria. LEACH has a fixed and remote base station. Each node has homogeneous and energy constrained. In this protocol, CHs rotated in a cluster with an objective to reduce energy consumption and to distribute load uniformly among the nodes. CHs aggregate data from their neighboring nodes in a cluster and pass on the aggregated data to the base station in WSN.

In this paper, Cluster Heads are formed by using their area and nodes. The two topology control approaches are load balancing and network scalability. CHs are selected first considering performance parameter in a wireless sensor network and then the clusters are formed. In WSNs, number of nodes that can be accommodating in the cluster without demeaning network performance is considered. A sensor node is the collection of hundreds and thousands of small and tiny nodes. The CHs are formed by using the parameter of the network and for uniform load distribution, a local clustering system is proposed. This mechanism will be called in the cluster so that cluster head revolution can take place within the cluster in wireless sensor network and that too when some specific condition is met.

V. CONCLUSION

A cluster head selection technique is proposed by lessens the node degree. Cluster head not only reduces energy consumption but also reduce noise in spectrum and balance load by selecting cluster head nodes first and then forming the well distributed clusters. The CHs are distributed by using the performance parameter in a wireless sensor network. Cluster the whole network with the selected cluster head. Cluster based network algorithm is mostly used for low energy consumption and increase the lifetime of network. It reduces the noise in the network and prolongs network lifetime. Thus, clustering is the important in wireless sensor network.

REFERENCES

  1. V. Pandey and A. N. Chand, “A review on data aggregation techniques in wireless sensor network”, Journal of Elecronics & Electrical Engineering, vol. 1, no. 2, (2010), pp. 01-08.
  2. Y. Yu, K. Li, W. Zhou, and P. Li, “Trust mechanisms in wirelesssensor networks: Attack analysis and countermeasures,” J. Netw.Comput. Appl., vol. 35, no. 3, pp. 867–880, 2012.
  3. H.-S. Lim, G. Ghinita, E. Bertino, and M. Kantarcioglu, “A gametheoreticapproach for high-assurance of data trustworthiness insensor networks,” in Proc. IEEE 28th Int. Conf. Data Eng., Apr.2012, pp. 1192–1203.
  4. Wagner and David, “Resilient aggregation in sensor networks”, In: Proceedings of the 2nd ACM workshop on Security of ad hoc and sensor networks (SASN 04) , pages 78–87, New York, NY, USA, ACM, 2004. ISBN 1-58113-972-1.
  5. He, W., Liu, X., Nguyen, H. V., Nahrstedt, K., andAbdelzaher, T. 2011. “Privacy preserving dataaggregation for information collection “ACMTransaction Sensor Network. Article 6 (August2011.DOI = 10.1145/1993042.199)3048.
  6. H.-S. Lim, Y.-S. Moon, and E. Bertino, “Provenancebasedtrustworthiness assessment in sensor networks,” inProceedings of the Seventh International Workshop onData Management for Sensor Networks, ser. DMSN ‟10,2010, pp. 2–7.
  7. S. Ganeriwal, L. K. Balzano, and M. B. Srivastava,“Reputationbased framework for high integrity sensornetworks,” ACM Trans. Sen. Netw., vol. 4, no. 3, pp.15:1–15:37, Jun. 2008.
  8. H.-L. Shi, K. M. Hou, H. ying Zhou, and X. Liu,“Energy efficient and fault tolerant multicore wirelesssensor network: E2MWSN,” in WirelessCommunications, Networking and Mobile Computing(WiCOM), 2011 7th International Conference on, 2011,pp. 1–4.
  9. X.-Y. Xiao, W.-C. Peng, C.-C. Hung, and W.-C. Lee,“Using SensorRanks for in-network detection of faultyreadings in wireless sensor networks,” in Proceedings ofthe 6th ACM.
  10. Ossama Younis and Sonia Fahmy. 2004. Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy-Efficient Approach.In Proceedings of IEEE INFOCOM, Hong Kong, an extended version appeared in IEEE Transactions on Mobile Computing, 3(4) .
  11. Z. Fan and Z. Jin, “A Multi-weight Based Clustering Algorithm for Wireless Sensor Networks,” College of Computer Science & Educational Software Guangzhou University, 2012

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