Optimizing Energy Consumption in Wireless Sensor Network Through Distributed Weighted Clustering Algorithm Anup Singh Yadav and Christian Bach Abstract: - In recent years, wireless sensor network have attracted the researchers because if their wide range of applications in many area like military, aerospace etc. In wireless network ad-hoc performance and security are the main concern. Efficient use of energy is essential for improve network performance. In wireless sensor network performance and scalability are major constraint. Clustering algorithm is used in wireless sensor and network scaling for optimization. Weighted clustering provide long term and enhanced optimization. Various lustering technique is used to for EE like LEECH,K-hop, heed, EECs. RELATED WORK Distributed Weighted Clustering Algorithm TL-Leach: - It uses two level hierarchy of cluster to reduce the number of nodes so that no. of nodes for data transmission reduces and energy consumption is reduced. EECS: - It is a k-hop clustering algorithm which consider some parameters like residual heat based on which cluster head is elected. It improves energy distribution and better utilization of resources. HEED: - This is a multi-hop clustering algorithm. It uses two parameter residual energy and intra-cluster communication. It minimizes the control of overhead over the network. K-hop: - This connectivity based on clustering algorithm which consider two parameter lowest ID and connectivity of nodes. Node which has lowest id and highest connectivity will elect the cluster. The routing technique we are using works in two steps. Step 1:- We calculate the quality of node service and weighted calculation. Step 2:- We do the cluster head selection. Node quality estimation Signal to noise ratio: is about the level of a coveted flag to the level of foundation clamour. It is characterized as the proportion of flag energy to the clamour control, frequently communicated in decibels. The hub sends an information bundle to neighbour hub and between both the flag quality the values are assessed utilizing the given recipe. Availability: in this system the hubs are said to interface which are in radio scope of a hub. Therefore Most extreme quantities of hubs are in associated through this hub causes the all the more serving capacity. Objective The key goal of the proposed algorithm is to develop energy efficient technique by which network will grow lifetime. Study of energy efficient routing technique. In order to design energy efficient routing technique required to find previous contribution of the energy efficient routing design concept. Design and implementation of new routing approach. After obtaining the most optimum technique, new modifications are required on that technique to make it compatible with others. In order to justify the new approach, comparisons are made with traditional approaches to show how good it is. Weight Calculation To process weights are help to locate the ideal hub in arrange in this way a rundown of effective hubs are made utilizing the figured weights. Cluster head selection In this phase all hub process the weights of their neighbour hubs utilizing a neighbour table. Along these lines after examination of assessed weights the bunch heads are chosen for a bunch of hubs. Conclusion Wireless sensor are advancing day by day and new research and development is emerging. The proposed algorithm consume less energy from traditional algorithm even when traffic is more. The proposed algorithm take less time as compared to traditional algorithm and delivers more packet. By deploying this algorithm we are increasing the life of sensor. Reference Ye, M., C. Li, et al. (2005). EECS: an energy efficient clustering scheme in wireless sensor networks. Performance, Computing, and Communications Conference, 2005. IPCCC 2005. 24th IEEE International, IEEE. Younis, O. and S. Fahmy (2004). "HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks." IEEE Transactions on mobile computing 3(4): 366-379. Nocetti, F. G., J. S. Gonzalez, et al. (2003). "Connectivity based k-hop clustering in wireless networks." Telecommunication systems 22(1-4): 205-220. Loscri, V., G. Morabito, et al. (2005). A two-levels hierarchy for low-energy adaptive clustering hierarchy (TL-LEACH). Vehicular Technology Conference, 2005. VTC-2005-Fall. 2005 IEEE 62nd, IEEE.