Abstract: Wireless Sensor Network (WSN) is spatially distributed independent network to sense the physical or environmental characteristics such as temperature, sound, pressure, energy with minimum number of sensor nodes. Most of the existing research works had concentrated on energy efficient routing, but the multi-hop clustering is a major issue in distributed wireless sensor network. In this study, a Generalized Iterative Scaling based Energy Effective Distributed Multi-hop Clustering (GISEDC) method is developed to improve the multi-hop clustering with minimal energy consumption. The Generalized Iterative Scaling is a log linear model to maximize the clustering efficiency and also reduce the energy consumption. Initially, the multi-hop clustering is performed on the distributed sensor nodes with multi-sink for energy savings in WSN. Accordingly, the cluster head is selected by using two factors in Generalized Iterative Scaling approach. The GISEDC method uses the principle of maximum entropy model to improve the clustering efficiency on the distributed sensor nodes and the principle of minimum entropy model to reduce the energy consumption. The formation of cluster head in Generalized Iterative Scaling consists of the grid includes cluster id, number of nodes grouped in that cluster and adjacent cluster information. GISEDC method uses the adjacent cluster information and identifies the node in more than one cluster called periphery node. The periphery node is used to improve the network performance with minimum delay time for a particular large scale sensor network environment. Finally, an energy effective distributed multi-hop clustering algorithm is applied to increase the network throughput with minimum energy consumption. Experimental analysis is performed using NS2 simulator to prove the effectiveness of the proposed GISEDC method in terms of clustering efficiency, energy consumption, delay time and throughput. The results demonstrate the advantage of the proposed model over state-of-art methods.