Distinguished from traditional wireless networks, sensor networks are characterized with severe power, computation, and memory constraints. Due to strict energy constraint, innovative techniques that improve energy efficiency to prolong the network lifetime are highly required.

Many solutions and algorithms for overcoming the problem depend on decomposing the network into a number of administrative entities called clusters. The structure imposed by clustering makes it somewhat easier to manage the problem introduced by the complexity of large-scale WSNs. Hierarchical (clustering) mechanisms are especially effective in increasing network scalability and reducing energy depletion, which have been extensively exploited.

We propose and evaluate an energy efficient clustering scheme based on LEACH (low energy adoptive clustering hierarchy), that is, LEACH-Energy Distance (LEACH-ED). In LEACH-ED, cluster heads are elected by a probability based on the ratio between residual energy of node and the total current energy of all of the sensor nodes in the network. In addition, it also considers the distance of any two cluster heads to uniform the clustering, namely, if the distance between some node and an existing cluster head in the round less than the distance threshold, the node cannot be elected as a cluster head. The method uses two different sorts of nodes which are called special node and normal node.

Assume that special nodes are uniformly distributed in the interested area. In the algorithm, it sets a distance threshold Td=SN∗Pt, where S is the acreage of interested filed, N is the number of sensor nodes in the entire network, and p is the desired percentage of cluster heads in each round. Each node generates a random probability p at the beginning of a new round and computes the threshold value (T(n)). In case of p < pt, the node is selected as a cluster head. The algorithm should consider the distance threshold between the sensor nodes and existing cluster heads while other cluster heads are selected.

Compared with the classical clustering algorithm LEACH and extended LEACH, it makes uniform the clustering and balances the network load by considering the residual energy of nodes and the restrained factor of distances between two cluster heads, so that extends the network's lifetime. Meanwhile, it also takes the acreage of area and the number of nodes into account and analyzes their impact on the performance of the networks.

The simulation results show that LEACH-ED greatly prolongs the network lifetime compared with LEACH and LEACH-C.