Prolonging the Lifetime of Wireless Sensor Networks via Unequal Clustering Stanislava Soro and Wendi B. Heinzelman Department of Electrical and Computer Engineering University of Rochester Rochester, NY 14627 Email: {soro, wheinzel}@ece.rochester.edu Abstract—Organizing wireless sensor networks into clusters enables the efficient utilization of the limited energy resources of the deployed sensor nodes. However, the problem of unbalanced energy consumption exists, and it is tightly bound to the role and to the location of a particular node in the network. If the network is organized into heterogeneous clusters, where some more powerful nodes take on the cluster head role to control network operation, it is important to ensure that energy dissipation of these cluster head nodes is balanced. Oftentimes the network is organized into clusters of equal size, but such equal clustering results in an unequal load on the cluster head nodes. Instead, we propose an Unequal Clustering Size (UCS) model for network organization, which can lead to more uniform energy dissipation among the cluster head nodes, thus increasing network lifetime. Also, we expand this approach to homogeneous sensor networks and show that UCS can lead to more uniform energy dissipation in a homogeneous network as well. I INTRODUCTION One of the most restrictive factors on the lifetime of wireless sensor networks is the limited energy resources of the deployed sensor nodes. In order to achieve high energy efficiency and assure long network lifetime, sensor nodes can be organized hierarchically by grouping them into clusters, where data is collected and processed locally at the cluster head nodes before being sent to a base station. In many sensor network applications where data collection and processing can be done “in place”, this hierarchical approach is a promising method for efficiently organizing the network. Also, many signal processing algorithms used for extraction of final information from the data gathered by the sensors are well- suited for local processing of data within the clusters. Communication within a cluster as well as communication between different clusters can be organized as a combination of one-hop and multi-hop communication. In one-hop communication, every sensor node can directly reach the destination, while in multi-hop communication, nodes have limited transmission range and therefore are forced to route their data over several hops until the data reach the final destination. In both models, there is an unavoidable problem of unbalanced energy dissipation among different nodes, leading to the situation where some nodes lose energy at a higher rate and die much faster than others, possibly reducing sensing coverage and leading to network partitioning. For single-hop communication, the nodes furthest away from the base station are the most critical nodes, while in multi-hop communication, the nodes closest to the base station are burdened with a heavy relay traffic load and die first (i.e., the “hot spot” problem). Clustered sensor networks can be broadly classified as heterogeneous and homogeneous with respect to the type and functionality of the nodes in the network. In homogeneous networks, all nodes have the same hardware and processing capabilities. The cluster head role is usually periodically rotated among the nodes to balance the load. Although rotating the cluster head role ensures that sensors consume energy more uniformly, the hot spot problem described above cannot be completely avoided. In heterogeneous networks, a certain number of nodes with much higher processing capabilities and complex hardware are deployed over the field together with numerous sensor nodes. As cluster head nodes, the more powerful nodes need to encompass several functions, serving as data collectors and processing centers for data gathered by sensor nodes. Because heterogeneous networks assume static cluster head assignment, the network lifetime is determined by the cluster heads’ functioning time, which is directly related to cluster head activity and energy consumption. The cluster heads can form a backbone network and use multi-hop routing to route the data to the base station. This leads to “hot spots” in the network, where cluster heads in the hot spot use their energy at a much higher rate and die much faster than the other cluster heads. Managing the load becomes necessary in order to prevent the problem of premature battery drainage for particular cluster head nodes. The positions of cluster heads in a network affect the total energy consumption of all nodes. Cluster heads can be dispersed in the sensor field randomly, or they can be deployed in a deterministic fashion. In the latter case, for example, these nodes can have the ability to move, and therefore change their positions until they reach some locations determined a priori. Although a randomly deployed heterogeneous sensor network is more common and easier to deploy, it is much harder to control the actual sizes of clusters and to effectively balance the traffic among the cluster head nodes. Therefore, the hot spot problem can easily appear as a result of excessive energy consumption of particular cluster head nodes. We are interested in exploring a deterministic approach, where the cluster head nodes have the ability to move and to adjust their locations, managing at the same time the size of their clusters and the expected load from other clusters further away. We are dealing with the problem of unbalanced energy consumption, particularly among the cluster head nodes, assuming that this type of node is much more expensive than the simple sensor nodes, and that the loss of one cluster head