Effective data aggregation using a hierarchical multi-layered scheme for large-scale sensor networks

Energy conservation is one of the most important challenges in wireless sensor networks. In more applications, sensor networks are composed of hundreds or thousands nodes which are dispersed in a wide field. In large-scale sensor networks, hierarchical architecture and data aggregation are effective approaches to prolong the lifetime of the network as long as possible. Clustering is a well-known two-layered architecture that has been extremely investigated for wireless sensor networks by researchers as yet. In this paper, we propose a novel adaptive Energy-Efficient Multi-layered Architecture (EEMA) protocol for large-scale sensor networks, where utilizes both hierarchical architecture and data aggregation mechanisms efficiently. In EEMA, the network is divided into some layers that the data are gathered in the first layer, and are aggregated in the next layers to reach the base station. Also each layer is segmented into some groups as clusters. EEMA selects the nodes with the proper residual energy, centrality, and proximity to the other neighboring heads as the head of each group. Performance evaluation is performed via simulations which confirms the effectiveness of the proposed EEMA protocol in terms of the network lifetime and reduced routing delay, in contrast with the existing hierarchical approaches for large-scale sensor networks.