Distributed Information Storage and Retrieval in 3-D Sensor Networks With General Topologies

Abstract

Distributed in-network data-centric processing aims to reduce energy consumed for communication and establish a self-contained data storage, retrieval, aggregation, and query sensor system that focuses more on the data itself rather than the identities of the individual network nodes. Double-ruling-based schemes support efficient in-network data-centric information storage and retrieval, especially for aggregated data, since all data with different types generated in a network can be conveniently retrieved along any single retrieval curve. Previous double-ruling-based research focuses on two-dimensional (2-D) wireless sensor networks where a 2-D planar setting is assumed. With increasing interests in deploying wireless sensors in three-dimensional (3-D) space for various applications, it is urgent yet fundamentally challenging to design double-ruling-based approach in general 3-D sensor networks because double-ruling-based schemes in general have much harder geometric constraints than other distributed in-network data-centric processing schemes. In this research, we propose a geographic location-free double-ruling-based approach for general 3-D sensor networks with possibly complicated topology and geometric shapes. Without the knowledge of the geographic location and the distance bound, a query simply travels along a simple curve with the guaranteed success to retrieve aggregated data through time and space with one or different types across the network. Extensive simulations and comparisons show the proposed scheme with low cost and a balanced traffic load.