Due to the proliferation and widespread use of mobile devices and satellite based sensors there has been increased interest in storing and managing spatio-temporal and sensory data. It has been recognized that centralized and monolithic index structures are not scalable enough to address the highly dynamic nature (high update rates) and the unpredictable access patterns in such datasets. In this paper, we propose an adaptive networked index method designed to address the above challenges. Our method not only facilitates fast query and update response times via dynamic data partitioning but is also able to self-tune highly loaded sites. Our contributions consist of techniques that offer dynamic load balancing of computing sites, non-disruptive on-the-fly addition/removal of storing sites, distributed collaborative decision making for the self-administering of the manager, and statistics-based data reorganization. These features are incorporated into a distributed software layer prototype used to evaluate the design choices made. Our experimentation compares the performance of a baseline configuration with our multi-site system, examines the attained speed-up as a function of the sites participating, investigates the effect of data reorganization on query/update response times, asserts the effectiveness of our proposed dynamic load balancing method, and examines the behavior of the system under diverse types of multi-dimensional data. Keywords: Data Management in Cluster of Workstations, Networked Storage Manager, Self-tuning Storage Nodes, and Multi-dimensional Data.