The proliferation of interconnected devices in the Internet of Things (IoT) has led to an exponential increase in data, commonly known as Big IoT Data. Efficient retrieval of this heterogeneous data demands a robust indexing mechanism for effective organization. However, a significant challenge remains: the overlap in data space partitions during index construction. This overlap increases node access during search and retrieval, resulting in higher resource consumption, performance bottlenecks, and impedes system scalability. To address this issue, we propose three innovative heuristics designed to quantify and strategically reduce data space partition overlap. The volume-based method (VBM) offers a detailed assessment by calculating the intersection volume between partitions, providing deeper insights into spatial relationships. The distance-based method (DBM) enhances efficiency by using the distance between partition centers and radii to evaluate overlap, offering a streamlined yet accurate approach. Finally, the object-based method (OBM) provides a practical solution by counting objects across multiple partitions, delivering an intuitive understanding of data space dynamics. Experimental results demonstrate the effectiveness of these methods in reducing search time, underscoring their potential to improve data space partitioning and enhance overall system performance.

翻译：物联网（IoT）中互联设备的激增导致数据呈指数级增长，通常被称为物联网大数据。高效检索这种异构数据需要一个鲁棒的索引机制以实现有效组织。然而，一个重大挑战依然存在：索引构建过程中数据空间划分的重叠。这种重叠增加了搜索和检索过程中的节点访问，导致更高的资源消耗、性能瓶颈，并阻碍系统可扩展性。为解决这一问题，我们提出了三种创新的启发式方法，旨在量化并策略性地减少数据空间划分的重叠。基于体积的方法（VBM）通过计算分区之间的交集体积提供详细评估，从而更深入地洞察空间关系。基于距离的方法（DBM）通过利用分区中心与半径之间的距离来评估重叠，提高了效率，提供了一种简化而准确的方法。最后，基于对象的方法（OBM）通过统计跨多个分区的对象数量，提供了一种实用的解决方案，从而直观地理解数据空间动态。实验结果证明了这些方法在减少搜索时间方面的有效性，突显了它们在改善数据空间划分和提升整体系统性能方面的潜力。