A Distributed Relation Detection Approach in the Internet of Things

• Main Authors: Weiping Zhu, Hongliang Lu, Xiaohui Cui, Jiannong Cao
• Format: Article
• Language: English
• Published: Hindawi Limited 2017-01-01
• Series: Mobile Information Systems
• Online Access: http://dx.doi.org/10.1155/2017/4789814

In the Internet of Things, it is important to detect the various relations among objects for mining useful knowledge. Existing works on relation detection are based on centralized processing, which is not suitable for the Internet of Things owing to the unavailability of a server, one-point failure, computation bottleneck, and moving of objects. In this paper, we propose a distributed approach to detect relations among objects. We first build a system model for this problem that supports generic forms of relations and both physical time and logical time. Based on this, we design the Distributed Relation Detection Approach (DRDA), which utilizes a distributed spanning tree to detect relations using in-network processing. DRDA can coordinate the distributed tree-building process of objects and automatically change the depth of the routing tree to a proper value. Optimization among multiple relation detection tasks is also considered. Extensive simulations were performed and the results show that the proposed approach outperforms existing approaches in terms of the energy consumption.