Integrating Sensing Perspectives for Better Self Organization of Ad Hoc Wireless Sensor Networks

摘要

Wireless sensor networks represent a new paradigm shift in ad hoc networks. In addition to ad hoc deployment and wireless communication capabilities, sensor nodes use on-board sensing and processing to sense (or detect) application specified events of interest. An ad hoc network of randomly deployed wireless sensor nodes is formed by having nodes pursue neighbor discovery and subsequent self organization. Since sensors typically run on batteries that have a limited lifetime, an energy-efficient self organized sensor network architecture becomes important. The design of a self organization protocol for sensor networks should, thus, incorporate not only the communication characteristics of the wireless medium but also several quality metrics associated with the sensing phenomenon. The sensing phenomenon is concerned with the characteristics of the sensors, the events to be detected, and their topological manifestations, both in the spatial and temporal domains. For example, it is obvious that sensors in close proximity to each other would have correlated readings. A temporal dual of this observation implies that sensor readings among neighboring sensors also have some correlation within some nearby time intervals. In addition to supporting the properties associated with the sensing phenomenon, it is also necessary to support hierarchical event processing as it makes it possible to have an incremental comprehensive global view of an area of deployment. In this paper, we propose a self organization algorithm that forms a hierarchical connected dominating set (CDS) network organization for wireless sensor networks. In this network hierarchy, we also assign specific roles (or tasks) to sensors based on their physical wireless connectivity and sensing characteristics. The resulting self-organized sensor network establishes a network-wide infrastructure consisting of a hierarchy of backbone nodes, and sensing zones that include sensor coordinators and sensing collaborators (or sensing zone members). We demonstrate the effectiveness of our design through complexity analysis and simulation.