images CHAPTER 13

Self-Localization of Sensor Networks

Joshua N. Ash and Randolph L. Moses

Ohio State University, Columbus, Ohio


Measurements from geographically distributed sensors enable signal processing algorithms to make inference in the spatial domain about the environment in which they are placed. Applications of inference and control in spatially distributed arrays range from precision agriculture, where sensors monitor the spatial variation in soil and crop conditions [1, 2], to noninvasive habitat monitoring [3, 4], to applications of object detection, classification, and tracking [5, 6]. Many other military and commercial applications, such as forest fire monitoring, inventory control, and structural monitoring, are given in the survey studies [7, 8].

In order to perform inference from spatially distributed sensors, knowledge of the sensor positions is typically required. With advances in micro-electro-mechanical systems (MEMS) and wireless communications, the size of sensor networks—as measured by both the number of nodes and size of deployment area—is rapidly increasing, with some current networks exceeding 1000 nodes [9]. Due to the large-scale and ad hoc deployment methodologies of such networks, an automated self-localization mechanism is a key enabling technology for modern sensor networks. This chapter will present popular localization algorithms while ...

Get Handbook on Array Processing and Sensor Networks now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.