Wireless sensor networks have been proposed for many location-dependent applications. In such applications, the requirement of low system cost prohibits many range-based methods for sensor node localization; on the other hand, range-free localization depending only on connectivity may underutilize the proximity information embedded in neighborhood sensing. In response to the above limitations, this paper presents a range-free approach to capturing a relative distance between 1-hop neighboring nodes from their neighborhood orderings that serve as unique high-dimensional location signatures for nodes in the network. With little overhead, the proposed design can be conveniently applied as a transparent supporting layer for many state-of-the-art connectivity-based localization solutions to achieve better positioning accuracy. We implemented our design with three well-known localization algorithms and tested it in two types of outdoor test-bed experiments: an 850-foot-long linear network with 54 MICAz motes, and a regular 2D network covering an area of 10000 square feet with 49 motes. Results show that our design helps eliminate estimation ambiguity with sub-hop resolution, and reduces localization errors by as much as 35%. In addition, extensive simulations reveal an interesting feature of robustness for our design under unevenly distributed radio propagation path loss, and confirm its effectiveness for large-scale networks.
A range-free approach beyond connectivity is proposed in REF .
Achieving range-free localization beyond connectivity
SenSys '09
Computer Science