Open AccessENHANCING THE COVERAGE OF INDOOR RADIO LOCALIZATION BY DISTRIBUTED COMPUTATIONS
Author(s) -
Andreas Fink,
Helmut Beikirch
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.12.3.606
Subject(s) - rss , computer science , wireless sensor network , centroid , position (finance) , euclidean distance , computation , default gateway , node (physics) , algorithm , real time computing , data mining , computer network , artificial intelligence , structural engineering , finance , engineering , economics , operating system
The prevalent evaluation criterion for indoor local positioning systems (ILPS) is the achievable accuracy in terms of Euclidean distance between estimated and true position. Systems relying on received signal strength (RSS) ranging often use a distributed collection of RSS sensor data at reference nodes and a centralized position estimation. For this direct remote positioning, the accuracy is dependent on the reference node density and thus, is indirect proportional to the achievable coverage. To split up the dependency between these two criteria, we propose a distributed weighted centroid localization (dWCL) strategy with a hierarchical sensor data field bus. Accuracy and coverage of centralized and distributed WCL algorithms are compared for a one-dimensional tracking simulation and 196 reference nodes, arranged in up to 28 gateway segments. Using distributed computations, the localization system’s coverage is increased by factor ten while the location estimation error increases only slightly.