Abstract
In wireless indoor positioning system designs, reference node (RN) failures during the online phase cause received signal strength values to be unavailable. This leads to accuracy performance degradation and a lack of system reliability in smart office systems. Moreover, the major design concern in the reliability of indoor positioning systems under the faulty RNs during the online phase has not been yet investigated in previous works. To address these gaps, we propose a novel mathematical formulation using a Binary Integer Linear Programming (BILP) approach that employs the Simulated Annealing (SA) solution technique. The proposed robust system design aims to put in place a suitable number of RNs and to determine their optimum locations, which may be located on a single floor or on multiple floors. In particular, the proposed system design provisions to support robust operation both during a normal situation and when there are some RN failures. Experimental results and comparative performance evaluation revealed that the proposed robust system design outperformed other system designs and was able to achieve the highest location accuracy performance in both fault-free and RN-failure scenarios. Specifically, when nine of the RNs in a three-story building failed, the proposed system design achieved 84.6%, 54.7%, and 32.9% more accurate performance than the Uniform, the MSMR, and the PhI-Uni, respectively.
Highlights
In wireless indoor positioning system designs, reference node (RN) failures during the online phase cause received signal strength values to be unavailable. is leads to accuracy performance degradation and a lack of system reliability in smart office systems
We developed the robust system design in order to solve the RN-failure problem in wireless indoor positioning systems. e proposed system design achieves high positioning accuracy during normal situations and yields reliable location results under unexpected situations such as RN failures. e main objective of the proposed RobustMaximum Summation of Max RSSI (R-Maximize-Sum of Maximum RSS (MSMR)) is to place a sufficient number of RNs in optimum locations so that the system can achieve a maximizing summation of the maximum received signal strength (RSS) at the signal test points (STPs) received from the RNs installed in the service area as written in the objective function: Maximize ∑∀i∈T m∀ja∈BxSijPij
We found that the uniform placement (Uniform) has the lowest number of RNs installed in all three scenarios, while the R-MSMR with R 2 has the highest number of RNs installed for all three scenarios. e reason for the R-MSMR requesting a higher number of RNs to be installed than other system designs is that the proposed R-MSMR structure is designed to provide high positioning accuracy during a normal situation and achieve reliable location results when some RNs in the system fail. erefore, our algorithm seeks to distribute the RN locations across the service area while maintaining high quality radio signal coverage
Summary
In wireless sensor network (WSN) system design, the positioning of the nodes can affect numerous network performance metrics. e placement of any node will affect overall data collection and must take into account the condition of the physical environment. Based on the system design solutions presented in the literature, node failures in a telecommunications network may cause the network either to become disconnected or to have unavailable wireless links in the network This may result in complete network outage. To address this problem, several research studies have proposed reliable system designs, whereby those developed systems could still function after some failure of certain network components, usually based on the provision of backup links or bandwidth management schemes. Ey focused on achieving the optimal coverage of a certain area while simultaneously minimizing the necessary number of RNs. In the literature, existing system designs for indoor positioning systems limit their focuses to the achievement of location accuracy or the provision of signal radio coverage in the service area. Our proposed model aims to place a suitable number of RNs and to determine their locations whereby their placement is provisioned to support robust system operation both during a normal situation and when some RNs have failed
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
