Local Wireless Sensor Networks Positioning Reliability Under Sensor Failure

Abstract

Local Positioning Systems are collecting high research interest over the last few years. Its accurate application in high-demanded difficult scenarios has revealed its stability and robustness for autonomous navigation. In this paper, we develop a new sensor deployment methodology to guarantee the system availability in case of a sensor failure of a five-node Time Difference of Arrival (TDOA) localization method. We solve the ambiguity of two possible solutions in the four-sensor TDOA problem in each combination of four nodes of the system by maximizing the distance between the two possible solutions in every target possible location. In addition, we perform a Genetic Algorithm Optimization in order to find an optimized node location with a trade-off between the system behavior under failure and its normal operating condition by means of the Cramer Rao Lower Bound derivation in each possible target location. Results show that the optimization considering sensor failure enhances the average values of the convergence region size and the location accuracy by 31% and 22%, respectively, in case of some malfunction sensors regarding to the non-failure optimization, only suffering a reduction in accuracy of less than 5% under normal operating conditions.

Comparative Performance Analysis of Time Local Positioning Architectures in NLOS Urban Scenarios

“…This study requires the characterization of the system noise errors [22], the consideration of the clock errors [26], the detection of LOS/NLOS paths in the positioning signal links of each architecture [41], the guarantee of the availability of the TBS under possible sensor failures [47] and a methodology that enables a costeffective node deployment for each architecture [25].…”

“…The definition of the necessary nodes for covering the target area (i.e. coverage problem [52]), the consideration of possible sensor failures in the deployment [47], the reduction of the energy consumption [53] or the minimization of the clock [26] and noise [41] uncertainties are some of the most important issues in WSN that require an optimized sensor location for achieving acceptable results.…”

“…In our previous works, we have modeled the path losses [22], the clock instabilities [26], and Non-Line-of-Sight links [41] into the covariance matrix of the CRB for characterizing the architecture errors in LPS applications. We have later applied this model for constructing optimized cost-effective node deployments [25] considering sensor failures in the CS nodes [47].…”

Hybrid Memetic Algorithm for the Node Location Problem in Local Positioning Systems

“…This allows us to avoid synchronism among the system receivers, consequently reducing the architecture clock errors [ 20 ] but increasing the signal paths and noise errors [ 21 ], since they rely on a receive-and-retransmit strategy of the positioning signals in the Target Sensor (TS), producing longer path signals. In addition, a possible CS malfunction may produce temporal system unavailability [ 22 ] due to the particular architecture dependence on the CS.…”

“…In our previous research, we have applied GA to the NLP in LPS [ 7 , 20 , 21 , 22 , 30 ]. In these papers, we have observed that the dimensions of the space of solutions which increases with the number of sensors, the resolution of the pre-defined possible space locations for them and the complexity of the fitness function evaluation, significantly affect the stable performance of the GA.…”