A Weighted and Distributed Algorithm for Range-Based Multi-Hop Localization Using a Newton Method

Abstract

Wireless sensor networks are used in many location-dependent applications. The location of sensor nodes is commonly carried out in a distributed way for energy saving and network robustness, where the handling of these characteristics is still a great challenge. It is very desirable that distributed algorithms invest as few iterations as possible with the highest accuracy on position estimates. This research proposes a range-based and robust localization method, derived from the Newton scheme, that can be applied over isotropic and anisotropic networks in presence of outliers in the pair-wise distance measurements. The algorithm minimizes the error of position estimates using a hop-weighted function and a scaling factor that allows a significant improvement on position estimates in only few iterations. Simulations demonstrate that our proposed algorithm outperforms other similar algorithms under anisotropic networks.

Node Localization Algorithm for Irregular Regions Based on Particle Swarm Optimization Algorithm and Reliable Anchor Node Pairs

“…At present, scholars at home and abroad have also begun to optimize the node positioning algorithm using strategies such as swarm intelligence optimization algorithm, mobile nodes and deep learning. Abd El Aziz M [24] designed a localization method based on time difference of arrival (TDOA) and frequency difference of arrival (FDOA), which improves the localization accuracy of the algorithm by introducing the free gradient method and solves the problem of slow convergence of cuckoo algorithm; Roman et al [25] designed a new distributed localization algorithm (RWNM-DV-Hop) based on the Newton-Raphson method, which effectively reduces the error introduced in the distance estimation phase by weighting the number of hops between neighboring sensor nodes using dynamic scaling parameters; Zhao et al [26] proposed an improved localization algorithm by combining RSSI and back propagation neural network (BP) model for the problem that the classical localization algorithm produces a large localization error during the localization process, and experiments proved that this algorithm consumes slightly more energy than other algorithms, but the localization effect is significantly improved; Yang et al [27] proposed a probabilistic KNN (k-Nearest Neighbor) algorithm (P-KNN), which uses the probability of RSSI in the radio map as a weight for calculating the Euclidean distance and filters RSSI values with probability less than 3% . Meanwhile, for passive indoor localization scenarios, the access point (AP) collects RSSI when the mobile terminal (MT) is not connected to the access point.…”

Research on Compression Sensing Positioning Algorithm of Indoor Complex Environment Visible Light Indoor Based on Hybrid APIT

“…Bluetooth location technology is easy to integrate into mobile devices such as mobile phones and is ideal for commercial promotion [ 5 ]. The disadvantage is that the cost of the positioning system is relatively high, the stability is poor, and the interference information in the indoor environment is considerable [ 6 ]. Radio Frequency Identification (RFID): Determines the use of radio frequency for contactless two-way communication to exchange data bit.…”