Localization of Distributed Wireless Sensor Networks using Two Sage SDP Optimization

2017 ◽  
Vol 7 (3) ◽  
pp. 1255
Author(s):  
Reza Shahbazian ◽  
Seyed Ali Ghorashi
Keyword(s):  
Optimization Problem ◽  
Low Cost ◽  
Reference Points ◽  
Wireless Sensor ◽  
Relaxation Techniques ◽  
Problem Size ◽  
Distance Measurements ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Cost Efficient

<span class="fontstyle0">A wireless sensor network (WSN) may comprise a large distributed set of low cost, low power sensing nodes. In many applications, the location of sensors is a necessity to evaluate the sensed data and it is not energy and cost efficient to equip all sensors with global positioning systems such as GPS. In this paper, we focus on the localization of sensors in a WSN by solving an optimization problem. In WSN localization, some sensors (called anchors) are aware of their location. Then, the distance measurements between sensors and anchors locations are used to localize the whole sensors in the network. WSN localization is a non-convex optimization problem, however, relaxation techniques such as semi-definite programming (SDP) are used to relax the optimization. To solve the optimization problem, all constraints should be considered simultaneously and the solution complexity order is O(n2) </span><span class="fontstyle0">where </span><span class="fontstyle2">n </span><span class="fontstyle0">is the number of sensors. The complexity of SDP prevents solving large size problems. Therefore, it would be beneficial to reduce the problem size in large and distributed WSNs. In this paper, we propose a two stage optimization to reduce the solution time, while provide better accuracy compared with original SDP method. We first select some sensors that have the maximum connection with anchors and perform the SDP localization. Then, we select some of these sensors as virtual anchors. By adding the virtual anchors, we add more reference points and decrease the number of constraints. We propose an algorithm to select and add virtual anchors so that the total solution complexity and time decrease considerably, while improving the localization accuracy.</span>

scholarly journals Three-Dimensional Empirical AoA Localization Technique for Indoor Applications

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5544 ◽  
Author(s):  
Abdallah Alma’aitah ◽  
Baha’ Alsaify ◽  
Raed Bani-Hani
Keyword(s):  
Antenna Arrays ◽  
Low Cost ◽  
Three Dimensional ◽  
Reference Points ◽  
Average Error ◽  
Navigation Systems ◽  
Angle Of Arrival ◽  
Received Signal Strength Indicator ◽  
Localization Technique ◽  
Localization Accuracy

Small and pervasive devices have been increasingly used to identify and track objects automatically. Consequently, several low-cost localization schemes have been proposed in the literature based on angle of arrival (AoA), time difference of arrival (TDoA), received signal strength indicator (RSSI) or their combinations. In this paper, we propose a three-dimensional empirical AoA localization (TDEAL) technique for battery-powered devices. The proposed technique processes the AoA measurements at fixed reader nodes to estimate the locations of the tags. The proposed technique provides localization accuracy that mitigates non-linear empirical errors in AoA measurements. We utilize two omni-directional antenna arrays at each fixed reader node to estimate the location vector. With multiple location estimations from different fixed reader nodes, each estimated location is assigned a weight that is inversely proportional to the AoA phase-difference error. Furthermore, the actual AoA parabolic formula of the location is approximated to a cone to simplify the location calculation process. The proposed localization technique has a low hardware cost, low computational requirements, and precise location estimates. Based on the performance evaluation, significant location accuracy is achieved by TDEAL; where, for instance, an average error margin of less than 13 cm is achieved using 10 readers in an area of   10   m ×   10   m . TDEAL can be utilized to provide reference points when integrated with a relative (e.g., inertial navigation systems) localization systems.

scholarly journals Hard Decision-Based Cooperative Localization for Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4665 ◽  
Author(s):  
Zhaoyang Wang ◽  
Xuebo Jin ◽  
Xiaoyi Wang ◽  
Jiping Xu ◽  
Yuting Bai
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Low Cost ◽  
Distance Estimation ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Localization Accuracy ◽  
Range Free ◽  
Hard Decision

Reliable and accurate localization of objects is essential for many applications in wireless networks. Especially for large-scale wireless sensor networks (WSNs), both low cost and high accuracy are targets of the localization technology. However, some range-free methods cannot be combined with a cooperative method, because these range-free methods are characterized by low accuracy of distance estimation. To solve this problem, we propose a hard decision-based cooperative localization method. For distance estimation, an exponential distance calibration formula is derived to estimate distance. In the cooperative phase, the cooperative method is optimized by outlier constraints from neighboring anchors. Simulations are conducted to verify the effectiveness of the proposed method. The results show that localization accuracy is improved in different scenarios, while high node density or anchor density contributes to the localization. For large-scale WSNs, the hard decision-based cooperative localization is proved to be effective.

scholarly journals A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8204
Author(s):  
Milica Petrović ◽  
Maciej Ciężkowski ◽  
Sławomir Romaniuk ◽  
Adam Wolniakowski ◽  
Zoran Miljković
Keyword(s):  
Neural Network ◽  
Main Idea ◽  
Signal Propagation ◽  
Calibration Method ◽  
Position Estimation ◽  
Reference Points ◽  
Positioning System ◽  
Feed Forward Neural Network ◽  
Distance Measurements ◽  
Positioning Systems

Positioning systems based on the lateration method utilize distance measurements and the knowledge of the location of the beacons to estimate the position of the target object. Although most of the global positioning techniques rely on beacons whose locations are known a priori, miscellaneous factors and disturbances such as obstacles, reflections, signal propagation speed, the orientation of antennas, measurement offsets of the beacons hardware, electromagnetic noise, or delays can affect the measurement accuracy. In this paper, we propose a novel hybrid calibration method based on Neural Networks (NN) and Apparent Beacon Position Estimation (ABPE) to improve the accuracy of a lateration positioning system. The main idea of the proposed method is to use a two-step position correction pipeline that first performs the ABPE step to estimate the perceived positions of the beacons that are used in the standard position estimation algorithm and then corrects these initial estimates by filtering them with a multi-layer feed-forward neural network in the second step. In order to find an optimal neural network, 16 NN architectures with 10 learning algorithms and 12 different activation functions for hidden layers were implemented and tested in the MATLAB environment. The best training outcomes for NNs were then employed in two real-world indoor scenarios: without and with obstacles. With the aim to validate the proposed methodology in a scenario where a fast set-up of the system is desired, we tested eight different uniform sampling patterns to establish the influence of the number of the training samples on the accuracy of the system. The experimental results show that the proposed hybrid NN-ABPE method can achieve a high level of accuracy even in scenarios when a small number of calibration reference points are measured.

scholarly journals Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 307
Author(s):  
Winfred Ingabire ◽  
Hadi Larijani ◽  
Ryan M. Gibson ◽  
Ayyaz-UI-Haq Qureshi
Keyword(s):  
Neural Networks ◽  
Long Range ◽  
Urban Areas ◽  
Large Scale ◽  
Low Cost ◽  
Wireless Sensor ◽  
Node Localization ◽  
Positioning Systems ◽  
Power Characteristics ◽  
Random Neural Networks

Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m.

Research and Improvement of DV-Hop Localization Algorithm for Wireless Sensor Network

2010 ◽  
Vol 44-47 ◽  
pp. 4028-4032
Author(s):  
Xue Guang Wang ◽  
Jie Ke Lin
Keyword(s):  
Energy Consumption ◽  
Low Cost ◽  
Wireless Sensor ◽  
Low Power Consumption ◽  
Localization Algorithm ◽  
Practical Application ◽  
Coverage Area ◽  
Localization Accuracy ◽  
Simulation Results ◽  
Average Size

Rang-free distributed localization algorithm has advantages of low power consumption, low cost etc, but it is weak in the localization accuracy, energy consumption of localization and the algorithm extend ability.That can not meet to the needs of practical application. To overcome the disadvantage of DV-Hop algorithm, in this paper, Two aspects improved algorithm are proposed based on the characteristics research of the original DV-Hop localization algorithm, which are the coverage area of beacon nodes and average size for one hop received by unknown nodes. Simulation results prove their validity and show the performance of the improvement algorithms is superior to the original DV-Hop algorithm.

scholarly journals Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors

2020 ◽  
Vol 10 (6) ◽  
pp. 2003 ◽  
Author(s):  
Liu Liu ◽  
Bofeng Li ◽  
Ling Yang ◽  
Tianxia Liu
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Reasonable Accuracy ◽  
Positioning Accuracy ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Smartphone Sensors ◽  
Pedestrian Dead Reckoning

For localization in daily life, low-cost indoor positioning systems should provide real-time locations with a reasonable accuracy. Considering the flexibility of deployment and low price of iBeacon technique, we develop a real-time fusion workflow to improve localization accuracy of smartphone. First, we propose an iBeacon-based method by integrating a trilateration algorithm with a specific fingerprinting method to resist RSS fluctuations, and obtain accurate locations as the baseline result. Second, as turns are pivotal for positioning, we segment pedestrian trajectories according to turns. Then, we apply a Kalman filter (KF) to heading measurements in each segment, which improves the locations derived by pedestrian dead reckoning (PDR). Finally, we devise another KF to fuse the iBeacon-based approach with the PDR to overcome orientation noises. We implemented this fusion workflow in an Android smartphone and conducted real-time experiments in a building floor. Two different routes with sharp turns were selected. The positioning accuracy of the iBeacon-based method is RMSE 2.75 m. When the smartphone is held steadily, the fusion positioning tests result in RMSE of 2.39 and 2.22 m for the two routes. In addition, the other tests with orientation noises can still result in RMSE of 3.48 and 3.66 m. These results demonstrate our fusion workflow can improve the accuracy of iBeacon positioning and alleviate the influence of PDR drifting.

scholarly journals Geometric Location Based on TDOA for Wireless Sensor Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Xin-long Luo ◽  
Wei Li ◽  
Jia-ru Lin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Low Cost ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Wireless Localization ◽  
Localization Accuracy ◽  
Sensor Performance ◽  
New Strategy ◽  
Performance Analysis And Simulation

Wireless sensor networks (WSNs) consist of a large number of low-cost miniature sensors, which can be applied to battlefield surveillance, environmental monitoring, target tracking, and other applications related to the positions of sensors. The location information of sensors is of great importance for wireless sensor networks. In this paper, we propose a new localization algorithm for the wireless sensor network based on time difference of arrival (TDOA), which is a typical algorithm in the wireless localization field. In order to improve the localization accuracy of a sensor, a new strategy is proposed for a localized sensor being upgraded to an anchor node, which is used to localize the position of the next sensor. Performance analysis and simulation results show that the revised TODA localization algorithm has the higher localization accuracy when compared with the original TDOA location method.

scholarly journals Discrete Path Selection and Entropy Based Sensor Node Failure Detection in Wireless Sensor Networks

2016 ◽  
Vol 16 (3) ◽  
pp. 137-153 ◽  
Author(s):  
K. Spoorthi ◽  
Saha Snehanshu ◽  
Mathur Archana
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Low Cost ◽  
Failure Detection ◽  
Path Selection ◽  
Wireless Sensor ◽  
Sensing Model ◽  
Cost Efficient ◽  
Almost All

Abstract Exertion of wireless sensor networks has been increasing in recent years, and it imprints in almost all the technologies such as machine industry, medical, military and civil applications. Due to rapid growth in electronic fabrication technology, low cost, efficient, multifunctional and accurate sensors can be produced and thus engineers tend to incorporate many sensors in the area of deployment. As the number of sensors in the field increases, the probability of failure committed by these sensors also increases. Hence, efficient algorithms to detect and recover the failure of sensors are paramount. The current work concentrates mainly on mechanisms to detect sensor node failures on the basis of the delay incurred in propagation and also the energy associated with sensors in the field of deployment. The simulation shows that the algorithm plays in the best possible way to detect the failure in sensors. Finally, the Boolean sensing model is considered to calculate the network coverage of the wireless sensor network for various numbers of nodes in the network.

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