101 research outputs found
GNSS-free outdoor localization techniques for resource-constrained IoT architectures : a literature review
Large-scale deployments of the Internet of Things (IoT) are adopted for performance
improvement and cost reduction in several application domains. The four main IoT application
domains covered throughout this article are smart cities, smart transportation, smart healthcare, and
smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be
time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor
localization and received significant attention from the research community due to low-power,
low-cost, and long-range communication. In addition, its signals can be used for communication
and localization simultaneously. There are different proposed localization methods to obtain the
IoT relative location. Each category of these proposed methods has pros and cons that make them
useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization
methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated
this work and provided the following contributions: (1) definition of the main requirements and
limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most
relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey
the most relevant methods used within the IoT ecosystem for improving GNSS-free localization
accuracy, and (4) discussion covering the open challenges and future directions within the field.
Some of the important open issues that have different requirements in different IoT systems include
energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview
of research works that have been published between 2018 to July 2021 and made available through
the Google Scholar database.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/
Integrated Satellite-terrestrial networks for IoT: LoRaWAN as a Flying Gateway
When the Internet of Things (IoT) was introduced, it causes an immense change in
human life. Recently, different IoT emerging use cases, which will involve an even higher
number of connected devices aimed at collecting and sending data with different purposes
and over different application scenarios, such as smart city, smart factory, and smart
agriculture. In some cases, the terrestrial infrastructure is not enough to guarantee the
typical performance indicators due to its design and intrinsic limitations. Coverage is
an example, where the terrestrial infrastructure is not able to cover certain areas such
as remote and rural areas. Flying technologies, such as communication satellites and
Unmanned Aerial Vehicles (UAVs), can contribute to overcome the limitations of the
terrestrial infrastructure, offering wider coverage, higher resilience and availability, and
improving user\u2019s Quality of Experience (QoE). IoT can benefit from the UAVs and satellite
integration in many ways, also beyond the coverage extension and the increase of the
available bandwidth that these objects can offer. This thesis proposes the integration
of both IoT and UAVs to guarantee the increased coverage in hard to reach and out of
coverage areas. Its core focus addresses the development of the IoT flying gateway and
data mule and testing both approaches to show their feasibility.
The first approach for the integration of IoT and UAV results in the implementing of
LoRa flying gateway with the aim of increasing the IoT communication protocols\u2019
coverage area to reach remote and rural areas. This flying gateway examines the
feasibility for extending the coverage in a remote area and transmitting the data to the IoT cloud in real-time. Moreover, it considers the presence of a satellite between the
gateway and the final destination for areas with no Internet connectivity and
communication means such as WiFi, Ethernet, 4G, or LTE. The experimental results
have shown that deploying a LoRa gateway on board a flying drone is an ideal option
for the extension of the IoT network coverage in rural and remote areas.
The second approach for the integration of the aforementioned technologies is the
deployment of IoT data mule concept for LoRa networks. The difference here is the
storage of the data on board of the gateway and not transmitting the data to the IoT
cloud in real time. The aim of this approach is to receive the data from the LoRa
sensors installed in a remote area, store them in the gateway up until this flying
gateway is connected to the Internet. The experimental results have shown the
feasibility of our flying data mule in terms of signal quality, data delivery, power
consumption and gateway status.
The third approach considers the security aspect in LoRa networks. The possible
physical attacks that can be performed on any LoRa device can be performed once its
location is revealed. Position estimation was carried out using one of the LoRa signal
features: RSSI. The values of RSSI are fed to the Trilateration localization algorithm to
estimate the device\u2019s position. Different outdoor tests were done with and without the
drone, and the results have shown that RSSI is a low cost option for position estimation
that can result in a slight error due to different environmental conditions that affect
the signal quality.
In conclusion, by adopting both IoT technology and UAV, this thesis advances the
development of flying LoRa gateway and LoRa data mule for the aim of increasing the
coverage of LoRa networks to reach rural and remote areas. Moreover, this research
could be considered as the first step towards the development of high quality and
performance LoRa flying gateway to be tested and used in massive LoRa IoT networks
in rural and remote areas
A DEEP LEARNING MODEL IMPLEMENTATION BASED ON RSSI FINGERPRINTING FOR LORA-BASED INDOOR LOCALIZATION
LoRa technology has received a lot of attention in the last few years. Numerous success stories about using LoRa technology for the Internet of Things in various implementations. Several studies have found that the use of LoRa technology has the opportunity to be implemented in indoor-based applications. LoRa technology is found more stable and is more resilient to environmental changes. Environmental change of the indoor is a major problem to maintain accuracy in position prediction, especially in the use of Received Signal Strength (RSS) fingerprints as a reference database. The variety of approaches to solving accuracy problems continues to improve as the need for indoor localization applications increases. Deep learning approaches as a solution for the use of fingerprints in indoor localization have been carried out in several studies with various novelties offered. Let’s introduce a combination of the use of LoRa technology's excellence with a deep learning method that uses all variations of measurement results of RSS values at each position as a natural feature of the indoor condition as a fingerprint. All of these features are used for training in-deep learning methods. It is DeepFi-LoRaIn which illustrates a new technique for using the fingerprint data of the LoRa device's RSS device on indoor localization using deep learning methods. This method is used to find out how accurate the model produced by the training process is to predict the position in a dynamic environment. The scenario used to evaluate the model is by giving interference to the RSS value received at each anchor node. The model produced through training was found to have good accuracy in predicting the position even in conditions of interference with several anchor nodes. Based on the test results, DeepFi-LoRaIn Technique can be a solution to cope with changing environmental conditions in indoor localizatio
Long-Range Indoor Emitter Localization from 433MHz and 2.4GHz WLAN Received Signal Strengths
An improved search method for localizing a radio emitter in a building from its signal strength is proposed and implemented. It starts from floor level determination, which samples the signal strength on each floor and determines the floor level of the emitter. Then the search is conducted iteratively on a specific floor. For each round of search, one-dimensional (1-D) or two-dimensional (2-D) signal strength is collected according to the actual structure of the floor. The signal strength data are processed to fit a 1-D curve or a 2-D surface with regression models to establish an indicator or trend, which can either locate the emitter or provide direction for the next round of search. The main contribution of this thesis is that the data processing results for 2- D signal strength data can locate the emitter or show the direction of the emitter through gradient, which is helpful to future search. Our approach has been implemented with two wireless protocols: 433MHz protocol and 2.4GHz wireless local area network (WLAN) protocol. A 433MHz module with LoRa modulation is chosen to provide long propagation distance. A 2.4GHz WLAN tester is used for close range search where 433MHz signal does not show enough attenuation spread to be effective. 433MHz implementation consists of an emitter, a radio tester and an Android APP on a smartphone. The emitter is a board with an Arduino Uno and a 433MHz transceiver. The radio tester is a board with an Arduino Uno, a 433MHz transceiver and a Bluetooth-to-serial module to communicate with a smartphone. The radio tester and the APP work together to localize the emitter. 2.4GHz WLAN implementation is composed of an emitter, which is emulated with a smartphone, a radio tester which consists of a smartphone, and a router and two Android APPs. Both phones are connected through the router and socket communication is initiated with the radio tester working as a server and the emitter working as a client. The APP on the emitter implements the client functions. The radio tester controls data acquisition process. The APP on the tester establishes the server functions and deals with received data. It compares signal strengths in different locations and finds the position that has the strongest signal strength to locate the emitter. The innovative idea of this thesis is to use 1-D and 2-D signal strength with regression models as it is convenient to provide location or unique search direction of the emitter. 1-D data is processed with linear and polynomial regressions to fit curves in order to find possible location of the emitter in either a narrow strip or a half a plane. 2-D data is processed with multiple regressions to fit contour-line surfaces in order to find either location of the emitter on the top of a surface or a unique search direction of the location of the emitter as indicated by the highest surface gradient. Our approach is compared with the centroid algorithm with an example. The centroid algorithm assumes the emitter is located in the search area and it is also easily influenced by sampling location biases. Our approach has two advantages over the centroid algorithm. The first advantage is that our approach can work even when the emitter is out of the initial search area since it searches iteratively. The second advantage is that when the emitter is in the initial search area, our approach is not influenced by sampling location biases
Low-Cost Traffic Sensing System Based on LoRaWAN for Urban Areas
The advent of Low Power Wide Area Networks (LPWAN) has enabled the
feasibility of wireless sensor networks for environmental traffic sensing
across urban areas. In this study, we explore the usage of LoRaWAN end nodes as
traffic sensing sensors to offer a practical traffic management solution. The
monitored Received Signal Strength Indicator (RSSI) factor is reported and used
in the gateways to assess the traffic of the environment. Our technique
utilizes LoRaWAN as a long-range communication technology to provide a
largescale system. In this work, we present a method of using LoRaWAN devices
to estimate traffic flows. LoRaWAN end devices then transmit their packets to
different gateways. Their RSSI will be affected by the number of cars present
on the roadway. We used SVM and clustering methods to classify the approximate
number of cars present. This paper details our experiences with the design and
real implementation of this system across an area that stretches for miles in
urban scenarios. We continuously measured and reported RSSI at different
gateways for weeks. Results have shown that if a LoRaWAN end node is placed in
an optimal position, up to 96% of correct environment traffic level detection
can be obtained. Additionally, we share the lComment: 7 pages, accepted to Emerging Topics in Wireless (EmergingWireless)
in CoNEXT 202
Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks
[EN] In this paper, we proposed a new wireless localization technique based on the ideology of social network analysis (SNA), to study the different properties of networks as a graph. Centrality is a main concept in SNA, so we propose using closeness centrality (CC) as a measurement to denote the importance of the node inside the network due to its geo-location to others. The node with highest degree of CC is chosen as a cluster heads, then each cluster head can form its trilateration process to collect data from its cluster. The selection of closest cluster based on CC values, and the unknown node's location can be estimated through the trilateration process. To form a perfect trilateration, the cluster head chooses three anchor nodes. The proposed algorithm provides high accuracy even in different network topologies like concave shape, O shape, and C shape as compared to existing received signal strength indicator (RSSI) techniques. Matlab simulation results based on practical radio propagation data sets showed a localization error of 0.32 m with standard deviation of 0.26 m.This work was fully supported by the Vice Chancellor Doctoral Scholarship at Auckland University of Technology, New Zealand.Ahmad, T.; Li, XJ.; Seet, B.; Cano, J. (2020). Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks. Electronics. 9(5):1-19. https://doi.org/10.3390/electronics9050738S11995Zhou, B., Yao, X., Yang, L., Yang, S., Wu, S., Kim, Y., & Ai, L. (2019). Accurate Rigid Body Localization Using DoA Measurements from a Single Base Station. Electronics, 8(6), 622. doi:10.3390/electronics8060622Ahmad, T., Li, X., & Seet, B.-C. (2017). Parametric Loop Division for 3D Localization in Wireless Sensor Networks. Sensors, 17(7), 1697. doi:10.3390/s17071697Kaur, A., Kumar, P., & Gupta, G. P. (2019). A weighted centroid localization algorithm for randomly deployed wireless sensor networks. Journal of King Saud University - Computer and Information Sciences, 31(1), 82-91. doi:10.1016/j.jksuci.2017.01.007Khelifi, F., Bradai, A., Benslimane, A., Rawat, P., & Atri, M. (2018). A Survey of Localization Systems in Internet of Things. Mobile Networks and Applications, 24(3), 761-785. doi:10.1007/s11036-018-1090-3Sanchez-Iborra, R., G. Liaño, I., Simoes, C., Couñago, E., & Skarmeta, A. (2018). Tracking and Monitoring System Based on LoRa Technology for Lightweight Boats. Electronics, 8(1), 15. doi:10.3390/electronics8010015Sayed, A. H., Tarighat, A., & Khajehnouri, N. (2005). Network-based wireless location: challenges faced in developing techniques for accurate wireless location information. IEEE Signal Processing Magazine, 22(4), 24-40. doi:10.1109/msp.2005.1458275Maşazade, E., Ruixin Niu, Varshney, P. K., & Keskinoz, M. (2010). Energy Aware Iterative Source Localization for Wireless Sensor Networks. IEEE Transactions on Signal Processing, 58(9), 4824-4835. doi:10.1109/tsp.2010.2051433Yang, X., Kong, Q., & Xie, X. (2009). One-Dimensional Localization Algorithm Based on Signal Strength Ratio. International Journal of Distributed Sensor Networks, 5(1), 79-79. doi:10.1080/15501320802571822Xie, S., Wang, T., Hao, X., Yang, M., Zhu, Y., & Li, Y. (2019). Localization and Frequency Identification of Large-Range Wide-Band Electromagnetic Interference Sources in Electromagnetic Imaging System. Electronics, 8(5), 499. doi:10.3390/electronics8050499Zhu, X., Wu, X., & Chen, G. (2013). Relative localization for wireless sensor networks with linear topology. Computer Communications, 36(15-16), 1581-1591. doi:10.1016/j.comcom.2013.07.007Meng, W., Xiao, W., & Xie, L. (2011). An Efficient EM Algorithm for Energy-Based Multisource Localization in Wireless Sensor Networks. IEEE Transactions on Instrumentation and Measurement, 60(3), 1017-1027. doi:10.1109/tim.2010.2047035Lim, H., & Hou, J. C. (2009). Distributed localization for anisotropic sensor networks. ACM Transactions on Sensor Networks, 5(2), 1-26. doi:10.1145/1498915.1498917Xiaohong Sheng, & Yu-Hen Hu. (2005). Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks. IEEE Transactions on Signal Processing, 53(1), 44-53. doi:10.1109/tsp.2004.838930Yun Wang, Xiaodong Wang, Demin Wang, & Agrawal, D. P. (2009). Range-Free Localization Using Expected Hop Progress in Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 20(10), 1540-1552. doi:10.1109/tpds.2008.239Huang, H., & Zheng, Y. R. (2018). Node localization with AoA assistance in multi-hop underwater sensor networks. Ad Hoc Networks, 78, 32-41. doi:10.1016/j.adhoc.2018.05.005Zà ruba, G. V., Huber, M., Kamangar, F. A., & Chlamtac, I. (2006). Indoor location tracking using RSSI readings from a single Wi-Fi access point. Wireless Networks, 13(2), 221-235. doi:10.1007/s11276-006-5064-1Singh, M., & Khilar, P. M. (2015). An analytical geometric range free localization scheme based on mobile beacon points in wireless sensor network. Wireless Networks, 22(8), 2537-2550. doi:10.1007/s11276-015-1116-8Yiqiang Chen, Qiang Yang, Jie Yin, & Xiaoyong Chai. (2006). Power-efficient access-point selection for indoor location estimation. IEEE Transactions on Knowledge and Data Engineering, 18(7), 877-888. doi:10.1109/tkde.2006.112Alzoubi, K., Li, X.-Y., Wang, Y., Wan, P.-J., & Frieder, O. (2003). Geometric spanners for wireless ad hoc networks. IEEE Transactions on Parallel and Distributed Systems, 14(4), 408-421. doi:10.1109/tpds.2003.1195412Safa, H. (2014). A novel localization algorithm for large scale wireless sensor networks. Computer Communications, 45, 32-46. doi:10.1016/j.comcom.2014.03.020Kaemarungsi, K., & Krishnamurthy, P. (2012). Analysis of WLAN’s received signal strength indication for indoor location fingerprinting. Pervasive and Mobile Computing, 8(2), 292-316. doi:10.1016/j.pmcj.2011.09.003Patwari, N., Hero, A. O., Perkins, M., Correal, N. S., & O’Dea, R. J. (2003). Relative location estimation in wireless sensor networks. IEEE Transactions on Signal Processing, 51(8), 2137-2148. doi:10.1109/tsp.2003.814469Niculescu, D. (2003). Telecommunication Systems, 22(1/4), 267-280. doi:10.1023/a:1023403323460Mahyar, H., Hasheminezhad, R., Ghalebi K., E., Nazemian, A., Grosu, R., Movaghar, A., & Rabiee, H. R. (2018). Compressive sensing of high betweenness centrality nodes in networks. Physica A: Statistical Mechanics and its Applications, 497, 166-184. doi:10.1016/j.physa.2017.12.145Plets, D., Bastiaens, S., Martens, L., & Joseph, W. (2019). An Analysis of the Impact of LED Tilt on Visible Light Positioning Accuracy. Electronics, 8(4), 389. doi:10.3390/electronics8040389RSSI Datasethttps://github.com/pspachos/RSSI-DatasetAhmad, T., Li, X. J., & Seet, B.-C. (2019). Noise Reduction Scheme for Parametric Loop Division 3D Wireless Localization Algorithm Based on Extended Kalman Filtering. Journal of Sensor and Actuator Networks, 8(2), 24. doi:10.3390/jsan8020024Benson, S. J., Ye, Y., & Zhang, X. (2000). Solving Large-Scale Sparse Semidefinite Programs for Combinatorial Optimization. SIAM Journal on Optimization, 10(2), 443-461. doi:10.1137/s105262349732800
- …