scholarly journals On the Realistic Radio and Network Planning of IoT Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3264 ◽  
Author(s):  
Loizos Kanaris ◽  
Charalampos Sergiou ◽  
Akis Kokkinis ◽  
Aris Pafitis ◽  
Nikos Antoniou ◽  
...  
Keyword(s):  
Large Scale ◽  
Open Systems ◽  
Urban Environments ◽  
Radio Propagation ◽  
Correct Usage ◽  
Open Systems Interconnection ◽  
Physical Information ◽  
Network Simulators ◽  
And Performance ◽  
Real Test

Planning and deploying a functional large scale Wireless Sensor Network (WSN) or a Network of Internet of Things (IoTs) is a challenging task, especially in complex urban environments. A main network design bottleneck is the existence and/or correct usage of appropriate cross layer simulators that can generate realistic results for the scenario of interest. Existing network simulators tend to overlook the complexity of the physical radio propagation layer and consequently do not realistically simulate the main radio propagation conditions that take place in urban or suburban environments, thus passing inaccurate results between Open Systems Interconnection (OSI) layers. This work demonstrates through simulations and measurements that, by correctly passing physical information to higher layers, the overall simulation process produces more accurate results at the network layer. It is demonstrated that the resulting simulation methodology can be utilized to accomplish realistic wireless planning and performance analysis of the deployed nodes, with results that are very close to those of real test-beds, or actual WSN deployments.

scholarly journals LONG-TERM VISUAL LOCALIZATION IN LARGE SCALE URBAN ENVIRONMENTS EXPLOITING STREET LEVEL IMAGERY

2020 ◽  
Vol V-2-2020 ◽  
pp. 57-63
Author(s):  
J. Meyer ◽  
D. Rettenmund ◽  
S. Nebiker
Keyword(s):  
Large Scale ◽  
3D Structure ◽  
Urban Environments ◽  
Visual Localization ◽  
Street Level ◽  
Reference Images ◽  
And Performance ◽  
Localization Approach ◽  
Better Than

Abstract. In this paper, we present our approach for robust long-term visual localization in large scale urban environments exploiting street level imagery. Our approach consists of a 2D-image based localization using image retrieval (NetVLAD) to select reference images. This is followed by a 3D-structure based localization with a robust image matcher (DenseSfM) for accurate pose estimation. This visual localization approach is evaluated by means of the ‘Sun’ subset of the RobotCar seasons dataset, which is part of the Visual Localization benchmark. As the results on the RobotCar benchmark dataset are nearly on par with the top ranked approaches, we focused our investigations on reproducibility and performance with own data. For this purpose, we created a dataset with street-level imagery. In order to have independent reference and query images, we used a road-based and a tram-based mapping campaign with a time difference of four years. The approximately 90% successfully oriented images of both datasets are a good indicator for the robustness of our approach. With about 50% success rate, every second image could be localized with a position accuracy better than 0.25 m and a rotation accuracy better than 2°.

Large-scale cost function learning for path planning using deep inverse reinforcement learning

2017 ◽  
Vol 36 (10) ◽  
pp. 1073-1087 ◽  
Author(s):  
Markus Wulfmeier ◽  
Dushyant Rao ◽  
Dominic Zeng Wang ◽  
Peter Ondruska ◽  
Ingmar Posner
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Cost Model ◽  
High Capacity ◽  
Urban Environments ◽  
Training Dataset ◽  
Inverse Reinforcement Learning ◽  
Dataset Size ◽  
And Performance ◽  
The Cost

We present an approach for learning spatial traversability maps for driving in complex, urban environments based on an extensive dataset demonstrating the driving behaviour of human experts. The direct end-to-end mapping from raw input data to cost bypasses the effort of manually designing parts of the pipeline, exploits a large number of data samples, and can be framed additionally to refine handcrafted cost maps produced based on manual hand-engineered features. To achieve this, we introduce a maximum-entropy-based, non-linear inverse reinforcement learning (IRL) framework which exploits the capacity of fully convolutional neural networks (FCNs) to represent the cost model underlying driving behaviours. The application of a high-capacity, deep, parametric approach successfully scales to more complex environments and driving behaviours, while at deployment being run-time independent of training dataset size. After benchmarking against state-of-the-art IRL approaches, we focus on demonstrating scalability and performance on an ambitious dataset collected over the course of 1 year including more than 25,000 demonstration trajectories extracted from over 120 km of urban driving. We evaluate the resulting cost representations by showing the advantages over a carefully, manually designed cost map and furthermore demonstrate its robustness towards systematic errors by learning accurate representations even in the presence of calibration perturbations. Importantly, we demonstrate that a manually designed cost map can be refined to more accurately handle corner cases that are scarcely seen in the environment, such as stairs, slopes and underpasses, by further incorporating human priors into the training framework.

scholarly journals LoRaWANSim: A Flexible Simulator for LoRaWAN Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 695
Author(s):  
Riccardo Marini ◽  
Konstantin Mikhaylov ◽  
Gianni Pasolini ◽  
Chiara Buratti
Keyword(s):  
Large Scale ◽  
Research Community ◽  
Area Network ◽  
Wide Area Network ◽  
Performance Limits ◽  
Medium Access ◽  
Trade Offs ◽  
Physical Medium ◽  
Network Simulators ◽  
And Performance

Among the low power wide area network communication protocols for large scale Internet of Things, LoRaWAN is considered one of the most promising, owing to its flexibility and energy-saving capabilities. For these reasons, during recent years, the scientific community has invested efforts into assessing the fundamental performance limits and understanding the trade-offs between the parameters and performance of LoRaWAN communication for different application scenarios. However, this task cannot be effectively accomplished utilizing only analytical methods, and precise network simulators are needed. To that end, this paper presents LoRaWANSim, a LoRaWAN simulator implemented in MATLAB, developed to characterize the behavior of LoRaWAN networks, accounting for physical, medium access control and network aspects. In particular, since many simulators described in the literature are deployed for specific research purposes, they are usually oversimplified and hold a number of assumptions affecting the accuracy of their results. In contrast, our simulator has been developed for the sake of completeness and it is oriented towards an accurate representation of the LoRaWAN at the different layers. After a detailed description of the simulator, we report a validation of the simulator itself and we then conclude by presenting some results of its use revealing notable and non-intuitive trade-offs present in LoRaWAN. Such simulator will be made available via open access to the research community.

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