A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion

With the rapid development in wireless technologies and the Internet, the Internet of Things (IoT) is envisioned to be an integral part of our daily lives. Localization-based services are among the most attractive applications related to the IoT. They are actually, thanks to the deployment of networks of sensors, able to collect and transmit data in order to determine the targets position. A plethora of localization systems are proposed in the literature. These localization systems are based on different positioning approaches, different techniques and different technologies, making them appropriate for some applications and inappropriate for other applications. This survey provides a general overview of the localization in Wireless Sensor Networks (WSN) and surveys technical details related to approaches and algorithms of various important localization techniques using different technologies. Based on the localization approaches, we propose to classify the localization systems to centralized, distributed and interactive. Considering the techniques of localization, we classify them to distance measurement, angle measurement, arear measurement and hop-count measurement based. Finally, Depending on the manner of the wireless devices interaction with the target, we classify the localization systems to two categories: device-based and device-free systems. In device-based techniques, localization is linked to the target, and localization is determined thanks to the cooperation with other deployed wireless devices. Whereas in the device-free systems, the target does not include any wireless device according to the localization. We compare exhaustively each system in terms of precision, cost, evolution and energy efficiency. Furthermore, we show the importance of localization in modern IoT application such as smart city, smart transportation and mobility. In this concern, we provide an overview of the main challenges of localization in IoT exposed recently in the literature. Finally, we suggest in this paper some future directions in localization studies. This paper intends to help new researchers in the field of localization and IoT by providing a comprehensive survey on recent advances in this field.

A Survey of Localization Systems in Internet of Things

Odometry techniques are key to autonomous robot navigation, since they enable self-localization in the environment However, designing a robust odometry system is particularly challenging when camera and LiDAR are uninformative or unavailable In this paper, we leverage recent advances in deep learning and variational inference to correct dynamical and observation models for state-space systems The methodology trains Gaussian processes on the residual between the original model and the ground truth, and is applied on publicly available datasets for robot navigation based on two wheel encoders, a fiber optic gyro, and an Inertial Measurement Unit (IMU) We also propose to build an Extended Kalman Filter (EKF) on the learned model using wheel speed sensors and the fiber optic gyro for state propagation, and the IMU to update the estimated state Experimental results clearly demonstrate that the (learned) corrected models and EKF are more accurate than their original counterparts

/pdf/learning-wheel-odometry-and-imu-errors-for-localization-n93w5jc08a.pdf

Learning Wheel Odometry and IMU Errors for Localization

Smart cities (SC) promote economic development, improve the welfare of their citizens, and help in the ability of people to use technologies to build sustainable services. However, computational methods are necessary to assist in the process of creating smart cities because they are fundamental to the decision-making process, assist in policy making, and offer improved services to citizens. As such, the aim of this research is to present a systematic review regarding data mining (DM) and machine learning (ML) approaches adopted in the promotion of smart cities. The Methodi Ordinatio was used to find relevant articles and the VOSviewer software was performed for a network analysis. Thirty-nine significant articles were identified for analysis from the Web of Science and Scopus databases, in which we analyzed the DM and ML techniques used, as well as the areas that are most engaged in promoting smart cities. Predictive analytics was the most common technique and the studies focused primarily on the areas of smart mobility and smart environment. This study seeks to encourage approaches that can be used by governmental agencies and companies to develop smart cities, being essential to assist in the Sustainable Development Goals.

https://www.mdpi.com/2071-1050/11/4/1077/pdf

Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018

The purpose of smart city is to enhance the optimal utilization of scarce resources and improve the resident’s quality of live. The smart cities employed Internet of Things (IoT) to create a sustainable urban life. The IoT devices such as sensors, actuators, and smartphones in the smart cities generate data. The data generated from the smart cities are subjected to analytics to gain insight and discover new knowledge for improving the efficiency and effectiveness of the smart cities. Recently, the application of deep learning in smart cities has gained a tremendous attention from the research community. However, despite raise in popularity and achievements made by deep learning in solving problems in smart cities, no survey has been dedicated mainly on the application of deep learning in smart cities to show recent progress and direction for future development. To bridge this gap, this paper proposes to conduct a dedicated survey on the applications of deep learning in smart cities. In this paper, recent progress, new taxonomies, challenges and opportunities for future research opportunities on the application of deep learning in smart cities have been unveiled. The paper can provide opportunities for experts in the research community to propose a novel approach for developing the research area. On the other hand, new researchers interested in the research area can use the paper as an entry point.

Deep learning application in smart cities: recent development, taxonomy, challenges and research prospects

A smart city’s main purpose is to provide intelligent responses to different problems of the rapid urban population growth. For instance, integrating fleet management solutions into intelligent transportation systems (ITS) can efficiently resolve transportation problems relying on each vehicle information. Usually, the position estimate is ensured by the integration of the Global Positioning System (GPS) and Inertial Navigation Systems (INS). For multisensor data fusion, the Extended Kalman Filter (EKF) is generally applied using the sensor’s measures and the GPS position as a helper. However, the INS are expensive and require more complex computing which induces restrictions on their implementation. Furthermore, the EKF performance depends on the vehicle dynamic variations and may quickly diverge because of environmental changes. In this paper, we present a robust low cost approach using EKF and Support Vector Machines (SVM) to reliably estimate the vehicle position by limiting the EKF drawbacks. The sensors used are a GPS augmented by a low cost wireless sensor network. When GPS signals are available, we train SVM on different dynamics and outage times to learn the position errors so we can correct the future EKF predictions during GPS signal outages. We obtain empirically an improvement of up to 94% over the simple EKF predictions in case of GPS failures.

Improving Vehicle Localization in a Smart City with Low Cost Sensor Networks and Support Vector Machines

Improving low cost sensor based vehicle positioning with Machine Learning

A smart city is emerging as an application of information and communication technologies to mitigate the problems generated by the urban population growth. One of the smart city solutions is to establish an efficient fleet management relating to the use of a fleet of vehicles (e.g., ambulances and police vehicles). The most basic function in a fleet management system is the real time vehicle tracking component. This component is usually the Differential Global Positioning System (DGPS) or the integration of Global Positioning System (GPS) and Inertial Navigation Systems (INS). To predict the position, the Extended Kalman Filter (EKF) is generally applied using the sensor's measures and the GPS position as a helper. However, the DGPS high cost solution still suffers from GPS satellite signals loss due to multipath errors and the INS require more complex computing. Furthermore, the EKF performance depends on the vehicle dynamic variations and may quickly diverge because of environment changes (i.e, GPS failures by obstructions from building and trees). In this paper, we present a robust low cost approach using EKF and neural networks (NN) with Genetic Algorithm (GA) to reliably estimate the real time vehicle position using GPS enhanced with low cost Dead Reckoning (DR) sensors. While GPS signals are available, we train the NN with GA on different dynamics and outage times to learn the position errors so we can correct the future EKF predictions during GPS signal outages. We obtain empirically an improvement of up to 95% over the simple EKF predictions in case of GPS failures.

A robust low cost approach for real time car positioning in a smart city using Extended Kalman Filter and evolutionary machine learning

Global Positioning System (GPS) and Inertial Navigation Systems (INS) are usually used for real time vehicle positioning. Unfortunately, this high cost solution still suffers from GPS outages due to multipath errors. To perform data fusion, the Extended Kalman filter (EKF) has been widely adopted as an optimal estimation tool for non-linear systems. However, it has some drawbacks in terms of stability, immunity to noise effects and observability. Furthermore, the filter is unfluenced by the vehicle dynamic variations and environment changes. On the other hand, neural networks can map input data to output data without any prior knowledge of the relationship involved. This paper presents a hybrid EKF and neural networks approach to improve the real time vehicle positioning performance while using GPS with nothing else than an odometer and a gyrometer low cost aiding sensors. While GPS signals are available, the neural networks are trained on different dynamics and outage times to learn the position errors so they can correct the future EKF predictions during GPS signal outages. The simulation results of this approach show a minimal improvement of 39% over the EKF predictions.

A hybrid low cost approach using Extended Kalman filter and neural networks for real time positioning

The Global Positioning System (GPS) enhanced with low cost Dead Reckoning (DR) sensors allows to estimate in real time a vehicle position with more accuracy while maintaining a low cost. The Extended Kalman Filter (EKF) is generally used to predict the position using the sensor’s measures and the GPS position as a helper. However, the filter performance tails off during periods of GPS failure and may quickly diverge (e.g., in tunnels or due to multipath phenomenon). In this paper, we propose a novel hybrid approach based on neural networks (NN) and autoregressive integrated moving average (ARIMA) models to circumvent the EKF limitations and improve the accuracy of vehicle position estimation. While GPS signals are available, we train NN and ARIMA models to learn the non-linear and linear structures in the vehicle position; therefore they can provide good predictions during GPS signal outages. We obtain empirically an improvement of up to 95 % over the simple EKF predictions in case of GPS failures.

Ikram Belhajem

Papers

Improving Vehicle Localization in a Smart City with Low Cost Sensor Networks and Support Vector Machines

Improving low cost sensor based vehicle positioning with Machine Learning

A robust low cost approach for real time car positioning in a smart city using Extended Kalman Filter and evolutionary machine learning

A hybrid low cost approach using Extended Kalman filter and neural networks for real time positioning

A Hybrid Machine Learning Based Low Cost Approach for Real Time Vehicle Position Estimation in a Smart City