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The development of urban environments leads to many issues; one of them is the traffic blockage. For this fast world, adequate traffic measures should be taken with the help of modern technology. To control this traffic issue, we need effective computation and prediction of traffic flow. To achieve this, the traffic flow data should be collected and analyzed properly (Wang et al., 2020). Wireless Sensor Network (WSN) is an auto-configured group of sensors that monitor and record physical parameters of the environment like temperature, pressure, sound, motion, etc. and organize the data at a central location. These WSNs find their application in various fields like military for battlefield surveillance, health monitoring systems, etc. One such application can be used in the traffic management system. The traffic flow data can be collected using the WSN; it constitutes wireless modules and magnetometer sensors, which give the vehicle flow data along with their speed and location accurately. This system is cost-effective, consumes less power and requires less maintenance. The data is collected and transmitted to the cloud server using the mobile internet. The traffic flow data is then computed using a method based on incremental noise addition for chaotic identification. This method involves the addition of noise signals with different intensities incrementally by the Pseudo-Periodic Surrogate (PPS) method, and the mean value of delayed Mutual Information (MI) is used to measure the complexity of the signal. Depending upon the trends of the changes in the noise mixed-signal, the characteristics can be determined. Hence, we conclude that this method helps in developing out an organized traffic management system. [ABSTRACT FROM AUTHOR]

Location has become an essential part of the next-generation Internet of Things systems. This paper proposes a multi-sensor-based 3D indoor localization approach. Compared with the existing 3D localization methods, this paper presents a wireless received signal strength (RSS)-profile-based floor-detection approach to enhance RSS-based floor detection. The profile-based floor detection is further integrated with the barometer data to gain more reliable estimations of the height and the barometer bias. Furthermore, the data from inertial sensors, magnetometers, and a barometer are integrated with the RSS data through an extend Kalman filter. The proposed multi-sensor integration algorithm provided more robust and smoother floor detection and 3D localization solutions than the existing methods.

Indoor positioning technology has attracted the attention of researchers due to the increasing pervasiveness of smartphones and the development of sensor technology, along with the increase of indoor time. Sensor technology, which is one of the most commonly used data sources for indoor positioning, has the advantage that sensors can receive data from a smartphone without installing any additional device. However, the readings of built-in sensors are easily affected by the surrounding environment and are even occasionally different from each other which adversely influence the accuracy of indoor positioning. Moreover, once an error occurs, it can accumulate because there is not any reference point in the sensor, only in indoor positioning. In this paper, we present an accurate indoor positioning technology, which uses smartphone built-in sensors and Bluetooth beacon-based landmarks. Our proposed algorithm chooses proper one between values of sensors alternately based on their characteristics. It exploits landmarks as the reference points of indoor positioning. It also allows individuals to add the location where they repeatedly detect the same and special beacon received signal strength indicator values as a crowdsourced landmark. Extensive experimental results show that our proposed algorithm facilitates the acquisition of accurate heading direction and coordinates of the user.

Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework. [ABSTRACT FROM AUTHOR]

Indoor positioning technology has attracted the attention of researchers due to the increasing pervasiveness of smartphones and the development of sensor technology, along with the increase of indoor time. Sensor technology, which is one of the most commonly used data sources for indoor positioning, has the advantage that sensors can receive data from a smartphone without installing any additional device. However, the readings of built-in sensors are easily affected by the surrounding environment and are even occasionally different from each other which adversely influence the accuracy of indoor positioning. Moreover, once an error occurs, it can accumulate because there is not any reference point in the sensor, only in indoor positioning. In this paper, we present an accurate indoor positioning technology, which uses smartphone built-in sensors and Bluetooth beacon-based landmarks. Our proposed algorithm chooses proper one between values of sensors alternately based on their characteristics. It exploits landmarks as the reference points of indoor positioning. It also allows individuals to add the location where they repeatedly detect the same and special beacon received signal strength indicator values as a crowdsourced landmark. Extensive experimental results show that our proposed algorithm facilitates the acquisition of accurate heading direction and coordinates of the user. [ABSTRACT FROM AUTHOR]