Localization plays an important role in the field of Wireless Sensor Networks (WSNs) and robotics. Currently, localization is a very vibrant scientific research field with many potential applications. Localization offers a variety of services for the customers, for example, in the field of WSN, its importance is unlimited, in the field of logistics, robotics, and IT services. Particularly localization is coupled with the case of human-machine interaction, autonomous systems, and the applications of augmented reality. Also, the collaboration of WSNs and distributed robotics has led to the creation of Mobile Sensor Networks (MSNs). Nowadays there has been an increasing interest in the creation of MSNs and they are the preferred aspect of WSNs in which mobility plays an important role while an application is going to execute. To overcome the issues regarding localization, the authors developed a framework of three algorithms named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF) Localization algorithms. In our previous study, the authors only focused on EKF-based localization. In this paper, the authors present a modified Kalman Filter (KF) for localization based on UKF and PF Localization. In the paper, all these algorithms are compared in very detail and evaluated based on their performance. The proposed localization algorithms can be applied to any type of localization approach, especially in the case of robot localization. Despite the harsh physical environment and several issues during localization, the result shows an outstanding localization performance within a limited time. The robustness of the proposed algorithms is verified through numerical simulations. The simulation results show that proposed localization algorithms can be used for various purposes such as target tracking, robot localization, and can improve the performance of localization.

Security surveillance is critical to social harmony and people’s peaceful life. It has a great impact on strengthening social stability and life safeguarding. Detecting anomaly timely, effectively and efficiently in video surveillance remains challenging. This paper proposes a new approach, called S2-VAE, for anomaly detection from video data. The S2-VAE consists of two proposed neural networks: a Stacked Fully Connected Variational AutoEncoder (SF - VAE) and a Skip Convolutional VAE (SC-VAE). The SF -VAE is a shallow generative network to obtain a model like Gaussian mixture to fit the distribution of the actual data. The SC-VAE, as a key component of S2- VAE, is a deep generative network to take advantages of CNN, VAE and skip connections. Both SF -VAE and SC-VAE are efficient and effective generative networks and they can achieve better performance for detecting both local abnormal events and global abnormal events. The proposed S2-VAE is evaluated using four public datasets. The experimental results show that the S2-VAE outperforms the state-of-the-art algorithms. The code is available publicly at https://github.com/tianwangbuaa/.

Underwater localization is used as a key element in most applications of underwater communications. Despite Global Positioning System (GPS) receivers are usually employed in Terrestrial Wireless Sensor Networks, they cannot be exploited for underwater localization. In fact, GPS signals are highly attenuated by the water, and higher than a couple of meters and can not propagate underwater and especially through the salt water. In place of RF signals, acoustic signals is the most common mode of communication, and the so-called Underwater Acoustic Sensor Networks attracted a significant interest due to their great impact on ocean monitoring and exploration. Hydroacoustics, as the study and application of sound in water, is the foundation of underwater localization, but the existing available methods, classified in range-based vs. range-free techniques are affected by several open problems and research challenges. Therefore, an accurate range-based algorithm for localization needs to be developed, and the demand for expeditiously employing the energy of the sensor nodes is still remaining a distinct feature for Underwater Wireless Sensor Networks. Because of these argues, an improved interpretation for underwater localization is presented, by first presenting a general localization algorithm, and afterwards deploying the ordinary, beacon nodes in order to find the error and accuracy of sensor localization. After that, we present two localization algorithms named as distance-based and angle-based algorithms. We consider a realistic case where sensor nodes are not time synchronized and the sound speed in water is unknown. Simulation results exhibit that our algorithms compensate for time synchronization, estimate the mean errors in localization and achieve a good localization accuracy.

The Internet of things (IoT) that allows connectivity of network devices embedded with sensors undergoes severe data exchange interference as the unlicensed spectrum band becomes overcrowded. By applying cognitive radio (CR) capabilities to IoT, a novel cognitive radio internet of things (CR-IoT) network arises as a promising solution to tackle the spectrum scarcity problem in conventional IoT network. CR is a form of wireless communication whereby a radio is dynamically programmed and configured to detect available spectrum channels. This enhances the spectrum utilization efficiency of radio frequency while avoiding interference and overcrowding to other users. Energy efficiency in CR-IoT network must be carefully formulated since the sensor nodes consume significant energy to support CR operations such as in dynamic spectrum sensing and switching. In this paper, we study channel spectrum sensing to boost energy efficiency in clustered CR-IoT networks. We propose a two-way information exchange dynamic spectrum sensing algorithms to improve energy efficiency for data transmission in licensed channels. In addition, the concern of the energy consumption in dynamic spectrum sensing and switching, we propose an energy efficient optimal transmit power allocation technique to enhance the dynamic spectrum sensing and data throughput. Simulation results validate that the proposed dynamic spectrum sensing technique can significantly reduce the energy consumption in CR-IoT networks.