Localization is an essential task in Wireless Sensor Networks (WSN) for various use cases such as target tracking and object monitoring. Anchor nodes play a critical role in this task since they can find their location via GPS signals or manual setup mechanisms and help other nodes in the network determine their locations. Therefore, the optimal placement of anchor nodes in a WSN is of particular interest for reducing the energy consumption while yielding better accuracy at finding locations of the nodes. In this paper, we propose a novel approach for finding the optimal number of anchor nodes and an optimal placement strategy of them in a large-scale WSN, based on the output of Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) methods. As an initial step in this approach, the virtual localization process is executed over a virtual coordinate system in order to optimize the efficiency of the localization process. GWO and PSO methods are compared with a coverage-based analytical method and machine learning approaches such as Support Vector Machine (SVM) regression and Multiple Regression. The simulations we run with different numbers of nodes in a WSN and different communication ranges of nodes demonstrate that the proposed approaches are superior for minimizing the localization errors while reducing the number of anchor nodes.

The industrial Internet of Things (IIoT) is a new field of Internet of Things (IoT) that has gained more popularity recently in industrial units and makes it possible to access information anywhere and anytime. In other words, geographic coordinates cannot prevent obtaining equipment and its data. Today, it is possible to manage and control equipment simply without spending time in an operational area and just by using the IIoT. This system collects data from manufacturing and production units by using wireless sensor networks or other networks for classification of fault detection. These data are then used after analysis to allow operational decisions to be made in shorter amounts of time. In fact, the IIoT increases the efficiency and accuracy of the "connection, collection, analysis, and operation" cycle. The information collected through different sensors in the IIoT is unreliable and uncertain due to the sensitivity of the sensors to noise, failure, and loss of information during transmission. One of the most important techniques offered to deal with this uncertainty in information is the decision fusion method. Among the decision fusion techniques, the Dempster-Shafer and improved Dempster-Shafer theory, which is also known as Yager theory, are efficient and effective ways to manage the uncertainty and have been used in many types of research. This paper offers an architecture for decision fusion in a small IIoT using Dempster-Shafer and Yager theories. In this architecture, data collected from the desired environment are fed to classifiers for classification. In this architecture, artificial neural networks and a dendrogram-based support vector machine are used as classifiers. To increase the accuracy of classifier results, the Dempster-Shafer and Yager theories are used to combine these results. To prove the performance, the proposed method was applied for detection of faults in an induction motor and human activity detection in an environment. This proposed method improved the accuracy of the system and decreased its uncertainty significantly according to obtained results from these two example use cases.

Today, automatic identification of individuals from biometric features is widely used in identification and authentication, security, and monitoring applications. Since facial recognition is a more user-friendly and comfortable method than other biometric methods, it has grown rapidly in recent years. However, most facial recognition systems are vulnerable to spoofing attacks. Therefore, face liveness detection (FLD) methods are of great importance. On the other hand, unlike traditional methods, deep learning techniques promise to significantly increase the accuracy of facial liveness detection systems and eliminate the difficulties of the real-world implementation of these systems. Therefore, in this paper, the application of some deep learning models to detect face liveness is reviewed and compared with each other.

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With the proliferation of Internet of Things (IoT), a large number of devices are expected to connect to the Internet over different networks due to various demands and characteristics of IoT devices (IoTDs). One of the networks that will connect IoTDs to the Internet is cellular networks. The 3rd Generation Partnership Project (3GPP) proposes a connectivity model for cellular networks in which IoTDs connect to the network via relay devices. In this model, discovering the relay devices is a challenging task due to energy budget constraints, mobility of devices, and collision of discovery messages. Since IoTDs are constrained devices, it is important that they perform discovery with the minimum energy consumption by intermittently sleeping and waking up. In this paper, we propose signature-based and energy-efficient relay discovery protocol (SERDP) using Zadoff-Chu sequences. Since Zadoff-Chu is a Constant Amplitude Zero Autocorrelation (CAZAC) sequence, we use the sequence for pointing the active slots of the devices within the proposed three-stage original period structure. The probabilistic analysis and simulation results show that SERDP discovers more devices with less energy consumption as well as preventing collisions, as compared with the existing protocols.