Summary Mobile Wireless Sensor Networks (MWSNs) have emerged as a new evolution trend in Wireless Sensor Networks (WSNs) technology as a result of the growth and popularization of mobile terminal technology. WSNs' feature spectrum is greatly expanded by the mobility of nodes introduced by MWSNs. MWSN is a network with lots of sensors that are initially distributed randomly and collaborate to gather, process, and transmit information around targets to the sink node. MWSN is an overgrowth and emerging technology with significant applications that provide free moving for sensors and adaptable communicating with each other without fixed infrastructure. Because of their small size, they have limited radio capability, storage, and power. Therefore, MWSNs are utilized to improve network lifetime, energy expenditure, and channel capacity than fixed WSNs. Many routing protocols have recently been introduced to perform advances in the field of energy expenditure for MWSN data collection. This paper introduces a survey on hierarchical‐based routing protocols for MWSNs. The survey outlines the classification of the routing techniques in the aspect of mobility. The comparisons between Mobile Sensor Node (MSN) protocols, Mobile Sink (MS) protocols, and protocols for mobile of both sink and sensor node together are introduced individually. Moreover, the commercial software programs for MWSNs are discussed. Finally, the challenges and future trends in MWSNs are presented.
Automatic modulation classification (AMC) is an important stage in intelligent wireless communication receivers. It is a necessary process after signal detection, and before demodulation. It plays a vital role in various applications. Blind modulation classification is a very difficult task without information about the transmitted signal and the receiver parameters like carrier frequency, signal power, timing information, phase offset, existence of frequency-selective multipath fading, and time-varying channels in real-world applications. The AMC methods are divided into traditional and advanced methods. Traditional methods include likelihood-based (LB) and feature-based (FB) methods. The advanced methods include deep learning (DL) methods. In addition, the AMC methods are used to classify different modulation schemes such as ASK, PSK, FSK, PAM, and QAM with different orders and different signal-to-noise ratios (SNRs). This paper focuses on summarizing the AMC methoods, comparing between them, presenting the commercial software packages for AMC, and finally considering the new challenges in the implementation of AMC. K E Y W O R D S automatic modulation classification (AMC), deep learning (DL), feature-based (FB) methods, likelihood-based (LB) methods | INTRODUCTIONAutomatic modulation classification (AMC) is important in wireless communication systems used in military and civilian applications to enhance the efficiency of the spectrum utilization, redue the overhead, and resolve the shortage problems. Unfortunately, the restricted spectrum resources barely satisfy the ever-increasing demand for 5G 1,2 and Internet of Things (IoT) networks. 3 The AMC can be used for better management of the available spectrum. A simple block diagram of a communication system based on AMC is presented in Figure 1. 4 The AMC architecture contains two steps: signal preprocessing and a proper algorithm for classification. The preprocessing tasks involve reduction of noise, carrier frequency estimation, symbol period estimation, equalization, and signal power evaluation. On the other hand, the AMC methods comprise traditional methods including decisiontheoretic methods and feature-based methods 4,5 along with advanced methods 6 as shown in Figure 2.
The issue of cybersecurity is one of the important fields which is involved in different research trends. Biometric security is one of these trends which is involved in several applications such as access control systems and online identity verification. The protection of human biometrics can be performed using both bi-directional and unidirectional encryption. The unidirectional encryption is carried out based on cancelable biometric techniques. This paper proposes a cancelable biometric system based on image composition, deep dream, and hashing techniques. The objective of the proposed system is to generate visual and text cancelable biometrics. The visual cancelable templates are generated using image composition and deep dream, while the text templates are generated using SHA hashing techniques. The proposed system is validated by multi-biometric inputs including iris, palm, face, and fingerprint biometrics. In addition, it is evaluated in both visual and text forms. The simulation results reveal that the proposed system appears a superior performance among the works which handle this problem.
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