Security is a major concern for wireless multimedia networks because of their role in providing various services. Traditional security techniques have inadequacies in identifying emerging security threats and also lacks in computing efficiency. Furthermore, conventional upper-layer authentication doesn’t provide any protection for physical layer, thus leading to leakage of privacy data. Keep these issues in mind, the paper has envisioned an artificial intelligence-based security authentication system that is lightweight, adaptive and doesn’t require any explicit programming. The neural network is built on convolutional filters which explore the data and learns the features or characteristic of the data. With this learned feature, the model will be able to recognize whether a wireless multimedia device present in a network is legitimate or not. Experimental analysis and validation have been performed on the trained model and ensure that the authentication of wireless multimedia devices can be achieved and also ensuring lightweight authentication system, which ensures less computation needs. The different neural model is also trained using gaussian noise of different standard deviation so that it can be used in a practical scenario like smart industry etc.  

World has been changed; every person is using a number of software, websites, and other systems that are using text-based passwords as a method of authentication.  These passwords need to be strong, hard to guess, and need to be stored in a secure environment. Major problems with passwords are caused by human limitations to remember passwords for different accounts. A trade-off between password security and human-memorability made it difficult to create passwords that are strong enough and easy to remember. No satisfactory solutions have been offered to problems associated with a password such as shoulder surfing, eavesdropping, keylogging programs, Trojan horse, brute force attacks, etc. This study suggests a new easy to use approach for creating a password that is easy to remember even for a large number of accounts. Here in this paper, we proposed two methods one is pattern-based passwords, a simple method that is solving the problem of memorability, another is the idea of Time-Synchronized Passwords (TSP), a novel method for creating passwords that are dynamic in nature and change with the passage of time. The novality of TSP is that instead of storing the passwords in database the patterns are stored, and these patterns are related linked with time. The significance of storing pattern instead of actual password is that at a specific time, the password will have only one instance known to the creator of the password, and this particular instance will be different from instances at other times and thus avoids shoulder surfing, eavesdropping, keylogging, and other problems associated with passwords. These methods are easy to implement and can be used in any system.

The burgeoning demand of high data rate in next generation wireless communication, the antecedent of the evolution of new multicarrier modulation technique FBMC (Filter Bank Multicarrier) system is OFDM (Orthogonal Frequency Division Multiplexing). Although OFDM has numerous merits but it consumes higher bandwidth due to its cyclic prefix to eliminate ISI (Inter Symbol Interference) in fading channel and shows large PAPR (Peak to Average Power Ratio) in transmission system.  To mitigate these problems, FBMC is more suitable candidate in high speed MIMO (multiple input multiple output) system. At first, in this paper, the responses of OFDM and FBMC system have been analyzed with AWGN and Rayleigh fading channel under theoretical and simulation environment. Besides this, Peak to Average Power Ratio (PAPR) with reduction technique, clipping is applied to know the behavior of these systems. Finally, in the area of MIMO scheme, each system is concatenated with MRC, Alamouti STBC and Generalized STBC to find FBMC as superior contender for 5G. Results show that using 16 QAM modulation, BER performance of FBMC significantly achieves 2dB compared to error rate of OFDM in theoretical and empirical analyses under AWGN and Rayleigh channel. Moreover, the clipping technique enhances the response of FBMC in terms of PAPR compared to OFDM approximately 0.5 dB. In MIMO configurations, MRC-FBMC exhibits the improved performance with 2 dB approximately over MRC-OFDM system. Results also show that in the case of Alamouti STBC, at 30dB and 35dB, FBMC (2×1) and (2×2) configurations achieve BER improvement 5×10-4, 3×10-4 respectively than OFDM (2×1) and (2×2) configurations. Generalized STBC investigation reveals that to reach FBMC BER performance, OFDM needs at least 2dB increment at specific error rate. In a nutshell, this paper brings up three distinct types of appealing features of FBMC such as good BER response, reduced PAPR, and suitability in MIMO configuration compared to OFDM. These distinct features make FBMC an ultimate choice in the emerging areas of MIMO networks.

In this article, a novel design for the bandwidth improvement of a substrate integrated waveguide (SIW) cavity-backed slot antenna (CBSA) is presented. The introduced structure utilizes an inverted L-shaped slot and unbalanced metalized vias. The proposed antenna is of low height and holds a planar geometry while sustaining lower losses and lightweight. The inverted L-shaped slot at the bottom plate is responsible for radiation. The quality factor of the SIW cavity is significantly reduced by the inverted L-shaped slot which leads to attaining a wideband response. The proposed antenna resonates at two frequencies 9.4025 GHz and 10.0075 GHz in the first band from the range 9.27 GHz to 10.13 GHz with the fractional bandwidth of 8.915% and at 12.07 GHz in the second band from the range 11.96 GHz to 12.2 GHz with fractional bandwidth of 1.9%.

Cloud of Things (CoT) relates to the convergence between Cloud Computing (CC) and the Internet of Things (IoT) and has significantly transformed the way services are delivered in the ubiquitous realm of devices. This integration has become essential because of the huge data being generated by IoT devices, requiring an infrastructure for storage and processing. Such infrastructure is provided by Cloud Computing services with massive space for data storage and exceptional platform to process complex data. IoT networks are vulnerable to multiple security breaches because of the growing usage of IoT devices in user personal systems. This leads to security and privacy threats that need to be addressed. IoT consists of resource limited devices which have feeble computing power, battery source and storage capacity. This paper addresses security issue by proposing usage of obfuscation and encryption techniques to scramble the data at IoT devices which is later on stored in encrypted form at the cloud server. The data at IoT devices is classified into highly critical or less critical and accordingly the appropriate technique between encryption and obfuscation is applied. The proposed mechanism is evaluated in terms of processing time for cryptographic operations at IoT devices. Evaluation results depict that the proposed mechanism is 1.17 times faster than [22] in terms of encryption and decryption times.