This paper presents the numerical study of a polarization-insensitive energy harvesting metasurface. The proposed metasurface is designed to harvest ambient electromagnetic (EM) energy at 2.45 GHz. The basic constituent element of the metasurface is an electric-field-coupled (ELC) resonator, which is used to synthesize a 2 x 2 super-cell with polarization-insensitive features. Finally, the metasurface is realized as a 3 x 3 array of ELC super-cells, and presents an energy harvesting efficiency of 95.4% at 2.45 GHz. The achieved energy harvesting efficiency is maintained irrespective of the polarization of the incident excitation. The proposed metasurface configuration holds promise for the implementation of ambient EM harvesters, able to scavenge energy from wireless technologies operating in the 2.45 GHz band.

Our life nowadays relies much on technologies and online services net banking, e-voting and so on. So, there is a necessity to secure the data that is transmitted through the internet. However, while performing decryption, it sometimes led to privacy violation so there is need to operate on users encrypted data without knowing the original plaintext.
This paper represents the implementation of two-layer cryptosystem using paillier and elgamal algorithm both following asymmetric encryption. It is mainly focusing the challenges of privacy protection and secure utilization of information, where homomorphy encryption is gaining attention. Additive homomorphism is used in paillier cryptosystem which is used in fields like secure biometrics and electronic voting. Elgamal ensures that paillier encrypted data is secured that ensures two-layer encryption.

SQL injection attack is a major threat to web application security. It has been rated as one of the most dangerous vulnerabilities for a web-based application. Based on the Open Web Application Security Project (OWASP), it is measured as one of the top ten.  Many types of research have been made to face this attack either by preventing the threat or at least detecting it. We aim in this paper to give an overview of the SQL injection (SQLI) attack and classify these attacks and prevention and detection tools. We introduce the most current techniques and tools that are used to prevent and detect SQLI and highlight their strengths and weaknesses.

The increasing exploration of smart space voice assistants and audio monitoring IoT devices leads to significant trust and privacy concerns. Among them, some of the data are sensitive and a user may not aware of when and where audio recordings are sent for processing. This provides burning questions- how do users gain knowledge of, and control the amount of data captured in the physical world. Moreover, the sending and processing of these sensitive data to the far cloud do not provide an efficient solution for sensitive data which causes serious privacy and security concerns. In this paper, a new architecture for providing audio transparency and privacy in the IoT-rich environment is proposed. The proposed privacy enforcement module is used to operate within a nearby fog node, which situated close to the data sources and enforces privacy according to the data owner's desire. To show the effectiveness of the proposed architecture, a well-known privacy threat framework is investigated.  

With the rapid development of 5G technology, the era of interconnection of all things has arrived. At the same time, a variety of hardware and software are getting more and more location information through sensors, and the accuracy of location information is increasingly important. Because traditional positioning relies on satellite signals, it achieves good results outdoors without obstruction, but indoors, due to the obstruction of various walls, such as Beidou satellite navigation system and U.S. Global Positioning System, it is difficult to meet the accuracy requirements for indoor positioning. Therefore, how to improve the positioning accuracy of indoor nodes has become a research hotspot in the field of wireless sensor. In order to improve the indoor positioning accuracy, this paper combines artificial neural network, intelligent optimization algorithm and node positioning to improve the accuracy of indoor positioning. One of the essences of the neural network is to solve the regression problem. Through the analysis of indoor node positioning, it can be concluded that the accuracy of distance-based positioning method lies in finding the relationship between signal strength and distance value. Therefore, the neural network can be used to regression analysis of signal strength and distance value and generate related models. In order to further improve the accuracy and stability of indoor node positioning, a method combining whale optimization algorithm with neural network is proposed. By using the whale optimization algorithm to find the optimal parameters of the neural network model, the training accuracy and speed of the neural network are improved. Then, using the excellent fitting ability of the neural network, the mapping relationship between RSSI value and distance value of indoor nodes is fitted, and the corresponding regression analysis model is generated, which can minimize the noise problem caused by abnormal signal attenuation and reduce the indoor positioning error. Finally, the data is processed by the neural network to get the parameters needed in the positioning algorithm. The experimental results show that the node positioning model based on the optimized neural network and the single optimization algorithm has significantly improved the positioning accuracy and stability.