Flexible handover solution for vehicular ad-hoc networks based on software defined networking and fog computing

Vehicular ad-hoc networks (VANET) suffer from dynamic network environment and topological instability that caused by high mobility feature and varying vehicles density. Emerging 5G mobile technologies offer new opportunities to design improved VANET architecture for future intelligent transportation system. However, current software defined networking (SDN) based handover schemes face poor handover performance in VANET environment with notable issues in connection establishment and ongoing communication sessions. These poor connectivity and inflexibility challenges appear at high vehicles speed and high data rate services. Therefore, this paper proposes a flexible handover solution for VANET networks by integrating SDN and fog computing (FC) technologies. The SDN provides global knowledge, programmability and intelligence functions for simplified and efficient network operation and management. FC, on the other hand, alleviates the core network pressure by providing real time computation and transmission functionalities at edge network to maintain the demands of delay sensitive applications. The proposed solution overcomes frequent handover challenges and reduces the processing overhead at core network. Moreover, the simulation evaluation shows significant handover performance improvement of the proposed solution compared to current SDN based schemes, especially in terms of handover latency and packet loss ratio under various simulation environments

An efficient method for audio watermarking using SWT and mean value quantization

Digital watermarking has been capturing the interests in programming society several decades ago due to the development of software and programming techniques that cause an increase in illegal use of digital files. In addition, with the widespread use of Internet which support sharing any digital files easily, this had simplified distribution of illegal digital files without the owner’s permission. As the multimedia files; which include images, audio, and video clips; are prone to piracy, the multimedia industry and owner’s of digital media are coping with this issue to protect their intellectual property. Furthermore, the multimedia digital market needs to find solutions for copyright protection. A robust, imperceptible and high capacity algorithm is proposed by using the stationary wavelet transform and the quantization index modulation technique with new synchronization method. The results obtained show high robustness towards signal processing and manipulating attacks specially the de-synchronization attacks such as jittering, cropping, and zero inserting attacks. In addition, the imperceptibility and capacity obtained are considered high with respect to signal to noise values. A subjective test with volunteer’s listeners had been conducted for the proposed method. The findings show high imperceptibility with subject difference grade SDG of 4.76; meanwhile high payload capacity with mean value of 176.4 bps is achieved. Hence, based on these results, the proposed audio watermarking method outperforms most of the previous studie

An Integrated Grasshopper Optimization Algorithm with Artificial Neural Network for Trusted Nodes Classification Problem

Wireless Body Area Network (WBAN) is a tool that improves real-time patient health observation in hospitals, asylums, especially at home. WBAN has grown popularity in recent years due to its critical role and vast range of medical applications. Due to the sensitive nature of the patient information being transmitted through the WBAN network, security is of paramount importance. To guarantee the safe movement of data between sensor nodes and various WBAN networks, a high level of security is required in a WBAN network. This research introduces a novel technique named Integrated Grasshopper Optimization Algorithm with Artificial Neural Network (IGO-ANN) for distinguishing between trusted nodes in WBAN networks by means of a classification approach, hence strengthening the safety of such networks. Feature extraction process is done by using Linear Regression-Based Principal Component Analysis (LR-PCA). The test results demonstrated that the proposed IGO-ANN method attains the greatest performance in terms of accuracy, end to end delay and packet delivery ratio regarding trusted WBAN nodes classification than certain existing methods