Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices

Wireless Link Selection Methods for Maritime Communication Access Networks-A Deep Learning Approach

In recent years, we have been witnessing a growing interest in the subject of communication at sea. One of the promising solutions to enable widespread access to data transmission capabilities in coastal waters is the possibility of employing an on-shore wireless access infrastructure. However, such an infrastructure is a heterogeneous one, managed by many independent operators and utilizing a number of different communication technologies. If a moving sea vessel is to maintain a reliable communication within such a system, it needs to employ a set of network mechanisms dedicated for this purpose. In this paper, we provide a short overview of such requirements and overall characteristics of maritime communication, but our main focus is on the link selection procedure-an element of critical importance for the process of changing the device/system which the mobile vessel uses to retain communication with on-shore networks. The paper presents the concept of employing deep neural networks for the purpose of link selection. The proposed methods have been verified using propagation models dedicated to realistically represent the environment of maritime communications and compared to a number of currently popular solutions. The results of evaluation indicate a significant gain in both accuracy of predictions and reduction of the amount of test traffic which needs to be generated for measurements.

A CPW fed quad-port MIMO DRA for sub-6 GHz 5G applications

The present work investigates a novel four-port, multiple-input multiple-output (MIMO), single element dielectric resonator antenna (DRA) for sub-6 GHz band. The DRA is designed and fabricated into a symmetric cross shape and fed using a coplanar waveguide (CPW) feed. A single radiator with four ports is rarely found in the literature. The -10 dB impedance bandwidth covered by the antenna is from 5.52 GHz to 6.2 GHz (11.6%) which covers fifth generation (5G) new radio (NR) bands N47 and wireless local area network (WLAN) IEEE 802.11a band. The isolation between orthogonal ports is about 15 dB while the isolation between opposite ports is 12 dB. The radiation pattern of the proposed antenna is bidirectional due to the absence of a ground plane below the DRA. The orthogonal modes excited in the DRA are TEδ21x and TE2δ1y through the four symmetrical CPW feeds. The simulated and measured results of the proposed design show that MIMO characteristics are achieved by pattern diversity between the ports. Due to the perfect symmetry of the design, the proposed work could be extended to MIMO array applications as well.

SCNN-Attack: A Side-Channel Attack to Identify YouTube Videos in a VPN and Non-VPN Network Traffic

Encryption Protocols e.g., HTTPS is utilized to secure the traffic between servers and clients for YouTube and other video streaming services, and to further secure the communication, VPNs are used. However, these protocols are not sufficient to hide the identity of the videos from someone who can sniff the network traffic. The present work explores the methodologies and features to identify the videos in a VPN and non-VPN network traffic. To identify such videos, a side-channel attack using a Sequential Convolution Neural Network is proposed. The results demonstrate that a sequence of bytes per second from even one-minute sniffing of network traffic is sufficient to predict the video with high accuracy. The accuracy is increased to 90% accuracy in the non-VPN, 66% accuracy in the VPN, and 77% in the mixed VPN and non-VPN traffic, for models with two-minute sniffing.