arXiv subjects

Mohammed Atiquzzaman

Publications and source records attributed to Mohammed Atiquzzaman.

22 records · Page 2Linked to original sources

arXiv2020

Message Dissemination in Connected Vehicles

Advances in connected vehicles based on Vehicular Ad-hoc Networks (VANETs) in recent years have gained significant attention in Intelligent Transport Systems (ITS) in terms of disseminating messages in an efficient manner. VANET uses Dedicated Short Range Communication (DSRC) for disseminating messages between vehicles and between infrastructures. Though DSRC based communications are viable, it is still challenging to disseminate messages in a timely manner when vehicles are not in the transmission range of each other. Furthermore, DSRC communication channels are heavily congested when the vehicle density increases on the road. To address these limitations, two emerging paradigms: 1) vehicular cloud computing and 2) vehicular fog computing are been adopted to disseminate message between the vehicles in a connected vehicular environment. Vehicular fog computing uses fog nodes for the dissemination of messages among vehicles. Any real-world object can be formed as a fog node by acquiring the properties such as 1) network connectivity, 2) computation, and 3) storage. In this book chapter, we highlight the significance of message dissemination in connected vehicles based on techniques like DSRC, vehicular cloud computing, and vehicular fog computing. Our objective is to help the readers better understand the fundamentals of connected vehicles and communication techniques while disseminating messages between vehicles and between infrastructures.

cs.NI↗

arXiv2018

Hybrid-Vehcloud: An Obstacle Shadowing Approach for VANETs in Urban Environment

Routing of messages in Vehicular Ad-hoc Networks (VANETs) is challenging due to obstacle shadowing regions with high vehicle densities, which leads to frequent disconnection problems and blocks radio wave propagation between vehicles. Previous researchers used multi-hop, vehicular cloud or roadside infrastructures to solve the routing issue among the vehicles, but they suffer from significant packet delays and frequent packet losses arising from obstacle shadowing. We proposed a vehicular cloud based hybrid technique called Hybrid-Vehcloud to disseminate messages in obstacle shadowing regions, and multi-hop technique to disseminate messages in non-obstacle shadowing regions. The novelty of our approach lies in the fact that our proposed technique dynamically adapts between obstacle shadowing and non-obstacle shadowing regions. Simulation based performance analysis of Hybrid-Vehcloud showed improved performance over Cloud-assisted Message Downlink Dissemination Scheme (CMDS), Cross-Layer Broadcast Protocol (CLBP) and Cloud-VANET schemes at high vehicle densities.

cs.NI↗

arXiv2018

DFCV: A Novel Approach for Message Dissemination in Connected Vehicles using Dynamic Fog

Vehicular Ad-hoc Network (VANET) has emerged as a promising solution for enhancing road safety. Routing of messages in VANET is challenging due to packet delays arising from high mobility of vehicles, frequently changing topology, and high density of vehicles, leading to frequent route breakages and packet losses. Previous researchers have used either mobility in vehicular fog computing or cloud computing to solve the routing issue, but they suffer from large packet delays and frequent packet losses. We propose Dynamic Fog for Connected Vehicles (DFCV), a fog computing based scheme which dynamically creates, increments and destroys fog nodes depending on the communication needs. The novelty of DFCV lies in providing lower delays and guaranteed message delivery at high vehicular densities. Simulations were conducted using hybrid simulation consisting of ns-2, SUMO, and Cloudsim. Results show that DFCV ensures efficient resource utilization, lower packet delays and losses at high vehicle densities.

cs.NI↗

arXiv2018

NHAD: Neuro-Fuzzy Based Horizontal Anomaly Detection In Online Social Networks

Use of social network is the basic functionality of today's life. With the advent of more and more online social media, the information available and its utilization have come under the threat of several anomalies. Anomalies are the major cause of online frauds which allow information access by unauthorized users as well as information forging. One of the anomalies that act as a silent attacker is the horizontal anomaly. These are the anomalies caused by a user because of his/her variable behaviour towards different sources. Horizontal anomalies are difficult to detect and hazardous for any network. In this paper, a self-healing neuro-fuzzy approach (NHAD) is used for the detection, recovery, and removal of horizontal anomalies efficiently and accurately. The proposed approach operates over the five paradigms, namely, missing links, reputation gain, significant difference, trust properties, and trust score. The proposed approach is evaluated with three datasets: DARPA'98 benchmark dataset, synthetic dataset, and real-time traffic. Results show that the accuracy of the proposed NHAD model for 10% to 30% anomalies in synthetic dataset ranges between 98.08% and 99.88%. The evaluation over DARPA'98 dataset demonstrates that the proposed approach is better than the existing solutions as it provides 99.97% detection rate for anomalous class. For real-time traffic, the proposed NHAD model operates with an average accuracy of 99.42% at 99.90% detection rate.

cs.NI↗