Network & Communication Research Group

Cihan University - Erbil, KRG-Iraq

Description

This research group focuses on a range of networking architectures from mobile cellular systems to Internet of Things (IoT), ad-hoc sensor networks and vehicular networks. Techniques involve the design of advanced algorithms for improving network. The Group uses state-of-the-art wireless and mobile network system level simulators and analytic tools for advanced network architecture and algorithm design/optimization, and performance evaluation of high mobile wireless networks.

Research Areas

Below are a range of research topics under which we are currently seeking collaboration from both academic and industry partners.

4G/5G Mobile Cellular Systems and Technologies

According to recent Cisco report, almost half a billion mobile devices and connections were added in 2016; mobile data traffic grew 18-fold over the past 5 years and is predicted to increase 7-fold within the next five years. There is a big gap between the capacity of mobile cellular networks and mobile data demands. Intensive research on the 5th generation (5G) cellular network architecture and technologies is undertaken to address the problem. In this research field of study, we focus on Resource management and scheduling for ultra-dense small cells and heterogeneous networks, Device to device communications, LTE assisted access (LAA) and cognitive radio: sharing unlicensed spectrum with non-cellular devices such as IEEE 802.11 devices, Millimeter wave technologies

Internet of Things Systems and Technologies

Internet of Things (IoT) is the inter-working of physical devices (such as smart meters, buildings, vehicles). With recent advances of RFID, embedded electronics, wireless communications, physical devices can be easily connected and become smarter. These devices can be sensed and controlled remotely, creating enormous opportunities for direct integration of physical world into cyber space. IoT provide smartness on many sectors, such as transportation, environment monitoring, health care, water, energy, smart city and smart grid. It is expected by Cisco there will be 30 billion IoT devices by 2020 and these devices may generate more than two exabytes of data each day. The data can provide opportunities for big data analysis but also poses big challenges on data processing, transport and storage. The vulnerability of IoT devices also raise big concerns on the IoT security.

Ad-hoc networks

Ad-hoc networks are dynamic in that nodes on the network can join and leave at any time as well as being able to move while connected. Thus the network topology and hence the routing tables are dynamic. This can happen on slow time scales (i.e. the topology changes more slow than the time it takes to update the routes) where traditional approaches will work. If the topology changes on a time scale that is comparable or faster than the routing update time then new approaches are needed.

Connected Autonomous Vehicles

Vehicle to everything (V2X) technology: V2X is a collective term referring to vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to pedestrian (V2P). V2X is a core component of CAV system. Two mainstream V2X technologies are IEEE 802.11p and 3GPP LTE-V. IEEE 802.11p is a variant of the general 802.11 technology tailed for vehicle connectivity. The nature of distributed random channel access in 802.11p offers high scalability and easy management, but also presents unpredictable quality of services (e.g. reliability and latency). 3GPP LTE-V is promising with large coverage, centralized management and high efficiency, but there are also many open research issues to be solved before it can be applied to CV, such as D2D resource allocation for V2V with low latency and high reliability.

Advanced object detection and cooperation: detection of objects (such as vehicles, pedestrians and cyclists) is critical for ADAS and AD. Even with advanced sensors such as Lidar and high resolution cameras, accidents are still likely to happen. Fast and efficient deep neural networks based models and technologies are developed for practical application to ADAS with improved driving object detection performance. In addition, driving hazards are detected and shared with neighbour drivers via V2X to enable cooperative driving. Cooperative road safety applications such as forward car collision avoidance (FCCA) and forward pedestrian collision avoidance (FPCA) can be effectively supported.

Working team

  1. Dr. Kayhan Zrar Ghafour
  2. Dr. Khalid Fadhil Jasim
  3. Dr. Halgurd S. Maghdid
  4. Dr. Ali Safa Sadiq / Malaysia
  5. Dr. Reem Jafar Ismail

Advisory Board

  1. Prof. Jaime Lloret / Spain
  2. Prof. Sherali Zeadally / USA
  3. Prof. Linghe Kong / China
  4. Prof. Danda B. Rawat / USA
  5. Prof. Igor Bisio / Italy