Najmul Hassan

Bio: Najmul Hassan is an academic researcher from Sunway University. The author has contributed to research in topics: Dynamic network analysis & Cellular network. The author has an hindex of 2, co-authored 3 publications receiving 97 citations.

Edge Computing in 5G: A Review

Abstract: 5G is the next generation cellular network that aspires to achieve substantial improvement on quality of service, such as higher throughput and lower latency. Edge computing is an emerging technology that enables the evolution to 5G by bringing cloud capabilities near to the end users (or user equipment, UEs) in order to overcome the intrinsic problems of the traditional cloud, such as high latency and the lack of security. In this paper, we establish a taxonomy of edge computing in 5G, which gives an overview of existing state-of-the-art solutions of edge computing in 5G on the basis of objectives, computational platforms, attributes, 5G functions, performance measures, and roles. We also present other important aspects, including the key requirements for its successful deployment in 5G and the applications of edge computing in 5G. Then, we explore, highlight, and categorize recent advancements in edge computing for 5G. By doing so, we reveal the salient features of different edge computing paradigms for 5G. Finally, open research issues are outlined.

Maritime Networking: Bringing Internet to the Sea

Abstract: Maritime networks establish wireless multi-hop networks to provide wireless broadband service at sea, connecting various kinds of ships, maritime buoys, and beacons. The maritime networks possess two distinguishing characteristics highly affected by maneuver at sea-dynamic link quality and bandwidth constrained, and dynamic network topology-that warrant specific attention. Unlike land vehicles, maneuver at sea is affected by sea surface movement and wave occlusions, which can cause unstable environment with a high rate of link breakages caused by low link stability, as well as low and highly variable bandwidth. In spite of the need to achieve performance close to high-speed terrestrial wireless broadband service on land, there is only a perfunctory effort to investigate maritime networks. There is an urgent need to refresh the interest to investigate, as well as to further enhance, maritime networks. This paper presents a review of the limited research works of this topic, which revolve around the networking issues in the link, network, and upper layers, in the literature. The objective is to establish a foundation in order to motivate a new research interest in maritime networks. Open issues are also presented to foster new research initiatives in this burgeoning and exciting area.

A Game Theory Based Clustering Scheme (GCS) for 5G-based Smart Healthcare

Abstract: 5G technology will play a significant role in the next-generation smart healthcare network. D2D communication, one the key enabling technology of 5G, makes use of devices in the vicinity for resources utilization, reducing latency, improving data rates and increasing system capacity. The integration of multi-hop and cellular networks not only delivers reliability and Quality of Service (QoS) but also make the network adaptable and flexible. In next-generation multi-hop cellular D2D networks, clustering and routing are presents significant challenges. Therefore, if the clustering and routing decisions are not appropriately implemented, multi-hop network performance can be worse as compared to the traditional network. This is because clustering and routing play a significant role to manage network fragmentation and dynamic network topology, which are not present in a traditional cellular network. In this paper, we propose a clustering scheme based on game theory (i.e., mixed strategy) to select optimal cluster heads (CHs) and transmit data from a cluster head (CH) to base station (BS). The simulation results show that our proposed scheme is better than LEACH protocol in terms of a network lifetime and energy consumption. We used MATLAB environment to simulate our proposed scheme and compared with LEACH protocol.

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Abstract: Federated learning (FL) is a distributed machine learning approach that can achieve the purpose of collaborative learning from a large amount of data that belong to different parties without sharing the raw data among the data owners. FL can sufficiently utilize the computing capabilities of multiple learning agents to improve the learning efficiency while providing a better privacy solution for the data owners. FL attracts tremendous interests from a large number of industries due to growing privacy concerns. Future vehicular Internet of Things (IoT) systems, such as cooperative autonomous driving and intelligent transport systems (ITS), feature a large number of devices and privacy-sensitive data where the communication, computing, and storage resources must be efficiently utilized. FL could be a promising approach to solve these existing challenges. In this paper, we first conduct a brief survey of existing studies on FL and its use in wireless IoT. Then, we discuss the significance and technical challenges of applying FL in vehicular IoT, and point out future research directions.

A Survey on Beyond 5G Network With the Advent of 6G: Architecture and Emerging Technologies

Abstract: Nowadays, 5G is in its initial phase of commercialization. The 5G network will revolutionize the existing wireless network with its enhanced capabilities and novel features. 5G New Radio (5G NR), referred to as the global standardization of 5G, is presently under the $3^{\mathrm {rd}}$ Generation Partnership Project (3GPP) and can be operable over the wide range of frequency bands from less than 6GHz to mmWave (100GHz). 3GPP mainly focuses on the three major use cases of 5G NR that are comprised of Ultra-Reliable and Low Latency Communication (uRLLC), Massive Machine Type Communication (mMTC), Enhanced Mobile Broadband (eMBB). For meeting the targets of 5G NR, multiple features like scalable numerology, flexible spectrum, forward compatibility, and ultra-lean design are added as compared to the LTE systems. This paper presents a brief overview of the added features and key performance indicators of 5G NR. The issues related to the adaptation of higher modulation schemes and inter-RAT handover synchronization are well addressed in this paper. With the consideration of these challenges, a next-generation wireless communication architecture is proposed. The architecture acts as the platform for migration towards beyond 5G/6G networks. Along with this, various technologies and applications of 6G networks are also overviewed in this paper. 6G network will incorporate Artificial intelligence (AI) based services, edge computing, quantum computing, optical wireless communication, hybrid access, and tactile services. For enabling these diverse services, a virtualized network slicing based architecture of 6G is proposed. Various ongoing projects on 6G and its technologies are also listed in this paper.

Collaborative Learning of Communication Routes in Edge-Enabled Multi-Access Vehicular Environment

Abstract: Some Internet-of-Things (IoT) applications have a strict requirement on the end-to-end delay where edge computing can be used to provide a short delay for end-users by conducing efficient caching and computing at the edge nodes. However, a fast and efficient communication route creation in multi-access vehicular environment is an underexplored research problem. In this paper, we propose a collaborative learning-based routing scheme for multi-access vehicular edge computing environment. The proposed scheme employs a reinforcement learning algorithm based on end-edge-cloud collaboration to find routes in a proactive manner with a low communication overhead. The routes are also preemptively changed based on the learned information. By integrating the “proactive” and “preemptive” approach, the proposed scheme can achieve a better forwarding of packets as compared with existing alternatives. We conduct extensive and realistic computer simulations to show the performance advantage of the proposed scheme over existing baselines.

Hybrid Satellite-UAV-Terrestrial Networks for 6G Ubiquitous Coverage: A Maritime Communications Perspective

Abstract: In the coming smart ocean era, reliable and efficient communications are crucial for promoting a variety of maritime activities. Current maritime communication networks (MCNs) mainly rely on marine satellites and on-shore base stations (BSs). The former generally provides limited transmission rate, while the latter lacks wide-area coverage capability. Due to these facts, the state-of-the-art MCN falls far behind terrestrial fifth-generation (5G) networks. To fill up the gap in the coming sixth-generation (6G) era, we explore the benefit of deployable BSs for maritime coverage enhancement. Both unmanned aerial vehicles (UAVs) and mobile vessels are used to configure deployable BSs. This leads to a hierarchical satellite-UAV-terrestrial network on the ocean. We address the joint link scheduling and rate adaptation problem for this hybrid network, to minimize the total energy consumption with quality of service (QoS) guarantees. Different from previous studies, we use only the large-scale channel state information (CSI), which is location-dependent and thus can be predicted through the position information of each UAV/vessel based on its specific trajectory/shipping lane. The problem is shown to be an NP-hard mixed integer nonlinear programming problem with a group of hidden non-linear equality constraints. We solve it suboptimally by using Min-Max transformation and iterative problem relaxation, leading to a process-oriented joint link scheduling and rate adaptation scheme. As observed by simulations, the scheme can provide agile on-demand coverage for all users with much reduced system overhead and a polynomial computation complexity. Moreover, it can achieve a prominent performance close to the optimal solution.