Showing papers by "Mohammad Patwary published in 2022"

A Secure and Intelligent Framework for Vehicle Health Monitoring Exploiting Big-Data Analytics

Abstract: The dependency on vehicles is increasing tremendously due to its excellent transport capacity, fast, efficient, flexible, pleasant journey, minimal physical effort, and substantial economic impact. As a result, the demand for smart and intelligent feature enhancement is growing and becoming a prime concern for maximum productivity based on the current perspective. In this case, the Internet of Everything (IoE) is an emerging concept that can play an essential role in the automotive industry by integrating the stakeholders, process, data, and things via networked connections. But the unavailability of intelligent features leads to negligence about proper maintenance of vehicle vulnerable parts, reckless driving and severe accident, lack of instructive driving, and improper decision, which incurred extra expenses for maintenance besides hindering national economic growth. For this, we proposed a conceptual framework for a central VHMS exploiting IoE-driven Multi-Layer Heterogeneous Networks (HetNet) and a machine learning technique to oversee individual vehicle health conditions, notify the respective owner-driver real-timely and store the information for further necessary action. This article transparently portrayed an overview of central VHMS and proposed the taxonomy to achieve such an objective. Subsequently, we unveiled the framework for central VHMS, IoE-driven Multi-tire HetNet, with a secure and trustworthy data collection and analytics system. Finally, anticipating this proposition’s outcome is immense in the automotive sector. It may motivate the researcher to develop a central intelligent and secure vehicular condition diagnostic system to move this sector towards Industry 4.0.

Renewable Energy Re-Distribution via Multiscale IoT for 6G-Oriented Green Highway Management

Abstract: While recent works on investigating renewable energy sources for powering the highway offer promising solutions for sustainable environments, they are often impeded by unequal distribution of sources across the region due to variations in solar exposure and road intensity that electromagnetically and mechanically generate the energy. By exploiting viable gathering of massive renewable energy data using the Internet of Things (IoT), this paper proposes a framework for improved highway-energy management based on the unmanned aerial vehicle-assisted wireless energy re-distribution of the harvested renewable energy. Combining both massive low-rate sensing with high-speed 6G-envisioned transmission for data aggregation, the IoT architecture is of multi-scale, consisting of: i) global data exchange and analytics for energy mapping, re-distribution planning and forecasting, and ii) local data sensing and processing at individual highway lampposts for micro-energy management. The feasibility of the networked energy system is analyzed via analytical cost-reliability analyses. The cost analysis demonstrates the cost-effectiveness through the lowest Requirement of Energy and Cost of Energy for the setup and maintenance. The reliability analysis reveals the energy plus (E+) feature of the system in certain conditions with enhanced reliability in adverse weathers that impact energy generation. With multi-scale data connectivity to intelligently manage standalone renewable energy, this work puts forward a viable idea of 6G use cases with massively networked energy sensors with a vision of achieving super-connected and intelligence-equipped highways.

A New Beamforming Approach Using 60 GHz Antenna Arrays for Multi-Beams 5G Applications

Abstract: Recent studies and research have centred on new solutions in different elements and stages to the increasing energy and data rate demands for the fifth generation and beyond (B5G). Based on a new-efficient digital beamforming approach for 5G wireless communication networks, this work offers a compact-size circular patch antenna operating at 60 GHz and covering a 4 GHz spectrum bandwidth. Massive Multiple Input Multiple Output (M–MIMO) and beamforming technology build and simulate an active multiple beams antenna system. Thirty-two linear and sixty-four planar antenna array configurations are modelled and constructed to work as base stations for 5G mobile communication networks. Furthermore, a new beamforming approach called Projection Noise Correlation Matrix (PNCM) is presented to compute and optimise the fed weights of the array elements. The key idea of the PNCM method is to sample a portion of the measured noise correlation matrix uniformly in order to provide the best representation of the entire measured matrix. The sampled data will then be utilised to build a projected matrix using the pseudoinverse approach in order to determine the best fit solution for a system and prevent any potential singularities caused by the matrix inversion process. The PNCM is a low-complexity method since it avoids eigenvalue decomposition and computing the entire matrix inversion procedure and does not require including signal and interference correlation matrices in the weight optimisation process. The suggested approach is compared to three standard beamforming methods based on an intensive Monte Carlo simulation to demonstrate its advantage. The experiment results reveal that the proposed method delivers the best Signal to Interference Ratio (SIR) augmentation among the compared beamformers.

Secondary spectrum allocation framework via concurrent auctions for 5G and beyond networks

Abstract: Due to the dramatic increase of spectrum demand, efficient usage of the limited spectrum resources has become a crucial issue for the next-generation wireless networks. Auction-based spectrum trading, utilization and pricing have many promising features and have proven to be a fair and consistent way of secondary spectrum trading and management. In this paper, we present a mathematical approach to the future spectrum market where multiple buyers (secondary network operator) compete to gain spectrum resources through a number of auctions from multiple sellers (primary network operator, PNO). Through static and dynamic auctions, the secondary network operators borrow underutilized licensed spectrum resources from primary operators either through predefined contracts or through instantaneous contracts. Our main focus is on the optimal choice of the secondary operator, contiguous spectrum resource to maintain the quality and utilization history based fair allocation of the spectrum resources through auctions controlled by the third party spectrum regulators (SR), which has not been addressed previously. We first develop a matching problem to identify the most suitable auctions for secondary operators. A price-based optimal number of auctions and a utility-based ranking of the optimal auctions to be bid by the secondary operators are proposed, where the secondary operator maximizes the net utility surplus (NUS). The win or lose, pricing and allocation of spectrum resources are determined by a proposed Vickery-type mechanism. Finally, we provide simulation results to evaluate the performance of the proposed auction mechanism.

INGR Roadmap

Abstract: Fifth generation (5G) networks represent the first step from evolutionary to revolutionary networks. Use cases driving this transition for 5G networks focus on the need to support heterogeneous traffic such as enhanced Mobile Broad Band (eMBB), massive Machine-Type Communications (mMTC), and Ultra-Reliable Low-Latency Communications (URLLC). On the software and control side, 5G and beyond networks are expected to support Software-Defined Networking (SDN) and Network Function virtualization (NFV) technologies and will leverage the merging of communication and computing through the “wireless edge”. With the deployment of novel applications and the expected increase in their usage and demand, the scope of innovation within future networks will be governed by: (a) limitations and boundaries of available resources; (b) limitations of the adaptability of legacy solutions (scalability and flexibility); (c) limitations of available decision making entities (network slice orchestrators and SDN controllers will not be enough); and (d) lack of intelligent management and control solutions for multi-variate optimization. Technologies are available for efficient use and self-adaptive optimization of resources using enablers such as AI-powered autonomic control loops. With ever increasing complexity expected for beyond-5G networks, there is a necessity for novel design, planning and operations paradigms. There is a need for assessment of legacy tools versus new Artificial Intelligence solutions for applicability to systems optimization, and a need for introduction of novel methods to model and study the behavior of highly complex systems developed for the realization of 5G and beyond networks. The goal of this working group (WG) is to assess complexity challenges for the 5G era and beyond, explore novel design, planning and operations techniques for networks and services, and explore intelligence sciences to create the roadmap of the IEEE Future Networks Initiative (FNI) Systems Optimization WG.