Sabur Baidya

University of Louisville | UL · Department of Computer Engineering and Computer Science

Deep learning is a proven method in many applications. However, it requires high computation resources and usually has a constant architecture. Mobile systems are good candidates to benefit from deep learning applications since they are closely integrated in people’s life. However, mobile systems experience varying conditions due to the same reason...

Deep learning algorithms are used in various advanced applications, including computer vision, large language models and many others due to their increasing success over traditional approaches. Enabling these algorithms on various embedded systems is essential to extend the success of deep learning methods in the cutting-edge real-world systems and...

Multi-vehicle perception fusion is an emerging advanced vehicular application providing Vehicle Users (VUs) with comprehensive driving assistance. This involves each VU's individual vehicular perception tasks and additional fusion tasks at the end. However, performing these perception fusion applications on some VUs may not be feasible due to the a...

In the forthcoming era of 6G, the mmWave communication is envisioned to be used in dense user scenarios with high bandwidth requirements, that necessitate efficient and accurate beam prediction. Machine learning (ML) based approaches are ushering as a critical solution for achieving such efficient beam prediction for 6G mmWave communications. Howev...

Vehicular networking has seen continued evolution over decades with the recently emerging paradigm of Cellular Vehicle-to-Everything (C-V2X) communications beginning to pick up momentum for adoption on today's roadways. Initial iterations of C-V2X grew from the LTE Device-to-Device framework and targeted application use cases that required the exch...

Digital Twin technology is playing a pivotal role in the modern industrial evolution. Especially, with the technological progress in the Internet-of-Things (IoT) and the increasing trend in autonomy, multi-sensor equipped robotics can create practical digital twin, which is particularly useful in the industrial applications for operations, maintena...

Digital Twin technology is being envisioned to be an integral part of the industrial evolution in modern generation. With the rapid advancement in the Internet-of-Things (IoT) technology and increasing trend of automation, integration between the virtual and the physical world is now realizable to produce practical digital twins. However, the exist...

Real-time machine vision applications running on resource-constrained embedded systems face challenges for maintaining performance. An especially challenging scenario arises when multiple applications execute at the same time, creating contention for the computational resources of the system. This contention results in increase in inference delay o...

In this paper, we explore the feasibility of solar-powered road-side unit (SRSU)-assisted vehicular edge computing (VEC) system, where SRSU is equipped with small cell base station (SBS) and VEC server, both of which are powered solely by solar energy. However, the limited capacity of solar energy, VEC server's computing, and SBS's bandwidth resour...

Real-time machine vision applications running on resource-constrained embedded systems face challenges for maintaining performance. An especially challenging scenario arises when multiple applications execute at the same time, creating contention for the computational resources of the system. This contention results in increase in inference delay o...

As the complexity of Deep Neural Network (DNN) models increases, their deployment on mobile devices becomes increasingly challenging, especially in complex vision tasks such as image classification. Many of recent contributions aim either to produce compact models matching the limited computing capabilities of mobile devices or to offload the execu...

Emerging connected and autonomous vehicles involve complex applications requiring not only optimal computing resource allocations but also efficient computing architectures. In this paper, we unfold the critical performance metrics required for emerging vehicular computing applications and show with preliminary experimental results, how optimal cho...

Almost all the WiFi networks today provide single band (either 2.4 GHz or 5.8 GHz) wireless communication functionality for connected mobile nodes. In a single band network, the interference depends on number of nodes in the network and the presence of other networks in the proximity. As the number of nodes in a Network increases, the interference...

In this paper, we present a framework for the dynamic selection of the wireless channels used to deliver information-rich data streams to edge servers. The approach we propose is data-driven, where a predictor, whose output informs the decision making of the channel selector, is built from available data on the transformation imposed by the network...

Offloading the execution of complex Deep Neural Networks (DNNs) models to compute-capable devices at the network edge, that is, edge servers, can significantly reduce capture-to-output delay. However, the communication link between the mobile devices and edge servers can become the bottleneck when channel conditions are poor. We propose a framework...

The ability to execute complex signal processing and machine learning tasks in real-time is the core of autonomy. In airborne devices such as Unmanned Aerial Vehicles (UAV), the hardware limitations imposed by the weight constraint make the continuous execution of these algorithms challenging. Edge and fog computing can mitigate such limitations an...

Infrastructure assistance has been proposed as a viable solution to improve the capabilities of commercial Unmanned Aerial Vehicles (UAV), especially toward fully autonomous operations. The airborne nature of these devices imposes constrains limiting the onboard available energy supply and computing power. The assistance of the surrounding communic...

Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of applications requiring extensive communications, either to interconnect the UAVs with each other or with ground resources. Focusing either on the modeling of UAV operations or communication and network dynamics, available simulation tools fail to capture the compl...

Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of applications requiring extensive communications, either to interconnect the UAVs with each other or with ground resources. Focusing either on the modeling of UAV operations or communication and network dynamics, available simulation tools fail to capture the compl...

By placing computation resources within a one-hop wireless topology, the recent edge computing paradigm is a key enabler of real-time Internet of Things (IoT) applications. In the context of IoT scenarios where the same information from a sensor is used by multiple applications at different locations, the data stream needs to be replicated. However...

The Urban Internet of Things (IoT) supports city-scale data collection and processing. It’s practical deployment poses several technical and technological challenges to overcome. In this chapter, we illustrate the main aspects of Urban IoT solutions based on Edge computing architectures. The potential to boost efficiency granted by such architectur...

Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of scenarios and applications. However, their deployment in urban areas poses important technical challenges. One of the most prominent concerns is the robustness of communications between the ground stations and the UAVs in a highly dynamic and crowded spectrum. Ind...

A novel cognitive interference control framework for heterogeneous local access networks supporting computing and data processing in Urban Internet of Things (IoT) systems is presented. The notion of cognitive content-aware interference control is introduced, where the transmission pattern of cognitive nodes is dynamically adapted to the state of “...

A novel interference management approach is proposed for modern communication scenarios, where multiple applications and networks coexist on the same channel resource. The leading principle behind the proposed approach is that the interference level should be adapted to the content being transmitted by the data links to maximize the amount of deliv...

In the Urban Internet of Things devices and systems are interconnected at the city scale to provide innovative services to the citizens.However, the traffic generated by the sensing and processing systems may overload local access networks. A coexistence problem arises where concurrent applications mutually interfere and compete for available resou...

A novel interference management approach is proposed for modern communication scenarios, where multiple applications and networks coexist on the same channel resource. The leading principle behind the proposed approach is that the interference level should be adapted to the content being transmitted by the data links to maximize the amount of deliv...

Multipath TCP (MPTCP) is an extension of TCP, developed by Internet Engineering Task Force (IETF) to support communication between source and destination through multiple flows under a single connection session. We have conducted several experiments with real hardware on flows with extreme variation in path quality and found that unless receive buf...