Palwasha Waheed Shaikh

Connected and Autonomous Electric Vehicles (CAEVs) and Uncrewed Aerial Vehicles (UAVs) are critical components of future Intelligent Transportation Systems (ITS), yet their deployment remains constrained by fragmented charging infrastructures and the lack of coordinated reservation and trip planning across static, dynamic wireless, and vehicle-to-vehicle (V2V) charging networks using magnetic resonance and laser-based power transfer. Existing solutions often struggle with misalignment sensitivity, unpredictable arrivals, and disconnected ground–aerial scheduling. This work introduces a three-layer architecture that integrates a handshake protocol for coordinated charging and billing, a misalignment correction algorithm for magnetic resonance and laser-based systems, and three scheduling strategies: Static Heuristic Charging Scheduling and Planning (SH-CSP), Dynamic Heuristic Charging Scheduling and Planning (DH-CSP), and the Safety, Scheduling, and Sustainability-Aware Feasibility-Enhanced Deep Deterministic Policy Gradient (SAFE-DDPG). SAFE-DDPG extends vanilla DDPG with feasibility-aware action filtering, prioritized replay, and adaptive exploration to enable real-time scheduling in heterogeneous and congested charging networks. Results show that SAFE-DDPG significantly improves scheduling efficiency, reducing average wait times by over 70% compared to DH-CSP and over 85% compared to SH-CSP, demonstrating its potential to support scalable and coordinated ground–aerial charging ecosystems.

In today’s era of rapid technological advancement, unmanned aerial vehicles (UAVs) are transforming sectors such as remote delivery, surveillance, and disaster response. However, challenges related to energy consumption and operational efficiency continue to hinder their broader adoption. To address these issues, this study proposes an integrated system design combining dynamic wireless charging (DWC), intelligent trip planning, and intelligent edge computing (IEC). The proposed system leverages IEC for local data processing to reduce latency and optimize energy management, 6G networks for real-time vehicle-to-infrastructure (V2I) communication, and DWC to enable efficient, on-the-go energy replenishment. Additionally, a dynamic arrival management algorithm is introduced to minimize UAV wait times to enhance operational efficiency. Simulations of this system demonstrated significant improvements: larger UAVs achieved an average charging efficiency of 91.2%, while smaller UAVs achieved 92.75%, with dynamic arrival management reducing wait times by an average of 1.5 min for smaller UAVs and 5.0 min for larger UAVs. These findings underscore the system’s effectiveness in optimizing UAV operations and charging efficiency. This integrated approach offers a scalable framework to enhance UAV capabilities and sets a benchmark for future advancements in operational efficiency and charging technology for urban and environmental applications.

The intersection of 6G technology, the Internet of Things (IoT), and Intelligent Transportation System (ITS) to address the persistent challenge of Unmanned Aerial Vehicles (UAVs) with limited battery life is explored. Despite the proliferation and versatility of UAVs, their mission time is hampered due to battery constraints. This paper highlights the role of dynamic wireless charging (DWC) for UAVs in enabling charging automation, ease of use, and prolonged mission lifetimes. A literature review underscores the shortcomings of traditional charging methods and advocates for the use of laser beaming technology as an efficient DWC solution. The proposed system design leverages 6 G technology to enable real-time communication and seamless connectivity for optimizing UAV charging and operational efficiency. Simulation results demonstrate the efficiency of DWC and propel UAVs towards increased autonomy and efficiency, paving the way for newer applications of UAVs in the $\mathbf{6 G}$-enabled ITS of smart cities.

6G networks, characterized by ultra-low latency and ubiquitous computing, herald a new era where Connected and Autonomous Electric Vehicles (CAEVs) and Unmanned Aerial Vehicles (UAVs) are redefining smart mobility. Equipped with real-time data processing, Artificial Intelligence (AI), and seamless connectivity, these vehicles seek efficient charging solutions facilitated by Intelligent Edge Computing (IEC). IEC, through latency reduction and optimized charging processes, promises rapid charging, grid stability, enhanced security, and renewable energy integration for intelligent transportation systems (ITS). This survey comprehensively examines IEC techniques and architectures, uncovering their impact on charging efficiency, security, and reliability within smart mobility frameworks. Through a review of research papers, the survey provides insights into real-world applications and IEC advancements, revealing key challenges and emerging research directions. The survey envisions an efficient, secure, and interconnected 6G-era charging ecosystem, with future directions including ultra-fast charging, renewables integration, enhanced security, standardization, AV-V2X synergy, predictive maintenance, and blockchain transparency, fundamentally reshaping AI-driven smart mobility.

An overview of the dynamic wireless charging (DWC) system design and architectures is presented to highlight its profound impact within the dynamic framework of 6G-enabled intelligent transportation systems (ITS). A historical overview of DWC architecture is provided to trace its evolution and highlight emerging trends. Thus, establishing a foundational understanding of this rapidly developing field. This paper also focuses on the management of charging requirements for both connected and autonomous electric vehicles (CAEVs) and unmanned aerial vehicles (UAVs). A survey of innovative charging reservation strategies that are vital for optimal use of DWC infrastructure is presented. Further, the effect of early and late-arriving vehicles on DWC reservation systems is explored to highlight areas of improvement for research. Finally, we propose our meticulously designed architecture for the DWC reservation and trip planning for efficient charging of both CAEVs and UAVs. This is concluded by the presentation of innovative strategies to manage early and late arrival scenarios of both CAEVs and UAVs. Thus, promoting a more sustainable, efficient, and interconnected future of CAEV and UAV charging within the 5G/6G ITS.

In the 6G era, addressing charging challenges for electric vehicles is paramount. This paper focuses on mitigating concerns for both Connected Autonomous Electric Vehicles (CAEVs) and Unmanned Aerial Vehicles (UAVs). CAEV users face range anxiety, while UAV operators contend with limited battery sizes. The paper introduces a dynamic wireless charging (DWC) reservation and trip planning system, powered by 6G, vital for future smart cities. It provides a literature review on CAEV and UAV charge scheduling, proposing a novel system design tailored for 6G-enabled Intelligent Transportation Systems (ITS), where UAVs serve CAEVs. Notably, for vehicle-to-vehicle DWC and communication, laser technology is utilized, ensuring reliability and efficiency. A dynamic arrival handling protocol addresses unpredictable vehicle arrivals. Simulation results showcase the system’s efficiency, optimizing charging and reducing wait times. With the impending 6G era, the paper aims to advance charging infrastructure for intelligent, flexible, and harmonized electric vehicle charging operations.

The rapid development of urban cities coupled with the rise in population has led to an exponentially growing number of vehicles on the roads for the latter to commute. This is adding to the already overbearing problem of traffic congestion. Short term, costly and short-sighted solutions of road infrastructure expansions are no longer suitable. One effective method of road resource allocation is focusing on the widely used traffic signal controllers’ timing schedules. Searching for a suitable or an optimal schedule for the prior via brute force to ease traffic congestion might not be the most elegant or feasible solution. Nature-inspired algorithms including evolutionary and swarm intelligence algorithms are gaining a lot of momentum. Many of these algorithms have been used in the last two decades to address different applications in the smart city era including traffic signal control (TSC). This paper conducts a comprehensive literature review on applications of evolutionary and swarm intelligence algorithms to TSC. Surveyed work is categorized based on the set of decision variables, optimization objective(s), problem modeling and solution encoding. The paper, based on gaps identified by the conducted review, identifies promising future research directions and discusses where the future research is headed.