In urban environments, tall buildings or structures can pose limits on the direct channel link between a base station (BS) and an Internet of Thing device (IoTD) for wireless communication. Unmanned aerial vehicles (UAVs) with a mounted reconfigurable intelligent surface (RIS), denoted as UAV-RIS, have been introduced in recent works to enhance the system throughput capacity by acting as a relay node between the BS and the IoTDs in wireless access networks. Uncoordinated UAVs or RIS phase shift elements will make unnecessary adjustments that can significantly impact the signal transmission to IoTDs in the area. The concept of Age of Information (AoI) is proposed in wireless network research to categorize the freshness of the received update message. To minimize the Average Sum of AoI (ASoA) in the network, two model-free deep reinforcement learning (DRL) approaches—Off-Policy deep Q -network (DQN) and On-Policy proximal policy optimization (PPO)—are developed to solve the problem by jointly optimizing the RIS phase shift, the location of the UAV-RIS, and the IoTD transmission scheduling for large-scale Internet of Things wireless networks. Analysis of loss functions and extensive simulations is performed to compare the stability and convergence performance of the two algorithms. The results reveal the superiority of the On-Policy approach, PPO, over the Off-Policy approach, DQN, in terms of stability, convergence speed, and under diverse environment settings.

Currently, rovers have been on the surface of Mars for more than two decades, beginning with the first wheeled land rover called the Sojourner from NASA's Pathfinder mission in 1997. In its over 14-year successful journey operating on the red planet, NASA’s Mars Opportunity Rover has held the record for the farthest distance driven on another planetary body (over 45 km). Rough terrain, obstacles, and environmental conditions make it challenging for these land rovers to explore the Martian surface and cover large amounts of terrain. There is an interest in exploring new methodologies and concepts with energy harvesting techniques that can withstand the harsh Martian climate and have the ability to survey and explore areas that might restrict land rovers. In this work, a new concept is explored involving a team of spherical robots, each housing small Dandelion seed bio-inspired Micro-Flying Rovers to be driven by the Martian winds. Each rover structure is equipped with various sensors needed for climate monitoring, surface mapping, communication, scientific experimentation, exploration, and localization on Mars. A spherical robot is used to house and disperse the dandelion inspired rovers into the Martian atmosphere in various locations. The success of this project would enable “unexplorable” areas on Mars to now be explored, and at a much faster rate.

This paper aims to design and fabricate a prototype of a solar-powered, fixed-wing, Unmanned Aerial Vehicle (UAV) with energy harvesting capabilities that can inspect and monitor panel arrays in solar power plants. In the energy sector, workers are susceptible to hazards such as working at large heights, high voltage contact, confined areas, and variable weather. Drone technology can be used to provide real-time, high-quality data that plant managers can use to conduct safe inspections, all at a low-cost. The primary goal for the UAV concept presented in this paper is use a lightweight, renewable energy source called Thermophotovoltaics, and attach the system to the wing of a drone to significantly extend its flight time without compromising the UAV’s weight, size, or maneuverability and provide power for long-duration missions without having to land. The UAV concept will incorporate three technologies: machine learning algorithms, artificial intelligence and path-planning, and recognition methods. These methods will be used to achieve high accuracy and precision information on the degradation or defect presence on individual solar panel modules. In addition, thermal and optical imaging may reveal compromises in the solar panel array via electrical errors or structural issues.

This paper aims to design and prototype a swarm of dandelion-inspired micro-robots for Martian exploration. Rough terrain, obstacles, dust storms, and environmental conditions make it challenging for land rovers or drones to explore the surface and cover large amounts of terrain. In this research, a new concept of robot design is proposed for a wind-driven aerial flight based on the physical structure of the Dandelion flower. A spherical robot is designed to house 1000 dandelion-seed shaped micro robots to be driven by the common Martian winds, equipped with various sensors needed for climate monitoring, exploration, analysis, scientific experimentation, and localization on Mars. This technology harnesses a hybrid power system using local Martian winds for long-range and long-duration missions, expanding the region covered by land rovers. The dandelion micro-robot concept will incorporate RF and/or optical technologies, machine learning, and artificial intelligence-based algorithms. The combination of these methods will allow the robots to have a pre-defined objective path based on the most recently measured wind patterns and their current understanding of the surrounding environment.

In this paper, we focus on investigating the performance of the mathematical software program Maple and the programming language MATLAB when using these respective platforms to compute the method of steps (MoS) and the Laplace transform (LT) solutions for neutral and retarded linear delay differential equations (DDEs). We computed the analytical solutions that are obtained by using the Laplace transform method and the method of steps. The accuracy of the Laplace method solutions was determined (or assessed) by comparing them with those obtained by the method of steps. The Laplace transform method requires, among other mathematical tools, the use of the Cauchy residue theorem and the computation of an infinite series. Symbolic computation facilitates the whole process, providing solutions that would be unmanageable by hand. The results obtained here emphasize the fact that symbolic computation is a powerful tool for computing analytical solutions for linear delay differential equations. From a computational viewpoint, we found that the computation time is dependent on the complexity of the history function, the number of terms used in the LT solution, the number of intervals used in the MoS solution, and the parameters of the DDE. Finally, we found that, for linear non-neutral DDEs, MATLAB symbolic computations were faster than Maple. However, for linear neutral DDEs, which are often more complex to solve, Maple was faster. Regarding the accuracy of the LT solutions, Maple was, in a few cases, slightly better than MATLAB, but both were highly reliable.

Deploying unmanned aerial vehicle (UAV) mounted base stations with a renewable energy charging infrastructure in a temporary event (e.g., sporadic hotspots for light reconnaissance mission or disaster-struck areas where regular power-grid is unavailable) provides a responsive and cost-effective solution for cellular networks. Nevertheless, the energy constraint incurred by renewable energy (e.g., solar panel) imposes new challenges on the recharging coordination. The amount of available energy at a charging station (CS) at any given time is variable depending on: the time of day, the location, sunlight availability, size and quality factor of the solar panels used, etc. Uncoordinated UAVs make redundant recharging attempts and result in severe quality of service (QoS) degradation. The system stability and lifetime depend on the coordination between the UAVs and available CSs. In this paper, we develop a reinforcement learning time-step based algorithm for the UAV recharging scheduling and coordination using a Q-Learning approach. The agent is considered a central controller of the UAVs in the system, which uses the $\epsilon$ -greedy based action selection. The goal of the algorithm is to maximize the average achieved throughput, reduce the number of recharging occurrences, and increase the life-span of the network. Extensive simulations based on experimentally validated UAV and charging energy models reveal that our approach exceeds the benchmark strategies by 381% in system duration, 47% reduction in the number of recharging occurrences, and achieved 66% of the performance in average throughput compared to a power-grid based infrastructure where there are no energy limitations on the CSs.