Limited Battery Capacity Research Articles

Techno-economic performance of different DC power distribution networks in official buildings based on a real-time analysis method

The integration of photovoltaic (PV) and energy storage systems into official buildings has garnered considerable attention, which are recognized as DC power sources. Notably, DC power can satisfy the majority of electrical demands in official buildings. The primary objective of this research is to enhance overall system efficiency by efficiently utilizing renewable energy through direct current (DC) power distribution compared to alternating current (AC) systems. This study proposes a techno-economic analysis framework to guide designers in selecting system configuration and voltage levels. The analysis covers various PV capacities, load compositions, and battery capacities, with the aim of optimizing power distribution efficiency and achieving a more competitive levelized cost of energy (LCOE). The proposed model integrates the dynamic efficiency of actual converters and wire gauges, enabling real-time computation of the energy supply–demand balance within the power distribution system, including associated conversion and transmission losses. The results indicate that conversion loss accounts for approximately 80% of the overall energy loss within power distribution networks. The implementation of DC power distribution significantly improves system efficiency and economic viability. Specifically, the DC375 system exhibits superior techno-economic performance in scenarios involving high PV and battery capacities. In scenarios with limited PV and battery capacity, the building power distribution system should employ a hybrid AC-DC topology to efficiently distribute the available PV output to building loads powered by DC

Offloading Strategy for Forest Monitoring Network Based on Improved Beetle Optimization Algorithm

In forest monitoring networks, the computational capabilities of sensors cannot meet the latency requirements for complex tasks, and the limited battery capacity of these sensors hinders the long-term execution of monitoring tasks. Mobile edge computing (MEC) acts as an effective solution for this issue by offloading tasks to edge servers, significantly reducing both task latency and energy consumption. However, the computational capacity of MEC servers and the bandwidth in the system are limited, and the communication environment in forested areas is complex. To simulate the complexity of the forest communication environment, we incorporate empirical path loss and multipath fading into the calculation of signal transmission rates. The computational offloading problem is then converted into a minimum-cost optimization problem with multiple constraints related to energy consumption and latency, which we formulate as an NP-hard problem. We propose a dung beetle optimization (DBO) strategy for computational offloading, enhancing it with an improved circle chaotic mapping, a dimension decomposition strategy, and Cauchy disturbance. This algorithm has the beauty of symmetry in the search range, and the symmetrical features can comprehensively search for existing solutions. Experimental results demonstrate that the improved dung beetle optimization algorithm (IDBO) achieves better convergence, lower complexity, and superior optimization outcomes compared to local offloading strategies and other metaheuristic algorithms, confirming the effectiveness of the proposed algorithm and ensuring the service quality of the forest monitoring network.

A Comprehensive Multi-Objective Energy Management Approach for Wearable Devices with Dynamic Energy Demands

Recent advancements in low-power electronics and machine-learning techniques have paved the way for innovative wearable Internet of Things (IoT) devices. However, these devices suffer from limited battery capacity and computational power. Hence, energy harvesting from ambient sources has emerged as a promising solution for powering low-energy wearables. Optimal management of the harvested energy is crucial for achieving energy-neutral operation and eliminating the need for frequent recharging. This task is challenging due to the dynamic nature of harvested energy and battery energy constraints. To tackle this challenge, we propose tinyMAN, a reinforcement learning-based energy management framework for resource-constrained wearable IoT devices. tinyMAN maximizes the target device utilization under battery energy constraints without relying on the harvested energy forecast, making it a prediction-free approach. It achieves up to 17% higher utility while reducing battery constraint violations by 80% compared to prior work. We also introduce tinyMAN-MO, a multi-objective extension of tinyMan for applications with time-varying energy demands. It learns the tradeoff between meeting the application’s energy demand and maintaining the battery energy level. We deployed our framework on a wearable device prototype using TensorFlow Lite for Micro, leveraging its small (less than 120 KB) memory footprint. Evaluations show that tinyMAN-MO operates within 10% of the Pareto-optimal solutions with only 1.98 ms execution time and 23.17 μJ energy consumption overhead.

UAV p­­­arcel Delivery System with Deep Reinforcement Learning Based Collision Avoidance and Route Optimization

Unmanned Aerial Vehicles (UAVs), or drones, have recently become a favorable solution for fast parcel delivery due to their maneuverability and advances in navigation technologies. With the limitation of battery capacity and payload of drones, it is crucial to consider both efficiency and cost while conducting the tasks. Meanwhile, UAVs should not collide with each other while traveling to customers. In this article, we propose a UAV parcel delivery system involving deep reinforcement learning (DRL) approach for collision avoidance and a genetic algorithm for route optimization. Specifically, a delivery center generates near-optimal routes, loading UAV with parcels according to demands. Each UAV takes charge of delivering packages in compliance with the assigned route while avoiding collision with each other. We utilize DRL to achieve collision avoidance without having prior knowledge about the trajectories of other UAVs. Additionally, we adopt a genetic algorithm to obtain the lowest energy cost path for each UAV. To find such an optimized path, we solve a capacitated vehicle routing problem (CVRP) with a modified cost function and extra constraints. Realistic simulations using a physics engine and software-in-the-loop (SITL) are conducted to evaluate the feasibility of the proposed methods.

A novel energy-efficiency framework for UAV-assisted networks using adaptive deep reinforcement learning

In the air-to-ground transmissions, the lifespan of the network is based on the “unmanned aerial vehicle's (UAV)” life span because of the limited battery capacity. Thus, the enhancement of energy efficiency and the outage of the ground candidate’s minimization are significant factors of the network functionality. UAV-aided transmission can highly enhance the spectrum efficacy and coverage. Because of their flexible deployment and the high maneuverability, the UAVs can be the best alternative for the situations where the “Internet of Things (IoT)” systems utilize more energy to attain the essential information rate, when they are far away from the terrestrial base station. Therefore, it is significant to win over the few troubles in the conventional UAV-aided efficiency approaches. Thus, this proposed work is aimed to design an innovative energy efficiency framework in the UAV-assisted network using a reinforcement learning mechanism. The energy efficiency optimization in the UAV offers better wireless coverage to the static and mobile ground user. Presently, reinforcement learning techniques effectively optimize the energy efficiency rate of the system by employing the 2D trajectory mechanism, which effectively removes the interference rate attained in the nearby UAV cells. The main objective of the recommended framework is to maximize the energy efficiency rate of the UAV network by performing the joint optimization using UAV 3D trajectory, with the energy utilized during interference accounting, and connected user counts. Hence, an efficient Adaptive Deep Reinforcement Learning with Novel Loss Function (ADRL-NLF) framework is designed to provide a better energy efficiency rate to the UAV network. Moreover, the parameter of ADRL is tuned using the Hybrid Energy Valley and Hermit Crab (HEVHC) algorithm. Various experimental observations are performed to observe the effectualness rate of the recommended energy efficiency model for UAV-based networks over the classical energy efficiency framework in UAV Networks.

Performance Evaluation of Non-Lambertian SLIPT for 6G Visible Light Communication Systems

Visible light communication (VLC) has emerged as one promising candidate technique to improve the throughput performance in future sixth-generation (6G) mobile communication networks. Due to the limited battery capacity of VLC systems, light energy harvesting has been proposed and incorporated for achieving the simultaneous lightwave information and power transfer (SLIPT) function and for improving the overall energy efficiency. Nevertheless, almost all reported works are limited to SLIPT scenarios adopting a basic and well-discussed Lambertian optical transmitter, which definitely cannot characterize the potential and essential scenarios employing distinctive non-Lambertian optical transmitters with various spatial beam characteristics. For addressing this issue, in this work, SLIPT based on a distinct non-Lambertian optical beam configuration is investigated, and for further enhancing the harvested energy and the achievable data rate, the relevant flexible optical beam configuration method is presented as well. The numerical results show that, for a typical receiver position, compared with about 1.14 mJ harvested energy and a 31.2 Mbps achievable data rate of the baseline Lambertian configuration, a harvested energy gain of up to 1.55 mJ and an achievable data rate gain of 21.1 Mbps can be achieved by the non-Lambertian SLIPT scheme explored here.

Energy‐efficient optimal sink placement using extended pelican optimization‐based clustering with Voronoi‐based node deployment

SummaryA wireless sensor network (WSN) is a network of spatially distributed autonomous sensor nodes that collaborate to monitor physical or environmental conditions, collect data, and transmit it to a sink node. WSNs have a wide range of applications across various domains due to their ability to provide real‐time data collection, remote monitoring, and data analysis. Still, in a WSN with a fixed sink, sensor nodes closer to the sink tend to have higher traffic loads because they forward data to nodes further away. This can lead to hotspots and uneven energy consumption. Introducing a mobile sink can distribute the traffic more evenly across the network, reducing congestion and balancing the energy consumption among nodes. Hence, this research proposes a novel WSN environment with a focus on energy‐efficient routing. The network is deployed using Voronoi‐based criteria to address network coverage issues. The clustering of nodes is employed using the proposed extended pelican optimization (ExPo) algorithm to improve network lifetime and energy efficiency, critical concerns in WSNs due to limited sensor node battery capacity. Cluster heads (CHs) aggregate and process data locally, reducing the energy needed for long‐range communication. Then, an energy‐efficient optimal sink placement (EEOSP) approach is used to optimize the placement of the mobile sink. The proposed system model is evaluated based on various metrics, including average residual energy, delay, network lifetime, packet delivery ratio, and throughput and acquired the values of 0.99 J, 3.68 ms, 99.55%, 99.55%, and 81 Mbps, respectively.

Municipal solid waste management using electric vehicle by variable neighbourhood search algorithm

Efficient solid waste management is essential for addressing the multifaceted challenges associated with waste disposal, including environmental, economic, technical, and social aspects. Municipal solid waste management (MSWM) incurs significant costs and contributes to environmental pollution primarily due to the collection process. This research presents a comprehensive case study that focuses on optimising the routing of electric vehicles for solid waste collection. Specifically, it addresses the Half-Open Time-Dependent Multi-Depot Electric Vehicle Routing Problem considering Battery Recharging and Swapping (HOTDMDEVRPBRS) in the context of waste collection. This problem aims to optimise the routes taken by electric vehicles across multiple depots, considering constraints such as limited battery capacity, multiple technologies for recharging battery capacity, the impact of truckload on energy consumption, and time-dependent travel times. To tackle this problem, we propose a highly efficient Variable Neighborhood Search (VNS) meta-heuristic algorithm, which outperforms the Simulated Annealing (SA) approach. The proposed algorithm achieves an average improvement of 11.88% in solution quality and reduces execution time by 73% for last-mile delivery. We evaluate the effectiveness of the proposed approach using real-world data from New York's recycling management system. The findings of this study emphasise the potential for enhancing solution quality and operational efficiency in solid waste management.

Robust iterative value conversion: Deep reinforcement learning for neurochip-driven edge robots

A neurochip is a device that reproduces the signal processing mechanisms of brain neurons and calculates Spiking Neural Networks (SNNs) with low power consumption and at high speed. Thus, neurochips are attracting attention from edge robot applications, which suffer from limited battery capacity. This paper aims to achieve deep reinforcement learning (DRL) that acquires SNN policies suitable for neurochip implementation. Since DRL requires a complex function approximation, we focus on conversion techniques from Floating Point NN (FPNN) because it is one of the most feasible SNN techniques. However, DRL requires conversions to SNNs for every policy update to collect the learning samples for a DRL-learning cycle, which updates the FPNN policy and collects the SNN policy samples. Accumulative conversion errors can significantly degrade the performance of the SNN policies. We propose Robust Iterative Value Conversion (RIVC) as a DRL that incorporates conversion error reduction and robustness to conversion errors. To reduce them, FPNN is optimized with the same number of quantization bits as an SNN. The FPNN output is not significantly changed by quantization. To robustify the conversion error, an FPNN policy that is applied with quantization is updated to increase the gap between the probability of selecting the optimal action and other actions. This step prevents unexpected replacements of the policy’s optimal actions. We verified RIVC’s effectiveness on a neurochip-driven robot. The results showed that RIVC consumed 1/15 times less power and increased the calculation speed by five times more than an edge CPU (quad-core ARM Cortex-A72). The previous framework with no countermeasures against conversion errors failed to train the policies. Videos from our experiments are available: https://youtu.be/Q5Z0-BvK1Tc.

En-route recharging scheduling strategy for electric trucks serving inter-terminal container transportation

ABSTRACT Container ports are increasingly deploying electric trucks (ETs) for inter-terminal transportation (ITT). However, en-route recharging activities, which reduce ITT efficiency, are necessary for ETs because of limited battery capacity. This study proposes a real-time strategy to schedule the en-route recharging activities for ETs. The strategy dynamically monitors the ITT system, and simultaneously designates charging piles for ETs and determines their positions in the service sequences. An ET may be designated to a remote but shorter-queued pile. The total queue time of the ETs served by a designated pile can be reduced by adjusting the service sequence. The effectiveness is demonstrated in comparison with an alternative strategy in a real-world case. The results indicate that the proposed strategy effectively reduces container throughput time by 21.8% and ET energy consumption by 12.3%. Sensitivity analyses suggest that the strategy is applicable for ports with multiple charging stations and moderate ET fleet size.

Anti-Jamming Resource-Allocation Method in the EH-CIoT Network through LWDDPG Algorithm.

In the Energy-Harvesting (EH) Cognitive Internet of Things (EH-CIoT) network, due to the broadcast nature of wireless communication, the EH-CIoT network is susceptible to jamming attacks, which leads to a serious decrease in throughput. Therefore, this paper investigates an anti-jamming resource-allocation method, aiming to maximize the Long-Term Throughput (LTT) of the EH-CIoT network. Specifically, the resource-allocation problem is modeled as a Markov Decision Process (MDP) without prior knowledge. On this basis, this paper carefully designs a two-dimensional reward function that includes throughput and energy rewards. On the one hand, the Agent Base Station (ABS) intuitively evaluates the effectiveness of its actions through throughput rewards to maximize the LTT. On the other hand, considering the EH characteristics and battery capacity limitations, this paper proposes energy rewards to guide the ABS to reasonably allocate channels for Secondary Users (SUs) with insufficient power to harvest more energy for transmission, which can indirectly improve the LTT. In the case where the activity states of Primary Users (PUs), channel information and the jamming strategies of the jammer are not available in advance, this paper proposes a Linearly Weighted Deep Deterministic Policy Gradient (LWDDPG) algorithm to maximize the LTT. The LWDDPG is extended from DDPG to adapt to the design of the two-dimensional reward function, which enables the ABS to reasonably allocate transmission channels, continuous power and work modes to the SUs, and to let the SUs not only transmit on unjammed channels, but also harvest more RF energy to supplement the battery power. Finally, the simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods under multiple jamming attacks.

GLIDE: Multi-Agent Deep Reinforcement Learning for Coordinated UAV Control in Dynamic Military Environments

Unmanned aerial vehicles (UAVs) are widely used for missions in dynamic environments. Deep Reinforcement Learning (DRL) can find effective strategies for multiple agents that need to cooperate to complete the task. In this article, the challenge of controlling the movement of a fleet of UAVs is addressed by Multi-Agent Deep Reinforcement Learning (MARL). The collaborative movement of the UAV fleet can be controlled centrally and also in a decentralized fashion, which is studied in this work. We consider a dynamic military environment with a fleet of UAVs, whose task is to destroy enemy targets while avoiding obstacles like mines. The UAVs inherently come with a limited battery capacity directing our research to focus on the minimum task completion time. We propose a continuous-time-based Proximal Policy Optimization (PPO) algorithm for multi-aGent Learning In Dynamic Environments (GLIDE). In GLIDE, the UAVs coordinate among themselves and communicate with the central base to choose the best possible action. The action control in GLIDE can be controlled in a centralized and decentralized way, and two algorithms called Centralized-GLIDE (C-GLIDE), and Decentralized-GLIDE (D-GLIDE) are proposed on this basis. We developed a simulator called UAV SIM, in which the mines are placed at randomly generated 2D locations unknown to the UAVs at the beginning of each episode. The performance of both the proposed schemes is evaluated through extensive simulations. Both C-GLIDE and D-GLIDE converge and have comparable performance in target destruction rate for the same number of targets and mines. We observe that D-GLIDE is up to 68% faster in task completion time compared to C-GLIDE and could keep more UAVs alive at the end of the task.

Deep reinforcement learning based resource provisioning for federated edge learning

With the rapid development of mobile internet technology and increasing concerns over data privacy, Federated Learning (FL) has emerged as a significant framework for training machine learning models. Given the advancements in technology, User Equipment (UE) can now process multiple computing tasks simultaneously, and since UEs can have multiple data sources that are suitable for various FL tasks, multiple tasks FL could be a promising way to respond to different application requests at the same time. However, running multiple FL tasks simultaneously could lead to a strain on the device’s computation resource and excessive energy consumption, especially the issue of energy consumption challenge. Due to factors such as limited battery capacity and device heterogeneity, UE may fail to efficiently complete the local training task, and some of them may become stragglers with high-quality data. Aiming at alleviating the energy consumption challenge in a multi-task FL environment, we design an automatic Multi-Task FL Deployment (MFLD) algorithm to reach the local balancing and energy consumption goals. The MFLD algorithm leverages Deep Reinforcement Learning (DRL) techniques to automatically select UEs and allocate the computation resources according to the task requirement. Extensive experiments validate our proposed approach and showed significant improvements in task deployment success rate and energy consumption cost.

A novel ECG compression algorithm using Pulse-Width Modulation integrated quantization for low-power real-time monitoring

Cardiac monitoring systems in Internet of Things (IoT) healthcare, reliant on limited battery and computational capacity, need efficient local processing and wireless transmission for comprehensive analysis. Due to the power-intensive wireless transmission in IoT devices, ECG signal compression is essential to minimize data transfer. This paper presents a real-time, low-complexity algorithm for compressing electrocardiogram (ECG) signals. The algorithm uses just nine arithmetic operations per ECG sample point, generating a hybrid Pulse Width Modulation (PWM) signal storable in a compact 4-bit resolution format. Despite its simplicity, it performs comparably to existing methods in terms of Percentage Root-Mean-Square Difference (PRD) and space-saving while significantly reducing complexity and maintaining robustness against signal noise. It achieves an average Bit Compression Ratio (BCR) of 4 and space savings of 90.4% for ECG signals in the MIT-BIH database, with a PRD of 0.33% and a Quality Score (QS) of 12. The reconstructed signal shows no adverse effects on QRS complex detection and heart rate variability, preserving both the signal amplitude and periodicity. This efficient method for transferring ECG data from wearable devices enables real-time cardiac activity monitoring with reduced data storage requirements. Its versatility suggests potential broader applications, extending to compression of various signal types beyond ECG.

[formula omitted] : An adaptive data aggregation algorithm for IoT infrastructure

In IoT infrastructure, high-frequency sensing and subsequent transmission of sensed data to computational facilities can lead to redundant data storage and processing, consuming significant storage and processing capacity. As a result, the IoT infrastructure needs more data transmission cycles, leading to data redundancy and low network up-time due to the drainage of limited battery capacity. Conversely, if the data is communicated at a lower rate, it may cause absolute data delivery to the processing unit, which is useless. As a result, a well-designed data aggregation algorithm is required. This paper proposes the ADA2−IoT, an Adaptive Data Aggregation Algorithm for IoT Infrastructure tailored to optimize parameters such as low data redundancy, limited data communication cycles, and high IoT infrastructure up times. The proposed algorithm consists of two key components: the Route Data Aggregator (RDA) performs aggregation during data transit towards the Edge node or gateway, and the Node Data Aggregator (NDA) performs data aggregation during capturing or sensing data. The algorithm employs metrics like Age of Information (AoI) and data freshness factor during the node and route data aggregation phase to capture and timely deliver data to the Edge node, where this data is processed for informed decision-making. The proposed algorithm was efficiently tested on a simulation and IoT hardware deployment environment. Both simulation and hardware results demonstrate a substantial improvement in QoS parameters, such as a decrease in data redundancy and packet exchanges, leading to considerable energy savings and prolonging the lifespan of IoT infrastructure.

Machine Learning Applied to Electric Vehicle Routing Problem: Optimizing Costs for a Sustainable Environment

The global move towards Electric Vehicles (EVs) marks a crucial step towards sustainable transportation. However, effectively integrating EVs into the current infrastructure demands more than technological advancements. One of the key challenges is optimizing the routing of EVs to minimize costs and environmental impact. This editorial examines the role of Machine Learning (ML) in addressing the electric vehicle routing problem (ESVRP), highlighting its potential to transform cost optimization and sustainability in transportation. Routing is a fundamental part of transportation logistics, influencing efficiency, cost, and environmental impact. While traditional internal combustion engine vehicles have established routing systems, EVs present unique challenges such as limited battery capacity, longer refueling times, and fewer charging stations. These factors require advanced routing solutions that can dynamically adapt to various constraints.

Convex Approach to Real-Time Multiphase Trajectory Optimization for Urban Air Mobility

Electric vertical takeoff and landing technology has emerged as a promising solution to alleviate ground transportation congestion. However, the limited capacity of onboard batteries enforces hard time constraints for vehicle operations in a complex urban environment. Therefore, a constrained, real-time trajectory optimization method is necessary to enable safe and energy-efficient vehicle flight. Unfortunately, most existing trajectory optimization methods suffer from low computational efficiency, unpredictable convergence processes, and strong dependence on a good initial trajectory. In this paper, we employ a successive convex programming approach to solve the multiphase minimum-energy-cost electric vertical takeoff and landing vehicle trajectory optimization problem for urban air mobility applications. The main contribution is the development and validation of two novel convexification methods that find approximate optimal solutions to the multiphase nonlinear trajectory optimization problem in real time by solving a sequence of convex subproblems. Specifically, the first method transforms the original nonlinear problem into a sequence of second-order cone programming problems through a convenient change of variables and lossless convexification, while the second approach achieves a similar goal without the need for control convexification. The resulting convex subproblems can be solved reliably in real time by advanced interior point methods. The proposed methods are demonstrated through numerical simulations of two different urban air mobility scenarios and compared with the solution obtained from GPOPS-II, a state-of-the-art general-purpose optimal control solver. Our proposed sequential convex programming methods can obtain near-optimal solutions with faster computational speed than GPOPS-II.

Eco-FL: Enhancing Federated Learning sustainability in edge computing through energy-efficient client selection

In the realm of edge cloud computing (ECC), Federated Learning (FL) revolutionizes the decentralization of machine learning (ML) models by enabling their training across multiple devices. In this way, FL preserves privacy and minimizes the need for centralized data by processing data near the source. From a communication standpoint, only the model weights are exchanged between devices. By avoiding the need to send data to a centralized location for processing, FL reduces the energy required for data transfer and supports more efficient use of computing resources at the edge. FL is particularly advantageous for resource-constrained devices, such as smartphones and IoT devices. However, this limited computational power and battery capacity and the challenge of energy consumption are critical aspects of FL systems. This paper introduces Eco-FL, an innovative methodology designed to optimize energy consumption in FL systems, in the field of Green Edge Cloud Computing (GECC). Our approach employs a device selection process that considers the entropy of the data held by the devices and their available energy reserves. This ensures that devices with lower energy availability are less likely to participate in the training rounds, prioritizing those with higher energy capacities. To evaluate the efficacy of our methodology, we utilize FedEntropy, an entropy-based aggregation method, alongside established aggregation methods such as FedAvg and FedProx for performance comparison. The effectiveness of Eco-FL in reducing energy consumption without compromising the accuracy of the FL process is demonstrated through analyses conducted on three distinct datasets. These analyses vary the β parameter of the Dirichlet distribution and account for scenarios with both homogeneous and heterogeneous initial device charges. Our findings validate Eco-FL’s potential to enhance the sustainability of FL systems by judiciously managing client participation based on energy criteria, presenting a significant step forward in the development of energy-efficient FL.

Reinforcement learning based autonomous multi-rotor landing on moving platforms

Multi-rotor UAVs suffer from a restricted range and flight duration due to limited battery capacity. Autonomous landing on a 2D moving platform offers the possibility to replenish batteries and offload data, thus increasing the utility of the vehicle. Classical approaches rely on accurate, complex and difficult-to-derive models of the vehicle and the environment. Reinforcement learning (RL) provides an attractive alternative due to its ability to learn a suitable control policy exclusively from data during a training procedure. However, current methods require several hours to train, have limited success rates and depend on hyperparameters that need to be tuned by trial-and-error. We address all these issues in this work. First, we decompose the landing procedure into a sequence of simpler, but similar learning tasks. This is enabled by applying two instances of the same RL based controller trained for 1D motion for controlling the multi-rotor’s movement in both the longitudinal and the lateral directions. Second, we introduce a powerful state space discretization technique that is based on i) kinematic modeling of the moving platform to derive information about the state space topology and ii) structuring the training as a sequential curriculum using transfer learning. Third, we leverage the kinematics model of the moving platform to also derive interpretable hyperparameters for the training process that ensure sufficient maneuverability of the multi-rotor vehicle. The training is performed using the tabular RL method Double Q-Learning. Through extensive simulations we show that the presented method significantly increases the rate of successful landings, while requiring less training time compared to other deep RL approaches. Furthermore, for two comparison scenarios it achieves comparable performance than a cascaded PI controller. Finally, we deploy and demonstrate our algorithm on real hardware. For all evaluation scenarios we provide statistics on the agent’s performance. Source code is openly available at https://github.com/robot-perception-group/rl_multi_rotor_landing.

Discharging driven energy sharing protocol for V2V communication in vehicular energy networks

With the growing concern over range anxiety among electric vehicle (EV) owners due to limited battery capacity and sparse charging infrastructure, EV-to-EV (V2V) energy sharing emerges as a crucial solution to extend driving range. Leveraging the vehicular energy network (VEN) enabled by dynamic wireless power transfer (DWPT) technology, energy sharing among EVs in motion becomes feasible. However, the effective establishment of communication and identification of suitable V2V energy sharing pairs pose significant challenges, particularly for EVs with discharging demands. To address this challenge, we propose a new discharging driven energy sharing protocol based on vehicular ad-hoc networks (VANETs). Firstly, we present a routing approach for transmitting discharging information through VANETs, considering key factors such as distance, state of charge, number of neighbor vehicles, and vehicle speed. This routing scheme facilitates efficient relay node selection on road segments and intersections, ensuring optimal communication paths. Subsequently, we formulate a charging requester selection model to identify the most suitable requester for energy sharing. This model optimizes individual utility while accounting for the state of charge of the requesters, ensuring a comprehensive and inclusive approach to V2V energy sharing. Finally, we develop an acknowledgment message transmission scheme to ensure the completion of selection acknowledgment between discharging EVs and charging EVs. This scheme includes provisions for recovery in case of forwarding link failures, ensuring robust communication in dynamic vehicular environments. Extensive simulations conducted using network simulator 2 (NS-2) demonstrate the superior performance of the proposed protocol in terms of packet delivery ratio, end-to-end delay, and overall V2V energy sharing efficiency.