Limited Battery Capacity Research Articles

Multi-agent reinforcement learning based computation offloading and resource allocation for LEO Satellite edge computing networks

Due to the limitations caused by geographical conditions and economic requirements, it is difficult to provide computing services by terrestrial networks for mobile terminals in remote areas. To address this issue, mobile edge computing (MEC) servers can be deployed in the low earth orbit (LEO) satellites to act as a complement and accommodate the unserved terminals. However, offloading computing tasks to servers in satellites may increase the energy consumption of ground terminals. Considering the limited battery capacity of ground terminals, how to perform the computation offloading and resource allocation are key challenges in the LEO satellite edge computing networks. Therefore, in this paper, we investigate the energy minimization problem for LEO satellite edge computing networks, where a multi-agent deep reinforcement learning algorithm with global rewards is proposed to optimize the transmit power, CPU frequency, bit allocation, offloading decision and bandwidth allocation via a decentralized method. Simulation results show that our proposed algorithm can converge faster. Most importantly, compared with the random algorithm, the proximal policy optimization (PPO) algorithm, and the deep deterministic policy gradient (DDPG) algorithm, the ground terminals’ energy consumption can be effectively reduced by our proposed algorithm.

Energy-efficient mobile edge computing assisted by layered UAVs based on convex optimization

The utilization of unmanned aerial vehicles (UAVs) as aerial mobile edge computing (MEC) servers presents an effective approach for mitigating the computational limitations of intelligent mobile devices. However, the limited battery capacity of UAVs poses a significant challenge in ensuring sustained service provision. To counter this obstacle and enhance energy efficiency, we introduce a partial offloading MEC scheme assisted by layered UAVs (POAL). Our objective is to minimize the total energy consumption of UAVs by jointly optimizing several factors, including wireless channels, transmit power, task partition, computing power, and UAV trajectories. Due to the non-convexity of the problem, we decompose it into three sub-problems and address them iteratively. Firstly, we employ the CVX optimization tool to allocate wireless channels and utilize Lagrangian duality to determine the optimal transmit power. Secondly, leveraging the monotonicity of the optimization objective, we derive the optimal computing power. Lastly, we employ the successive convex approximation (SCA) technique to tackle the trajectory planning sub-problem. Numerical results demonstrate that the proposed algorithm can effectively position UAVs within 15 iterations. Furthermore, our layered scheme significantly reduces overall energy consumption.

Wireless Power Transfer System in Electric Vehicle Application

Abstract: As an alternate form in the road transportation system, electric vehicle (EV) can help reduce the fossil-fuel consumption. However, the usage of EVs is constrained by the limited capacity of battery. Wireless Power Transfer (WPT) can increase the driving range of EVs by charging EVs in motion when they drive through a wireless charging lane embedded in a road. The amount of power that can be supplied by a charging lane at a time is limited. A problem here is when a large number of EVs pass a charging lane, how to efficiently distribute the power among different penetrations levels of EVs? However, there has been no previous research devoted to tackling this challenge. To handle this challenge, we propose a system to balance the State of Charge (called BSoC) among the EVs. It consists of three components: i) fog-based power distribution architecture, ii) power scheduling model, and iii) efficient vehicle-to-fog communication protocol. The fog computing center collects information from EVs and schedules the power distribution. We use fog closer to vehicles rather than cloud in order to reduce the communication latency. The power scheduling model schedules the power allocated to each EV. In order to avoid network congestion between EVs and the fog, we let vehicles choose their own communication channel to communicate with local controllers.

A novel electro-hydraulic unit design based on a shaftless integration of an internal gear machine and a permanent magnet electric machine

In recent years, increasingly stringent emission regulations have spurred an electrification trend in off-highway vehicle technology. To address challenges such as the high cost of power electronics components, limited battery capacity, and the substantial modifications required for the vehicles, there is a pressing need to develop high-speed, cost-effective, compact, and efficient electro-hydraulic units capable of powering vehicle functions. In response to these demands, this paper introduces an innovative morphology for an electro-hydraulic unit and outlines the integration method for a crescent-type internal gear machine with a permanent magnet synchronous electric machine. The proposed morphology aims to minimize component count through a shaftless solution while incorporating a cooling system that utilizes the same working fluid as the hydraulic machine. The design approach utilizes a genetic algorithm optimization process to maximize overall energy efficiency and compactness. Insights gained from the optimization results shed light on the relationship between key design parameters and unit performance, enhancing the understanding of this electro-hydraulic unit. A prototype of the unit was manufactured and tested, demonstrating a volumetric efficiency ranging from 81 % to 97 % at a maximum rotational velocity of the pinion of 6000 rpm. These results validate both the morphology and the design approach, indicating the feasibility of designing compact electro-hydraulic units that leverage hydraulic machines with higher maximum rotational velocities than commercially available counterparts as a mean to enhance efficiency and compactness.

A Sustainable Multi-Objective Model for Capacitated-Electric-Vehicle-Routing-Problem Considering Hard and Soft Time Windows as Well as Partial Recharging.

Due to the high pollution of the transportation sector, nowadays the role of electric vehicles has been noticed more and more by governments, organizations, and environmentally friendly people. On the other hand, the problem of electric vehicle routing (EVRP) has been widely studied in recent years. This paper deals with an extended version of EVRP, in which electric vehicles (EVs) deliver goods to customers. The limited battery capacity of EVs causes their operational domains to be less than those of gasoline vehicles. For this purpose, several charging stations are considered in this study for EVs. In addition, depending on the operational domain, a full charge may not be needed, which reduces the operation time. Therefore, partial recharging is also taken into account in the present research. This problem is formulated as a multi-objective integer linear programming model, whose objective functions include economic, environmental, and social aspects. Then, the preemptive fuzzy goal programming method (PFGP) is exploited as an exact method to solve small-sized problems. Also, two hybrid meta-heuristic algorithms inspired by nature, including MOSA, MOGWO, MOPSO, and NSGAII_TLBO, are utilized to solve large-sized problems. The results obtained from solving the numerous test problems demonstrate that the hybrid meta-heuristic algorithm can provide efficient solutions in terms of quality and non-dominated solutions in all test problems. In addition, the performance of the algorithms was compared in terms of four indexes: time, MID, MOCV, and HV. Moreover, statistical analysis is performed to investigate whether there is a significant difference between the performance of the algorithms. The results indicate that the MOSA algorithm performs better in terms of the time index. On the other hand, the NSGA-II-TLBO algorithm outperforms in terms of the MID, MOCV, and HV indexes.

Towards Energy-Aware Federated Learning via Collaborative Computing Approach

This research delves into the consequences of the high complexity of on-device operations executed during the federated learning process. We investigate how the varying computational capabilities and battery levels among mobile devices can introduce performance disparities and influence training quality. Hence, in order to deal with these challenges, we propose EAFL+, a novel energy optimization technique, that focuses on managing power consumption in devices with limited battery capacity. EAFL+ is a cloud–edge–terminal collaborative approach that provides a new architectural design for achieving power-aware FL training by leveraging resource diversity and computation offloading. The innovative scheme enables the efficient selection of an approximately-optimal offloading target, from a set of Cloud-tier, Edge-tier, and Terminal-tier resources and achieves the best cost-quality tradeoff for the devices taking part in the FL system. Our evaluation shows EAFL+ can help conserve the devices’ energy participating in training, which improves the participation rates and increases the clients’ contributions, hence achieving higher accuracy and faster convergence. Through experiments on real datasets and traces in an emulated FL environment, EAFL+ reduces the drop-out of clients to zero and enhances accuracy by up to 24% and 9% compared to EAFL and Oort, respectively.

SMS2DC: Synchronous mobile sinks scheduling for data collection in internet of things‐enabled wireless sensor networks

SummaryEnergy‐efficient data collection in wireless sensor networks (WSNs) is crucial due to the limited battery capacity of sensor nodes (SNs). Using a mobile sink (MS) for data collection can lower the energy consumption of SNs to avoid relaying in WSNs. However, a single MS is not a feasible solution for large‐scale WSNs, so it was necessary to use multiple MSs to collect data. A synchronous MS scheduling strategy for data collection (SMS2DC) is proposed in this paper, which uses two types of MS, a local MS to collect data from SN and a global MS to collect data from local MS. In this process, we begin by partitioning the network based on chemical reaction optimization. For each partition, a MS is assigned and scheduled using a path construction strategy according to a geometric path construction approach. In addition, a global MS is scheduled based on a local MS trajectory by identifying the most appropriate collision point to collect data. As a result, the algorithm increases data collection accuracy while minimizing network data loss. The asymptotic time complexity of the proposed SMS2DC algorithm needed . The comparison results show the superiority of the proposed SMS2DC strategy under multiple scenarios under various deployment conditions.

Decentralized computation offloading via multi-agent deep reinforcement learning for NOMA-assisted mobile edge computing with energy harvesting devices

Supporting latency-sensitive and computation-intensive applications is hardly possible for mobile devices (MDs) with limited battery capacity and low computing resources. Therefore, mobile edge computing (MEC) and wireless power transfer (WPT) have emerged as promising technologies that allow MDs to offload part or all of their workloads to MEC servers and harvest energy for prolonging their battery lifetime. However, the MEC server’s limited computing resources, available communication channel quality, and time-limited energy harvesting (EH) challenge the computation offloading. In this paper, we study the joint problem of decentralized computation offloading and resource allocation (JDCORA) in the environment of non-orthogonal multiple access (NOMA)-assisted MEC with multiple EH-enabled MDs. To learn decentralized offloading policies, we propose a multi-agent deep reinforcement learning (MADRL)-based scheme towards minimizing energy consumption and task completion time, which considers the cooperation between MDs to adjust their strategies. In particular, we improve the multi-agent deep deterministic policy gradient (MADDPG) by applying the features of double actors, double centralized critics, soft value estimation, critic regularization and proportional-based prioritized experience replay (pPER) and propose an algorithm called multi-agent twin actors regularized critics (MATARC). Simulation results demonstrate that the proposed MATARC has a better convergence performance compared to other baseline methods and also reduces the average energy consumption, task completion time and task drop rate.

Energy Consumption Optimization based on Economic Benefit in WSN-based IoT via Global Hierarchical Caching Strategy

Recent advances in Wireless Sensor Network (WSN) have significantly contributed to the prevalence of Internet of Things (IoT) devices and their pervasive integration into everyday life and industrial operations. A WSN-based IoT comprises numerous small, scattered, battery-operated sensors that are designed to perform collaborative tasks. These sensor nodes are prone to energy drain because of their limited battery capacity when they needed to run efficiently over extended periods of time. Enhancing the large-scale network’s lifespan and controlling the economic costs become relevant since battery replacement or recharging is impractical in severe environments. In this paper, we present a novel energy optimization algorithm tailored for WSNs, which not only takes into account the reduction in replacement costs stemming from prolonged equipment lifespan but also incorporates the operational savings resulting from enhanced energy efficiency. It aims to enhance energy efficiency by leveraging a global hierarchical caching mechanism to simultaneously balance exploration and exploitation of energy resources in both the uplink and downlink of the networks. The simulation results demonstrate that our algorithm effectively minimizes energy consumption while maintaining optimal economic efficiency by decreasing the frequency of state transitions. It can consume 13% less energy than original system and extend the network lifetime by 10%.

Flood Prediction System Using IOT & Artificial Neural Network

Floods pose significant challenges as one of nature's most devastating disasters, making the development of accurate forecast model’s complex. This issue has led to severe consequences such as crop loss, population displacement, damage to infrastructure, and disruption of essential services. Advanced research on flood prediction models has played a crucial role in providing policy recommendations, mitigating risks, reducing human casualties, and minimizing property damage caused by floods. In this context, we propose an Internet of Things (IoT)-based flood prediction and forecasting model that prioritizes energy efficiency. Given the limited battery and memory capacity of IoT sensor nodes, we employ an energy-saving strategy within the fog layer, leveraging data diversity to minimize energy consumption. Additionally, cloud technology offers an effective storage solution. To accurately calibrate flood phases, we investigate climatic factors such as humidity, temperature, rainfall, as well as water body parameters including water flow and elevation. Neural networks are commonly used in constructing forecast systems, as they can replicate the complex calculations involved in flood physical processes, resulting in improved performance and cost-effectiveness. In our approach, the Artificial Neural Network (ANN) technique is employed for flood forecasting, and the effectiveness of different algorithms, such as Logistic Regression and Decision Tree, is assessed by comparing them to ANN. Accuracy values are computed using a classification report assessment, and graph parameters are carefully evaluated. Ultimately, our proposed system utilizes the ANN technique to train a predictive model by examining the dataset. This model generates real-time flood risk forecasts through a user-friendly graphical interface.

A Survey on Methods to Optimize Power Harvesting in Drones

This paper explores the challenges associated with limited battery capacity in drones and presents a comprehensive analysis of strategies to optimize energy harvesting and extend flight durations. Various energy generation methods, including piezoelectric and solar harvesting, are discussed, along with electrical circuit generation for wireless charging and communication-enabled energy delivery. The interdisciplinary nature of drone technology is highlighted, emphasizing the need for ongoing research in renewable energy models and innovative solutions like laser charging. The paper concludes with recommendations for further exploration and refinement of these strategies to enhance the future of UAVs.

Study on Distribution Path Planning of Electric Logistics Vehicles Under Partial Charging Strategy

The current situation of energy consumption and environmental pollution is becoming more and more serious, electric vehicles have gradually become an important distribution tool for logistics companies due to their advantages of low energy consumption and low pollution, however, the problems of limited battery capacity and long charging time are still the main obstacles to their effective promotion in the field of logistics and distribution. In order to improve the utilization rate of electric logistics vehicles, the charging method of the vehicle is studied in detail, and it is proposed to use partial charging strategy to optimize the distribution path scheme of electric logistics vehicles with soft time window, and to establish an optimization model with the goal of minimizing the total cost of distribution, and at the same time, consider the constraints of the vehicle's loading, the customer's soft time window, and the battery endurance; the genetic algorithm is improved by using the idea of destruction and repair in the large neighborhood search algorithm, and the problem is solving, and verify the effectiveness of the model and algorithm using the Solomon arithmetic set; and conduct a sensitivity analysis of the distribution cost by adjusting 2 parameters, the battery capacity and charging price of the electric vehicle. The results show that the partial charging strategy can effectively reduce the cost compared with the full charging strategy in the distribution activities of electric logistics vehicles. In the example, the total cost is reduced by 7.69%, the electricity cost is reduced by 8.85%, and the total distance is shortened by 7.4%. In this case, the total cost, electricity cost and time window cost increased by 6.22%, 19.83% and 6.83% respectively. At the same time, some charging strategies can also significantly improve the power utilization rate of the vehicle battery, and reduce the idle and waste of vehicle resources and power resources. This paper proposes a distribution route optimization model of electric logistics vehicles under the partial charging strategy, which can provide beneficial enlightenment for logistics enterprises in the scheme scheduling and distribution model selection in urban distribution, and is of great significance for reducing the distribution cost of logistics enterprises.

Power-Aware Inverse-Search Machine Learning for Low Resource Multi-Objective Unmanned Underwater Vehicle Control (Student Abstract)

Flapping-fin unmanned underwater vehicle (UUV) propulsion systems enable high maneuverability for tasks ranging from station-keeping to surveillance but are often constrained by their limited computational power and battery capacity. Previous research has demonstrated that time-series neural network models can accurately predict the thrust and power of certain fin kinematics based on the specified gait coupled with the fin configuration, but can not fit an inverse neural network that takes a thrust request and tunes the kinematics by weighting thrust generation, smooth movement transitions, and power attributes. We study various combinations of the three weights and fin materials to create different ‘modes’ of movement for a multi-objective UUV, based on controller intent using an inverse neural network. Finally, we implement and validate an enhanced power-aware inverse model by benchmarking on the Raspberry Pi Model 4B system and testing through generated simulated movements.

Impact of Drone Battery Recharging Policy on Overall Carbon Emissions: The Traveling Salesman Problem with Drone

This study investigates the traveling salesman problem with drone (TSP-D) from a sustainability perspective. In this problem, a truck and a drone simultaneously serve customers. Due to the limited battery and load capacity, the drone temporarily launches from and returns to the truck after each customer visit. Previous studies indicate the potential of deploying drones to reduce delivery time and carbon emissions. However, they assume that the drone battery is swapped after each flight. In this study, we analyze the carbon emissions of the TSP-D under the recharging policy and provide a comparative analysis with the swapping policy. In the recharging policy, the drone is recharged simultaneously on top of the truck while the truck travels. A simulated annealing algorithm is proposed to solve this problem. The computational results demonstrate that the recharging policy can provide faster delivery and lower emissions than the swapping policy if the recharging is fast enough.

Improved DRL-based energy-efficient UAV control for maximum lifecycle

Unmanned aerial vehicles (UAVs) operating as airborne base stations (UAV-BSs) provide efficient on-demand services to ground users. UAV-BSs are inherently flexible and mobile, allowing them to be strategically deployed based on ground user distribution and quality of service requirements, including coverage rate, system lifecycle, and user fairness. Owing to the limited battery capacity and coverage range of the UAVs, managing them to extend their operational lifecycle, ensure service fairness, and maintain a specific real-time coverage rate is challenging. Therefore, a multi-objective optimization problem with constrained Pareto dominance is formulated. Subsequently, a novel assisted deep reinforcement learning model is developed to maximize the minimum remaining energy while simultaneously considering user fairness and coverage-rate requirements. The particle swarm optimization algorithm is adopted to assist multi-agent cooperative deep reinforcement learning. Finally, the simulation results show that the proposed model outperforms the other popular methods in terms of user fairness, system lifecycle, coverage rate, and energy efficiency in the context of multi-objective, multi-agent cooperative coverage control deployment.

Locating charging stations and routing drones for efficient automated stocktaking

Drones have received growing attention in logistics recently. One possible application is deploying drones for auditing inventory in warehouses. With the use of drones, warehouses are able to increase inventory record accuracy and decrease labor costs. In this research, we introduce the stocktaking drone routing problem (STDRP), which consists of routing a fleet of drones through a warehouse for stocktaking purposes as well as deciding on the location of charging stations on the warehouse floor, which is necessary due to the limited battery capacity of the drones. Subsequently, we develop an adaptive large neighborhood search-based heuristic (ALNS) with novel solution encoding and decoding approaches to solve the STDRP. In a numerical study, we show that ALNS can solve realistic instances in reasonable time. We also derive recommendations regarding the ideal size of the drone fleet, the charging infrastructure, and battery capacity. Finally, we investigate the interplay between the storage assignment policy (such as the popular ABC rule) and stocktaking efficiency using drones.

Optimal Location of EV Charging Stations in the Distribution System Considering GWO Algorithm

Objectives: The transition from conventional vehicles to electric vehicles (EVs) has been significantly influenced by the current scarcity of fossil fuels and the environmental concerns surrounding greenhouse gas emissions. The exploration of the optimal location of electric vehicle charging stations has been prompted by the electrification of the transportation system and the increasing demand for EVs. Methods: One of the main challenges is the detrimental impact of improper positioning of charging stations on the power distribution network. The Grey Wolf Optimization (GWO) approach is used to determine the effective Charging Stations (CS) locations to minimize the losses, which is the major objective of the proposed technique. An IEEE 33 bus system is used to implement the suggested technique. Findings : The proposed method finds the suitable deployments for EVCS within the distribution system, which is of utmost importance due to the constraints imposed by limited battery capacity and prolonged charging periods. This article proposes the optimal location for EVCS to minimize losses inside the distribution network. Novelty: The effectiveness of the proposed GWO is evaluated by comparing it with the PSO optimal algorithm. Finally, a comparison of system voltage and nominal voltage is presented and the probability comparison of EV location with the base case and the proposed algorithms is presented. Keywords: Electric Vehicle Charging Station, Distribution System, GWO, System and Nominal Voltage

Automatic Guided Vehicle Scheduling in Automated Container Terminals Based on a Hybrid Mode of Battery Swapping and Charging

Automatic guided vehicles (AGVs) in the horizontal area play a crucial role in determining the operational efficiency of automated container terminals (ACTs). To improve the operational efficiency of an ACT, it is essential to decrease the impact of battery capacity limitations on AGV scheduling. To address this problem, this paper introduces battery swapping and opportunity charging modes into the AGV system and proposes a new AGV scheduling problem considering the hybrid mode. Firstly, this study describes the AGV scheduling problem of the automated container terminals considering both loading and unloading tasks under the hybrid mode of battery swapping and charging. Thereafter, a mixed-integer programming model is established to minimize the sum of energy costs and delay costs. Secondly, an effective adaptive large neighborhood search algorithm is proposed to solve the problem, in which the initial solution construction, destroy operators, and repair operators are designed according to the hybrid mode. Finally, numerical experiments are conducted to analyze the effectiveness of the model and the optimization performance of the algorithm. The results demonstrate that the hybrid mode of battery swapping and charging can effectively reduce the number of battery swapping times and scheduling costs compared to the existing mode.

Optimized task offloading strategy in IoT edge computing network

As the IoT devices are highly ubiquitous and connected, their computational tasks can be processed on the Multi-access edge computing (MEC) servers because of the IoT devices’ limited battery, computing power and storage capacities. Due to the heterogeneity of IoT devices and MEC devices’ limited capabilities, tasks should be offloaded efficiently to MEC servers mainly and then to the Clouds (fog nodes) if necessary for fulfilling tasks’ computational requirements. The proposed solution presents a task offloading decision using base stations of the network and queued task scheduling. The base station, which is the edge orchestrator, will decide on where to offload (edge servers or cloud) or compute locally. For handling heterogeneous tasks, a maximum tolerable latency is included. It is shown that a 3% to 7% improvement in the number of failed tasks, more than 7.4% in the number of successfully completed tasks, an improvement by more than 0.85 s in the average execution time and more than 7% in network communication, which directly impacts the energy of edge devices. The simulation results show that this work performed better in decreasing the number of failed tasks due to latency, decreasing energy consumption and minimizing the average execution time of tasks.

Joint Optimization of Deployment and Flight Planning of Multi-UAVs for Long-Distance Data Collection From Large-Scale IoT Devices

Internet of Things (IoT) devices have been widely deployed to build smart cities. How to efficiently collect data from large-scale IoT devices is a valuable and challenging research topic. Benefiting from agility, flexibility, and deployability, an unmanned aerial vehicle (UAV) has great potential to be an aerial base station. However, given the limited battery capacity, the flight time of a UAV is limited. This paper focuses on using multi-UAVs to execute long-distance data collection from large-scale IoT devices. We design a multi-UAVs-assisted large-scale IoT data collection system. The core facilities of this system are the data center and charging stations, which are equipped with a limited number of charging piles to provide charging services for UAVs. To ensure the efficient operation of the system, the problem of deployment and flight planning of UAVs is formulated as a joint optimization problem. To solve the problem, a population-based optimization algorithm with a three-layer structure, namely EDDE-DPDE, is proposed. It includes two core components: elite-driven differential evolution (EDDE) and differential evolution with a dynamic population (DPDE), which are two variants of differential evolution. Thanks to ideas of reusing elite individuals and historical information, the proposed EDDE-DPDE shows an improvement of at least 11.11% compared with four powerful algorithms in terms of average travel time.