Low Energy Consumption Requirements Research Articles

Interactions between bacteria and microalgae in microalgal-bacterial symbiotic wastewater treatment systems: mechanisms and influencing factors

Microalgal-bacterial symbiotic wastewater treatment systems (MBSWTSs) have received widespread attention due to their capacity to achieve high pollutant removal efficiency during wastewater treatment, with low energy consumption requirements, efficient carbon sequestration, and wastewater resource utilization. This paper provides an overview of the treatment performance and current research status of MBSWTSs, including a detailed summary of the mechanisms of nitrogen, phosphorus, and carbon removal by MBSWTSs and the interactions between bacteria and microalgae. In particular, this review focuses on the influence of operational parameters on the regulation of the symbiotic system, such as the microalgal:bacterial ratio, N:P ratio, external carbon source, dissolved oxygen concentration, aeration mode, and light availability. Among these factors, the microalgal:bacterial ratio, carbon source, and light availability have an important influence on microalgal-bacterial competition, as well as the trophic mode of the system, biomass production, and the capacity for the process to be practically applied on a large scale. MBSWTSs still have some challenging aspects that have hindered their development and application, such as the unknown mechanism of microalgal-bacterial co-metabolism, limited previous practical applications, algal contamination, and harvesting difficulties. To overcome these challenges, future research requires a multidisciplinary approach, incorporating life sciences, material science, and other disciplines. Comprehensive research should be conducted on the metabolic mechanisms of MBSWTSs, the optimization of process performance and waste resource utilization, providing a theoretical and practical foundation for the practical application of MBSWTSs.

Edge collaborative caching solution based on improved NSGA II algorithm in Internet of Vehicles

With the rapid development of the Internet of Vehicles technology and the continuous advancement of intelligent transportation, vehicle network traffic is growing exponentially. Traditional caching solutions transmit content through the core network, causing significant latency. A vehicle-cloud collaborative edge caching network architecture was proposed to address this type of problem. Using this architecture, both the on-board terminals and cloud/edge servers could provide computing services. A communication model, caching model, latency model, energy consumption model, load model and multi-objective optimization problem model were designed. The edge collaborative caching solution based on the improved NSGA II algorithm was proposed. By caching some services to edge nodes and nearby vehicles, reducing content access latency and improving resource utilization. The results indicate that the caching solution outperforms the comparison scheme in terms of the comprehensive costs of latency, energy consumption, and load balancing in simulation experiments. It can meet the caching requirements of low latency and low energy consumption for in-vehicle applications, laying a solid foundation for achieving more efficient and reliable connected vehicles and autonomous driving.

Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks.

The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum and the power limitations seriously constrains the improvement of WSN. Therefore, in this paper, we proposed a deep reinforcement learning-based energy consumption optimization for P2P communication in WSN. Specifically, P2P sensors (PUs) are considered agents to share the spectrum of authorized sensors (AUs). An authorized sensor has permission to access specific data or systems, while a P2P sensor directly communicates with other sensors without needing a central server. One involves permission, the other is direct communication between sensors. Each agent can control the power and select the resources to avoid interference. Moreover, we use a double deep Q network (DDQN) algorithm to help the agent learn more detailed features of the interference. Simulation results show that the proposed algorithm can obtain a higher performance than the deep Q network scheme and the traditional algorithm, which can effectively lower the energy consumption for P2P communication in WSN.

Hydrolysate derived from sugarcane bagasse by hydrothermal and enzymatic treatment as an external carbon source for wastewater nitrogen removal

Research on the preparation of denitrifying carbon sources from agricultural wastes persists with problems of environmentally unfriendly pretreatment methods and unstable carbon release. In this study, a hydrolysis method without secondary waste liquid generation was used and the obtained hydrolysate was applied as a denitrifying carbon source. Firstly, the optimum hydrothermal condition of sugarcane bagasse (SCB) was explored, which showed a higher reducing sugar concentration (13.68 g/L) and COD concentration (20,691.67 mg/L) at 200 °C, 40 min with relatively low energy consumption (EC) requirement (1.81 kW·h). Afterward, all three bio-additives (tea saponins, rhamnolipids, and whey proteins) were found to enhance enzymatic hydrolysis, with whey proteins being the most effective, increasing the reducing sugar and COD concentrations by 29.40 % and 17.51 %, respectively. The enzymatic reaction was optimized using the following conditions: whey protein concentration of 0.2 %, pH 5.0, temperature 50 °C, cellulase to the β-glucosidase ratio of 4:1, and total enzyme amount of 30 mg. Moreover, the nitrogen removal effect of SCB hydrolysate as a carbon source was determined, and the average TN removal was 98.23 % at C/N of 5 and HRT of 8. Finally, the microbial community was analyzed, and the dominant phylum was denitrifying bacteria (Proteobacteria), and organic matter utilizing bacteria (Bacteroidetes, Acidobacteriota, and Patescibacteria), and the relative abundance of the latter both improved with increasing C/N, which further demonstrated the feasibility of SCB hydrolysate as a carbon source. This research provides a reference for the application of agricultural waste hydrolysate in wastewater nitrogen removal.

A configurable hardware-efficient ECG classification inference engine based on CNN for mobile healthcare applications

—Electrocardiogram (ECG) processors for healthcare have been widely used, however most of them can only adapt to specific applications, lacking flexibility. For achieving scalable on-chip ECG classification, a flexible inference engine based on one-dimensional (1-D) convolutional neural network (CNN) is proposed. By utilizing the proposed computing strategy based on systolic arrays, filter level parallelism and output channel parallelism are achieved. The configurable processing units (PUs) and modifiable instruction registers allow this inference engine to support computing of 1-D convolutional layers or fully connected layers with different scales. Based on the proposed data buffers system with multi-level storage structure, the input feature values and weight values are reused, thereby reducing hardware overhead and memory access power. This design has been validated on FPGA using our proposed two arrhythmia classification models that represent low energy consumption and high accuracy requirements, achieving accuracy of 98.9% and 99.3%, respectively, with energy consumption of 2.05 μJ and 14.27 μJ per classification at 200 MHz. The hardware implementation results indicate that good configurability enables our design to adapt to different ECG classification scenarios, effectively improving the hardware universality in mobile healthcare applications.

Radio Map Assisted Multi-UAV Target Searching

When acting as dynamic receivers, unmanned aerial vehicles (UAVs) have several advantages over the conventional static base stations for received signal strength (RSS) based target searching. It is highly likely that the RSS is becoming stronger as the UAV is moving towards the estimated coordinate, and this leads to a higher estimation precision for each localization update. On the other hand, despite its low cost, low energy consumption and simple hardware requirement, RSS can be severely influenced by land layouts, therefore the assumption of a simple free space propagation model may lead to poor estimation precision and even failure, especially for an area where the topography is highly complex. In this paper, we propose multi-UAV target searching with the assistance of radio maps, which contain abundant information of the topography influence on RSS. We generate radio maps from real topographic data, and derive the minimum mean square error estimator for multi-UAV target searching with the radio map knowledge for memoryless observations. Simulations for areas with complex topography, show that the localization precision and searching efficiency can be considerably improved with the assistance of radio maps.

Survey on Multiple Methods for Task Offloading in Mobile Cloud-Edge Environment

Mobile edge computing (MEC) is an emerging technology that extends cloud computing to the edge of the network. It offloads computing intensive tasks to the edge server to solve the problem of insufficient computing power and resource of the terminal device, meets the low energy consumption and low latency requirements of the mobile user for application task computing, and greatly releases the pressure of the cloud center. Cloud-edge integration architecture has become a trend. How to perform optimal offload scheduling for terminal tasks has always been one of the key issue in the field of MEC research. This paper reviews the research directions and achievements of task offloading technology in today’s cloud-edge environment. First, the development status of MEC environment and task offloading technology are introduced, and the concepts and applications of classic schemes such as heuristic algorithm, metaheuristic algorithm, and reinforcement learning are elaborated. Based on the mainstream solutions in the research literature, two types of task offloading solutions are summarized: traditional task offloading solutions based on algorithm optimization and interactive task offloading solutions based on reinforcement learning. Second, the research literature on the two mainstream directions mentioned above is summarized and organized from aspects such as problem constraints, optimization objectives, and contributions made. Meanwhile, schemes are compared through the classic algorithms and innovations they used. Finally, the current challenges and future research objectives in this field are summarized to provide reference and help for the follow-up research.

Spherical Joint Actuator with Backstepping Sliding Mode Control for Robotic Manipulator Rigid-Flexible Coupling System

At present, the output shaft of a permanent magnet spherical actuator (PMSA) is a rigid structure, which has the problems of small load weight ratio and high energy consumption. It is difficult to meet the application requirements of lightweight, high speed, and low energy consumption. In this paper, a PMSA is proposed as the driving motor for the rigid-flexible coupling system (R-FCS) of the robotic manipulator, and a flexible mechanical arm is used to replace the rigid output shaft of the PMSA to connect the hand claw to form a spherical joint. Firstly, the partial differential dynamics model of the robotic manipulator in the coordinate system is established based on the Hamilton method, and the R-FCS is constructed according to the momentum conservation equation combined with the rigid axis dynamics model of the PMSA. Then, a backstepping sliding mode controller (BSMC) is designed to form a closed-loop control system by selecting an appropriate nonlinear gain function to estimate the unknown disturbances of the system, and the stability of the controller is guaranteed by the Lyapunov theory. Finally, the simulation and experimental results verify that the R-FCS can achieve better trajectory tracking and provide an effective driving method for robotic manipulator spherical joint.

Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply

Mobile edge computing (MEC), as a new distributed computing model, satisfies the low energy consumption and low latency requirements of computation-intensive services. The task offloading of MEC has become an important research hotspot, as it solves the problems of insufficient computing capability and battery capacity of Internet of things (IoT) devices. This study investigates task offloading scheduling in a dynamic MEC system. By integrating energy harvesting technology into IoT devices, we propose a hybrid energy supply model. We jointly optimize local computing, offloading duration, and edge computing decisions to minimize system cost. On the basis of stochastic optimization theory, we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called DTOME. DTOME can make task offloading decisions by weighing system cost and queue stability. We quote dynamic programming theory to obtain the optimal task offloading strategy. Simulation results verify the effectiveness of DTOME, and show that DTOME entails lower system cost than two baseline task offloading strategies.

Delay-Aware Edge-Terminal Collaboration in Green Internet of Vehicles: A Multiagent Soft Actor-Critic Approach

The high performance requirement of task processing and low energy consumption requirement of network operation result in a contradiction for the Internet of Vehicles (IoV). This paper addresses the contradiction and proposes an efficient edge-terminal collaboration scheme to sustain the diverse task requirements for a green IoV network. Specifically, two types of tasks are first modeled in terms of data size and delay requirements and then an energy minimization problem was formulated under the diverse task requirements. Thereafter, a multi-agent reinforcement learning framework is constructed, based on which the time-varying environment of the IoV can be modeled as a partially observable Markov decision process. Furthermore, a multi-agent soft actor-critic (MASAC) scheme is proposed to train the edge-terminal collaboration policy in terms of task offloading, spectrum sharing as well as vehicle power control. Finally, the effectiveness of the proposed scheme is validated by comparing with other baseline schemes through extensive simulation experiments.

Joint heterogeneity-aware personalized federated search for energy efficient battery-powered edge computing

The limited battery capacity of edge devices has a significant impact on the deployment of Federated Learning (FL). As a result, maintaining computation sustainability is a critical issue for edge FL. Furthermore, the heterogeneities of deployed edge devices reduce FL efficiency and effectiveness, making FL computation sustainability more challenging to maintain. To address these issues raised by heterogeneities, we perform a joint heterogeneity-aware personalized federated search for energy-efficient edge computing. To achieve energy-efficient on-device inference and training, a one-training process is adopted to search for personalized partial network structures on each device. We begin by tailoring the network scale on each node based on the efficiency of model inference, which also serves as the search space for optimization. This strategy can mitigate the straggler problem and improve the energy efficiency of FL by guiding the efficient FL training process in each round. To further optimize the energy consumption of edge devices, we design a lightweight search controller during the search process. This controller meets the low energy consumption requirements of the edge devices and reduces their energy consumption during the search process. Finally, we introduce an adaptive search strategy to guarantee personalized training convergence on each device. By reducing the energy consumption of each training round and ensuring the training convergence of personalized models, we can significantly improve the energy efficiency of FL on battery-powered edge devices. Our framework can obtain competitive accuracy with state-of-the-art methods while improving inference efficiency by up to 1.43× and training energy efficiency by up to 2.63×.

High-Speed 9.6-μm Long-Wave Infrared Free-Space Transmission With a Directly-Modulated QCL and a Fully-Passive QCD

Free-space optics (FSO) in the mid-infrared (mid-IR) contains rich spectral resources for future ultrahigh-speed wireless communications yet is currently under-exploited. Two atmospheric transmission windows at the mid-IR, namely, the mid-wave IR (MWIR, 3–5 μm) and the long-wave IR (LWIR, 8–12 μm), show great potential in supporting free-space communications for both terrestrial and space application scenarios. Particularly, the LWIR signal with a longer wavelength has high intrinsic robustness against aerosols’ scattering and turbulence-induced scintillation and beam broadening effects, which are the main concerns hindering the wide deployment of practical FSO systems. In this context, high-bandwidth semiconductor-based mid-IR FSO transceivers will be desirable to meet the requirements of low energy consumption and small footprints for large-volume development and deployment. Quantum cascade devices, including quantum cascade lasers (QCLs) and quantum cascade detectors (QCDs), appear promising candidates to fulfill this role. In this work, we report a high-speed LWIR FSO transmission demonstration with a 9.6-μm directly-modulated (DM)-QCL and a fully passive QCD without any active cooling or bias voltage. Up to 8 Gb/s, 10 Gb/s, and 11 Gb/s signal transmissions are achieved when operating the DM-QCL at 10 °C, 5 °C, and 0 °C, respectively. These results indicate a significant step towards an envisioned fully-connected mid-IR FSO solution empowered by the quantum cascade semiconductor devices.

Review on Battery Packing Design Strategies for Superior Thermal Management in Electric Vehicles

In the last decades of electric vehicle (EV) development, battery thermal management has become one of the remaining issues that must be appropriately handled to ensure robust EV design. Starting from researching safer and more durable battery cells that can resist thermal exposure, battery packing design has also become important to avoid thermal events causing an explosion or at least to prevent fatal loss if the explosion occurs. An optimal battery packing design can maintain the battery cell temperature at the most favorable range, i.e., 25–40 °C, with a temperature difference in each battery cell of 5 °C at the maximum, which is considered the best working temperature. The design must also consider environmental temperature and humidity effects. Many design strategies have been reported, including novel battery pack constructions, a better selection of coolant materials, and a robust battery management system. However, those endeavors are faced with the main challenges in terms of design constraints that must be fulfilled, such as material and manufacturing costs, limited available battery space and weight, and low energy consumption requirements. This work reviewed and analyzed the recent progress and current state-of-the-art in designing battery packs for superior thermal management. The narration focused on significant findings that have solved the battery thermal management design problem as well as the remaining issues and opportunities to obtain more reliable and enduring batteries for EVs. Furthermore, some recommendations for future research topics supporting the advancement of battery thermal management design were also discussed.

Deadline-Aware Deep-Recurrent-Q-Network Governor for Smart Energy Saving

Complex cyber-physical-social systems (CPSS) consist of battery-supplied devices with low energy consumption requirements. It is essential to maintain the timing performance of computing or communication tasks while saving the device energy. Linux OS provides built-in frequency governors for power management. However, these governors are not able to incorporate the timing requirements of the application for decision-making and cannot adapt the power management decision to the specific application on target devices. This paper presents an intelligent Linux frequency Deep-Recurrent-Q-Network (DRQN) governor for dedicated applications with deadline requirements running on CPSS devices through machine learning. Although machine learning algorithms have made considerable breakthroughs in recent years, deploying them to real small devices is challenging because of the computational overhead. To tackle the computation overhead problem, an interactive learning framework is designed where the DRQN model performs only the lightest inference in the kernel while using the online data (time-series) to learn and update itself in real-time at the user level. The governor is tested on both standalone devices and networked devices. The experiment shows that DRQN can self-develop tradeoff policy to meet the user's need with low overhead. The energy saved by DRQN ranges from 7% to 33% for various deadlines.

Evolutionary Game Analysis of Green Building Development Dynamic System under Government Regulation: From the Perspective of the Contractor

To speed up the development of green buildings (GB) and meet the requirements of low energy consumption, low carbon dioxide emissions, and green sustainable development, the key is to build a high-efficient green building dynamic system (GBDS). The government, the developer, and the contractor are the main power of GB promotion, and they are the main participants of GBDS. This paper aims to study how to improve the motivation of contractors’ investment and participation in GB under the government regulation measures, to make up for the gap of research on the contractor’s dynamic mechanism in the GBDS. In the research, evolutionary game theory was applied to build a dynamic model of the evolutionary game among the government, the developer, and the contractor, and the evolutionary laws and stability strategies of their game behaviors were analyzed. The research results show that the contractor’s motivation of building GBs depends on the sum of receipts after the mix of income from construction and government rewards and penalties. The higher the sum of receipts is, the more stable the contractor’s decision to participate in GBs is. When the government gives better incentive measures to the contractor, it is easier for the government, the developer, and the contractor to form an unanimous decision on the GB strategy, so the development dynamic system of GB is more stable.

Cloud Computing Image Processing Application in Athlete Training High-Resolution Image Detection

The rapid development of Internet of things mobile application technology and artificial intelligence technology has given birth to a lot of services that can meet the needs of modern life, such as augmented reality technology, face recognition services, and language recognition and translation, which are often applied to various fields, and some other aspects of information communication and processing services. It has been used on various mobile phone, computer, or tablet user clients. Terminal equipment is subject to the ultralow latency and low energy consumption requirements of the above-mentioned applications. Therefore, the gap between resource-demanding application services and resource-limited mobile devices will bring great problems to the current and future development of IoT mobile applications. Based on the local image features of depth images, this paper designs an image detection method for athletes' motion posture. First, according to the characteristics of the local image, the depth image of the athlete obtained through Kinect is converted into bone point data. Next, a 3-stage exploration algorithm is used to perform block matching calculations on the athlete's bone point image to predict the athlete's movement posture. At the same time, using the characteristics of the Euclidean distance of the bone point image, the movement behavior is recognized. According to the experimental results, for some external environmental factors, such as sun illumination and other factors, the image detection method designed in this paper can effectively avoid their interference and influence and show the movement posture of athletes, showing excellent accuracy and robustness in predicting the movement posture of athletes and action recognition. This method can simplify a series of calibration tasks in the initial stage of 3D video surveillance and infer the posture of the observation target and recognize it in real time. The one that has good application values has specific reference values for the same job.

Controllable oxygen-doping of two-dimensional heterostructure as diode-type gas sensor with high performance

To meet the low energy consumption requirement of the Internet of things, there is an eagerness for 2D gas sensors to realize the ultralow-power or self-powered sensing at room temperature. Here, we demonstrate the controllable Oxygen-doping of SnS2/G heterostructure as a diode-type gas sensor with excellent and robust performances. The oxygen substitution controlled by ion irradiation modifies the homogeneous SnS2/G heterostructure into a heterogeneous SnS2-0.22O0.22/G-SnS2/G. The boundary of SnS2-0.22O0.22/G-SnS2/G forms a lateral Schottky junction, which outputs a stable open-circuit voltage of 1.5 V under the indoor light illumination. Adsorbed with gas molecules, the SnS2-0.22O0.22/G-SnS2/G junction has the variation of the photoconductance instead of the build-in electric field, ensuring the linear response to the gas concentration. This diode-type gas sensor exhibits long-term stability for at least 50 days (50th−100th day) and has an ultrafast gas-sensing performance with a response/recovery time of ∼54 s/9.2 s. Besides, it exhibits high selectivity to NO2 (42.8%) compared to gas species of H2S (0.6%), SO2 (7.8%), and NH3 (0%), based on different sensitivities. This work presents controllable oxygen doping as a novel avenue for preparing extremely energy-efficient sensors with fast response/recovery, linear response, and robust performances catering to future ubiquitous and real-time sensing requirements.

Design of a Lightweight and Deployable Soft Robotic Arm

Soft robotics represents a rising trend in recent years, due to the ability to work in unstructured environments or in strict contact with humans. Introducing soft parts, robots can adapt to various contexts overcoming limits relative to the rigid structure of traditional ones. Main issues of soft robotics systems concern the relatively low force exertion and control complexity. Moreover, several fields of application, as space industry, need to develop novel lightweight and deployable robotic systems, that can be stored into a relatively small volume and deployed when required. In this paper, POPUP robot is introduced: a soft manipulator having inflatable links and rigid joints. Its hybrid structure aims to match the advantages of rigid robots and the useful properties of having a lightweight and deployable parts, ensuring simple control, low energy consumption and low compressed gas requirement. The first robot prototype and the system architecture are described highlighting design criteria and effect of internal pressure on the performances. A pseudo-rigid body model is used to describe the behavior of inflatable links looking forward to control design. Finally, the model is extended to the whole robot: multi-body simulations are performed to highlight the importance of suitable sensor equipment for control development, proposing a visual servoing solution.

An Energy-Efficient Authentication Scheme Based on Chebyshev Chaotic Map for Smart Grid Environments

Electric vehicle charging is becoming more commonplace, but a number of challenges remain. For example, the wireless communications between vehicle users and aggregators can be subject to exploitation and hence, several authentication schemes have been designed to support varying levels of privacy protection. However, there are a number of limitations observed in existing authentication schemes, and examples include lack of anonymity and not considering charging peak in their design (and consequently, not meeting low energy consumption requirement in smart grid environments). More recently, there have been attempts to utilize Chevyshev chaotic map in the design of authentication mechanism, with the aims of reducing computational costs yet achieving high security. However, the security requirements of Chebyshev polynomials pose new challenges to the construction of Chebyshev chaotic maps-based authentication schemes. To solve these limitations, we propose an efficient Chebyshev polynomials algorithm by adopting a square matrix-based binary exponentiation algorithm to provide secure and efficient Chebyshev polynomial computation. We further construct an energy-efficient authentication and key negotiation scheme for the smart grid environments based on the proposed algorithm. Compared with five other competing schemes, our proposed authentication scheme achieves reduced computational and communication costs. In addition, the ProVerif tool is used to analyze the security of our proposed authentication scheme. The results show that the proposed scheme outperforms these five other schemes in terms of computation and communication overheads while achieving privacy preserving.

Quantum-based wireless sensor networks: A review and open questions

Applications of quantum computing are growing at a very fast pace, for example, from quantum computers to quantum algorithms and even to the development of the quantum Internet. However, the use of quantum technology in wireless sensor networks has not been thoroughly investigated just yet. This is in part due to the complexity of using big, costly, and highly energy-consuming machines that are quantum computers to this date, compared to the nodes used in wireless sensor networks which are small, inexpensive, and operate with very low energy consumption requirements. However, we can expect that in the future (possibly in the next decade) quantum computers will be commercial and reduced in size, and hence, they can be used for sensor network applications which are the basis of the Internet of Things. In this review, we study the road from quantum computing to quantum wireless sensor networks and how the analysis and design of these systems have to change to accommodate quantum capabilities in sensors, processors, communication links, and overall performance of these monitoring networks.