Asiful Islam, Md. Sajjad-Ul Islam, Md. Minhaj Hossain et al.

2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS) • 2024

Solar power is an excellent, environmentally friendly, and dependable option for meeting our energy needs in the future. The program is based on a policy from the Department of Energy (DoE) that encourages the expansion of solar renewable energy appliances and their positive impact on both the national economy and the global environment. This study takes advantage of the recent market advantage of induction cooking technologies to apply more efficient and cost-effective ways that align with future energy sector desires. Induction cookers have been demonstrated to transport at least 80% of the power generated to the cookware, whereas electric stoves and gas burners create more power to compensate for the power that is passed to the atmosphere in the form of heat (i.e. roughly 55% efficient). At the moment, the induction cooker is the most advanced form of cooking technology that has been developed. Although all appliances, including induction cookers, are entirely reliant on grid electricity. According to studies, solar energy might replace our dependence on non-renewable resources. The product is self-sufficient, easy, dependable, fuel-free, and adaptable. The study proposed a refugee camp induction cooker that is renewable and carbon-free. For this research, the induction stove was mostly powered by solar energy. Pulse width modulation increases solar power and system efficiency. Ultimately, this energy is used to charge the battery. This solves the renewable-based cooking system's intermittency issue.

Majd Al-Homoud, Ola Samarah

Urban Science • 2025

The Zaatari Camp in Jordan exemplifies how Syrian refugees transform a planned grid settlement into an organic urban environment through socio-spatial adaptation, reflecting their cultural identity and territorial practices. This study investigates the camp’s morphological evolution, analyzing how refugees reconfigure public and private spaces to prioritize privacy, security, and community cohesion. Using qualitative methods—including archival maps, photographs, and field observations—the research reveals how formal public areas are repurposed into private shelter extensions, creating zones of influence that mirror traditional Arab-Islamic urban patterns. Key elements such as mosques, markets, and hierarchical street networks emerge as cultural anchors, shaped by refugees’ prior urban experiences. However, this organic growth introduces challenges, such as blocked streets and undefined spaces, which hinder safety and service delivery, underscoring tensions between informal urbanization and structured planning. The findings advocate urban resilience and participatory planning frameworks that integrate socio-cultural values, emphasizing defensible boundaries, interdependence, and adaptable design. Refugees’ territorial behaviors—such as creating diagonal streets and expanding shelters—highlight their agency in reshaping urban systems, challenging conventional top-down approaches. This research focuses on land-use dynamics, sustainable cities, and adaptive urban systems in crisis contexts. By bridging gaps between displacement studies and urban theory, the study offers insights into fostering social inclusion and equitable infrastructure in transient settlements. Future research directions, including comparative analyses of refugee camps and cognitive mapping, aim to deepen understanding of socio-spatial resilience. Ultimately, this work contributes to global dialogues on informal urbanization and culturally responsive design, advocating for policies that align with the Sustainable Development Goals to rebuild cohesive, resilient urban environments in displacement settings.

Shafiq Ahmed, M. Anisi

IEEE Transactions on Industrial Cyber-Physical Systems • 2024

The rapid advancement of intelligent transportation systems and the growing demand for sustainable energy solutions have elevated the Vehicle-to-Grid (V2G) paradigm in Industrial Cyber-Physical Systems (ICPS). This paper presents an AI-Enhanced Secure Protocol for V2G Energy Management, integrating Artificial Intelligence (AI) through Long Short-Term Memory (LSTM) networks with advanced cryptographic techniques for optimizing energy distribution between smart grids and electric vehicles. This protocol enhances system security and device integrity, effectively countering cyber threats and physical tampering. Emphasizing practical applicability, it demonstrates scalability and versatility across various smart grid environments, marking a significant step in AI-integrated cybersecurity for sustainable energy management. Comparative analysis reveals reductions in computation and communication costs by 49.79% and 23.24%, respectively, highlighting the efficiency of the protocol and its potential to enhance smart grid security frameworks.

Jinbo Wen, Jiawen Kang, D. Niyato et al.

IEEE Transactions on Industrial Cyber-Physical Systems • 2024

Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs.

J. Gilbert, G. Dick, B. Jenkins et al.

Standards in Genomic Sciences • 2014

The National Science Foundation’s EarthCube End User Workshop was held at USC Wrigley Marine Science Center on Catalina Island, California in August 2013. The workshop was designed to explore and characterize the needs and tools available to the community that is focusing on microbial and physical oceanography research with a particular emphasis on ’omic research. The assembled researchers outlined the existing concerns regarding the vast data resources that are being generated, and how we will deal with these resources as their volume and diversity increases. Particular attention was focused on the tools for handling and analyzing the existing data, on the need for the construction and curation of diverse federated databases, as well as development of shared, interoperable, “big-data capable” analytical tools. The key outputs from this workshop include (i) critical scientific challenges and cyber infrastructure constraints, (ii) the current and future ocean ’omics science grand challenges and questions, and (iii) data management, analytical and associated and cyber-infrastructure capabilities required to meet critical current and future scientific challenges. The main thrust of the meeting and the outcome of this report is a definition of the ’omics tools, technologies and infrastructures that facilitate continued advance in ocean science biology, marine biogeochemistry, and biological oceanography.

Venkanna Udutalapally, S. Mohanty, Vishal Pallagani et al.

IEEE Sensors Journal • 2021

Agriculture Cyber-Physical System (A-CPS) is becoming increasingly important in enhancing crop quality and productivity by utilizing minimum cropland. This paper introduces the innovative idea of the Internet-of-Agro-Things (IoAT) with an explanation of the automatic detection of plant disease for the development of ACPS. Majority of the crops were infected by microbial diseases in conventional agriculture. Also, the constantly mutating pathogens cannot be known to the knowledge of the farmer, due to which, there arises a demand to develop a disease prediction system. To prevent this, we use a trained Convolutional Neural Network (CNN) model to perform an analysis of the crop image captured by a health maintenance system. The image capturing along with continuous sensing and intelligent automation is performed by the solar sensor node. The sensor node houses a developed soil moisture sensor which has a high longevity compared to its peers. A real time implementation of the proposed system is demonstrated using a solar sensor node with a camera module, a microcontroller and a smartphone application using which a farmer can monitor the field. The prototype was deployed for three months and has achieved a robust performance by remaining rust-free and sustaining the varied weather conditions. An accuracy of 99.24% is achieved by the proposed plant disease prediction framework.

Jiang Wan, Anthony Lopez, Mohammad Abdullah Al Faruque

ACM Transactions on Cyber-Physical Systems • 2019

Modern automotive Cyber-Physical Systems (CPS) are increasingly adopting wireless communications for Intra-Vehicular, Vehicle-to-Vehicle (V2V), and Vehicle-to-Infrastructure (V2I) protocols as a promising solution for challenges such as the wire harnessing problem, collision detection, and collision avoidance, traffic control, and environmental hazards. Regrettably, this new trend results in new security challenges that can put the safety and privacy of the automotive CPS and passengers at great risk. In addition, automotive wireless communication security is constrained by strict energy and performance limitations of electronic controller units and sensors. As a result, the key generation and management for secure automotive CPS wireless communication is an open research challenge. This article aims to help solve these security challenges by presenting a practical key generation technique based on the reciprocity and high spatial and temporal variation properties of the automotive wireless communication channel. Accompanying this technique is also a key length optimization algorithm to improve performance (in terms of time and energy) for safety-related applications constrained by small communication windows. To validate the practicality and effectiveness of our approach, we have conducted simulations alongside real-world experiments with vehicles and RC cars. Last, we demonstrate through simulations that we can generate keys with high security strength (keys with 67% min-entropy) with 20× reduction in code size overhead in comparison to the state-of-the-art security techniques.

Seyed Hamid Reza Hosseini, Adib Allahham, Charlotte Adams

IET Smart Grid • 2021

Abstract This study presents an evaluation framework for the techno‐economic‐environmental (TEE) performance of the integrated multi‐vector energy networks (IMVENs) including geothermal energy. Geothermal energy storage (GES) offers huge potential for both energy storage and supply and can play a critical role in decarbonising the heat load of smart multi‐energy grids. The two most common types of GES, that is, high‐temperature GES (HTGES) and low‐temperature GES (LTGES), were modelled and integrated within the framework. This framework evaluates the impact of different low carbon energy sources including HTGES, LTGES, wind and Photovoltaics (PV) on the amount of energy imported from upstream, operational costs and emissions of IMVENs to meet the heat load of a region. The evaluation framework performs TEE performance analysis of any configuration of IMVEN representing future energy system pathways to provide a basis for well‐informed design choices to decarbonise heat. The TEE evaluation framework was tested on a real‐world case study, and several IMVEN configurations were designed and analysed. The results reveal that the most efficient, cost effective and least carbon‐intensive configurations for meeting the heat load of the case study are the configurations benefitting from HTGES, from high penetration of heat pumps and from LTGES, respectively.

Sehee Bang, Jongseo Lee, Wonyoung Lee

ECS Meeting Abstracts • 2023

Solid oxide fuel cells are promising eco-friendly power generating devices directly utilizing various fuels such as hydrogen, methane, and carbon dioxide. However, a technical breakthrough is required for further commercialization by lowering the high operating temperature to the intermediate temperature regime. Introducing the anode functional layer (AFL) between the electrolyte and anode is one of the crucial methods in the development of high performance solid oxide fuel cells by maximizing the triple phase boundary (TPB) sites. To activate the TPB sites, ensuring the continuous oxygen ion conduction from the electrolyte to the TPB sites is essential to maximize their utilization for hydrogen oxidation reactions (HORs). In this study, we modify the connectivity of oxygen ion conduction pathways in the AFL by controlling the microstructure in AFLs. We calculated active reaction site using image processing of cross-sectional scanning electron microscopy (SEM) image and the strong correlation between the electrochemical performance and calculated active reaction site is revealed. The modified AFL with highly connected oxygen ion conduction pathways exhibits substantially higher maximum power density (MPD) compared with conventional AFL: ~1.7-fold higher MPD of 1.51 Wcm -2 at 550 ℃ with hydrogen and ~3.5-fold higher MPD of 1.11 Wcm -2 at 550 ℃ with methane and carbon dioxide, surpassing previously reported values. Moreover, excellent carbon tolerance is observed in the modified AFL, exhibiting nearly no degradation at 550 ℃ for 130 h. This result substantiates the role of connectivity of the oxygen ion conduction pathways in the HOR and carbon tolerance in AFLs. Figure 1

Lin Zhao, Chuanyue Yang, Yu-chen Zhao et al.

Remote Sensing • 2023

The spatial and temporal characteristics of land use carbon emissions are relevant to the sustainable use of land resources. Although spatial and temporal studies have been conducted on land use carbon emissions, the spatial correlation of land use carbon emissions at the city level still requires further research. Here, we estimated the distribution of carbon emissions at the city level in Shandong Peninsula urban agglomeration in spatial and temporal terms based on land use remote sensing data and fossil energy consumption data during 2000–2019. The results showed that the land use change in the 16 cities in the study area was the conversion of cropland to construction land. Carbon emissions from land use had an upward trend for all 16 cities overall during the period of 2000–2019, but the incremental carbon emissions trended downward after 2010. Among them, Jinan and Qingdao had higher carbon emissions than other cities. In addition, we also found that land use carbon emissions at the city level were characterized by stochasticity, while per capita carbon emissions displayed geospatial aggregation. Among them, Yantai displayed a spatial pattern of high–high clustering of carbon emissions, while Jining presented a spatial pattern of low–low clustering in terms of land-average carbon emissions and carbon emissions per capita during 2000–2019. The results of the study are important for guiding the achievement of urban carbon emission reduction and carbon neutrality targets at the city level.

G. Sahbeni, Maurice Ngabire, P. Musyimi et al.

Remote Sensing • 2023

Meeting current needs without compromising future generations’ ability to meet theirs is the only path toward achieving environmental sustainability. As the most valuable natural resource, soil faces global, regional, and local challenges, from quality degradation to mass losses brought on by salinization. These issues affect agricultural productivity and ecological balance, undermining sustainability and food security. Therefore, timely monitoring and accurate mapping of salinization processes are crucial, especially in semi-arid and arid regions where climate variability impacts have already reached alarming levels. Salt-affected soil mapping has enormous potential thanks to recent progress in remote sensing. This paper comprehensively reviews the potential of remote sensing to assess soil salinization. The review demonstrates that large-scale soil salinity estimation based on remote sensing tools remains a significant challenge, primarily due to data resolution and acquisition costs. Fundamental trade-offs constrain practical remote sensing applications in salinization mapping between data resolution, spatial and temporal coverage, acquisition costs, and high accuracy expectations. This article provides an overview of research work related to soil salinization mapping and monitoring using remote sensing. By synthesizing recent research and highlighting areas where further investigation is needed, this review helps to steer future efforts, provides insight for decision-making on environmental sustainability and soil resource management, and promotes interdisciplinary collaboration.

Abhasha Joshi, B. Pradhan, Shilpa Gite et al.

Remote Sensing • 2023

Reliable and timely crop-yield prediction and crop mapping are crucial for food security and decision making in the food industry and in agro-environmental management. The global coverage, rich spectral and spatial information and repetitive nature of remote sensing (RS) data have made them effective tools for mapping crop extent and predicting yield before harvesting. Advanced machine-learning methods, particularly deep learning (DL), can accurately represent the complex features essential for crop mapping and yield predictions by accounting for the nonlinear relationships between variables. The DL algorithm has attained remarkable success in different fields of RS and its use in crop monitoring is also increasing. Although a few reviews cover the use of DL techniques in broader RS and agricultural applications, only a small number of references are made to RS-based crop-mapping and yield-prediction studies. A few recently conducted reviews attempted to provide overviews of the applications of DL in crop-yield prediction. However, they did not cover crop mapping and did not consider some of the critical attributes that reveal the essential issues in the field. This study is one of the first in the literature to provide a thorough systematic review of the important scientific works related to state-of-the-art DL techniques and RS in crop mapping and yield estimation. This review systematically identified 90 papers from databases of peer-reviewed scientific publications and comprehensively reviewed the aspects related to the employed platforms, sensors, input features, architectures, frameworks, training data, spatial distributions of study sites, output scales, evaluation metrics and performances. The review suggests that multiple DL-based solutions using different RS data and DL architectures have been developed in recent years, thereby providing reliable solutions for crop mapping and yield prediction. However, challenges related to scarce training data, the development of effective, efficient and generalisable models and the transparency of predictions should be addressed to implement these solutions at scale for diverse locations and crops.

Nishan Bhattarai, Pradeep Wagle

Remote Sensing • 2021

Evapotranspiration (ET) plays an important role in coupling the global energy, water, and biogeochemical cycles and explains ecosystem responses to global environmental change. However, quantifying and mapping the spatiotemporal distribution of ET across a large area is still a challenge, which limits our understanding of how a given ecosystem functions under a changing climate. This also poses a challenge to water managers, farmers, and ranchers who often rely on accurate estimates of ET to make important irrigation and management decisions. Over the last three decades, remote sensing-based ET modeling tools have played a significant role in managing water resources and understanding land-atmosphere interactions. However, several challenges, including limited applicability under all conditions, scarcity of calibration and validation datasets, and spectral and spatiotemporal constraints of available satellite sensors, exist in the current state-of-the-art remote sensing-based ET models and products. The special issue on “Remote Sensing of Evapotranspiration II” was launched to attract studies focusing on recent advances in remote sensing-based ET models to help address some of these challenges and find novel ways of applying and/or integrating remotely sensed ET products with other datasets to answer key questions related to water and environmental sustainability. The 13 articles published in this special issue cover a wide range of topics ranging from field- to global-scale analysis, individual model to multi-model evaluation, single sensor to multi-sensor fusion, and highlight recent advances and applications of remote sensing-based ET modeling tools and products.

Mila Koeva, Rohan Bennett, Claudio Persello

Remote Sensing • 2022

Contemporary land administration (LA) systems incorporate the concepts of cadastre and land registration. Conceptually, LA is part of a global land management paradigm incorporating LA functions such as land value, land tenure, land development, and land use. The implementation of land-related policies integrated with well-maintained spatial information reflects the aim set by the United Nations to deliver tenure security for all (Sustainable Development Goal target 1.4, amongst many others). Innovative methods for data acquisition, processing, and maintaining spatial information are needed in response to the global challenges of urbanization and complex urban infrastructure. Current technological developments in remote sensing and geo-spatial information science provide enormous opportunities in this respect. Over the past decade, the increasing usage of unmanned aerial vehicles (UAVs), satellite and airborne-based acquisitions, as well as active remote sensing sensors such as LiDAR, resulted in high spatial, spectral, radiometric, and temporal resolution data. Moreover, significant progress has also been achieved in automatic image orientation, surface reconstruction, scene analysis, change detection, classification, and automatic feature extraction with the help of artificial intelligence, spatial statistics, and machine learning. These technology developments, applied to LA, are now being actively demonstrated, piloted, and scaled. This Special Issue hosts papers focusing on the usage and integration of emerging remote sensing techniques and their potential contribution to the LA domain.

Mariana Belgiu, Alfred Stein

Remote Sensing • 2019

In this paper, we discuss spatiotemporal data fusion methods in remote sensing. These methods fuse temporally sparse fine-resolution images with temporally dense coarse-resolution images. This review reveals that existing spatiotemporal data fusion methods are mainly dedicated to blending optical images. There is a limited number of studies focusing on fusing microwave data, or on fusing microwave and optical images in order to address the problem of gaps in the optical data caused by the presence of clouds. Therefore, future efforts are required to develop spatiotemporal data fusion methods flexible enough to accomplish different data fusion tasks under different environmental conditions and using different sensors data as input. The review shows that additional investigations are required to account for temporal changes occurring during the observation period when predicting spectral reflectance values at a fine scale in space and time. More sophisticated machine learning methods such as convolutional neural network (CNN) represent a promising solution for spatiotemporal fusion, especially due to their capability to fuse images with different spectral values.

Kunal Goel, A. Bindal

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) • 2018

This paper presents the importance of precision agriculture over the tradition agriculture that how farmers can get the exact details about their cultivation land while deploying the sensor and measure the various parameters of the land and then do the farming according to that parameters so they can earn more and through the deployment of sensor in land also helps the farmers in excellent crop yield. The wireless sensor system which is discussed in this paper tells about the various sensors used for measure the various parameters of land like moisture, temperature and soil salinity, which is the most important part of farming. Another thing is deployment and communication techniques through the hybrid network and the concept of microbial fuel cell membrane, in which anode and cathode is there and various bacterias will used to produce the current work as a acetate which will make the sensor node self powering. Bacteria's perform the reactions through the anode and cathode in the compartment and also added the various chemicals like methylene blue, neutral red, thionine produce which will accelerate the current for batteries used for sensor, which will expand the network lifespan.

Celal Erbay, Salvador Carreon-Bautista, E. Sánchez-Sinencio et al.

Environmental Science & Technology • 2014

Microbial fuel cell (MFC) that can directly generate electricity from organic waste or biomass is a promising renewable and clean technology. However, low power and low voltage output of MFCs typically do not allow directly operating most electrical applications, whether it is supplementing electricity to wastewater treatment plants or for powering autonomous wireless sensor networks. Power management systems (PMSs) can overcome this limitation by boosting the MFC output voltage and managing the power for maximum efficiency. We present a monolithic low-power-consuming PMS integrated circuit (IC) chip capable of dynamic maximum power point tracking (MPPT) to maximize the extracted power from MFCs, regardless of the power and voltage fluctuations from MFCs over time. The proposed PMS continuously detects the maximum power point (MPP) of the MFC and matches the load impedance of the PMS for maximum efficiency. The system also operates autonomously by directly drawing power from the MFC itself without any external power. The overall system efficiency, defined as the ratio between input energy from the MFC and output energy stored into the supercapacitor of the PMS, was 30%. As a demonstration, the PMS connected to a 240 mL two-chamber MFC (generating 0.4 V and 512 μW at MPP) successfully powered a wireless temperature sensor that requires a voltage of 2.5 V and consumes power of 85 mW each time it transmit the sensor data, and successfully transmitted a sensor reading every 7.5 min. The PMS also efficiently managed the power output of a lower-power producing MFC, demonstrating that the PMS works efficiently at various MFC power output level.

J. Mangaiyarkkarasi, J. Shanthalakshmi Revathy

Advances in Wireless Technologies and Telecommunication • 2024

This chapter explores the synergy between radar and radio frequency (RF) front-end systems, ushering in a new era of wireless connectivity. It discusses the collaborative potential of radar and RF, emphasizing their role in enhancing security, reducing interference, and boosting adaptability. The chapter covers radar-based spectrum sensing, which enhances network efficiency, particularly in high-frequency scenarios like 5G. Radar and RF enable precise localization for IoT and autonomous vehicles, surpassing the capabilities of GPS. The chapter highlights radar's contributions to security, threat detection, and reducing signal interference. Radar-assisted RF improves vehicle communication, cooperative driving, and traffic management. In environmental monitoring and disaster management, radar augments RF for early warnings. This integration offers transformative potential, benefiting diverse applications and offering theoretical and practical insights for researchers and engineers. Radar and RF convergence offers a more connected, adaptable, and efficient wireless future.

G. Jeeva, P. Mahalakshmi, S. Thenmalar

Advanced Computing Solutions for Healthcare • 2025

The integration of smart sensors in wearable devices, particularly smart watches, has revolutionized the landscape of personal health monitoring. This review paper provides a comprehensive analysis of recent advancements in smart sensor technology and their application in smartwatches for health monitoring. The paper begins with an overview of the evolution of smartwatches and their transition from timekeeping devices to sophisticated health monitoring tools. It then delves into the key components of smart sensor technology, encompassing biometric sensors, environmental sensors, and activity trackers. The review extensively covers the diverse range of health parameters that can be monitored by smartwatches, including physical activity levels, oxygen saturation, blood pressure, and heart rate. Furthermore, the paper evaluates the accuracy and reliability of these sensors, considering factors such as sensor placement, calibration, and data processing techniques. The paper also explores the potential integration of machine learning and artificial intelligence in data analysis and interpretation, highlighting their potential to enhance the effectiveness and efficiency of smartwatch health monitoring. In addition, the review addresses challenges and limitations associated with smartwatch health monitoring, including privacy concerns, data security, and battery life. This paper provides an up-to-date overview of smart sensor technology as applied to health monitoring in smartwatches. It serves as a valuable resource for researchers, healthcare professionals, and technology enthusiasts interested in understanding the potential and limitations of this rapidly evolving field.

Anna Espinoza-Tofalos, Francesca Formicola, Pierangela Cristiani et al.

ECS Meeting Abstracts • 2019

Bioelectrochemical Systems (BES) are a novel technology in which microorganisms degrade the organic matter in anaerobic conditions by using an electrode (anode) as final electron acceptor. Therefore, BES can be used as an effective strategy in environments where the absence of suitable electron acceptors limits classic bioremediation. Researches in progress demonstrated the possibility of apply innovative microbial electrochemical technologies for the monitoring and recovering of low concentration of organics, metals and micronutrients from polluted water environments, out of the electric grid. Recently BES have been also studied to stimulate the anaerobic degradation of hydrocarbons [1,2]. Although bioremediation is often inexpensive compared to physical-chemical methods, it typically requires more time and it is currently applied to a limited variety of pollutants. Benzene is a very toxic hydrocarbon and the pollution of this compound in fresh and groundwater causes many health and environmental problems. By monitoring the current produced by a BES, the rate of specific metabolic processes and the substrate concentration can be quantified in real time. The aim of this work is to study the correlation between the current produced in a BES and the concentration of benzene, in the rage of 10–60 mg/L. Tests were performed in single cell membraneless bioelectrochemical systems. The first run consisted in the inoculation of a BES with a refinery waste water in order to colonize the anode with an electroactive benzene-degrading bacterial community. Benzene was periodically supplemented to select a community able to degrade benzene. Current and benzene concentration were monitored, in order to correlate these parameters with the development of an electroactive biofilm. The application of this technology as biosensor for the monitoring of toxic compounds in water presents several advantages: low operational cost, versatility and the possibility to monitor in real time the concentration of pollutants from the environment. [1] M. Daghio, A. Espinoza Tofalos, B. Leoni, P. Cristiani, M. Papacchini, E. Jalilnejad, G. Bestetti, A. Franzetti. Bioelectrochemical BTEX removal at different voltages: assessment of the degradation and characterization of the microbial communities. Journal of Hazardous Materials, Volume 341, 5 November 2018, Pages 120-127 [2] S. Zhanga, J.Youa, C. Kennes, Z. Cheng, J. Ye, D. Chen, J. Chen, L. Wang. Current advances of VOCs degradation by bioelectrochemical systems: A review. Chemical Engineering Journal, Volume 334, 15 February 2018, Pages 2625-2637

Pachhaiammal Alias Priya M, P. Karthikeyani, N. Arunfred et al.

2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM) • 2024

The Internet of Things (IoT) in hydrogen transport and fuel cell vehicle infrastructure is a fundamental transformation. This work discusses how IoT devices transformed the system efficiently. By connecting IoT devices, this infrastructure improves effectiveness, security, and sustainability. Hydrogen sensors monitor storage, pipelines, and filling stations to optimize hydrogen supply. Flow meters control distribution and consumption, while pressure and temperature sensors maintain safety. Performance data from vehicle telematics optimizes fuel usage and battery health. Real-time IoT data optimizes hydrogen production and distribution with energy availability in energy management systems. Remote monitoring devices provide quick system health intervention. Communication gateways provide centralized control by connecting everything. Predictive maintenance sensors monitor equipment status to prevent downtime, and IoT strengthens security systems. Smart grid integration integrates renewable and hydrogen generation for sustainability. IoT solutions collect and analyze device data to provide performance insights. Environmental sensors optimize storage. This complete integration strengthens hydrogen transport and fuel cell vehicle infrastructure, making mobility safer, more efficient, and more sustainable.

Anwar Elhadad, Maryam Rezaie, Seokheun Choi

2022 21st International Conference on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS) • 2022

Miniaturized bio-solar cells have emerged as a potential sustainable power source for the Internet of Things (IoT) applications deployed in unattended, difficult-to-reach aquatic environments such as oceans, rivers, and lakes. Here, we create a scalable, high-power, long-lasting, and buoyant bio-solar cell stack that integrates significantly improved miniature bio-solar cells in an array. Each cell incorporates a symbiotic microbial consortium consisting of Synechocystis sp. PCC6803 and Bacillus subtilis. While Synechocystis sp. generates electricity through its photosynthetic and respiratory activities, it produces an organic fuel through its photosynthesis, which sustains B. subtilis in the consortium. Each cell was able to generate a sustainable maximum power density of ~70 μWcm−2.

Yongchao Tian, Chuwen Huang, Guohao Shangguan et al.

2025 IEEE 5th International Conference on Electronic Technology, Communication and Information (ICETCI) • 2025

This study focuses on the development of an innovative monitoring device that integrates sensors, IoT and software data processing technologies. First, the device is based on microbial electrochemistry and relies on the metabolic activities of electroactive microorganisms to generate electrical signals with the help of microbial fuel cell technology. Sensors play a key role in collecting the electrical signals with high precision to ensure that the acquired data are accurate and reliable. Secondly, the use of Internet of Things (IoT) technology realizes the remote and rapid transmission of the collected data, breaks the geographical limitation of data transmission, and enables the data to be summarized to the processing terminal in a timely manner. The software data processing system, on the other hand, analyzes and processes the transmitted data in depth, and through the establishment of specific algorithms and models, transforms the collected electrical signals into intuitive pollution indicator data, and realizes the storage, management and visualization display of the data. Finally, after testing and verification, the device has outstanding advantages such as realtime monitoring and accurate data, which greatly improves the monitoring efficiency and shows a broad application prospect in the field of monitoring system.

Gabriela Marcano, P. Pannuto

Proceedings of the 1st ACM Workshop on No Power and Low Power Internet-of-Things • 2021

This paper explores the power delivery potential of soil-based microbial fuel cells. We build a prototype energy harvesting setup for a soil microbial fuel cell, measure the amount of power that we can harvest, and use that energy to drive an e-ink display as a representative example of a periodic energy-intensive load. Microbial fuel cells are highly sensitive to environmental conditions, especially soil moisture. In near-optimal, super moist conditions our cell provides approximately 100 μW of power at around 500 mV, which is ample power over time to power our system several times a day. We further explore how cell performance diminishes and recovers with varying moisture levels as well as how cell performance is affected by the load from the energy harvester itself. In sum, we find that the confluence of ever lower-power electronics and new understanding of microbial fuel cell design means that "soil-powered sensors" are now feasible. There remains, however, significant future work to make these systems reliable and maximally performant.

Sanowar Hossain, Md Asif Adnan, Abu Shufian et al.

2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC) • 2023

Chemical manufacturing, textile processing, and other industrial activities have increased the importance of effective wastewater treatment. This study gives a systematic look at how well an artificially ventilated tidal flow Microbial Fuel Cell (MFC) wetland system cleans the water. The system uses IoT monitoring to continuously collect data on the MFC's performance, which can be used to optimize the system for wastewater treatment and energy generation. Integrating IoT monitoring with the ESP8266-12 and ADS1115 improves real-time evaluation and makes it possible to keep improving the performance of MFCs for efficient treatment of wastewater and production of energy. The main feature of this system is that - (i) it can generate electricity. Secondly, (ii) it has waste disposal facilities, and (iii) the chemical elements of wastewater can produce hydroponic plants. The study used three materials: Jhama brick, brick surkhi, and rubber tire fragments. By testing NH4_N, N02_N, N03_N, TN, TSS, TKN, BOD, and COD. The experiment results show that TKN (65.8%) and TN (76.9%) removed the most nitrogen due to better nitrification-denitrification and media-oriented chemical adsorption. The highest pollutant and coliform removed were found at around 87.5% and 95.09% of COD, respectively. The proposed system can produce bioenergy with a maximum voltage of 142 mV, a maximum current density of 27.85 mA/m3, and a maximum power density of 3954.03 mW/m3. Pollutant removal was highest when jhama brick was used as a medium in MFC and lowest when rubber tire fragments were used.

Ian E.Y. Li, Tiger Y. S. Cheng, Kelvin W. L. Wong

2024 7th International Conference on Green Technology and Sustainable Development (GTSD) • 2024

Based on the market research report by the International Market Analysis Research and Consulting Group (IMARC) Group, the global market for environmental monitoring has surpassed USD$20 billion in 2022. It is projected to grow to over USD$31 billion by 2028, primarily driven by the increasing demand for environmental monitoring in developing nations, particularly China. However, implementing an Environmental Monitoring System (EMS) poses significant challenges in terms of scale and cost. In rural areas, the deployment of EMS typically requires a substantial number of sensors, predominantly powered by battery packs, solar panels, and stationary power supplies. Except for stationary power supplies, the other two methods entail a considerable amount of resources for monitoring and replacement, leading to higher operational costs. To address these challenges, this research project aims to propose a self-sustaining Internet of Things (IoT) monitoring system. This system integrates a power generator based on moss-based microbial fuel cells (MFCs), which generate a voltage output through photosynthesis. Additionally, the system will incorporate a voltage booster circuit to amplify the power output to a usable level of 3.3 volts. This voltage level enables the system to power various IoT devices, such as MCUs and a wide range of sensors, enhancing its versatility and applicability. By eliminating the need for solar panels and reducing maintenance costs and frequency, the proposed system has the potential to reduce overall expenses significantly. This cost reduction would facilitate wider adoption of the system by companies and countries, contributing to the mitigation of environmental pollution.

Ruolin He, Jinyu Zhang, Yuanzhe Shao et al.

PLOS Computational Biology • 2023

Non-ribosomal peptide synthetase (NRPS) is a diverse family of biosynthetic enzymes for the assembly of bioactive peptides. Despite advances in microbial sequencing, the lack of a consistent standard for annotating NRPS domains and modules has made data-driven discoveries challenging. To address this, we introduced a standardized architecture for NRPS, by using known conserved motifs to partition typical domains. This motif-and-intermotif standardization allowed for systematic evaluations of sequence properties from a large number of NRPS pathways, resulting in the most comprehensive cross-kingdom C domain subtype classifications to date, as well as the discovery and experimental validation of novel conserved motifs with functional significance. Furthermore, our coevolution analysis revealed important barriers associated with reengineering NRPSs and uncovered the entanglement between phylogeny and substrate specificity in NRPS sequences. Our findings provide a comprehensive and statistically insightful analysis of NRPS sequences, opening avenues for future data-driven discoveries. Author Summary NRPS, a gigantic enzyme that produces diverse microbial secondary metabolites, provides a rich source for important medical products including antibiotics. Despite the extensive knowledge gained about its structure and the large amount of sequencing data available, the frequent failure of reengineering NRPS in synthetic biology highlights the fact that much is still unknown. In this work, we applied existing knowledge to data mining of NRPS sequences, using well-known conserved motifs to partition NRPS sequences into motif-intermotif architectures. This standardization allows for integrating large amounts of sequences from different sources, providing a comprehensive overview of NRPSs across different kingdoms. Our findings included new C domain subtypes, novel conserved motifs with implication in structural flexibility, and insights into why NRPSs are so difficult to reengineer. To facilitate researchers in related fields, we constructed an online platform “NRPS Motif Finder” for parsing the motif-and-intermotif architecture and C domain subtype classification (http://www.bdainformatics.org/page?type=NRPSMotifFinder). We believe that this knowledge-guided approach not only advances our understanding of NRPSs but also provides a useful methodology for data mining in large-scale biological sequences.

N. Madondo, S. Rathilal, B. Bakare et al.

Chemistry – An Asian Journal • 2023

The selectivity of catalytic materials suitable for oxygen reduction potential of bioelectrochemical systems is very affluent. In this study, the application of magnetite-nanoparticles and a static magnetic field on a microbial fuel cell (MFC) in anaerobic digestion was investigated. The experimental set-up included four 1 L biochemical methane potential tests: a) MFC, b) MFC with magnetite-nanoparticles (MFCM), c) MFC with magnetite-nanoparticles and magnet (MFCMM), and d) control. The highest biogas production obtained was 545.2 mL/g VSfed in the MFCMM digester, which was substantially greater than the 117.7 mL/g VSfed of the control. This was accompanied by high contaminant removals for chemical oxygen demand (COD) of 97.3%, total solids (TS) of 97.4%, total suspended solids (TSS) of 88.7%, volatile solids (VS) 96.1%, and color of 70.2%. The electrochemical efficiency analysis revealed greater maximum current density of 12.5 mA/m2 and coulombic efficiency of 94.4% for the MFCMM. Kinetically, the cumulative biogas produced data obtained were well fitted on the modified Gompertz models and the greatest coefficient of determination (R2 = 0.990) was obtained in the MFCMM. Therefore, the application of magnetite-nanoparticles and static magnetic field on MFC showed a high potential for bioelectrochemical methane production and contaminant removal for sewage sludge.

F. Ma, Yankai Yin, Shaopeng Pang et al.

IEEE Access • 2019

Microbial fuel cells (MFCs) are devices that transform organic matters in wastewater into green energy. Microbial fuel cells systems have strong nonlinearity and high coupling, which involves control science, microbiology, electrochemistry and other disciplines. According to the requirements of microbial fuel cell system for model robustness and accuracy, we designed a comprehensive model optimization framework. Firstly, the influence of uncertain parameters on system was analyzed by combining global sensitivity analysis with uncertainty analysis. In accordance with analysis results, the uncertain parameters were optimized. Secondly, based on the optimized stochastic model, a simplified model was proposed by combining variable selection with neural networks. The results shown that the proposed framework can deeply analysis the influence of uncertain parameters on output, and provide theoretical basis for experimental research. It fully simplifies the original MFCs model, and has guiding significance for other types of fuel cells.

F. Ma, Yankai Yin, Min Li

Mathematical Problems in Engineering • 2019

Sediment microbial fuel cells (SMFCs) are a typical microbial fuel cell without membranes. They are a device developed on the basis of electrochemistry and use microbes as catalysts to convert chemical energy stored in organic matter into electrical energy. This study selected a single-chamber SMFC as a research object, using online monitoring technology to accurately measure the temperature, pH, and voltage of the microbial fuel cell during the start-up process. In the process of microbial fuel cell start-up, the relationship between temperature, pH, and voltage was analysed in detail, and the correlation between them was calculated using SPSS software. The experimental results show that, at the initial stage of SMFC, the purpose of rapid growth of power production can be achieved by a large increase in temperature, but once the temperature is reduced, the power production of SMFC will soon recover to the state before the temperature change. At the beginning of SMFC, when the temperature changes drastically, pH will change the same first, and then there will be a certain degree of rebound. In the middle stage of SMFC start-up, even if the temperature will return to normal after the change, a continuous temperature drop in a short time will lead to a continuous decrease in pH value. The RBF neural network and ELM neural network were used to perform nonlinear system regression in the later stage of SMFC start-up and using the regression network to forecast part of the data. The experimental results show that the ELM neural network is more excellent in forecasting SMFC system. This article will provide important guidance for shortening start-up time and increasing power output.

Jia-rui Han, Shuai Li, Wen-Jun Li et al.

Advanced Biotechnology • 2024

Extreme environments such as hyperarid, hypersaline, hyperthermal environments, and the deep sea harbor diverse microbial communities, which are specially adapted to extreme conditions and are known as extremophiles. These extremophilic organisms have developed unique survival strategies, making them ideal models for studying microbial diversity, evolution, and adaptation to adversity. They also play critical roles in biogeochemical cycles. Additionally, extremophiles often produce novel bioactive compounds in response to corresponding challenging environments. Recent advances in technologies, including genomic sequencing and untargeted metabolomic analysis, have significantly enhanced our understanding of microbial diversity, ecology, evolution, and the genetic and physiological characteristics in extremophiles. The integration of advanced multi-omics technologies into culture-dependent research has notably improved the efficiency, providing valuable insights into the physiological functions and biosynthetic capacities of extremophiles. The vast untapped microbial resources in extreme environments present substantial opportunities for discovering novel natural products and advancing our knowledge of microbial ecology and evolution. This review highlights the current research status on extremophilic microbiomes, focusing on microbial diversity, ecological roles, isolation and cultivation strategies, and the exploration of their biosynthetic potential. Moreover, we emphasize the importance and potential of discovering more strain resources and metabolites, which would be boosted greatly by harnessing the power of multi-omics data.

Lauren F. Messer, Charlotte E. Lee, R. Wattiez et al.

Microbiome • 2024

Background Microbial functioning on marine plastic surfaces has been poorly documented, especially within cold climates where temperature likely impacts microbial activity and the presence of hydrocarbonoclastic microorganisms. To date, only two studies have used metaproteomics to unravel microbial genotype–phenotype linkages in the marine ‘plastisphere’, and these have revealed the dominance of photosynthetic microorganisms within warm climates. Advancing the functional representation of the marine plastisphere is vital for the development of specific databases cataloging the functional diversity of the associated microorganisms and their peptide and protein sequences, to fuel biotechnological discoveries. Here, we provide a comprehensive assessment for plastisphere metaproteomics, using multi-omics and data mining on thin plastic biofilms to provide unique insights into plastisphere metabolism. Our robust experimental design assessed DNA/protein co-extraction and cell lysis strategies, proteomics workflows, and diverse protein search databases, to resolve the active plastisphere taxa and their expressed functions from an understudied cold environment. Results For the first time, we demonstrate the predominance and activity of hydrocarbonoclastic genera ( Psychrobacter , Flavobacterium , Pseudomonas ) within a primarily heterotrophic plastisphere. Correspondingly, oxidative phosphorylation, the citrate cycle, and carbohydrate metabolism were the dominant pathways expressed. Quorum sensing and toxin-associated proteins of Streptomyces were indicative of inter-community interactions. Stress response proteins expressed by Psychrobacter , Planococcus , and Pseudoalteromonas and proteins mediating xenobiotics degradation in Psychrobacter and Pseudoalteromonas suggested phenotypic adaptations to the toxic chemical microenvironment of the plastisphere. Interestingly, a targeted search strategy identified plastic biodegradation enzymes, including polyamidase, hydrolase, and depolymerase, expressed by rare taxa. The expression of virulence factors and mechanisms of antimicrobial resistance suggested pathogenic genera were active, despite representing a minor component of the plastisphere community. Conclusion Our study addresses a critical gap in understanding the functioning of the marine plastisphere, contributing new insights into the function and ecology of an emerging and important microbial niche. Our comprehensive multi-omics and comparative metaproteomics experimental design enhances biological interpretations to provide new perspectives on microorganisms of potential biotechnological significance beyond biodegradation and to improve the assessment of the risks associated with microorganisms colonizing marine plastic pollution. Video Abstract

A. Shaheen, A. Elsayed, R. El-Sehiemy et al.

Engineering Optimization • 2021

A modified marine predators optimizer (MMPO) is proposed for simultaneous distribution network reconfiguration (DNR) associated with the allocation of distributed generators (DGs). In the MMPO, the predator’s strategies are merged to consider the possibilities for variation in the environmental and climatic circumstances. The suggested MMPO is contrasted with the standard marine predators optimizer (MPO) and genetic, harmony search, fireworks, firefly and improved sine–cosine optimizers. The proposed MMPO is validated on single and multiple objectives using 33- and 69-bus distribution systems at light, nominal and heavy loading levels. The results obtained by the proposed MMPO are compared with those obtained by the original MPO and other optimizers. The achieved simulation outputs reveal a great improvement over the standard MPO and demonstrate the superiority of the proposed MMPO for simultaneous DNR and DG allocation.

Nitin Liladhar Rane, Ömer Kaya, Jayesh Rane

Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry 5.0 • 2024

The use of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) significantly has the touch of transformational potential towards bringing the Sustainable Development Goals (SDGs) to be addressed in various industries. This research investigates the new developments and applications of these technologies in advancing sustainability programs in industry-intensive domains. Industries are beginning to undergo a major change by making today with the help of AI, ML, and DL that resources can be optimized, energy efficiency can be improved, and environmental impacts can be mitigated. A number of other trends - including predictive analytics and intelligent automation, allow for smarter and more efficient production, waste minimization and circular economy practices. AI-powered solutions are also now being used in the energy sector to maximize the generation of renewable energy, optimize grid management, and aid in the transition to low carbon energy systems. This will enable industries achieve better environmental benefits and higher operational efficiencies through big data analytics and IoT. AI and ML are also crucial in smart cities, urban planning, public services that delivery efficiency and overall support the sustainability agenda. The results reinforce the importance of strong regulatory structures and interdisciplinary collaboration to optimally leverage AI, ML, and DL to the SDGs, which will be intrinsic to designing for resilience and sustainability.

Nitin Liladhar Rane, Ömer Kaya, Jayesh Rane

Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry 5.0 • 2024

This book offers an insight into the applications of Artificial Intelligence (AI)- Machine Learning Algorithms and Deep Learning (DL) in Bigdata Analytics to Industry 4.0/5.0 and Society 5.0 with transformative power responsibly. It has delved into how these technologies are disrupting industries, fostering innovation, and solving age-old social problems-so that readers have an understanding of where the digital world is headed. These chapters cover the big picture subjects of using AI with Big data analytics aimed mostly at increasing industrial efficiency, healthcare optimization, retail transformation, construction industry transformation, autonomous vehicles development and environmental sustainability improvement. The book covers each of these technologies extensively applied to full chapters devoted to detail studies, methodologies and practical usages. One of the central concepts in the book is how we evolve from industry 4.0 to industry 5.0. Therefore, Industry 4.0 relies on the automation and data exchange in manufacturing technologies using cyber-physical systems, the Internet of Things and cloud computing route to intelligent factories. During this phase, it improves operational efficiency, predictive maintenance and real-time monitoring which lowers down time and other operating costs by considerable amount.

Song Gao

Geography • 2021

Nowadays, artificial intelligence (AI) is bringing tremendous new opportunities and challenges to geospatial research. Its fast development is powered by theoretical advancement, big data, computer hardware (e.g., the graphics processing unit, or GPU), and high-performance computing platforms that support the development, training, and deployment of AI models within a reasonable amount of time. Recent years have witnessed significant advances in geospatial artificial intelligence (GeoAI), which is the integration of geospatial studies and AI, especially machine learning and deep learning methods and the latest AI technologies in both academia and industry. GeoAI can be regarded as a study subject to develop intelligent computer programs to mimic the processes of human perception, spatial reasoning, and discovery about geographical phenomena and dynamics; to advance our knowledge; and to solve problems in human environmental systems and their interactions, with a focus on spatial contexts and roots in geography or geographic information science (GIScience). Thus, it would require the knowledge of AI theory, programming and computation practices as well as geographic domain knowledge to be competent in GeoAI research. There have already been increasingly collaborative GeoAI studies for GIScience, remote sensing, physical environment, and human society. It is a good time to provide a key reference list for educators, students, researchers, and practitioners to keep up with the latest GeoAI research topics. This bibliographical entry will first review the historical roots for AI in geography and GIScience and then list up to ten selective recent works with annotations that briefly describe their importance for each topic of interest in the GeoAI landscape, ranging from fundamental spatial representation learning to spatial predictions and to various advancements in cartography, earth observation, social sensing, and geospatial semantics.

Yiming Huang, Ravi U. Sheth, Shijie Zhao et al.

Nature Biotechnology • 2023

Pure bacterial cultures remain essential for detailed experimental and mechanistic studies in microbiome research, and traditional methods to isolate individual bacteria from complex microbial ecosystems are labor-intensive, difficult-to-scale and lack phenotype–genotype integration. Here we describe an open-source high-throughput robotic strain isolation platform for the rapid generation of isolates on demand. We develop a machine learning approach that leverages colony morphology and genomic data to maximize the diversity of microbes isolated and enable targeted picking of specific genera. Application of this platform on fecal samples from 20 humans yields personalized gut microbiome biobanks totaling 26,997 isolates that represented >80% of all abundant taxa. Spatial analysis on >100,000 visually captured colonies reveals cogrowth patterns between Ruminococcaceae , Bacteroidaceae , Coriobacteriaceae and Bifidobacteriaceae families that suggest important microbial interactions. Comparative analysis of 1,197 high-quality genomes from these biobanks shows interesting intra- and interpersonal strain evolution, selection and horizontal gene transfer. This culturomics framework should empower new research efforts to systematize the collection and quantitative analysis of imaging-based phenotypes with high-resolution genomics data for many emerging microbiome studies. A machine learning isolation and genotyping platform enable high-throughput bacterial culture generation.

K. Lesnik, Wenfang Cai, Hong Liu

Environmental Science & Technology • 2019

Stability as evaluated by functional resistance and resilience is critical to the effective operation of environmental biotechnologies. To date, limited tools have been developed that allow operators of these technologies to predict functional responses to environmental and operational disturbances. In the present study, 17 Microbial Fuel Cells (MFCs) were exposed to a low pH perturbation. MFC power dropped 52.7 ± 35.8% during the low pH disturbance. Following the disturbance, 3 MFCs did not recover while 14 took 60.7 ± 58.3 hours to recover to previous current output levels. Machine learning models based on genomic data inputs were developed and evaluated on their ability to predict resistance and resilience. Resistance and resilience levels corresponding to risk of deactivation could be classified with 70.47 ± 15.88% and 65.33 ± 19.71 % accuracy, respectively. Models predicting resistance and resilience coefficient values projected post-perturbation current drops within 6.7 - 15.8% and recovery times within 5.8 - 8.7% of observed values. Results suggest that abundance of specific genera are better predictors of resistance while overall microbial community structure more accurately predicts resilience. This approach can be used to assess operational risk and is a first step towards further understanding and improving overall stability of environmental biotechnologies.

M. S. Chaitanya, Uday Kiran Reddy B, S. K et al.

2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) • 2025

This research study proposes a novel Smart Irrigation System that integrates Microbial Fuel Cells (MFCs) with IoT and Machine Learning for sustainable agricultural practices. The system uses MFCs to generate electricity from soil microorganisms, providing a renewable energy source for irrigation. IoT sensors monitor real-time environmental parameters such as temperature, humidity, and soil moisture, and transmit data to cloud platforms for analysis. Machine learning algorithms are used to process the historical data, weather forecast, and sensor information in real-time to predict the irrigation requirement and optimize water usage. Renewable energy generation, IoT-based monitoring, and machine learning-driven decision support strategy are integrated in this system to improve water efficiency, reduce energy consumption, and support sustainable agricultural practice.

Jing Wang, Qilun Wang

Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering • 2018

Aiming at the online control problem of microbial fuel cells, this article presents a class of explicit model-predictive control methods based on the machine learning data model. The proposed method is divided into two stages: off-line design and on-line control. In the off-line design stage, (1) a feasible data set is collected by sampling the admissible state in the feasible region and solving the optimal model predictive control law for each sampling data point off-line, (2) a feasible sample discriminator is constructed based on the support vector machine–based binary classification in order to judge the whether the real sampling state is feasible, and (3) according to the feasible samples and the corresponding optimal control law, the control surface of explicit model predictive controller is constructed based on the machine learning methods. In the on-line control stage, the process data are collected in real time and the feasible control output is calculated by using the trained explicit predictive control surface. Extensive testing and comparison among the different machine learning algorithms, such as artificial neural network, extreme learning machine, Gaussian process regression, and relevance vector machine, are performed on the benchmark model of a class of microbial desalination fuel cells. These results demonstrate that the proposed explicit model predictive control method can avoid the exhausting optimization computing and is easy to realize on-line with good control performance.