Enhancing Microbial Fuel Cell Performance Prediction and IoT Integration Using Machine Learning and Renewable Energy

One of the most important challenges for our society is providing powerful devices for renewable energy production. Many technologies based on renewable energy sources have been developed, which represent a clean energy sources that have a much lower environmental impact than conventional energy technologies. Nowadays, many researches focus their attention on the development of renewable energy from solar, water, organic matter and biomass, which represent abundant and renewable energy sources. This research is mainly focused on the development of promising electrode materials and their potential application on emerging technologies such as artificial photosynthesis and microbial fuel cell (MFC). According to desired proprieties of functional materials, this research was focused on two main materials: (1) TiO2 for the development of electrodes for the water splitting reaction due to its demonstrated application potential as photocatalyst material and (2) carbon-based materials for the development of electrodes for MFC. In the first part of the investigation, different TiO2 nanostructures have been studied including: synthesis, characterization and test of TiO2-based materials with the aim of improving the limiting factors of the photocatalytic reaction: charge recombination and separation/migration processes. The photo-catalytic properties of different TiO2 nanostructures were evaluated including: TiO2 nanoparticles (NPs) film, TiO2 nanotubes (NTs) and ZnO@TiO2 core-shell structures. Photo-electrochemical activity measurements and electrochemical impedance spectroscopy analysis showed an improvement in charge collection efficiency of 1D-nanostructures, related to a more efficient electron transport in the materials. The efficient application of both the TiO2 NTs and the ZnO@TiO2 core-shell photoanodes opens important perspectives, not only in the water splitting application field, but also for other photo-catalytic applications (e.g. photovoltaic cells, degradation of organic substances), due to their chemical stability, easiness of preparation and improved transport properties. Additionally, in order to improve the photo-catalytic activity of TiO2 NPs, PANI/TiO2 composite film was synthesized. PANI/TiO2 composite film was successfully applied as anode material for the PEC water splitting reaction showing a significant increase in the photocatalytic activity of TiO2 NPs composite film essentially attributed to the efficient separation of the generated electron and hole pairs. To date, no cost-effective materials system satisfies all of the technical requirements for practical hydrogen production under zero-bias conditions. For this propose, to promote the sustainability of the process, the bias require to conduct PEC water splitting reaction could be powered by MFC systems in which many efforts have been done to improve power and electricity generation as is explained below. In this work, different strategies were also applied in order to improve the performance of anode materials for MFCs. The investigation of commercial carbon-based materials demonstrated that these materials, normally used for other ends are suitable electrodes for MFC and their use could reduce MFC costs and improve the energy sustainability of the process. In addition, to enhance power generation in MFC by using low-cost and commercial carbon-based materials, nitric acid activation (C-HNO3) and PANI deposition (C-PANI) were performed on commercial carbon felt (C-FELT) in order to increase the performance of MFC. Electrochemical determinations performed in batch-mode MFC reveled a strong reduction of the activation losses contribution and an important decrease of the internal resistance of the cell using C-HNO3 and C-PANI of about 2.3 and 4.4 times, respectively, with respect to C-FELT. Additionally, with the aim of solvent different MFC operational problems such as: biofouling, low surface area and large-scale MFC, an innovative three-dimensional material effectively developed and used as anode electrode. The conductive carbon-coated Berl saddles (C-SADDLES) were successfully used as anode electrode in batch-mode MFC. Electrochemical results suggested that C-SADDLES offer a low-cost solution to satisfy either electrical or bioreactor requirements, increasing the reliability of the MFC processes, and seems to be a valid candidate for scaled-up systems and for continuous mode application of MFC technology. In addition, the electrochemical performance and continuous energy production of the most promising materials obtained during this work were evaluated under continuous operation MFC in a long-term evaluation test. Remarkable results were obtained for continuous MFCs systems operated with three different anode materials: C-FELT, C-PANI and C-SADDLES. From polarization curves, the maximum power generation was obtained using C-SADDLES (102 mW•m-2) with respect to C-FELT (93 mW•m-2) and C-PANI (65 mW•m-2) after three months of operation. The highest amount of electrical energy was produced by C-PANI (1803 J) with respect to C-FELT (1664 J) and C-SADDLES (1674 J). However, it is worth to note that PANI activity was reduced during time by the operating conditions inside the anode chamber. In order to demonstrate the wide application potential MFC, this work reports on merging heterogeneous contributions and combining the advantages from three separate fields in a system which enables the ultra-low-power monitoring of a microbial fuel cell voltage status and enables pressure monitoring features of the internal conditions of a cell. The solution is conceived to provide an efficient energy source, harvesting wastewater, integrating energy management and health monitoring capabilities to sensor nodes which are not connected to the energy grid. Finally, this work presented a general concept of the integration of both devices into a hybrid device by interfacing PEC and MFC devices (denoted as PEC-MFC), which is proposed to generate electricity and hydrogen using as external bias the potential produce by microbial fuel cell

PurposeBuilt asset management processes require a long transition period to collect, edit and update asset conditions information from existing data sets. This paper aims to explore and explain whether and how digital technologies, including asset information model (AIM), Internet of Things (IoT) and blockchain, can enhance asset conditions assessment and lead to better asset management.Design/methodology/approachMixed methods are applied to achieve the research objective with a focus in universities. The questionnaire aims to test whether the integration of AIM, IoT and blockchain can enhance asset condition assessment (ACA). Descriptive statistical analysis was applied to the quantitative data. The mean, median, mode, standard deviation, variance, skewness and range of the data group were calculated. Semi-structured interviews were designed to answer how the integration of AIM, IoT and blockchain can enhance the ACA. Quantitative data was analysed to define and explain the essential factors for each sub-hypothesis. Meanwhile, to strengthen the evaluation of the research hypothesis, the researcher also obtained secondary data from the literature review.FindingsThe research shows that the integration of AIM, IoT and blockchain strongly influences asset conditions assessment. The integration of AIM, IoT and blockchain can improve the asset monitoring and diagnostics through its life cycle and in different aspects, including financial, physical, functional and sustainability. Moreover, the integration of AIM, IoT and blockchain can enhance cross-functional collaboration to avoid misunderstandings, various barriers and enhance trust, communication and collaboration between the team members. Finally, costs and risk could be reduced, and performance could be increased during the ACA.Practical implicationsThe contribution of this study indicated that the integration of AIM, IoT and blockchain application in asset assessment could increase the efficiency, accuracy, stability and flexibility of asset assessment to ensure the reliability of assets and lead to a high-efficiency working environment. More importantly, a key performance indicator for ACA based on the asset information, technology and people experience could be developed gradually.Originality/valueThis study can break the gap between transdisciplinary knowledge to improve the integration of people, technology (AIM, IoT and blockchain) and process value-based ACA in built asset management within universities.

Integrating Internet of Things (IoT) with Artificial Intelligence (AI) is one of the catalysts for improving traditional healthcare services. This integration has created many opportunities that have led to healthcare shifting towards enabling home care, the concept that harnesses the technologies advanced potential such as the IoT and deep learning for intelligent monitor and manage chronic diseases. As population growth increases, restrictions on traditional healthcare services increase. Some diseases, such as chronic diseases, require innovative solutions that go beyond the boundaries of traditional healthcare settings due to their impact on individuals’ health for example traditional healthcare systems have little capacity to provide high-quality and real-time services. Empowering home care services using deep learning and internet of things technology is promising. It enables continuous monitoring through interconnected devices and deep learning, which provides intelligent insights from massive data sets. This brief explores the key components of enabling home care, including continuous patient health monitoring, predictive analytics, medication management, and remote patient support by healthcare providers, and provides friendly interfaces for end-users. The conjunction between the IoT and deep learning in home-care signals a shift toward precision medicine, enhancing patient outcomes and creating a sustainable model for chronic disease management in the era of decentralized healthcare. This review article aims to discuss the following aspects: presenting the latest technologies in home care systems, showing the merit of combining the Internet of Medical Things (IoMT) and deep learning and its role in monitoring patient conditions and managing chronic disease to improve patient health status accurately, in real-time, and cost-effective, and lastly, debating future studies and providing recommendations for the ongoing development of home care remote monitoring applications.

The convergence of healthcare with the Internet of Things (IoT) and Artificial Intelligence (AI) is reshaping medical practice with promising enhanced data-driven insights, automated decision-making, and remote patient monitoring. It has the transformative potential of these technologies to revolutionize diagnosis, treatment, and patient care. This study aims to explore the integration of IoT and AI in healthcare, outlining their applications, benefits, challenges, and potential risks. By synthesizing existing literature, this study aims to provide insights into the current landscape of AI, IoT, and AIoT in healthcare, identify areas for future research and development, and establish a framework for the effective use of AI in health. A comprehensive literature review included indexed databases such as PubMed/Medline, Scopus, and Google Scholar. Key search terms related to IoT, AI, healthcare, and medicine were employed to identify relevant studies. Papers were screened based on their relevance to the specified themes, and eventually, a selected number of papers were methodically chosen for this review. The integration of IoT and AI in healthcare offers significant advancements, including remote patient monitoring, personalized medicine, and operational efficiency. Wearable sensors, cloud-based data storage, and AI-driven algorithms enable real-time data collection, disease diagnosis, and treatment planning. However, challenges such as data privacy, algorithmic bias, and regulatory compliance must be addressed to ensure responsible deployment of these technologies. Integrating IoT and AI in healthcare holds immense promise for improving patient outcomes and optimizing healthcare delivery. Despite challenges such as data privacy concerns and algorithmic biases, the transformative potential of these technologies cannot be overstated. Clear governance frameworks, transparent AI decision-making processes, and ethical considerations are essential to mitigate risks and harness the full benefits of IoT and AI in healthcare.

On the interplay of Internet of Things and Cloud Computing: A systematic mapping study

The Internet of Things (IoT) has emerged as a transformative technology that connects the physical and digital worlds, revolutionizing how devices, objects, and systems interact. As the world becomes increasingly interconnected, IoT paves the way for the development of smart cities, homes, industries, and healthcare systems. This paper presents a comprehensive analysis of the Internet of Things and its role in shaping the smart world.The first section of this paper delves into the foundational concepts of IoT, outlining its history, principles, and key components. The discussion highlights the rapid growth of IoT devices, sensors, and communication protocols, which have enabled seamless data exchange and connectivity on a global scale. The potential benefits and challenges of IoT adoption are addressed, emphasizing the need for robust security and privacy measures to protect against potential threats. The second section explores the pivotal role of IoT in shaping smart cities. With urbanization on the rise, the need for efficient resource management, sustainable infrastructure, and enhanced citizen services becomes more pressing. IoT-driven solutions empower cities to optimize traffic flow, manage energy consumption, improve waste management, and enhance public safety through real-time data analytics and predictive modeling.In the context of smart homes, the third section investigates how IoT applications have revolutionized the way people interact with their living spaces. Smart home devices, such as smart thermostats, lighting systems, and voice-activated assistants, provide users with increased convenience and energy efficiency. However, concerns surrounding data privacy and potential vulnerabilities necessitate a discussion on the best practices for securing smart home ecosystems. The fourth section examines the impact of IoT on industries, from manufacturing to agriculture. IoT-enabled industrial processes streamline production, enhance supply chain management, and facilitate predictive maintenance, leading to increased productivity and reduced operational costs. Additionally, precision agriculture applications of IoT offer farmers real-time data on soil conditions, weather patterns, and crop health, leading to improved crop yield and resource utilization.The fifth section addresses the transformation of healthcare systems through IoT integration. From remote patient monitoring to wearable health devices, IoT has opened new avenues for personalized and proactive healthcare. This section emphasizes the potential of IoT to revolutionize healthcare delivery by facilitating timely interventions, reducing hospital readmissions, and empowering patients to take an active role in their health management.The sixth section discusses the challenges and barriers hindering the widespread adoption of IoT and the smart world. Interoperability issues, standardization, and the complexity of integrating diverse systems pose significant challenges for stakeholders. Moreover, concerns regarding data ownership, privacy, and cyber-security remain key obstacles that need to be addressed to build public trust in IoT technologies. The final section explores future trends and possibilities for IoT and the smart world. With the proliferation of 5G networks, edge computing, and artificial intelligence, the potential for IoT applications is boundless. The paper concludes by emphasizing the importance of collaboration among governments, industries, and researchers to shape a sustainable and inclusive smart world powered by IoT technologies.In summary, this paper provides a comprehensive analysis of the Internet of Things and its role in shaping the smart world. From its foundational principles to its diverse applications across various sectors, IoT has the potential to transform societies and redefine how we interact with technology. However, addressing challenges related to security, privacy, and standardization is crucial to unlock the full potential of IoT and pave the way for a truly interconnected and intelligent future.

Purpose Integrating Internet of Things (IoT) networks with distributed ledger technology (DLT) and artificial intelligence (AI) presents critical challenges, particularly related to latency, scalability, hardware constraints and data security. Efficient data ingestion and validation are essential to enable real-time AI processing. The main contribution of this paper is the proposal of the Energy consensus algorithm, designed to minimize both latency and energy consumption in such environments. Design/methodology/approach Energy is a consensus algorithm tailored for public directed acyclic graph-based DLTs in IoT contexts. It introduces a flexible transaction validation mechanism that reduces or bypasses Proof of Work requirements. The algorithm’s performance is experimentally compared with IOTA under varying payload conditions. Findings Results show that Energy significantly reduces latency and energy consumption, especially for small payloads, which are common in IoT applications. These findings demonstrate Energy’s ability to enhance transaction efficiency and support real-time AI model updates based on verified IoT data streams. Research limitations/implications Future work should investigate the scalability of Energy in larger and more heterogeneous IoT ecosystems, as well as its compatibility with different AI frameworks. Evaluating its performance under diverse network conditions and hardware setups would further strengthen the generalizability of the results. Practical implications The Energy algorithm enables continuous AI model updates while ensuring data integrity, traceability and low latency. Its adaptability makes it a suitable solution for large-scale IoT deployments requiring secure and efficient data processing. Originality/value This paper presents a novel consensus algorithm that bridges the requirements of IoT, DLT and AI, with a particular focus on improving latency and energy efficiency. Energy offers a robust approach for optimizing data flow and transaction processing in real-time, AI-driven IoT systems.

Abstract: As the world moves towards digital transformation and sustainable development, the concept of Smart Villages is revolutionizing rural communities by leveraging the Internet of Things (IoT) and Artificial Intelligence (AI) to bridge the urban-rural divide. "Smart Villages: IoT and AI for Sustainable Rural Development" explores how cutting-edge technologies are transforming agriculture, healthcare, energy management, education, and governance in rural areas. The book examines AI-driven precision farming, IoT-powered smart irrigation, AI-based telemedicine, renewable energy microgrids, and blockchain-enabled governance as key enablers for sustainable rural development. It highlights the role of digital connectivity, real-time data analytics, and AI-driven decision-making in optimizing resource allocation, improving infrastructure, and fostering economic resilience. The book also delves into policy frameworks, investment strategies, and global initiatives that support scalable and replicable Smart Village models. Through real-world case studies, expert insights, and technological advancements, this book serves as a comprehensive guide for policymakers, researchers, technology developers, and entrepreneurs seeking to implement smart and inclusive rural transformation. Keywords: Smart Villages, IoT in rural development, AI for sustainable agriculture, precision farming, smart irrigation, AI-driven telemedicine, remote healthcare, wearable health sensors, renewable energy microgrids, smart energy management, blockchain in rural governance, digital rural transformation, AI in education, rural connectivity, fintech for rural development, AI-powered decision-making, smart supply chains, predictive analytics in farming, AI and IoT integration, sustainable rural development, cloud computing in rural areas, AI-powered infrastructure, digital inclusion, rural economic resilience, data-driven agriculture, smart public services.

The rapid proliferation of the Internet of Things (IoT) has ushered in a new era of technological advancement, with profound implications for industries and businesses across the globe. IoT, characterized by the interconnection of everyday objects and devices through the internet, has revolutionized the way data is collected, analyzed, and utilized, thereby offering unprecedented opportunities for enhanced operational efficiency and strategic decision-making. In the realm of industry, IoT has become a linchpin for the concept of Industry or fourth industrial revolution. The seamless sensors integration, actuators, and smart devices into industrial processes has enabled the creation of "smart factories”. IoT-driven insights have empowered manufacturers to adopt a more customer-centric approach by tailoring products andservices to individual preferences, fostering customer loyalty and opening up new revenue streams. In the business sector, IoT has transcended beyond mere operational enhancements and has catalyzed fundamental business model transformations. Retailers, for instance, harness IoT- generated data to personalize customer experiences, optimize inventory management, and refine marketing strategies. The integration of IoT with artificial intelligence has led to the emergence of predictive analytics, enabling businesses to forecast demand patterns, preempt disruptions, and make proactive strategic choices. In conclusion, the Internet of Things is reshaping industries and businesses by infusing them with data-driven intelligence and connectivity. As IoT continues to evolve, stakeholders must collaborate to overcome challenges and ensure its ethical, secure, and inclusive deployment.

The urgency of this research is to increase efficiency, transparency and sustainability in increasingly complex and challenging agribusiness supply chains. The aim of this research is to develop an integrated system that combines IoT capabilities in collecting agricultural data in real-time, AI to analyze data and provide recommendations for action, as well as security and transparency guaranteed by blockchain technology. The method used is a mixed methods approach , this approach combines qualitative and quantitative elements to obtain a deeper understanding. A qualitative approach is used to gain a contextual perspective, while a quantitative approach is used to measure performance empirically. This research uses a case study design on a sensor-based agricultural monitoring system because of its ability to provide in-depth and holistic insights. The research population consists of users and stakeholders in sensor-based agricultural monitoring systems. The sample was selected purposively to cover various aspects of the supply chain. Data was collected through in-depth interviews, direct observation, surveys of system users and collection of sensor and transaction data from agricultural monitoring systems. The research results show that the integration of IoT, AI, and blockchain significantly improves operational efficiency in agribusiness supply chains. Implementation of this integrated system resulted in an increase in productivity of up to 22%, a reduction in pesticide use by 35%, an increase in water use efficiency by 30%, and a reduction in operational costs by 18%. Statistical analysis confirmed a strong positive correlation between the use of integrated technology and increased operational efficiency (R=0.85, p<0.01).

This paper reviews the emerging and critical role of Artificial Intelligence (AI) and the Internet of Things (IoT) in transforming modern agriculture into a more sustainable and efficient practice. Faced with escalating global population demands and a myriad of challenges such as climate change, labor costs, and market fluctuations, the agricultural sector is at a pivotal juncture. The integration of AI and IoT offers a beacon of hope, enabling smarter farming methods through real-time monitoring, data analysis, and management of agricultural processes. This review delves into the application of these technologies in various facets of farming, including precision agriculture, crop yield forecasting, and the management of resources. It explores how AI's analytical prowess can predict and address crop diseases, automate farming operations, and assist in decision-making, while IoT's network of sensors and devices facilitates the real-time tracking and monitoring of farm conditions. Additionally, the synergy of these technologies with blockchain and cloud computing is examined, highlighting their potential in enhancing transparency, data security, and supply chain management. Collectively, these technological innovations are not just reshaping the agricultural landscape but are instrumental in steering it towards a more sustainable, productive, and resilient future.

<p>In the rapidly evolving technological landscape, modern innovations such as blockchain (BC), artificial intelligence (AI), and the Internet of Things (IoT) are playing a pivotal role in realizing the vision of Society 5.0. This concept envisions a harmonious integration of physical, digital, and human elements to create a sustainable and progressive future. Emerging technologies like robotics, biotechnology, quantum computing, 5G/6G networks, smart cities, and the metaverse are expected to further amplify the impact of Society 5.0, driving transformative changes across industries and societal structures. The integration of BC, AI, and IoT within cloud-based frameworks offers a robust solution to enhance security, privacy, and data management. The integration of blockchain, AI, and IoT within a cloud-based framework offers a transformative solution to address the challenges of modern societies. By leveraging these technologies, the proposed platform aims to create a secure, efficient, and sustainable ecosystem that enhances quality of life and drives progress toward the vision of Society 5.0. This approach not only tackles current societal issues but also paves the way for a more interconnected and innovative future. This article introduces a platform that seamlessly combines these technologies to provide a secure and reliable foundation for data collection, sharing, and analysis while enabling the development of smart applications that improve efficiency, productivity, and overall quality of life (QoL).</p>

Energy harvesting from the microbial fuel cells have a significant attention in the recent days, due to their cost efficiency, simple designing structure and self-powered system. Also, the emergence of internet of things plays a vital role in many real time application scenarios like agricultural purposes and activities. But, the incorporation of these techniques is one of challenging and interesting tasks in the research field. In the conventional works, the internet of things has been utilized as a cloud storage domain for activating the sensors used for environmental monitoring and controlling purposes. The main intention of this paper is to design a robust and cost-effective sludge water based microbial fuel cells, and utilize it for an internet of things incorporated ecological monitoring and farming applications by activating the smart sensors. It discusses about the various electrode combination with several mixture of substrate to study about the optimum performance of microbial fuel cells. To ease the comparative study, Thing Speak platform is used along with the necessary sensors for continuous monitoring. In addition to that, the efficiency of single and dual chamber microbial fuel cell is analyzed based on the set of parameters such as cost, size, and construction. In this work, the microbial fuel cell-based energy harvesting scheme is also developed with switched capacitance-based metal oxide semiconductor field effect transistor and relay-based charge pump circuit which can be incorporated to the internet of things based agriculture applications. Here, the cost analysis of microbial fuel cell with and without DC–DC converter have been compared for selecting the most suitable one for the application system. Moreover, the digital temperature and humidity sensor can be utilized with the proposed microbial fuel cell system for gathering the inputs of the ecological system, which acts as an interface of the microbial fuel cell and cloud systems. During experimentation, the results of both the energy harvesting schemes are evaluated and compared by using various performance indicators.

Toward a smarter and more sustainable agricultural future, Artificial Intelligence (AI) and the Internet of Things (IoT) have emerged as transformative technologies addressing challenges such as climate change, resource scarcity, and rising food demand.This study investigates the integration of AI and IoT in agriculture, focusing on their applications and socioeconomic impacts from a sustainability perspective.It examines the role of AI in accurately diagnosing plant diseases and highlights how IoT enables real-time monitoring of agricultural environments to optimize irrigation and fertilization.The research also explores behavioral changes among farmers, improvements in work conditions, and enhanced decisionmaking enabled by data-driven tools.Results show that applying AI and IoT increased agricultural productivity by over 20% and

The increasing demand for clean and reliable water resources, coupled with the growing threat of water pollution, has made real-time water quality (WQ) monitoring and assessment a critical priority in many urban areas. Urban environments encounter substantial challenges in maintaining WQ, driven by factors such as rapid population growth, industrial expansion, and the impacts of climate change. Effective real-time WQ monitoring is essential for safeguarding public health, promoting environmental sustainability, and ensuring adherence to regulatory standards. The rapid advancement of Internet of Things (IoT) sensor technologies and smartphone applications presents an opportunity to develop integrated platforms for real-time WQ assessment. Advances in the IoT provide a transformative solution for WQ monitoring, revolutionizing the way we assess and manage our water resources. Moreover, recent developments in Location-Based Services (LBSs) and Global Navigation Satellite Systems (GNSSs) have significantly enhanced the accessibility and accuracy of location information. With the proliferation of GNSS services, such as GPS, GLONASS, Galileo, and BeiDou, users now have access to a diverse range of location data that are more precise and reliable than ever before. These advancements have made it easier to integrate location information into various applications, from urban planning and disaster management to environmental monitoring and transportation. The availability of multi-GNSS support allows for improved satellite coverage and reduces the potential for signal loss in urban environments or densely built environments. To harness this potential and to enable the seamless integration of the IoT and LBSs for sustainable WQ monitoring, a systematic literature review was conducted to determine past trends and future opportunities. This research aimed to review the limitations of traditional monitoring systems while fostering an understanding of the positioning capabilities of LBSs in environmental monitoring for sustainable urban development. The review highlights both the advancements and challenges in using the IoT and LBSs for real-time WQ monitoring, offering critical insights into the current state of the technology and its potential for future development. There is a pressing need for an integrated, real-time WQ monitoring system that is cost-effective and accessible. Such a system should leverage IoT sensor networks and LBSs to provide continuous monitoring, immediate feedback, and spatially dynamic insights, empowering stakeholders to address WQ issues collaboratively and efficiently.