Green Internet Of Things Research Articles

GraphSmart: a method for green and accurate IoT water monitoring

Water scarcity is nowadays a critical global concern and an efficient management of water resources is paramount. This paper presents an original approach for monitoring Water Distribution Systems (WDSs) through Internet of Things (IoT) that involves the integration of multiple sensors placed across the distribution network to accurately measure water flow. To enhance energy efficiency for green monitoring and communication process, we harness the power of graph theory and graph signal processing to represent in a tunable and accurate way the water flow and simultaneously minimize the number of IoT sensors communicating those measurements. We propose a graph model where water flow is represented as signal on graph and we introduce an algorithm, named GraphSmart, designed to reconstruct the graph signal when certain measurements are unknown or missing. Our framework is applied on synthetic realistic environment within the context of LoRaWAN (Long Range Wide Area Network), an infrastructure and protocol designed for ultra-low-power IoT devices. Our findings show that GraphSmart significantly reduces energy consumption while ensuring precise flow estimation. Our research demonstrates high potential for energy-efficient and accurate water flow monitoring, paving the way to improve the management of WDSs and enabling water operators to address water scarcity challenges.

Green Wearable Sensors and Antennas for Bio-Medicine, Green Internet of Things, Energy Harvesting, and Communication Systems.

This paper presents innovations in green electronic and computing technologies. The importance and the status of the main subjects in green electronic and computing technologies are presented in this paper. In the last semicentennial, the planet suffered from rapid changes in climate. The planet is suffering from increasingly wild storms, hurricanes, typhoons, hard droughts, increases in seawater height, floods, seawater acidification, decreases in groundwater reserves, and increases in global temperatures. These climate changes may be irreversible if companies, organizations, governments, and individuals do not act daily and rapidly to save the planet. Unfortunately, the continuous growth in the number of computing devices, cellular devices, smartphones, and other smart devices over the last fifty years has resulted in a rapid increase in climate change. It is severely crucial to design energy-efficient "green" technologies and devices. Toxic waste from computing and cellular devices is rapidly filling up landfills and increasing air and water pollution. This electronic waste contains hazardous and toxic materials that pollute the environment and affect our health. Green computing and electronic engineering are employed to address this climate disaster. The development of green materials, green energy, waste, and recycling are the major objectives in innovation and research in green computing and electronics technologies. Energy-harvesting technologies can be used to produce and store green energy. Wearable active sensors and metamaterial antennas with circular split ring resonators (CSSRs) containing energy-harvesting units are presented in this paper. The measured bandwidth of the matched sensor is around 65% for VSWR, which is better than 3:1. The sensor gain is 14.1 dB at 2.62 GHz. A wideband 0.4 GHz to 6.4 GHz slot antenna with an RF energy-harvesting unit is presented in this paper. The Skyworks Schottky diode, SMS-7630, was used as the rectifier diode in the harvesting unit. If we transmit 20 dBm of RF power from a transmitting antenna that is located 0.2 m from the harvesting slot antenna at 2.4 GHz, the output voltage at the output port of the harvesting unit will be around 1 V. The power conversion efficiency of the metamaterial antenna dipole with metallic strips is around 75%. Wearable sensors with energy-harvesting units provide efficient, low-cost healthcare services that contribute to a green environment and minimize energy consumption. The measurement process and setups of wearable sensors are presented in this paper.

Exploring the role of the Internet of Things in green buildings

AbstractThe global energy crisis has been one of the significant challenges for decades, threatening the global economy, and the health of our environment. The government's efforts to enhance the welfare and lifestyle of citizens have been partially undermined by a significant rise in energy intensity, resulting in increased energy consumption. Over the years, researchers have utilized historical energy consumption data to enhance energy efficiency through various technologies. Innovative smart technologies drive energy efficiency, reducing energy usage throughout all areas of the energy industry, from production and supply to consumption. This creates a balance in all sectors and indicates a decrease in energy demand in all areas of building infrastructure. Green buildings that utilize Internet of Things (IoT) technology employ sensors and software to collect data, optimizing, and enhancing building performance. This includes reducing energy and electricity consumption, improving air quality, and optimizing lighting throughout the day. These buildings can contribute to reaching zero‐energy building targets. It becomes challenging to manage green buildings without using centralized control. Therefore, managing and integrating these buildings with intelligent technologies is vital in achieving environmentally friendly management. This study offers a broad overview of the green building concept and explores the use of the green IoT in enhancing services and conserving energy within green buildings. The article aims to deliver an extensive review of green buildings and their advantages, analyze the technology behind the IoT and its integration with solar panels to lower energy consumption, and ultimately identify the challenges present in this area of research.

Identifying the Internet of Things (IoT) implementation benefits for sustainable construction project

ABSTRACT Understanding the implementation of the Internet of Things (IoT) in sustainable construction projects. Implementing the IoT in sustainable construction schemes can offer many benefits, including resource management, remote control and monitoring, improving safety, optimizing building performance and data-driven decision-making. This research presents a unique effort to enhance our comprehension of IoT implementation in such projects. We analyzed 55 studies using a systematic approach to literature selection and filtration, using theoretical models for Internet of Things (IoT) implementation to study the sustainability of construction industries. The results revealed that monitoring constituted 9 (16.4%), machine/equipment control 2 (3.6%), safety in construction 5 (9.1%), water management 4 (7.3%), 4IR 2 (3.6%), BIM 5 (9.1%), green IoT 5 (9.1%), prefabricated design 2 (3.6%), IoT adoption level 6 (10.9%), and other aspects accounted for 15 (27.3%). The primary areas of IoT application in the construction industry include BIM, remote application monitoring, construction management, repair and equipment services, construction implements, and tracking equipment. Notably, there is a lack of research awareness regarding the potential integration of IoT and BIM by construction companies. Despite numerous studies on IoT adoption, limited literature addresses the challenges and awareness of how IoT tools impact implementation in the building industry. Our review proposes a framework for implementing sustainable construction projects to address this gap. However, further investigation is required to analyze potential security threats associated with IoT in a smart city setting, explore promising countermeasures, and delve into open research issues.

An intelligent FL-based vehicle route optimization protocol for green and sustainable IoT connected IoV

The intelligent Internet of Vehicles (IoV) provides superior results in effectively addressing complex transportation challenges. Predicting vehicle traffic, crashes, demand, location, communication, and travel safety are all critical issues in today’s transportation systems. The proposed paper optimizes vehicle traffic by incorporating reroute recommendations, increasing the use of public transportation, and providing onboard vehicle drivers with intelligent health assistance using Federated Learning (FL). This research also focuses on resolving complex transportation issues such as a vehicle’s current location, exact vehicle count information on each route, and onboard vehicle vacant seat information. Furthermore, vehicle communication contributes to the proposed system’s efficiency by avoiding communication delays or information loss to registered users and the cloud server. An intelligent FL-based scheme for vehicle route optimization has been proposed as part of this research to prevent vehicle traffic in a real-time Internet of Things (IoT) connected IoV transportation system. The vehicle detection approach determines the number of vehicles traveling on each route to recommend the best route to registered users. The effective implementation of cluster-based vehicle communication and location estimation models enhances the efficiency of the proposed system.

Utilization of green internet of things (GIoT) applications towards sustainable performance: The antecedents and consequences of carbon footprint

This study aims to propose and investigate the key factors that predict the Utilization of Green Internet of Things (U-GIoT) applications by business organizations. This study will also look at the effect of using GIoT applications on energy efficiency and carbon footprint reduction, which in turn, contributes to the sustainable green performance. Therefore, value-belief-norm theory (VBN) was chosen as the theoretical basis for this study's conceptual model. Two constructs derived from VBN (i.e. biospheric value and ecological worldview) are suggested as key predictors for the use of GIoT applications. The conceptual model is extended by considering the role of green marketing orientation (GMO); green energy awareness; and energy knowledge/technical capabilities. The current model also suggests that the utilization of GIoT applications would impact both energy efficiency and carbon footprint reduction. Online questionnaires are used to gather data from a purposive sample (n = 500) of managers and employees at different levels in different service organizations. Statistical results show strong evidence demonstrating the significant effect of biospheric value; ecological worldview; green marketing orientation (GMO); and energy knowledge/technical capabilities with GIoT. This study presents a valuable contribution that helps researchers; policy-makers, and practitioners to identify and understand the most important antecedents and consequences of U-GIoT. It is also worth noting for future studies looking at different applications of pro-environmental behaviour such as sustainable practices, green mobility and transportation, paperless work; recycling; reducing waste levels; and smart green solar. Policy-makers, decision-makers, practitioners and specialists in green energy systems (i.e. GIoT) will be able to use this information for future improvement.

Research on sustainable green building space design model integrating IoT technology.

"How can the integration of Internet of Things (IoT) technology enhance the sustainability and efficiency of green building (G.B.) design?" is the central research question that this study attempts to answer. This investigation is important because it examines how green building and IoT technology can work together. It also provides important information about how to use contemporary technologies for environmental sustainability in the building sector. The paper examines a range of IoT applications in green buildings, focusing on this intersection. These applications include energy monitoring, occupant engagement, smart building automation, predictive maintenance, renewable energy integration, and data analytics for energy efficiency enhancements. The objective is to create a thorough and sustainable model for designing green building spaces that successfully incorporates IoT, offering industry professionals cutting-edge solutions and practical advice. The study uses a mixed-methods approach, integrating quantitative data analysis with qualitative case studies and literature reviews. It evaluates how IoT can improve energy management, indoor environmental quality, and resource optimization in diverse geographic contexts. The findings show that there has been a noticeable improvement in waste reduction, energy and water efficiency, and the upkeep of high-quality indoor environments after IoT integration. This study fills a major gap in the literature by offering a comprehensive model for IoT integration in green building design, which indicates its impact. This model positions IoT as a critical element in advancing sustainable urban development and offers a ground-breaking framework for the practical application of IoT in sustainable building practices. It also emphasizes the need for customized IoT solutions in green buildings. The paper identifies future research directions, including the investigation of advanced IoT applications in renewable energy and the evaluation of IoT's impact on occupant behavior and well-being, along with addressing cybersecurity concerns. It acknowledges the challenges associated with IoT implementation, such as the initial costs and specialized skills needed.

A scoping review of green Internet of Things in construction and smart cities: current applications, adoption strategies and future directions

PurposeContemporary technological disruptions are espoused as though they stimulate sustainable growth in the built environment through the Green Internet of Things (G-IoT). Learning from discipline-specific experiences, this paper articulates recent advancements in the knowledge and concepts of G-IoT in relation to the construction and smart city sectors. It provides a scoping review for G-IoT as an overlooked dimension. Attention was paid to modern circularity, cleaner production and sustainability as key benefits of G-IoT adoption in line with the United Nations’ Sustainable Development Goals (UN-SDGs). In addition, this study also investigates the current application and adoption strategies of G-IoT.Design/methodology/approachThis study uses the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) review approach. Resources are drawn from Scopus and Web of Science repositories using apt search strings that reflect applications of G-IoT in the built environment in relation to construction management, urban planning, societies and infrastructure. Thematic analysis was used to analyze pertinent themes in the retrieved articles.FindingsG-IoT is an overlooked dimension in construction and smart cities so far. Thirty-three scholarly articles were reviewed from a total of 82 articles retrieved, from which five themes were identified: G-IoT in buildings, computing, sustainability, waste management and tracking and monitoring. Among other applications, findings show that G-IoT is prominent in smart urban services, healthcare, traffic management, green computing, environmental protection, site safety and waste management. Applicable strategies to hasten adoption include raising awareness, financial incentives, dedicated work approaches, G-IoT technologies and purposeful capacity building among stakeholders. The future of G-IoT in construction and smart city research is in smart drones, building information modeling, digital twins, 3D printing, green computing, robotics and policies that incentivize adoption.Originality/valueThis study adds to the normative literature on envisioning potential strategies for adoption and the future of G-IoT in construction and smart cities as an overlooked dimension. No previous study to date has reviewed pertinent literature in this area, intending to investigate the current applications, adoption strategies and future direction of G-IoT in construction and smart cities. Researchers can expand on the current study by exploring the identified G-IoT applications and adoption strategies in detail, and practitioners can develop implementation policies, regulations and guidelines for holistic G-IoT adoption.

Energy-Efficient Device-to-Device Communications for Green Internet of Things Using Unmanned Aerial Vehicle-Mounted Intelligent Reflecting Surface

The Internet of Things (IoT) serves as a crucial element in interconnecting diverse devices within the realm of smart technology. However, the energy consumption of IoT technology has become a notable challenge and an area of interest for researchers. With the aim of achieving an IoT with low power consumption, green IoT has been introduced. The use of unmanned aerial vehicles (UAVs) represents a highly innovative approach for creating a sustainable green IoT network. UAVs offer advantages in terms of flexibility, mobility, and cost. Moreover, device-to-device (D2D) communication is essential in emergency communications, due to its ability to support direct communication between devices. The intelligent reflecting surface (IRS) is also a hopeful technology which reconstructs the radio propagation environment and provides a possible solution to reduce co-channel interference resulting from spectrum sharing for D2D communications. The investigation in this paper hence focuses on energy-efficient UAV-IRS-assisted D2D communications for green IoT. In particular, a problem of optimization aimed at maximizing the system’s average energy efficiency (EE) is formulated, firstly, by simultaneously optimizing the power coefficients of all D2D transmitters, the UAV’s trajectory, and the base station (BS)’s active beamforming, along with the IRS’s phase shifts. Second, to address the problem, we develop a multi-agent twin delayed deep deterministic policy gradient (MATD3)-based scheme to find a near-optimal solution, where D2D transmitters, the BS, and the UAV cooperatively learn to improve EE and suppress the interference. To conclude, numerical simulations are conducted to assess the availability of the proposed scheme, and the simulation results demonstrate that the proposed scheme surpasses the baseline approaches in both convergence speed and EE performance.

Trust-Based Optimized Reporting for Detection and Prevention of Black Hole Attacks in Low-Power and Lossy Green IoT Networks.

The Internet of Things (IoT) is empowering various sectors and aspects of daily life. Green IoT systems typically involve Low-Power and Lossy Networks (LLNs) with resource-constrained nodes. Lightweight routing protocols, such as the Routing Protocol for Low-Power and Lossy Networks (RPL), are increasingly being applied for efficient communication in LLNs. However, RPL is susceptible to various attacks, such as the black hole attack, which compromises network security. The existing black hole attack detection methods in Green IoT rely on static thresholds and unreliable metrics to compute trust scores. This results in increasing false positive rates, especially in resource-constrained IoT environments. To overcome these limitations, we propose a delta-threshold-based trust model called the Optimized Reporting Module (ORM) to mitigate black hole attacks in Green IoT systems. The proposed scheme comprises both direct trust and indirect trust and utilizes a forgetting curve. Direct trust is derived from performance metrics, including honesty, dishonesty, energy, and unselfishness. Indirect trust requires the use of similarity. The forgetting curve provides a mechanism to consider the most significant and recent feedback from direct and indirect trust. To assess the efficacy of the proposed scheme, we compare it with the well-known trust-based attack detection scheme. Simulation results demonstrate that the proposed scheme has a higher detection rate and low false positive alarms compared to the existing scheme, confirming the applicability of the proposed scheme in green IoT systems.

Harmony in Connectivity: Toward a Sustainable Future with Green Internet

Abstract: The widespread adoption of Internet of Things (IoT) devices has resulted in remarkable progress across various domains, enriching connectivity and automation. However, the growing deployment of IoT presents substantial environmental challenges, raising issues related to energy consumption, electronic waste, and resource depletion. This research paper explores the concept of Green IoT, with a focus on developing and implementing sustainable practices within the IoT ecosystem. The primary goal of this study is to investigate strategies for enhancing the energy efficiency of IoT devices, reducing their carbon footprint, and advocating for environmentally conscious practices throughout their life cycle. We delve into cutting-edge technologies, such as low-power components, energy harvesting mechanisms, and optimization algorithms for communication protocols, aiming to establish a more eco-friendly infrastructure for IoT. Additionally, this paper emphasizes the significance of sustainable materials and design principles in the manufacturing of IoT devices, emphasizing the reduction of electronic waste and the promotion of recycling. We discuss the integration of renewable energy sources like solar and wind power to energize IoT devices, contributing to a cleaner and more sustainable energy supply for the IoT ecosystem.

Green IoT Based on Tropical Weather: The Impact of Energy Harvesting in Wireless Sensor Network

Wireless Sensor Networks (WSNs) are a key component of Green IoT, as they play a critical role in many applications. However, a major challenge faced by WSNs is their limited energy capacity, which can impede their effectiveness. To address this issue, energy harvesting techniques are used to harness ambient energy and power the nodes, eliminating the need for frequent battery replacements or recharging. This study proposes a solar energy harvesting technique to prolong the lifespan of each wireless sensor node in a network. The aim of the research is to assess the impact of energy harvesting on wireless sensor networks with a focus on solar ambient light energy, as it is a critical component of achieving a Green Internet of Things (IoT). The study is adapted to the tropical weather of Malaysia and its forecasted conditions. Through the use of NetSim simulations, the energy efficiency and reliability of the network are evaluated using solar energy harvesting. The recharge rate for energy harvesting in a wireless sensor network is calculated to be 2.34W per day using a 55x70mm solar photovoltaic panel based on Malaysia weather forecasts and sun hours. This proposed method is successfully executed using NetSim, which utilizes real device parameters for a WSN in a simulated agriculture environment. The results show an overall improvement of 22.4 percent in performance parameters in all areas, including energy consumption, network layer, routing performance, and IEEE 802.15.4 metrics, representing a significant improvement. With the implementation of solar energy harvesting, traditional energy of WSNs can benefit from developing smart agricultural monitoring systems. This efficient method and calculation for solar energy harvesting can help to sustain the infinite lifetime of WSN by optimizing the operational time to 18% of the duty cycle. Results demonstrate that the proposed method can effectively increase the energy efficiency and reliability of the network, making it a promising solution for WSNs operating in tropical regions. This research contributes to the development of sustainable and energy-efficient WSNs, promoting the Green IoT concept.

Implementation of Green IoT to Achieve Sustainable Environment for Improving Energy Efficiency

The Internet of Things (IoT) has emerged as a powerful tool in the pursuit of achieving a sustainable environment. This abstract highlights the key facets of IoT implementation to foster sustainability. IoT's ability to seamlessly connect devices, collect real-time data, and enable intelligent decision-making has revolutionized various sectors, including energy management, waste reduction, and conservation of natural resources. One of the pivotal applications of IoT in sustainability is in energy management. Smart grids, powered by IoT, optimize energy distribution, reduce wastage, and integrate renewable energy sources effectively. IoT-driven sensors and controls in buildings further enhance energy efficiency through automated lighting, heating, and cooling systems, resulting in reduced carbon emissions. IoT's impact extends to waste management by enabling smart bins that monitor and optimize waste collection routes. This minimizes fuel consumption, reduces traffic congestion, and mitigates the environmental footprint of waste disposal. Moreover, IoT-driven sensors in agricultural practices facilitate precision farming, optimizing resource utilization and minimizing environmental impact. Natural resource conservation is another realm where IoT plays a pivotal role. Smart sensors and remote monitoring devices enable real-time tracking of water quality, air pollution levels, and forest health. This data empowers policymakers and environmentalists to make informed decisions, mitigating the consequences of environmental degradation. To achieve a sustainable green environment through IoT, it's essential to consider data privacy, security, and interoperability between devices and systems.

REVIEW ON THE EVOLUTION AND IMPACT OF IOT-DRIVEN PREDICTIVE MAINTENANCE: ASSESSING ADVANCEMENTS, THEIR ROLE IN ENHANCING SYSTEM LONGEVITY, AND SUSTAINABLE OPERATIONS IN BOTH MECHANICAL AND ELECTRICAL REALMS

This study provides a comprehensive review of the evolution and impact of Internet of Things (IoT)-driven predictive maintenance, focusing on advancements in technology, their role in enhancing system longevity, and promoting sustainable operations in mechanical and electrical systems. The primary objective was to assess how IoT integration has transformed traditional maintenance approaches, leading to improved system durability and reliability. Utilizing a systematic literature review methodology, the study involved sourcing data from peer-reviewed journals, conference proceedings, and industry reports. A content analysis approach was employed to analyze the data, focusing on themes such as technological advancements, sustainability considerations, and industry-specific applications of IoT in predictive maintenance. Key findings reveal significant advancements in IoT applications, particularly the integration of advanced data analytics, artificial intelligence, and machine learning in predictive maintenance strategies. These advancements have led to more accurate and timely maintenance interventions, contributing to enhanced system longevity and operational efficiency. The study also highlights the emergence of green IoT practices and the challenges and opportunities in the future landscape of IoT in predictive maintenance. The study concludes that IoT-driven predictive maintenance is pivotal for sustainable industrial operations, with opportunities lying in addressing challenges through innovative solutions and robust regulatory frameworks. Recommendations for industry and policy include fostering sustainable IoT practices and prioritizing energy efficiency. Future research directions involve exploring the integration of IoT with emerging technologies and investigating the long-term environmental impacts of IoT deployments.
 Keywords: Predictive Maintenance, System Longevity, Sustainable Operations, Internet of Things.

Lightweight Secure Compression Scheme for Green IoT Applications

The Internet of Things (IoT) is a revolutionary paradigm that has gained significant prominence in recent years. It represents the interconnection of everyday objects, devices and machines to the internet, allowing them to collect, exchange and analyze data. As the number of connected objects grows exponentially, energy consumption and data security become essential challenges to ensure a sustainable development of green IoT. Data compression theory can be used as an enticing key to downsize the large amount of circulated information, since wireless communication is the prime energy consuming component in IoT devices. In addition, data encryption algorithms are crucial to protect sensitive information and ensure the integrity and confidentiality of IoT systems. Therefore, combining compression and encryption can lead to improved overall IoT system performance in terms of both power cost and data privacy. In this paper, a lightweight and secure compression approach is introduced to efficiently manage the energy expenditure and provide end-to-end security for energy constrained devices. This proposed algorithm combines the performance of LTC compression and AES encryption to securely encode collected data in IoT devices. The energy model analysis has shown that the suggested method is effective in terms of processing energy, reduces the volume of transmitted data and guarantees end-to-end information privacy.

Research on Data Privacy and Security in the Green Internet of Things

Research on Data Privacy and Security in the Green Internet of Things

DRL-Based Computation Offloading and Resource Allocation in Green MEC-Enabled Maritime-IoT Networks

The maritime Internet of Things (MIoT), a maritime version of the Internet of Things (IoT), is envisioned as a promising solution that can provide ubiquitous connectivity over land and sea. Due to the rapid development of maritime activities and the maritime economy, there is a growing demand for computing-intensive and latency-sensitive maritime applications requiring various energy consumption, communication, and computation resources, posing a significant challenge to MIoT devices due to their limited computational ability and battery capacity. Mobile Edge Computing (MEC), which can handle computation tasks at the network’s edge more efficiently and with less latency, is emerging as a paradigm for fulfilling the ever-increasing demands of MIoT applications. However, the exponential increase in the number of MIoT devices has increased the system’s energy consumption, resulting in increased greenhouse gas emissions and a negative impact on the environment. As a result, it is vital for MIoT networks to take traditional energy usage minimization into account. The integration of renewable energy-harvesting capabilities into base stations or MIoT devices possesses the potential to reduce grid energy consumption and carbon emissions. However, making an effective decision regarding task offloading and resource allocation is crucial for maximizing the utilization of the system’s potential resources and minimizing carbon emissions. In this paper, we first propose a green MEC-enabled maritime IoT network architecture to flexibly provide computing-intensive and latency-sensitive applications for MIoT users. Based on the architecture, we formulate the joint task offloading and resource allocation problem by optimizing the total system execution efficiency (including the total size of completed tasks, task execution latency, and the system’s carbon emissions) and then propose a deep-deterministic-policy-gradient-based joint optimization strategy to solve the problem, eventually obtaining an effective resolution through continuous action space learning in the changing environment. Finally, simulation results confirm that our proposal can yield good performance in system execution efficiency compared to other benchmarks; that is, it can significantly reduce the system’s carbon emissions and tasks’ delay and improve the total size of completed tasks.

Harnessing federated generative learning for green and sustainable Internet of Things

The rapid proliferation of devices in the Internet of Things (IoT) has ushered in a transformative era of data-driven connectivity across various domains. However, this exponential growth has raised pressing concerns about environmental sustainability and data privacy. In response to these challenges, this paper introduces One-shot Federated Learning (OSFL), an innovative paradigm that harmonizes sustainability and machine learning within IoT ecosystems. OSFL revolutionizes the traditional Federated Learning (FL) workflow by condensing multiple iterative communication rounds into a single operation, thus significantly reducing energy consumption, communication overhead, and latency. This breakthrough is coupled with the strategic integration of generative learning techniques, ensuring robust data privacy while promoting efficient knowledge sharing among IoT devices. By curtailing resource utilization, OSFL aligns seamlessly with the vision of green and sustainable IoT, effectively extending device lifespans and mitigating their environmental footprint. Our research underscores the transformative potential of OSFL, poised to reshape the landscape of IoT applications across domains such as energy-efficient smart cities and groundbreaking healthcare solutions. This contribution marks a pivotal step towards a more responsible, sustainable, and technologically advanced future.

Green internet of things and solar energy.

The Internet of Things (IoT) stands out as one of the most captivating technologies of the current decade. Its ability to connect people and things anytime and anywhere has led to its rapid expansion and numerous impactful applications that enhance human life. With billions of connected devices and substantial power and infrastructure requirements, the IoT system can pose a threat to the environment. However, the IoT's vast range of resources and capabilities can also be leveraged to assist in environmental conservation in the evolution of technologies due to massive CO2 emissions, climate change, and environmental and health issues. In this study, with the two-way integration of IoT and green practices, two distinct concepts for green IoT are presented. Among green practices, energy solutions play a vital role in greening the IoT. In this study, the energy solutions for the IoT system are divided as reducing energy consumption and using green energy sources. Solutions for reducing IoT energy consumption are studied systematically through a five-layer framework to simplify its modular design and implementation. Then, the use of green energy resources is discussed for all components of the IoT ecosystem. Leveraging IoT to make the environment and other technologies green is the other concept of green IoT. IoT technology plays a crucial role in enhancing both energy management systems and the efficient harvesting of renewable energy sources. Switching to solar energy from fossil fuel energy is one of the most fundamental green practices today. In this study, the mutual relationship between solar energy harvesting and the IoT is addressed specifically. Several promising research directions in the realm of green IoT are also highlighted.

Sustainable collaboration: Federated learning for environmentally conscious forest fire classification in Green Internet of Things (IoT)

Forests are an invaluable natural resource, playing a crucial role in the regulation of both local and global climate patterns. Additionally, they offer a plethora of benefits such as medicinal plants, food, and non-timber forest products. However, with the growing global population, the demand for forest resources has escalated, leading to a decline in their abundance. The reduction in forest density has detrimental impacts on global temperatures and raises the likelihood of forest fires. To address these challenges, this paper introduces a Federated Learning framework empowered by the Internet of Things (IoT). The proposed framework integrates with an Intelligent system, leveraging mounted cameras strategically positioned in highly vulnerable areas susceptible to forest fires. This integration enables the timely detection and monitoring of forest fire occurrences and plays its part in avoiding major catastrophes. The proposed framework incorporates the Federated Stochastic Gradient Descent (FedSGD) technique to aggregate the global model in the cloud. The dataset employed in this study comprises two classes: fire and non-fire images. This dataset is distributed among five nodes, allowing each node to independently train the model on their respective devices. Following the local training, the learned parameters are shared with the cloud for aggregation, ensuring a collective and comprehensive global model. The effectiveness of the proposed framework is assessed by comparing its performance metrics with the recent work. The proposed algorithm achieved an accuracy of 99.27 % and stands out by leveraging the concept of collaborative learning. This approach distributes the workload among nodes, relieving the server from excessive burden. Each node is empowered to obtain the best possible model for classification, even if it possesses limited data. This collaborative learning paradigm enhances the overall efficiency and effectiveness of the classification process, ensuring optimal results in scenarios where data availability may be constrained.