Sustainable Internet Of Things Research Articles

Integrating Internet of Things Technologies for Dynamic Sustainability in Architectural Design

The application of Internet of Things (IoT) technology is a significant step toward improving the sustainability and responsiveness of the built environment. The current work introduces the Adaptive and Sustainable IoT Integration Model (ASIIM), a novel framework designed to enhance the dynamic sustainability and adaptation of IoT in architecture in order to maximize its potential. Through a comprehensive review of the literature, this study analyzes the current state of IoT applications in architectural professions, highlighting the key benefits of IoT in improving building performance, occupant comfort, and energy efficiency. The ASIIM framework emerges as a comprehensive approach that encompasses key strategies for combining user interface systems, sustainability measures, IoT-enabled adaptive features, and fundamental design principles in order to promote a more responsive and sustainable architecture design. The paper identifies key obstacles to IoT integration, such as interoperability, data protection, and device sustainability, and offers collaborative ways to overcome them. The findings demonstrate the transformative potential of IoT in architecture, suggesting a future in which buildings will become dynamic systems that can adapt to the needs of both the surrounding environment and their occupants rather than static structures. This study contributes to the expanding body of knowledge on sustainable architecture by offering insights and a theoretical foundation for further study and practice in the integration of IoT technology in architectural design.

Realization of Self‐Powered High‐Performance Photodetection via Electrostatic Field Enhancement and Dark Current Suppression

Self‐powered photodetectors (PDs) are particularly attractive in the construction of environmentally friendly and sustainable Internet of Things. Utilizing dual functions of PTFE of electrostatic field enhancement and dark current suppression, a high‐performance self‐powered ITO/PTFE/TiO2/GaN UV PD is developed herein. The introduction of PTFE not only significantly reduces the dark current, but also facilitates the separation of photogenerated carriers by coupling the internal electrostatic field at the PTFE/TiO2 interface with the built‐in electric field of the TiO2/GaN heterojunction. With 0 V bias, the light‐to‐dark current (Ilight/Idark) ratio of the PD with PTFE is improved by 2297 times and the response time is faster by 1.69/2.96 times compared to the ITO/TiO2/GaN PD. In addition, a microstructured M‐ITO/PTFE/TiO2/GaN PD with micrometer‐sized cylindrical TiO2 arrays exhibits a high Ilight/Idark ratio of 3.65 × 105, a responsivity of 280.1 mA W−1, a high detectivity of 1.97 × 1013 Jones, and a response time of 4.3/3.0 ms under 360 nm illumination. Our strategy provides a promising way to develop high‐performance self‐powered PD.

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.

Driving Agricultural Transformation: Unraveling Key Factors Shaping IoT Adoption in Smart Farming with Empirical Insights

Driving Agricultural Transformation: Unraveling Key Factors Shaping IoT Adoption in Smart Farming with Empirical Insights

Assessing Malaysia’s Internet of Things (IoT) Readiness Based On CREATE-IoT Key Performance Indicators

The current nationwide deployment of Internet of Things (IoT) in Malaysia has never been assessed nor benchmarked against any Key Performance Indicators (KPIs). A proven IoT assessment standard called CREATE-IoT has successfully driven IoT projects in European countries, and it is hence used as a benchmarking tool in this research. The assessment that includes both technical and business perspectives concludes that 42 out of 50 KPIs (84%) in Malaysia are in the advanced state. Malaysia is thus rated as an IoT-ready country. However, rooms for improvement exist in the scopes of providing expected duration for completing an IoT service transaction to predict the level of customer satisfaction when using the service – and the ability to produce IoT sensors and devices locally to have sustainable, scalable, and economical continuous IoT deployments.

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.

Reconstructed Graph Neural Network With Knowledge Distillation for Lightweight Anomaly Detection.

The proliferation of Internet-of-Things (IoT) technologies in modern smart society enables massive data exchange for offering intelligent services. It becomes essential to ensure secure communications while exchanging highly sensitive IoT data efficiently, which leads to high demands for lightweight models or algorithms with limited computation capability provided by individual IoT devices. In this study, a graph representation learning model, which seamlessly incorporates graph neural network (GNN) and knowledge distillation (KD) techniques, named reconstructed graph with global-local distillation (RG-GLD), is designed to realize the lightweight anomaly detection across IoT communication networks. In particular, a new graph network reconstruction strategy, which treats data communications as nodes in a directed graph while edges are then connected according to two specifically defined rules, is devised and applied to facilitate the graph representation learning in secure and efficient IoT communications. Both the structural and traffic features are then extracted from the graph data and flow data respectively, based on the graph attention network (GAT) and multilayer perceptron (MLP) techniques. These can benefit the GNN-based KD process in accordance with the more effective feature fusion and representation, considering both structural and data levels across the dynamic IoT networks. Furthermore, a lightweight local subgraph preservation mechanism improved by the graph attention mechanism and downsampling scheme to better utilize the topological information, and a so-called global information alignment defined based on the self-attention mechanism to effectively preserve the global information, are developed and incorporated in a refined graph attention based KD scheme. Compared with four different baseline methods, experiments and evaluations conducted based on two public datasets demonstrate the usefulness and effectiveness of our proposed model in improving the efficiency of knowledge transfer with higher classification accuracy but lower computational load, which can be deployed for lightweight anomaly detection in sustainable IoT computing environments.

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.

Hierarchical, Attribute and Hash-Based Naming and Forwarding Aided Smart Campus of Things

In order to provide universal ability to access information and communication among Internet-connected devices, the Sustainable Internet of Things (IoT) is on a mission to bring all objects or devices under one roof. Future Internet architecture, especially Information-Centric Networking (ICN), can easily handle the connectivity offered and information created by the massive amount of devices to make it as sustainable IoT applications. Named Data Networking (NDN), one of the several future Internet designs that employ ICN as its foundation, shows promise. NDN integration with IoT-based applications gives solutions to numerous problems. However, this fusion makes accessing the IoT content easier, provided that an effective naming scheme is created to execute this operation. In this work, we build an innovative NDN-based naming scheme (NDN–NS) and put it into practise for consumer, producer, and content routers using our own secure forwarding schemes (NDN–NFS). Due to its scalability, heterogeneity, and security needs, IoT-based Smart Campus (IoT-SC) scenarios are taken into consideration for design and evaluation. We give a complete activity list based on NDN–NS that is split into two communication models (PusH Type Communication (PHTC) and PulL Type Communication (PLTC)) that can be applied to any IoT application. In terms of interest satisfaction rate (ISR), delay, and number of transmissions, we compare the NDN–NFS to legacy NDN. The outcomes demonstrate that NDN–NFS outperforms classic NDN in terms of performance and efficiency.

Wireless power transfer for a more sustainable Internet of Things

Perovskite solar cells could replace single-use batteries and erase the need for changing power sources for hard-to-reach devices.

Latency Minimization for IRS-Aided NOMA MEC Systems With WPT-Enabled IoT Devices

Mobile edge computing (MEC) and intelligent reflecting surface (IRS) are envisioned as two promising technologies that enable massive connectivity in the future Internet of Things (IoT) networks. MEC allows IoT devices (IDs) to offload their computation intensive tasks, and thus, can prolong their lifespan. In contrast, the IRS can enhance the channel condition between IDs and the access points (APs), which are colocated with the MEC server. Wireless power transfer technique enabling energy harvesting for IDs helps realizing sustainable IoT network. This paper applies IRS in a multi-ID MEC system for better latency performance. We first propose a multiple access scheme with hybrid frequency division and non-orthogonal access technologies and then design a timing protocol for the IDs. Based on the above design, we study the latency optimization problem with joint optimization of power allocation, the IRS phase shift matrix, and uplink and downlink beamformer under maximum power constraint for the IDs and AP. To tackle the formulated multi-variable non-convex problem, we split the target problem into several sub-problems and provide a nearoptimal low-complexity ID clustering scheme. Afterwards, we derive optimal solutions to these sub-problems, and a lowcomplexity fast-convergence alternating algorithm is proposed to minimize the overall latency. Presented simulation results verify the convergence of the alternating algorithm, and its superiority over the benchmarks.

A Lightweight Blockchain-Based Remote Mutual Authentication for AI-Empowered IoT Sustainable Computing Systems

Internet of Things (IoT) has led to significant advancements in communication technologies, specifically, concerning IoT Based Sustainable Information Systems. Lately, Industry-Academic communities have made great strides for the development of security in IoT-based applications, such as traffic management, industrial automation systems, military surveillance systems, transportation, parking, etc. The sustainable IoT converges AI and blockchain technologies for enhancing quality of individual’s life. As a result, emerging IoT applications operate a distributed ledger technology to provide robust-level of encryption and execution for contractual agreement that resolves interoperability and security issues. Thus, this paper proposes a blockchain-based remote mutual authentication (B-RMA) that considers smart devices and cloud networks to offer security and privacy. The proposed B-RMA can coexist with IoT based smart environment to decentralize the processing of user authentication requests. The prominence of the proposed strategies including security efficiency and privacy protection is evaluated using informal security analysis. Moreover, a run-time platform "Node.js" was used to analyze the communication metrics such as execution time, throughput, and overhead ratio over the concurrent requests. The investigation results prove that the B-RMA achieves a scalable environment, accordingly.

A High-Efficiency Diplexer for Sustainable 5G-Enabled IoT in Metaverse Transportation System and Smart Grids

Symmetry is essential in the design of complex systems like the Metaverse Transportation Sys-tem (MTS) and helps ensure that all components work together effectively. In the development of a microstrip diplexer for 5G-enabled IoT and MTS, maintaining symmetry is crucial to achieving flat responses with low group delays. By integrating transportation technology and the Metaverse, the Metaverse Transportation System (MTS) can greatly improve the effectiveness and intellect of transportation systems in reality. To establish a dependable network, it is essential to include 5G-enabled Internet of Things (IoT) and sensor networks with a sustainable design that focuses on efficiency and energy conservation. A three-channel microstrip lowpass-bandpass diplexer has been developed for 5G-enabled IoT and MTS implementation. Multi-channel designs are rare due to the complex design process, but this diplexer is very compact at only 0.004 λg2. All channels have flat responses with group delays of 0.34 ns, 1.7 ns, and 0.34 ns at the lower, middle, and upper passbands, respectively. The lowpass channel has a cut-off frequency of 1.22 GHz, suitable for mid-band 5G applications. Compared to previous work, this diplexer achieves the smallest size, lowest group delay, and insertion and return losses at the lower channel. It consists of a lowpass-bandpass section connected to a band-pass filter analyzed mathematically, and its performance has been verified through simulation and measurement with good accuracy.

Eternal-thing 2.0: Analog-Trojan-resilient Ripple-less Solar Harvesting System for Sustainable IoT

Recently, harvesting natural energy is gaining more attention than other conventional approaches for sustainable IoT. System on chip power requirement for the internet of things (IoT) and generating higher voltages on chip is a massive challenge for on-chip peripherals and systems. In this article, an on-chip reliable energy-harvesting system (EHS) is designed for IoT with an inductor-free methodology. The control section monitors the computational load and the recharging of the battery/super-capacitor. An efficient maximum power point tracking algorithm is also used to avoid quiescent power consumption. The reliability of the proposed EHS is improved by using an aging tolerant ring oscillator. The effect of Trojan on the performance of energy-harvesting system is analyzed, and proper detection and mitigation mechanism is proposed. Finally, the proposed ripple mitigation techniques further improves the performance of the aging sensor. The proposed EHS is designed and simulated in CMOS 90-nm technology. The output voltage is in the range of 3–3.55 V with an input 1–1.5 V with a power throughput of 0–22 μW. The EHS consumes power under the ultra-low-power requirements of IoT smart nodes.

Green IoT: A Review and Future Research Directions

The internet of things (IoT) has a significant economic and environmental impact owing to the billions or trillions of interconnected devices that use various types of sensors to communicate through the internet. It is well recognized that each sensor requires a small amount of energy to function; but, with billions of sensors, energy consumption can be significant. Therefore, it is crucial to focus on developing energy-efficient IoT technology and sustainable solutions. The contribution of this article is to support the implementation of eco-friendly IoT solutions by presenting a thorough examination of energy-efficient practices and strategies for IoT to assist in the advancement of sustainable and energy-efficient IoT technologies in the future. Four framework principles for achieving this are discussed, including (i) energy-efficient machine-to-machine (M2M) communications, (ii) energy-efficient and eco-sustainable wireless sensor networks (WSN), (iii) energy-efficient radio-frequency identification (RFID), and (iv) energy-efficient microcontroller units and integrated circuits (IC). This review aims to contribute to the next-generation implementation of eco-sustainable and energy-efficient IoT technologies.

GLEAN: Generalized-Deduplication-Enabled Approximate Edge Analytics

The Internet of Things (IoT) has brought about exponential growth in sensor data. This has led to increasing demands for efficient and novel data transmission, storage, and analytics solutions for sustainable IoT ecosystems. It has been shown that the generalized deduplication (GD) compression algorithm offers not only competitive compression ratio and throughput but also random access properties that enable direct analytics of compressed data. In this article, we thoroughly stress test existing methods for direct analytics of GD compressed data with a diverse collection of 103 data sets, identify the need to optimize GD for analytics, and develop a new version of GD to this end. We also propose the generalized deduplication-enabled approximate edge analytics (GLEAN) framework. This framework applies the aforementioned analytics techniques at the Edge server to deliver end-to-end lossless data compression and high-quality Edge analytics in the IoT, thereby addressing challenges related to data transmission, storage, and analytics. Impressive analytics performance was achieved using this framework, with a median increase in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering error of just 2% relative to analytics performed on uncompressed data, while running <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.5\times $ </tex-math></inline-formula> faster and requiring <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.9\times $ </tex-math></inline-formula> less storage at the Edge server compared to universal compressors.

An enhanced opportunistic rank-based parent node selection for sustainable & smart IoT networks

The Internet of Things (IoT) is a network of interconnected devices that includes low-end devices (sensors) and high-end devices (servers). The routing protocol used the Low-Power and Lossy Networks (RPL) protocol, which was designed to collect data in Low-Power and Lossy Networks (LLN) efficiently and reliably. The RPL rank property specifies how sensor nodes are placed in Destination Oriented Directed Acyclic Graphs (DODAG) based on an Objective Function (OF). The OF includes information such as the Expected Transmission Count (ETX) and packet delivery rate. The rank property aids in routing path optimization, reducing control overhead, and maintaining a loop-free topology by using rank-based data path validation. However, it causes many issues, such as optimal parent selection, next-hop node selection, and network instability. We proposed an Enhanced Opportunistic Rank-based Parent Node Selection for Sustainable and Smart IoT Networks to address these issues. The optimal parent node is determined by forecasting the expected energy of each node using Received Signal Strength (RSS) and an enhanced reinforcement learning algorithm. The proposed method addresses the issue of selecting the next-hop neighbor node and improves routing stability. Furthermore, when a large number of new nodes try to join the sustainable IoT-based smart cities, the proposed technique provides optimal load balance.

Energy-balanced neuro-fuzzy dynamic clustering scheme for green & sustainable IoT based smart cities

The Internet of Things (IoT) is a pervasive computing technology that provides solutions to critical sustainable smart city applications. Each sustainable application has its own set of requirements, including energy efficiency, Quality of Service (QoS), hardware, and software resources. Even though green IoT devices operate in a resource-constrained environment. Monitoring, recognizing, and responding to activities that entail continuous access to timely information in a partially or fully distributed ecosystem is a difficult task. To overcome the challenges of resource management in the IoT, we proposed an energy-efficient Dynamic Clustering Routing (DCR) protocol using a neuro-fuzzy technique for restricting the resources of IoT devices. The proposed protocol uses a dynamic self-organizing neural network to create dynamic clusters in a network. The test-bed analysis is for computing the real-time event detection and clustering sensor nodes using TinyOS. The simulation result shows that the proposed protocol achieved a significant gain over peer-competing well-known green communication routing protocols like Low-energy Adaptive Clustering Hierarchy (LEACH) and Low-energy Adaptive Clustering Hierarchy-Centralized (LEACH-C). The proposed model results show that using neuro-fuzzy logic is effective for sustainable IoT devices and green smart city applications in terms of resource management and dynamic clustering. The result analysis shows that the proposed protocol shows an average 35% significant gain on the First Node Dies (FND), Last Node Dies (LND), the number of packets sent to CH & BS, network convergence time, network overhead, and average packet delay to compare with the LEACH and LEACH-C.

A customer-centric IoT-based novel closed-loop supply chain model for WEEE management

Waste electrical and electronic equipment (WEEE) has been one of the widest and soaring solid wastes worldwide. Circular economy, as a sustainable paradigm, promotes closing the loop of e-products to prolong the life of WEEE. Hence, a sustainable Internet of things (IoT)-based closed-loop supply chain (CLSC) system is established considering the economic, environment and social dimensions. Due to the reality constraints, a simulation model with two scenarios is constructed to consider the customers’ behavior in the system. The attribute importance of customers’ behavior is analysed. Second-hand price and buy back price are shown to be two highly important factors, followed by peer pressure and social influence. Additionally, we present a sustainability assessment with the extant system using life cycle assessment, showing that our proposed system is more sustainable. Appropriate WEEE management using the proposed IoT-enabled system makes more profit and can reduce more greenhouse gas emissions and heavy metal contamination. In addition, with applying the proposed system, the number of people being protected from diseases caused by heavy metals can be significantly reduced.

High-Performance Multiband Ambient RF Energy Harvesting Front-End System for Sustainable IoT Applications—A Review

The increasing demand for wireless Internet of Things (IoT) calls for power efficient RF energy harvesting approach. The current dominant single-band RF energy harvesting front-end system restricts itself to a single frequency, which is at the risk of ineffective operation when the harvesting frequency is unavailable. This paper reviews and explores the alternative approach of a multiband RF energy-harvesting front-end system. It covers all essential circuitry of a multiband RF energy harvesting front-end system, starting from the recent RF surveys which investigate the typical, usable, and high-strength RF input, to the overview of the state-of-the-art antenna, impedance matching network (IMN), and RF-DC rectifier. The recent advancement of the multiple RF input harvesting abilities, reflection loss minimization, and performance improvement are comprehensively reviewed for different operating conditions, and this review also presents the advantages and disadvantages of the different circuit architecture combinations for multiband RF energy harvesting front-end systems. In summary, this review aims to fill the research gap in the further advancement of multiband RF energy harvesting towards enhancing its performance through optimal circuit integration of the front-end system.