Sustainable IoT Research Articles

UAV-Enabled Mobile RAN and RF-Energy Transfer Protocol for Enabling Sustainable IoT in Energy-Constrained Networks

UAV-Enabled Mobile RAN and RF-Energy Transfer Protocol for Enabling Sustainable IoT in Energy-Constrained Networks

INTERNET BASED SUSTAINABLE ENERGY FOR THE ECOSYSTEM

The world has a wealth of renewable and conventional energies, but the lack of technological capabilities and capacities combined with the water-energy requirements limit their benefits to its citizens. Sustainable energy research and innovation will be revolutionized by IoT technologies and the latest IoT communications and sensing approaches. Internet of Things can also be used to address energy challenges for the community by leveraging existing infrastructure such as renewable energy, microgrids and P2G hydrogen systems. This chapter presents the IoT for sustainable energy system approaches, me thods, scenarios and tools, along with a discussion on different sensing techniques and communication technologies. The IoT in Energy Systems is intended to improve the bidirectional exchange of network services by using Internet of Things on grid. This will lead to enhanced system resilience and reliable data flows, as well as connectivity optimization. The sustainable energy IoT challenges and opportunities for innovation are discussed in order to meet the energy needs and economic strength of the energy sector.

TEGBed: A thermal energy harvesting testbed for batteryless internet of things

This paper presents TEGBed, a testbed designed for the evaluation and testing of batteryless devices powered using thermal energy. TEGBed offers the capability to mimic real-life temperature gradients, providing researchers with a controlled environment for testing and evaluating batteryless devices. Researchers can leverage TEGBed to accelerate their investigations, gain insights into the behaviour of batteryless devices, and drive advancements in the field of sustainable and efficient IoT technologies. The TEGBed consists of a temperature emulator for emulating temperature differences, Joule Counter, a novel hardware tool for energy and power measurement, and a framework for non-supervised operation. By using the temperature emulator, TEGBed can emulate real-life energy harvesting situations in the lab. The Joule Counter allows gauging the power output from energy harvesting units for a wide range of output power. This enables researchers to gain insights into the energy harvesting capacity and efficiency of harvesting and power management units under different scenarios. With the help of different real-world use cases, we demonstrate the capabilities and effectiveness of TEGBed in assessing thermal energy harvesters and batteryless designs. In addition, we demonstrate how TEGBed can be used for the feasibility study of harvesting energy from two different underutilized heat sources; the temperature difference between soil and air and the temperature difference between the interior and exterior of a greenhouse. Emulations with the TEGBed show that, we could harvest an average of 0.89mW power from soil-air temperature differences and 0.60mW power from greenhouse temperature differences.

A Distributed Data-Driven and Machine Learning Method for High-Level Causal Analysis in Sustainable IoT Systems

A Distributed Data-Driven and Machine Learning Method for High-Level Causal Analysis in Sustainable IoT Systems

Digital Twin Empowered Approach for Sustainable IoT in Consumer Electronics Health: A Use Case

Digital Twin Empowered Approach for Sustainable IoT in Consumer Electronics Health: A Use Case

Towards truly sustainable IoT systems: the SUPERIOT project

This paper provides an overview of the SUPERIOT project, an EU SNS JU (Smart Networks and Services Joint Undertaking) initiative focused on developing truly sustainable IoT systems. The SUPERIOT concept is based on a unique holistic approach to sustainability, proactively developing sustainable solutions considering the design, implementation, usage and disposal/reuse stages. The concept exploits radio and optical technologies to provide dual-mode wireless connectivity and dual-mode energy harvesting as well as dual-mode IoT node positioning. The implementation of the IoT nodes or devices will maximize the use of sustainable printed electronics technologies, including printed components, conductive inks and substrates. The paper describes the SUPERIOT concept, covering the key technical approaches to be used, promising scenarios and applications, project goals and demonstrators which will be developed to the proof-of-concept stage. In addition, the paper briefly discusses some important visions on how this technology may be further developed in the future.

Stochastic Computation Model for Solar Panel Size and Cost of Sustainable IoT Networks

Stochastic Computation Model for Solar Panel Size and Cost of Sustainable IoT Networks

Optimal data transmission for decentralized IoT and WSN based on Type-2 Fuzzy Harris Hawks Optimization

Due to the widespread availability of services and smart devices, the decentralized internet of things and wireless sensor network-based systems (DIoT and WSN) have drawn the attention of academics and researchers and are still under constant development. Many challenges face the DIoT and WSN networks and need to be solved. The main issue among these challenges is reducing energy consumption in order to increase network lifetime. Energy-efficient routing protocols and the clustering method are the best solutions to this problem. In this paper, Type-2 Fuzzy Harris Hawks Optimization (T2FHHO) is used to choose the best path (route) from the source (cluster head (CH)) to the destination nodes (base station (BS)). Along with this, we propose a new fitness function to find the next hop based on residual energy, distance, traffic, and buffer size. Then, we apply the T2FHHO method to get the best CH. This protocol seeks to increase the long-term reward earned by each node. The best path in terms of the minimum cost obtained using the proposed method is found. The two main contributions of this study are as follows: firstly, we present a new efficient routing approach that minimizes costs and maximizes efficiency. Secondly, we apply the T2FHHO to the network parameters of energy consumption, network lifetime, and convergence curves. The results indicate that T2FHHO outperforms competing methods. The proposed method holds the promise of significantly advancing the state of the art in energy-efficient routing for DIoT and WSN networks, with significant implications in various sectors and applications, ensuring extended network lifetimes and sustainable IoT implementations.

Enhancing Elderly Fall Detection through IoT-Enabled Smart Flooring and AI for Independent Living Sustainability

In the realm of sustainable IoT and AI applications for the well-being of elderly individuals living alone in their homes, falls can have severe consequences. These consequences include post-fall complications and extended periods of immobility on the floor. Researchers have been exploring various techniques for fall detection over the past decade, and this study introduces an innovative Elder Fall Detection system that harnesses IoT and AI technologies. In our IoT configuration, we integrate RFID tags into smart carpets along with RFID readers to identify falls among the elderly population. To simulate fall events, we conducted experiments with 13 participants. In these experiments, RFID tags embedded in the smart carpets transmit signals to RFID readers, effectively distinguishing signals from fall events and regular movements. When a fall is detected, the system activates a green signal, triggers an alarm, and sends notifications to alert caregivers or family members. To enhance the precision of fall detection, we employed various machine and deep learning classifiers, including Random Forest (RF), XGBoost, Gated Recurrent Units (GRUs), Logistic Regression (LGR), and K-Nearest Neighbors (KNN), to analyze the collected dataset. Results show that the Random Forest algorithm achieves a 43% accuracy rate, GRUs exhibit a 44% accuracy rate, and XGBoost achieves a 33% accuracy rate. Remarkably, KNN outperforms the others with an exceptional accuracy rate of 99%. This research aims to propose an efficient fall detection framework that significantly contributes to enhancing the safety and overall well-being of independently living elderly individuals. It aligns with the principles of sustainability in IoT and AI applications.

The intellectual structure of sustainability accounting in the corporate environment: A literature review

The paper focuses on a current study on sustainable accounting as well as potential future research issues. 679 publications dealing with sustainable accounting produced between 2003 and 2022 were assessed using a bibliometric and visualization tool for the Web of Science cor collection database and viewer-based literature. The study looks at cluster analysis, all-keyword co-occurrence analysis, and bibliographic coupling mapping. This study allows us to suggest future research directions that may be beneficial in reflecting on the major influence that technology will have on the expansion of sustainable accounting in a business. The survey identified four major research trends in accounting for sustainability: reporting and disclosure, sustainability as part of management control systems, accounting and environmental management, stakeholder involvement and accountability, sustainability and business social responsibility. Education and Information Sharing About Sustainable Accounting, Green Sustainable Accounting, Technology Usage in Sustainable Accounting, Including AI, Big Data, and IoT, and ESG: Sustainable Accounting. This report presents a thorough examination of research trends in the intersection of sustainable accounting, as well as prospective research directions.

Sustainable resilient supplier selection for IoT implementation based on the integrated BWM and TRUST under spherical fuzzy sets

Supplier selection process plays a vital role in supply chain management and is the most important variable in its success. With increasing environmental considerations, organizations must consider sustainability considerations and economic goals to protect the environment. Furthermore, the destructive effects of disruptions on the supply chain performance of companies have prompted organizational experts to pay special attention to the concept of resilience. This study developed an integrated approach based on the extended version of Multi-Criteria Decision-Making (MCDM) methods in a spherical fuzzy (SFS) environment to address sustainable and resilient IoT supplier selection. In the proposed approach, the main criteria (i.e., resilience, and sustainability) have been used in the supplier selection process. Then, these criteria are weighted using the developed SFS-Best-Worst Method (BWM), which reduces uncertainty in pairwise comparisons. In the next step, the 14 selected IoT suppliers are evaluated and prioritized by applying SFS-mulTi-noRmalization mUlti-Distance aSsessmenT (TRUST) that considers a multi-normalization algorithm to reduce subjectivity in normalized data. The results of this study shows that the pollution control and risk-taking sub-criteria are placed in the first and second priorities, respectively. The comparison of the results of the SFS-TRUST with other MCDM methods and sensitivity analysis demonstrates the performance of the proposed approach and its ranking stability in various scenarios.

Securing Access to Internet of Medical Things Using a Graphical-Password-Based User Authentication Scheme

Digital healthcare services have seen significant growth in this decade and many new technologies have been thoroughly examined to provide efficient services through secure infrastructures. The Internet of Medical Things (IoMT) revitalizes a healthcare infrastructure by creating an interconnected, intelligent, accessible, and efficient network. While there have been many studies on possible device authentication techniques for the IoMT, there is still much work to be done in user authentication to provide sustainable IoT solutions. Graphical passwords, which use visual content such as images instead of traditional text-based passwords, can help users authenticate themselves. However, current schemes have limitations. Therefore, this paper proposes a novel graphical authentication scheme that uses multiple factors to register and authenticate users using simple arithmetic operations, machine learning for hand gesture recognition, and medical images for recall purposes. The proposed method is designed to keep the authentication process simple, memorable, and robust. To evaluate the proposed scheme, we use the Post-Study System Usability Questionnaire (PSSUQ) to compare it with PIN-based and pattern-based authentication techniques. While comparing treatment and comparison groups, system quality showed a 16.7% better score, information quality a 25% increase, interface quality a 40% increase, and overall quality showed a 25% increase. The proposed method successfully revitalizes the use of graphical passwords, specifically in the field of IoMT, by developing a user-friendly, satisfying, and robust authentication scheme.

Battery-Less Industrial Wireless Monitoring and Control System for Improved Operational Efficiency

An industrial wireless monitoring and control system, capable of supporting energy-harvesting devices through smart sensing and network management, designed for improving electro-refinery performance by applying predictive maintenance, is presented. The system is self-powered from bus bars, and features wireless communication and easy-to-access information and alarms. With cell voltage and electrolyte temperature measurements, the system enables real-time cell performance discovery and early reaction to critical production or quality disturbances such as short-circuiting, flow blockages, or electrolyte temperature excursions. Field validation shows an increase in operational performance of 30% (reaching 97%) in the detection of short circuits, which, thanks to a neural network deployed, are detected, on average, 10.5 h earlier compared to the traditional methodology. The developed system is a sustainable IoT solution, being easy to maintain after its deployment, and providing benefits of improved control and operation, increased current efficiency, and decreased maintenance costs.

Batteryless NB-IoT prototype for bidirectional communication powered by ambient light

IoT use-cases aim to provide high-quality services where IoT infrastructures are always linked with the Internet. They require large scale deployment of small devices acting as sensors and actuators in different places. These devices need to be energy efficient and run sustainably. The maintenance of such large scale devices is difficult if they are powered by batteries that need to be replaced after some time. Moreover, disposing of these batteries also pollutes the soil and contaminates groundwater. Narrowband IoT (NB-IoT) is one of the long-range technologies that supports connectivity even in deep indoor closed areas such as underground parking lots, elevators, and tunnels. Therefore, in this article, the suitability of an NB-IoT device or User Equipment (UE) to be powered without any batteries to enable a long-term sustainable maintenance-free IoT network is demonstrated. We develop a prototype using a small photovoltaic solar panel for indoor light harvesting using sunlight or an artificial light bulb to power the NB-IoT UE. Due to the unpredictability of energy harvesting, the batteryless UE can experience frequent switches between the ON and OFF states. Therefore, we evaluate the performance of the solution where the UE communicates either uni- (uplink only) or bi-directionally (uplink and downlink) for different capacitor sizes at different artificial light intensities. We also evaluate the solution under dynamic harvesting power using natural light during daytime. The experimental results conclude that the batteryless NB-IoT UE supports a transmission interval of 60 s without experiencing any restarts when harvesting 6 mW or more, which can be harvested by placing a 6 W artificial light bulb at a distance of 60 cm or less.

ETAP: Energy-aware Timing Analysis of Intermittent Programs

Energy harvesting battery-free embedded devices rely only on ambient energy harvesting that enables stand-alone and sustainable IoT applications. These devices execute programs when the harvested ambient energy in their energy reservoir is sufficient to operate and stop execution abruptly (and start charging) otherwise. These intermittent programs have varying timing behavior under different energy conditions, hardware configurations, and program structures. This article presents Energy-aware Timing Analysis of intermittent Programs (ETAP), a probabilistic symbolic execution approach that analyzes the timing and energy behavior of intermittent programs at compile time. ETAP symbolically executes the given program while taking time and energy cost models for ambient energy and dynamic energy consumption into account. We evaluate ETAP by comparing the compile-time analysis results of our benchmark codes and real-world application with the results of their executions on real hardware. Our evaluation shows that ETAP’s prediction error rate is between 0.0076% and 10.8%, and it speeds up the timing analysis by at least two orders of magnitude compared to manual testing.

CCD Sensor Based Cameras for Sustainable Streaming IoT Applications With Compressed Sensing

CCD Sensor Based Cameras for Sustainable Streaming IoT Applications With Compressed Sensing

A Sustainable and Efficient IoT Approach for Fire Department Alerting System

The creation of sophisticated fire detection systems is necessary given the rising danger of fire occurrences. The system described in this abstract uses IoT components such an Arduino board, flame sensor, buzzer, SIM800L module, and SIM800L to detect fires in real time and inform users. The flame sensor is the main element of the system, and it uses infrared radiation to identify flames when they are present. The Arduino board receives the signal from the sensor and activates the alarm system when it detects a flame. A loud audible alarm is produced by the buzzer when it is linked to the Arduino board to alert residents to a potential fire threat. The system includes the SIM800L module to support distant notifications in addition to local alerts. Once a fire has been identified, the Arduino board and SIM800L module connect to send an SMS or place a call to preset emergency contacts. This makes it possible to quickly take the necessary action, even if the affected properties are unattended. The IoT device connection with the Arduino board gives the fire detection system flexibility and scalability making it reliable and sustainable. By adding more sensors or integrating with current home automation systems, it is simple to customize and expand. Real-time fire detection, remote alerting capabilities, and IoT ecosystem integration are just a few of the advantages the suggested fire detection system has over conventional fire alarm systems.

Dependable Intrusion Detection System for IoT: A Deep Transfer Learning Based Approach

Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and therefore, sophisticated and dependable defense solutions are necessary against such threats. With the rapid development of IoT networks and evolving threat types, the traditional machine learning-based IDS must update to cope with the security requirements of the current sustainable IoT environment. In recent years, deep learning, and deep transfer learning have progressed and experienced great success in different fields and have emerged as a potential solution for dependable network intrusion detection. However, new and emerging challenges have arisen related to the accuracy, efficiency, scalability, and dependability of the traditional IDS in a heterogeneous IoT setup. This manuscript proposes a deep transfer learning-based dependable IDS model that outperforms several existing approaches. The unique contributions include effective attribute selection, which is best suited to identify normal and attack scenarios for a small amount of labeled data, designing a dependable deep transfer learning-based ResNet model, and evaluating considering real-world data. To this end, a comprehensive experimental performance evaluation has been conducted. Extensive analysis and performance evaluation show that the proposed model is robust, more efficient, and has demonstrated better performance, ensuring dependability.

DRDE: Dual Run Distribution Based Encoding Scheme for Sustainable IoT Applications

DRDE: Dual Run Distribution Based Encoding Scheme for Sustainable IoT Applications

Ecosense: An IoT System for Detecting Suitable and Sustainable Living Conditions

The EcoSense project intends to create a sustainable IoT system to detect and track whether living circumstances are suitable for individuals. The system gathers sensor data and measures important aspects including temperature, air quality, and noise level. The degree of sustainable air pollution is determined by air pollution sensors, which monitor several dangerous chemicals including CO and CO2 in the atmosphere. The noise sensor measures the level of noise and pinpoints the source of excessive noise. Environmental temperature and humidity are measured using sensors. These sensors sustainably and continuously track all of these parameters, sending out notifications when specified thresholds are reached or exceeded. In order to assess the habitat’s general compatibility, the sensor data is processed, saved in the cloud, and then examined. Following that, this information is used to give locals immediate feedback so they may decide for themselves how best to enhance their living arrangements as well as the environment and public health. their general wellbeing. This initiative helps to spread the word about living sustainably. The EcoSense initiative has the potential to have a big impact on the environmental sustainability industry. The initiative can assist people and communities in making educated decisions about their environment and taking constructive action to enhance it by offering real-time data on quality of life. The project has the potential to increase public awareness of environmental problems and inspire individuals to take action to safeguard the environment.