Towards Sustainable IoT: Challenges, Solutions, and Future Directions for Device Longevity

• URL: http://arxiv.org/abs/2405.16421v1
• Date: Sun, 26 May 2024 04:05:01 GMT
• Title: Towards Sustainable IoT: Challenges, Solutions, and Future Directions for Device Longevity
• Authors: Ghazaleh Shirvani, Saeid Ghasemshirazi,
• Abstract summary: This study explores the various complex difficulties which contributed to the early decommissioning of IoT devices. By examining factors such as security vulnerabilities, user awareness gaps, and the influence of fashion-driven technology trends, the paper underscores the need for legislative interventions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In an era dominated by the Internet of Things, ensuring the longevity and sustainability of IoT devices has emerged as a pressing concern. This study explores the various complex difficulties which contributed to the early decommissioning of IoT devices and suggests methods to improve their lifespan management. By examining factors such as security vulnerabilities, user awareness gaps, and the influence of fashion-driven technology trends, the paper underscores the need for legislative interventions, consumer education, and industry accountability. Additionally, it explores innovative approaches to improving IoT longevity, including the integration of sustainability considerations into architectural design through requirements engineering methodologies. Furthermore, the paper discusses the potential of distributed ledger technology, or blockchain, to promote transparent and decentralized processes for device provisioning and tracking. This study promotes a sustainable IoT ecosystem by integrating technology innovation, legal change, and social awareness to reduce environmental impact and enhance resilience for the digital future

Related papers

• Securing the Future: Proactive Threat Hunting for Sustainable IoT Ecosystems [0.0]
  This paper explores the concept of proactive threat hunting as a pivotal strategy for enhancing the security and sustainability of IoT systems. By improving the security posture of IoT devices this approach significantly contributes to extending IoT operational lifespan and reduces environmental impact.
  arXiv  Detail & Related papers  (2024-06-21T00:44:17Z)
• Powering the Future of IoT: Federated Learning for Optimized Power Consumption and Enhanced Privacy [0.0]
  Federated Learning emerges as a promising paradigm to address the inherent challenges of power consumption and data privacy in IoT environments. This paper explores the transformative potential of FL in enhancing the longevity of IoT devices by mitigating power consumption and enhancing privacy and security measures.
  arXiv  Detail & Related papers  (2024-05-05T22:18:22Z)
• Harnessing Federated Generative Learning for Green and Sustainable Internet of Things [9.699977999019977]
  One-shot Federated Learning (OSFL) is 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. 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.
  arXiv  Detail & Related papers  (2024-04-30T17:15:26Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Introduction to IoT [0.0]
  The Internet of Things has rapidly transformed the 21st century. The integration of smart devices and automation technologies has revolutionized every aspect of our lives. It is also essential to recognize the significant safety, security, and trust concerns in this technological landscape.
  arXiv  Detail & Related papers  (2023-12-09T10:29:16Z)
• Green Edge AI: A Contemporary Survey [49.47249665895926]
  We present a contemporary survey on green edge AI. Despite its potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of deep learning (DL) We explore energy-efficient design methodologies for the three critical tasks in edge AI systems, including training data acquisition, edge training, and edge inference.
  arXiv  Detail & Related papers  (2023-12-01T04:04:37Z)
• From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks [82.964958051535]
  Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making. By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society.
  arXiv  Detail & Related papers  (2023-10-27T02:58:11Z)
• Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges [55.82853124625841]
  Artificial General Intelligence (AGI) possesses the capacity to comprehend, learn, and execute tasks with human cognitive abilities. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the Internet of Things. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education.
  arXiv  Detail & Related papers  (2023-09-14T05:43:36Z)
• Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs [62.91362897985057]
  An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications. This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption.
  arXiv  Detail & Related papers  (2021-10-04T19:41:42Z)
• Constraint Programming Algorithms for Route Planning Exploiting Geometrical Information [91.3755431537592]
  We present an overview of our current research activities concerning the development of new algorithms for route planning problems. The research so far has focused in particular on the Euclidean Traveling Salesperson Problem (Euclidean TSP) The aim is to exploit the results obtained also to other problems of the same category, such as the Euclidean Vehicle Problem (Euclidean VRP), in the future.
  arXiv  Detail & Related papers  (2020-09-22T00:51:45Z)
• Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey [8.108571247838206]
  IoT platforms have been developed into a global giant that grabs every aspect of our daily lives by advancing human life with its unaccountable smart services. There are existing security measures that can be applied to protect IoT. Traditional techniques are not as efficient with the advancement booms as well as different attack types and their severeness. A huge technological advancement has been noticed in Machine Learning (ML) which has opened many possible research windows to address ongoing and future challenges in IoT.
  arXiv  Detail & Related papers  (2020-04-11T03:08:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.