Sustainable Data-Energy Networking for Light-Based Internet of Things

Core Concepts

This paper proposes a novel Data-Energy Networking-enabled Light-based Internet of Things (DE-LIoT) architecture that enhances both data communication and energy harvesting efficiency in densely configured indoor wireless sensor networks.

Abstract

The paper introduces the DE-LIoT concept, a novel data and energy network developed for densely populated indoor energy harvesting-based IoT sensor applications. The goal is to achieve prolonged operational lifetime by leveraging the directivity characteristics of optical wireless communication (OWC) and optical wireless power transfer (OWPT). The proposed architecture employs a distributed sensor and centralized controller model applicable to indoor illumination scenarios. The central optical access point (OAP) identifies resource-rich and resource-deficit nodes, and demonstrates how it can enhance the communication and energy harvesting capabilities of the deficit nodes. The feasibility of the DE-LIoT concept is validated through a comprehensive review of the literature and an examination of currently available suitable technologies. A proof-of-concept network is designed, implemented, and evaluated, utilizing energy-autonomous prototype nodes and access points compatible with the DE-LIoT framework. The evaluation demonstrates the effectiveness of the DE-LIoT concept in improving the lifetime of resource-limited nodes, confirming the effectiveness of this new data and energy networking model in enhancing sustainability and resource optimization in VLC-based wireless personal area networks (WPANs).

Stats

The scavenged energy EScavenge during a time interval TInt under static channel conditions by an SSN with M energy harvesters in the presence of K PSNs can be expressed as: EScavenge = Σ(i=1 to M) [ Σ(j=1 to K) Nj*PReceive(i,j)*tEnergyNet(j)*ηConv(i) + PIllum(i)TIntηConv(i) ]

Quotes

"The growing demand for Internet of Things (IoT) networks has sparked interest in sustainable, zero-energy designs through Energy Harvesting (EH) to extend the lifespans of IoT sensors." "Visible Light Communication (VLC) is particularly promising, integrating signal transmission with optical power harvesting to enable both data exchange and energy transfer in indoor network nodes."

Key Insights Distilled From

DE-LIoT: The Data-Energy Networking Paradigm for Sustainable Light-Based Internet of Things

by Amila Perera... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.14333.pdf

Deeper Inquiries

How can the DE-LIoT concept be extended to outdoor environments with varying illumination conditions?

To extend the DE-LIoT concept to outdoor environments with varying illumination conditions, several considerations need to be taken into account. Firstly, the selection of energy harvesting technologies should be optimized for outdoor settings, such as solar panels that can harness sunlight efficiently. Additionally, the design of the nodes should incorporate robust power management systems to adapt to fluctuating energy availability. In terms of communication, the use of directional antennas and advanced modulation techniques can enhance signal reliability in outdoor environments. Furthermore, the network architecture should be flexible to accommodate changes in network topology due to mobility and environmental factors. Overall, a comprehensive approach that considers the unique challenges of outdoor settings, such as weather conditions and varying light intensities, is essential for the successful extension of the DE-LIoT concept outdoors.

What are the potential security and privacy implications of the centralized control architecture in the DE-LIoT network, and how can they be addressed?

The centralized control architecture in the DE-LIoT network poses potential security and privacy implications, primarily related to data confidentiality, integrity, and availability. Centralized control points are attractive targets for cyber attacks, as compromising the central controller can lead to widespread network disruption. Additionally, the collection of sensitive data at a central point raises concerns about privacy breaches and unauthorized access to personal information. To address these security and privacy implications, several measures can be implemented. Firstly, robust encryption protocols should be employed to secure data transmission between nodes and the central controller. Access control mechanisms, such as authentication and authorization protocols, can restrict unauthorized access to the network. Regular security audits and updates to the network infrastructure can help identify and mitigate vulnerabilities. Privacy-enhancing technologies, such as data anonymization and differential privacy techniques, can safeguard sensitive information. Overall, a multi-layered security approach that combines technical controls, policies, and user awareness is essential to mitigate security and privacy risks in a centralized DE-LIoT network.

What are the opportunities for integrating emerging technologies like printed electronics and neuromorphic computing to further enhance the sustainability and intelligence of DE-LIoT systems?

The integration of emerging technologies like printed electronics and neuromorphic computing presents exciting opportunities to enhance the sustainability and intelligence of DE-LIoT systems. Printed electronics offer advantages such as flexibility, low-cost production, and environmental friendliness, making them ideal for creating energy-efficient and eco-friendly DE-LIoT nodes. By leveraging printed electronics for components like sensors and energy harvesters, DE-LIoT systems can achieve higher sustainability by reducing material waste and energy consumption in manufacturing processes. Neuromorphic computing, inspired by the human brain's architecture, can enhance the intelligence of DE-LIoT systems by enabling efficient data processing and decision-making. Neuromorphic chips can perform complex computations with low power consumption, making them suitable for edge computing in DE-LIoT networks. By integrating neuromorphic computing, DE-LIoT systems can analyze sensor data in real-time, adapt to changing environmental conditions, and optimize energy usage more effectively. Overall, the integration of printed electronics and neuromorphic computing in DE-LIoT systems opens up new possibilities for creating sustainable, intelligent, and energy-efficient IoT networks that can operate autonomously and adaptively in diverse environments.