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Journal ArticleDOI

Green Sensing and Communication: A Step Towards Sustainable IoT Systems

01 Apr 2020-Journal of the Indian Institute of Science (Springer India)-Vol. 100, Iss: 2, pp 383-398
TL;DR: This article surveys the existing green sensing and communication approaches to realize sustainable IoT systems for various applications and presents a few case studies that aim to generate sensed traffic data intelligently as well as prune it efficiently without sacrificing the required service quality.
Abstract: With the advent of Internet of Things (IoT) devices, their reconfigurability, networking, task automation, and control ability have been a boost to the evolution of traditional industries such as health-care, agriculture, power, education, and transport. However, the quantum of data produced by the IoT devices poses serious challenges on its storage, communication, computation, security, scalability, and system’s energy sustainability. To address these challenges, the concept of green sensing and communication has gained importance. This article surveys the existing green sensing and communication approaches to realize sustainable IoT systems for various applications. Further, a few case studies are presented that aim to generate sensed traffic data intelligently as well as prune it efficiently without sacrificing the required service quality. Challenges associated with these green techniques, various open issues, and future research directions for improving the energy efficiency of the IoT systems are also discussed.
Citations
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Journal ArticleDOI
TL;DR: This perspective paper concentrates on the “green AI” concept as an enabler of the smart city transformation, as it offers the opportunity to move away from purely technocentric efficiency solutions towards efficient, sustainable and equitable solutions capable of realizing the desired urban futures.
Abstract: Smart cities and artificial intelligence (AI) are among the most popular discourses in urban policy circles. Most attempts at using AI to improve efficiencies in cities have nevertheless either struggled or failed to accomplish the smart city transformation. This is mainly due to short-sighted, technologically determined and reductionist AI approaches being applied to complex urbanization problems. Besides this, as smart cities are underpinned by our ability to engage with our environments, analyze them, and make efficient, sustainable and equitable decisions, the need for a green AI approach is intensified. This perspective paper, reflecting authors’ opinions and interpretations, concentrates on the “green AI” concept as an enabler of the smart city transformation, as it offers the opportunity to move away from purely technocentric efficiency solutions towards efficient, sustainable and equitable solutions capable of realizing the desired urban futures. The aim of this perspective paper is two-fold: first, to highlight the fundamental shortfalls in mainstream AI system conceptualization and practice, and second, to advocate the need for a consolidated AI approach—i.e., green AI—to further support smart city transformation. The methodological approach includes a thorough appraisal of the current AI and smart city literatures, practices, developments, trends and applications. The paper informs authorities and planners on the importance of the adoption and deployment of AI systems that address efficiency, sustainability and equity issues in cities.

62 citations


Cites background from "Green Sensing and Communication: A ..."

  • ...The earlier use of the term mostly appeared in the context of wireless sensor networks (WSNs) [100]....

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Journal ArticleDOI
18 May 2020
TL;DR: This article presents an adaptive multi-sensing framework for a network of densely deployed solar energy harvesting wireless nodes where each node is mounted with heterogeneous sensors to sense multiple cross-correlated slowly-varying parameters/signals.
Abstract: This article presents an adaptive multi-sensing (MS) framework for a network of densely deployed solar energy harvesting wireless nodes. Each node is mounted with heterogeneous sensors to sense multiple cross-correlated slowly-varying parameters/signals. Inherent spatio-temporal correlations of the observed parameters are exploited to adaptively activate a subset of sensors of a few nodes and turn-OFF the remaining ones. To do so, a multi-objective optimization problem that jointly optimizes sensing quality and network energy efficiency is solved for each monitoring parameter. To increase energy efficiency, network and node-level collaborations based multi-sensing strategies are proposed. The former one utilizes spatial proximity (SP) of nodes with active sensors (obtained from the MS) to further reduce the active sensors sets, while the latter one exploits cross-correlation (CC) among the observed parameters at each node to do so. A retraining logic is developed to prevent deterioration of sensing quality in MS-SP. For jointly estimating all the parameters across the field nodes using under-sampled measurements obtained from MS-CC based active sensors, a multi-sensor data fusion technique is presented. For this ill-posed estimation scenario, double sparsity due to spatial and cross-correlation among measurements is used to derive principal component analysis-based Kronecker sparsifying basis, and sparse Bayesian learning framework is then used for joint sparse estimation. Extensive simulation studies using synthetic (real) data illustrate that, the proposed MS-SP and MS-CC strategies are respectively $48.2\ (52.09)\%$ and $50.30\ (8.13)\%$ more energy-efficient compared to respective state-of-the-art techniques while offering stable sensing quality. Further, heat-maps of estimated field signals corresponding to synthetically generated and parsimoniously sensed multi-source parameters are also provided which may aid in source localization Internet-of-Things applications.

24 citations


Cites methods from "Green Sensing and Communication: A ..."

  • ...the FC as suggested in the survey [30]....

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Journal ArticleDOI
TL;DR: In this article , the authors conduct a bibliometric study to investigate the current state of the IoT and agriculture in academic literature and identify those agricultural resources that are mostly impacted by the introduction of IoT (i.e., seeds, soil, water, fertilizers, pesticides, energy, livestock, human resources, technology infrastructure, business relations).
Abstract: The proliferation of the Internet of Things (IoT) has fundamentally reshaped the agricultural sector. In recent years, academic research on the IoT has grown at an unprecedented pace. However, the broad picture of how this technology can benefit the agricultural sector is still missing. To close this research gap, we conduct a bibliometric study to investigate the current state of the IoT and agriculture in academic literature. Using a resource-based view (RBV), we also identify those agricultural resources that are mostly impacted by the introduction of the IoT (i.e., seeds, soil, water, fertilizers, pesticides, energy, livestock, human resources, technology infrastructure, business relations) and propose numerous themes for future research.

15 citations

Journal ArticleDOI
01 Mar 2022
TL;DR: Backscatter communication (BackCom) is a recently emerged technique that enables green IoT through joint wireless communication and sensing and potentially allows IoT devices to operate without batteries as discussed by the authors , which is a key technology for enabling ubiquitous applications that interconnect with cyber-physical systems.
Abstract: Internet of Things (IoT) is a key technology for enabling ubiquitous applications that interconnect with cyber-physical systems in various environments. However, its large scale adoption is strongly impeded by the limited energy available for most IoT devices that are battery-powered, and further challenged by the growing demands to pack increasing functionalities into IoT devices while shrinking their sizes. To address these problems, researchers have developed techniques for energy harvesting, wireless power transfer, and minimizing power consumption in the sensing, communication and computation components of IoT nodes, as found in many surveys. In contrast, this paper surveys Backscatter Communication (BackCom), a recently emerged technique that enables green IoT through joint wireless communication and sensing and potentially allows IoT devices to operate without batteries. The operating principle of BackCom-based green IoT, its architecture and evolution are presented. Also state-of-the-art applications such as healthcare, agriculture, human activity recognition, transportation and mobile IoT are reviewed together with the operational and security challenges faced by these applications and potential solution techniques to address these challenges while ensuring a high energy efficiency. Lastly, some future applications of BackCom-based green IoT are discussed.

15 citations

Proceedings ArticleDOI
01 Sep 2020
TL;DR: The developed AAPMD system consumes 10-times less energy while using 5G NB-IoT communication module, which makes it a very competitive candidate for massive deployment in highly polluted metro cities like Delhi and Kolkata, in India.
Abstract: We have designed a 5G-capable environmental sensing network (ESN) node prototype, called Advanced Air Pollution Monitoring Device (AAPMD). The developed prototype system measures concentrations of NO 2 , Ozone, carbon monoxide, and sulphur dioxide using semiconductor sensors. Further, the system gathers other environmental parameters like temperature, humidity, PM 1 , PM 2 . 5 , and PM 10 . The prototype is equipped with a GPS sub-system for accurate geo-tagging. The board communicates through Wi-Fi and NB-IoT. AAPMD is also implemented with energy harvesting power management, and is powered through solar energy and battery backup. Compared to the conventional designs with Wi-Fi-based connectivity, the developed system consumes 10-times less energy while using 5G NB-IoT communication module, which makes it a very competitive candidate for massive deployment in highly polluted metro cities like Delhi and Kolkata, in India. The system can provide updated measurements of pollutant levels with controllable time granularity.

13 citations


Cites background from "Green Sensing and Communication: A ..."

  • ...To this end, a network-level data-driven green sensing framework has been recently proposed in [22]....

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References
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Journal ArticleDOI
TL;DR: In this paper, areas of power system synchrophasor data communication which can be improved by compressive sampling (CS) theory are identified and it is shown that missing and bad data can be reconstructed satisfactorily using CS.
Abstract: In this paper, areas of power system synchrophasor data communication which can be improved by compressive sampling (CS) theory are identified. CS reduces the network bandwidth requirements of Wide Area Measurement Systems (WAMS). It is shown that CS can reconstruct synchrophasors at higher rates while satisfying the accuracy requirements of IEEE standard C37.118.1-2011. Different steady state and dynamic power system scenarios are considered here using mathematical models of C37.118.1-2011. Synchrophasors of lower reporting rates are exempted from satisfying the accuracy requirements of C37.118.1-2011 during system dynamics. In this work, synchrophasors are accurately reconstructed from above and below Nyquist rates. Missing data often pose challenges to the WAMS applications. It is shown that missing and bad data can be reconstructed satisfactorily using CS. Performance of CS is found to be better than the existing interpolation techniques for WAMS communication.

59 citations

Journal ArticleDOI
TL;DR: The generalized extreme value distribution characteristic for household load data is proposed and then utilizes it to identify load features including load states and load events and a highly efficient lossy data compression format is designed to store key information of load features.
Abstract: In recent years, smart meters have been widely installed in households across the world, which has led to problems with big data. The huge amount of household load data requires highly efficient data compression techniques to reduce the great burden on data transmittance, storage, processing, application, etc. This paper proposes the generalized extreme value distribution characteristic for household load data and then utilizes it to identify load features including load states and load events. Finally, a highly efficient lossy data compression format is designed to store key information of load features. The proposed feature-based load data compression method can support highly efficient load data compression with little reconstruction error and simultaneously provide load feature information directly for application. A case study based on the Irish Smart Metering Trial Data validates the high performance of this new approach, including in-depth comparisons with the state-of-art load data compression methods.

57 citations

Journal ArticleDOI
TL;DR: This work develops the charging equation for replenishing an energy-depleted storage element by RFET, and the RF charging time distribution for a given residual voltage distribution is derived.
Abstract: Wireless energy transfer to the onboard energy storage element using dedicated radio frequency (RF) energy source has the potential to provide sustained network operations by recharging the sensor nodes on demand. To determine the efficiency of RF energy transfer (RFET), characterization of recharging process is needed. Different from classical capacitor-charging operation, the incident RF waves provide constant power (instead of constant voltage or current) to the storage element, which requires a new theoretical framework for analyzing the charging behavior. This work develops the charging equation for replenishing an energy-depleted storage element by RFET. Since the remaining energy on a sensor node is a random parameter, the RF charging time distribution for a given residual voltage distribution is also derived. The analytical model is validated through hardware experiments and simulations.

52 citations

Journal ArticleDOI
TL;DR: The maximum energy level that an additional wake-up radio can consume is determined to become a reasonable alternative of widely used duty-cycling techniques for typical IoT networks.
Abstract: Energy consumption has become dominant issue for wireless internet of things (IoT) networks with battery-powered nodes. The prevailing mechanism allowing to reduce energy consumption is duty-cycling. In this technique the node sleeps most of the time and wakes up only at selected moments to extend the lifespan of nodes up to 5–10 years. Unfortunately, the scheduled duty-cycling technique is always a trade-off between energy consumption and delay in delivering data to the target node. The delay problem can be alleviated with an additional wake-up radio (WuR) channel. In the paper we present original power consumption models for various duty-cycling schemes. They are the basis for checking whether WuR approach is competitive with scheduled duty-cycling techniques. We determine the maximum energy level that an additional wake-up radio can consume to become a reasonable alternative of widely used duty-cycling techniques for typical IoT networks.

52 citations


"Green Sensing and Communication: A ..." refers background in this paper

  • ...sumption of the node and delay [38] in data delivery to the target node....

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Proceedings ArticleDOI
01 Dec 2010
TL;DR: In this paper, the authors investigated the feasibility and potential benefits of using passive RFID as a wake-up radio and showed that using a passive radio offers significant energy efficiency at the expense of delay and additional low-cost RFID hardware.
Abstract: Energy efficiency is one of the crucial design criteria for wireless sensor networks. Idle listening constitutes a major part of energy waste, and thus solutions such as duty cycling and the use of wake-up radios have been proposed to reduce idle listening and save energy. Compared to duty cycling, wake-up radios save more energy by reducing unnecessary wake-ups and collisions. In this paper, we investigate the feasibility and potential benefits of using passive RFID as a wake-up radio. We first introduce a physical implementation of sensor nodes with passive RFID wake-up radios and measure their energy cost and wake-up probability. Then, we compare the performance of our RFID wake-up sensor nodes with duty cycling in a Data MULE scenario through simulations with realistic application parameters. The results show that using a passive RFID wake-up radio offers significant energy efficiency benefits at the expense of delay and the additional low-cost RFID hardware, making RFID wake-up radios beneficial for many delay-tolerant sensor network applications.

48 citations