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.
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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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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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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
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
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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01 Jul 2017
TL;DR: A multi-armed bandit (MAB) based reinforcement learning approach is proposed to achieve the optimum harmonization of feedback and feedbackless transmissions, and simulation results fully demonstrate the practicability of the proposed approach in supporting URLLC and mMTC.
Abstract: Different from the International Mobile Telecommunications Advanced (IMT-Advanced) system solely enhancing the transmission data rates regardless the variety of emerging wireless traffic, the IMT-2020 system supports enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable and low latency communications (URLCC) to fully capture diverse wireless services in 2020. To satisfactorily gratify the scope of IMT-2020, 3GPP has launched the standardization activity of the fifth generation (5G) New Radio (NR) to deploy the first phase (Release 15) system in 2018 and the ready (Release 16) system in 2020. As eMBB is a legacy system from IMT- Advanced, URLLC jointly demanding low latency and high reliability, and mMTC emphasizes on high reliability may consequently induce significant impacts on the designs of NR air interface. On the advert of the conventional feedback based transmission in LTE/LTE-A designed for eMBB imposing potential inefficiency in the support of URLLC and mMTC, in this paper, we revisit the feedbackless transmission framework, and reveal a tradeoff between these two transmission frameworks. A multi-armed bandit (MAB) based reinforcement learning approach is therefore proposed to achieve the optimum harmonization of feedback and feedbackless transmissions. Our simulation results fully demonstrate the practicability of the proposed approach in supporting URLLC and mMTC, to justify the potential of our approach in the design of 5G NR.
41 citations
"Green Sensing and Communication: A ..." refers background in this paper
...Centralized as well as decentralized implementation of the IoT systems [28], design of low-latency reliable communication systems [39], and energy-harvesting IoT systems [24] have gained significant research interest....
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TL;DR: Simulation results validate the effectiveness of the proposed method in terms of communication throughput and show that enhanced performance can be obtained by utilizing the sensed signal's temporal and spatial correlations.
Abstract: This paper considers the application of compressed sensing (CS) to a wireless sensor network for data measurement communication and reconstruction, where N sensor nodes compete for medium access to a single receiver. Sparsity of the sensor data in three domains due to time correlation, space correlation and multiple access are being utilized. We first provide an in-depth analysis on the CS-based medium access control schemes from a physical layer perspective and reveal the impact of communication signal-to-noise ratio on the reconstruction performance. We show the process of the sensor data converted to the modulated symbols for physical layer transmission and how the modulated symbols being recovered via compressed sensing. This paper further identifies the decision problem of distinguishing between active and inactive transmitters after symbol recovery and shows a comprehensive performance comparison between carrier sense multiple access and the proposed CS-based scheme. Second, a network data recovery scheme that exploits both spatial and temporal correlations is proposed. Simulation results validate the effectiveness of the proposed method in terms of communication throughput and show that enhanced performance can be obtained by utilizing the sensed signal's temporal and spatial correlations.
40 citations
TL;DR: Simulation results show that the proposed active node selection approach leads to an improved reconstruction performance, network lifetime, and spectrum usage, in comparison to various node selection schemes for compressive sleeping WSNs.
Abstract: In this paper, we propose an active node selection framework for compressive sleeping wireless sensor networks (WSNs) to improve signal acquisition performance, network lifetime, and the use of spectrum resources. While conventional compressive sleeping WSNs only exploit the spatial correlation of sensor nodes, the proposed approach further exploits the temporal correlation by selecting active nodes using the support of the data reconstructed in the previous time instant. The node selection problem is framed as the design of a specialized sensing matrix, where the sensing matrix consists of selected rows of an identity matrix. By capitalizing on a genie-aided reconstruction procedure, we formulate the active node selection problem into an optimization problem, which is then approximated by a constrained convex relaxation plus a rounding scheme. Simulation results show that our proposed active node selection approach leads to an improved reconstruction performance, network lifetime, and spectrum usage, in comparison to various node selection schemes for compressive sleeping WSNs.
39 citations
TL;DR: It is shown that compared to the existing characterization models, the proposed GM model provides a significantly better fit for smart meter data.
Abstract: In this paper, a novel characterization of smart meter data based on Gaussian mixture (GM) model is presented It is shown that compared to the existing characterization models, the proposed GM model provides a significantly better fit for smart meter data Furthermore, at each smart meter, sparsity of data is exploited to devise an adaptive data reduction algorithm using compressive sampling technique such that the bandwidth requirement for smart meter data transmission is reduced with minimum loss of information When compared to the closest competitive scheme, the proposed compressive sampling based data reduction algorithm is found to be noise robust and offers ${\text{128}}$ % and ${\text{74}}$ % higher bandwidth saving, respectively, at 1 s and 30 s sampling intervals for comparable reconstruction accuracy Proposed scheme is tested in real-time using RT-LAB
38 citations
"Green Sensing and Communication: A ..." refers background or methods in this paper
...More robust frameworks for effective characterization and reduction of high frequency smart meter data using adaptive compressive sampling are proposed in [63] and [52], respectively for single-variate and multivariate data samples, based on adaptive sparsity selection over optimum batch size before data transmission....
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...This section discusses green schemes developed by us in previous works [23] [24], [62], [63] for three IoT applications, namely, lab environment monitoring, smart grid monitoring, and smart metering....
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...A comparison of adaptive compressive sampling algorithm with the closest competitive technique based on resumable data compression [64] is presented in [63]....
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...To address these challenges, intelligence is imparted in the IoT devices and systems to acquire and communicate data in an energy-efficient manner [10,11,23, 63]....
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...In [63], it is observed that with the increase in number of samples in the compression window, bandwidth sav-...
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TL;DR: Several methods for preprocessing of phasor angles are presented, including a new method-frequency compensated difference encoding-that is able to significantly reduce angle data entropy, and an entropy encoder based on Golomb-Rice codes that is ideal for high-throughput signal compression.
Abstract: Phasor measurement units (PMUs) are being increasingly deployed to improve monitoring and control of the power grid due to their improved data synchronization and reporting rates in comparison with legacy metering devices. However, one drawback of their higher data rates is the associated increase in bandwidth (for transmission) and storage requirements (for data archives). Fortunately, typical grid behavior can lead to significant compression opportunities for phasor angle measurements. For example, operation of the grid at near-nominal frequency results in small changes in phase angles between frames, and the similarity in frequencies throughout the system results in a high level of correlation between phasor angles of different PMUs. This paper presents several methods for preprocessing of phasor angles that take advantage of these system characteristics, including a new method—frequency compensated difference encoding—that is able to significantly reduce angle data entropy. After the preprocessor stage, the signal is input to an entropy encoder, based on Golomb–Rice codes, that is ideal for high-throughput signal compression. The ability of the proposed methods to compress phase angles is demonstrated using a large corpus of data—over 1 billion phasor angles from 25 data sets—captured during typical and atypical grid conditions.
37 citations