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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TL;DR: The whole system was validated by the issuance of a landslide warning in the month of July (last monsoon season), which facilitated pre-emptive action by the local government and community to prevent loss of human life.
Abstract: We present extensive studies into data reduction and energy minimization in Sensor Networks for landslide detection. What started as a simple homogenous network of rainfall sensors has evolved into a complex heterogeneous network of 20 wireless probes with each probe consisting of four different types of sensors to measure rainfall, moisture, pore pressure, and movement, all of which have been in continuous operation for more than two years in the equatorial forests of Kerala, India. Each probe runs on solar power and the frequency of sensor data measurements from the probes is dynamically and adaptively throttled in real time, based on climatic conditions to minimize the total energy consumption. The probes also work together to identify who among them is sensing the maximum parameter, after which all other sensors are switched off for a predetermined duration. We present detailed analysis of data reduction and energy savings, and also relate them to the effectiveness of landslide detection. The whole system was validated by the issuance of a landslide warning in the month of July (last monsoon season), which facilitated pre-emptive action by the local government and community to prevent loss of human life.
30 citations
TL;DR: Comparison of simulation results with the results of a well-known method and a state-of-the-art one shows that the proposed method outperforms both in terms of prolonging coverage, network lifetime, and energy efficiency, besides the redundancy rate reduction.
26 citations
TL;DR: To pursue high sensing quality at low sample rate, an adaptive CS based sample scheduling mechanism (ACS) for WSNs is proposed, which estimates the minimum required sample rate subject to given sensing quality on a per-sampling-window basis and accordingly adjusts sensors' sample rates.
25 citations
TL;DR: This paper explores the sparsity embedded within the problem and proposes a sparsity-aware sensor selection paradigm and presents reasonably low-complexity and elegant distributed algorithms in order to solve the centralized problems with convergence guarantees within a bounded error.
Abstract: The selection of the minimum number of sensors within a network to satisfy a certain estimation performance metric is an interesting problem with a plethora of applications. The problem becomes even more interesting in a distributed configuration when each sensor has to decide itself whether it should contribute to the estimation or not. In this paper, we explore the sparsity embedded within the problem and propose a sparsity-aware sensor selection paradigm for both uncorrelated and correlated noise experienced at different sensors. We also present reasonably low-complexity and elegant distributed algorithms in order to solve the centralized problems with convergence guarantees within a bounded error. Furthermore, we analytically quantify the complexity of the distributed algorithms compared to centralized ones. Our simulation results corroborate our claims and illustrate a promising performance for the proposed centralized and distributed algorithms.
25 citations
TL;DR: CB-CS combines a novel sampling mechanism along with an original covariogram-based approach for the online estimation of the covariance structure of the signal and leverages the signal's spatio-temporal correlation structure through the Kronecker CS framework to provide compression versus energy tradeoffs that approach those of idealized CS schemes.
Abstract: In this paper, we propose covariogram-based compressive sensing (CB-CS), a spatio-temporal compression algorithm for environmental wireless sensor networks. CB-CS combines a novel sampling mechanism along with an original covariogram-based approach for the online estimation of the covariance structure of the signal and leverages the signal’s spatio-temporal correlation structure through the Kronecker CS framework. CB-CS’s performance is systematically evaluated in the presence of synthetic and real signals, comparing it against a number of compression methods from the literature, based on linear approximations, Fourier transforms, distributed source coding, and against several approaches based on CS. CB-CS is found superior to all of them and able to effectively and promptly adapt to changes in the underlying statistical structure of the signal, while also providing compression versus energy tradeoffs that approach those of idealized CS schemes (where the signal correlation structure is perfectly known at the receiver).
24 citations