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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08 Sep 2017
TL;DR: This paper proposes a new channel-adaptive strategy that depends on the rate of variation of wireless channel irrespective of the underlying channel fading distribution, and shows that the concept of average fade duration dependent retransmission is a special case of the proposed strategy.
Abstract: Widespread adoption of Internet-of-Things (IoT) technology depends on cost-affordability of the devices and convenience of their usage in terms of long life. Energy-efficiency of the miniaturized wireless IoT devices is a key to the cost and convenience factors. While there have been a few prior studies on wireless channel adaptive communication strategies, in this paper we relook at the problem aiming at effectively characterizing the wireless fading channel and devising energy-efficient link-layer retransmission strategy suitable for IoT devices. In particular, we propose a new channel-adaptive strategy that depends on the rate of variation of wireless channel irrespective of the underlying channel fading distribution. We further show that the concept of average fade duration dependent retransmission is a special case of the proposed strategy. Extensive simulations show that the proposed scheme most effectively characterizes the temporal variations of wireless channel in comparison with the other existing schemes. Performance of the proposed retransmission strategy is compared with the competitive protocols using the Markov model. Numerical results demonstrate that, the proposed scheme offers a gain of about 9% in terms data throughput and about 12% in terms of energy efficiency in comparison to its nearest existing benchmark scheme.
12 citations
01 Feb 2018
TL;DR: It is demonstrated that temporal correlation of pollutant concentration can be exploited to select optimum sampling period of an energy-intensive sensor to reduce sensing energy consumption without losing much information.
Abstract: Air pollution monitoring systems with energy-intensive sensors cannot afford to sample frequently in order to maximize time between successive recharges. In this paper, we propose an energy-efficient machine learning based sensor duty-cycling method for a sensor hub receiving data from the air-pollution sensors. In particular, we demonstrate that temporal correlation of pollutant concentration can be exploited to select optimum sampling period of an energy-intensive sensor to reduce sensing energy consumption without losing much information. Support Vector Regression is used to predict the missing samples during the period sensor is turned off.
12 citations
"Green Sensing and Communication: A ..." refers background in this paper
...based duty cycling scheme for air pollution sensing is proposed in the work [14]....
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TL;DR: It is observed that, the optimal deployment height of UAV increases as the antenna becomes more directional, and the ratio of loss in harvested power due to mismatch and harvested power without mismatch increases.
Abstract: In this letter, the impact of hovering inaccuracy on the performance of unmanned aerial vehicle (UAV)-aided RF energy transfer (RFET) is investigated. Hovering inaccuracy is measured by localization and orientation mismatch of UAV while it hovers above a sensor of interest. An analytical framework is presented that captures these mismatches, and its impact on the performance of UAV-aided RFET is studied. To evaluate the performance, a metric called mismatch index is defined as the ratio of loss in harvested power due to mismatch and harvested power without mismatch. A closed-form expression of the distribution of mismatch index is obtained. A UAV-based experimental setup is developed to collect the data of hovering inaccuracy parameters, and the performance is investigated for three antenna types with different radiation patterns. It is observed that, the optimal deployment height of UAV increases as the antenna becomes more directional.
11 citations
25 Aug 2008
TL;DR: This paper proposes a Wireless IP multisensor that is able to gather several types of data from the environment and transmit the result of their combination and a comparison with other wireless IP sensors is provided.
Abstract: Every sensor node in a wireless sensor network (WSN) has a microcontroller, a transmitter/receiver and a sensor. It is able to acquire data from specific point in a real environment and transmit it through the WSN. Sometimes it is useful to gather different type of data from the same place in order to obtain a final result. In the related literature, very few works are about sensing different parameters using a unique sensor. In this paper we propose a Wireless IP multisensor that is able to gather several types of data from the environment and transmit the result of their combination. Our proposal decision has being mainly based on its development costs, its expansion capacity and its flexibility to add more features to the sensor node. We will show all the characteristics of our proposal and its application areas. A comparison with other wireless IP sensors will be also provided.
11 citations
"Green Sensing and Communication: A ..." refers background in this paper
...In this context, multi-sensing platforms have been designed in works [4, 8]....
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25 Jul 2019
TL;DR: The authors propose simple-yet-efficient analytical circuit model for solar-assisted supercapacitor charging and statistical model for characterising the solar intensity distribution, and derive a novel solar charging rate distribution for super capacitor.
Abstract: Self-sustainability of wireless sensor nodes is the need of the hour to realise ubiquitous wireless networks. To this end, the authors investigate practical feasibility of a sustainable green sensor network with solar-powered nodes. They propose simple-yet-efficient (i) analytical circuit model for solar-assisted supercapacitor charging and (ii) statistical model for characterising the solar intensity distribution. Combining these circuit and statistical models, they derive a novel solar charging rate distribution for supercapacitor. For analytical insights, they also propose an ideal diode-based tight approximation for the practical supercapacitor charging circuit model. Accuracy of these proposed analytical models are validated by extensive numerical simulations based on real-world solar intensity profile and panel characteristics. The derived solar charging rate distribution is used to investigate the energy outage probability of a sensor node for a given sensing rate. The results suggest that, for an energy outage probability of 0.1, at New Delhi, a 40 F supercapacitor and a 3 W solar panel can support the operation of Waspmote with six on-board gas sensors at a rate of 65 samples per day. Furthermore, they use the proposed models to estimate the practical supercapacitor and panel sizes for sustainable operation at different geographical locations with varying sensing rate.
11 citations