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: This paper proposes a wavelet-based sampling algorithm for choosing the minimum sampling rate for ensuring the data reliability and develops mathematical modeling for CAD and CAE, which have been validated using real-time data collected in the past.
Abstract: Real-time wireless sensor networks are an emerging technology for continuous environmental monitoring. But real-world deployments are constrained by resources, such as power, memory, and processing capabilities. In this paper, we discuss a set of techniques to maximize the lifetime of a system deployed in south India for detecting rain-fall induced landslides. In this system, the sensing subsystem consumes 77.5%, the communication subsystem consumes 22%, and the processing subsystem consumes 0.45% of total power consumption. Hence, to maximize the lifetime of the system, the sensing subsystem power consumption has to be reduced. The major challenge to address is the development of techniques that reduce the power consumption, while preserving the reliability of data collection and decision support by the system. This paper proposes a wavelet-based sampling algorithm for choosing the minimum sampling rate for ensuring the data reliability. The results from the wavelet sampling algorithm along with the domain knowledge have been used to develop context aware data collection models that enhance the lifetime of the system. Two such models named context aware data management (CAD) and context aware energy management (CAE) have been devised. The results show that the CAD model extends the lifetime by six times and the CAE model does so by 20 times when compared with the continuous data collection model, which is the existing approach. In this paper, we also developed mathematical modeling for CAD and CAE, which have been validated using real-time data collected in the past.
36 citations
08 Mar 2016
TL;DR: The wavelet packet analysis provides satisfactory preservation of feature integrity and removal of redundancy to achieve better noise reduction and higher compression with controlled degradation in data fidelity.
Abstract: A technique based on wavelet packet decomposition (WPD) is proposed for the analysis, denoising, and compression of power system data in the smart grid (SG) communication. WPD is an expansion of wavelet decomposition (WD) tree algorithm to a full binary tree. The main advantage of WPD is better signal representation by finding the best tree from a number of bases of the WPD. Thus, the wavelet packet analysis provides satisfactory preservation of feature integrity and removal of redundancy to achieve better noise reduction and higher compression with controlled degradation in data fidelity. A cost function is used in this work for efficient searching of full binary tree of WPD to obtain an accurate representation of a given signal. The data analysis, compression, and denoising are performed through selection of a suitable wavelet function, decomposition the signal up to the optimum level, determination of the best tree representation, calculate threshold at different levels, application of the threshold to the coefficients, and reconstruction of the signal. The proposed method is evaluated using a set of frequency disturbance recorder (FDR), phasor measurement unit (PMU), and load voltage data. Comparative results are presented with the wavelet decomposition (WD) and the built-in Matlab function ‘wpdencmp.’
34 citations
TL;DR: This paper considers a Bayesian compressive sensing approach and proposes two efficient algorithms that decrease the number of active sensor nodes while maintaining high performance, and shows that the centralized greedy selection algorithm provides the best performance while the decentralized algorithm performs nearly as well as the centralized algorithm as thenumber of sensor nodes increases.
Abstract: Recently, compressive sensing has been studied in wireless sensor networks, which allows an aggregator to recover the desired sparse signal with fewer active sensor nodes. In this paper, we consider heterogeneous sensing environments, where the sensing quality varies due to the differences in the physical environment of each sensor node. We consider a Bayesian compressive sensing approach and propose two efficient algorithms that decrease the number of active sensor nodes while maintaining high performance. Both the selection algorithms aim to reduce the estimation error by minimizing the determinant of the error covariance matrix, which is proportional to the volume of the confidence ellipsoid. The first algorithm is the centralized greedy selection algorithm, which can achieve a nearly optimal solution in terms of the minimum confidence ellipsoid. It can also achieve almost the same level of performance as the combinatorial selection method, but has a lower complexity and outperforms the conventional convex relaxation method. The second algorithm is the decentralized selection algorithm, which is derived by approximating the determinant of the error covariance matrix. Unlike the centralized greedy algorithm, it can be done by each sensor node without heavy overhead or high complexity. Furthermore, we prove that the decentralized selection algorithm becomes equivalent to the centralized greedy algorithm as the number of sensor nodes increases. Our simulation results show that the centralized greedy selection algorithm provides the best performance while the decentralized algorithm performs nearly as well as the centralized algorithm as the number of sensor nodes increases.
32 citations
"Green Sensing and Communication: A ..." refers background or methods in this paper
...In this regard, a Bayesian approach is used to develop iterative centralized and decentralized sensor selection strategies for heterogeneous sensing applications in the work [28]....
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...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: Performance studies indicate that compared to the state-of-the-art, the proposed technique is able to achieve impressive bandwidth saving for transmission of data over communication network without compromising faithful reconstruction of data at the receiver.
Abstract: Recent advances in electric metering infrastructure have given rise to the generation of gigantic chunks of data. Transmission of all of these data certainly poses a significant challenge in bandwidth and storage constrained Internet of Things (IoT), where smart meters act as sensors. In this work, a novel multivariate data compression scheme is proposed for smart metering IoT. The proposed algorithm exploits the cross correlation between different variables sensed by smart meters to reduce the dimension of data. Subsequently, sparsity in each of the decorrelated streams is utilized for temporal compression. To examine the quality of compression, the multivariate data is characterized using multivariate normal–autoregressive integrated moving average modeling before compression as well as after reconstruction of the compressed data. Our performance studies indicate that compared to the state-of-the-art, the proposed technique is able to achieve impressive bandwidth saving for transmission of data over communication network without compromising faithful reconstruction of data at the receiver. The proposed algorithm is tested in a real smart metering setup and its time complexity is also analyzed.
32 citations
"Green Sensing and Communication: A ..." refers 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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TL;DR: This paper studies cost-effective and long-range WuRx solutions for range-based wake-up (RW) as well as directed wake-ups (DW) for RF energy harvesting wireless sensor node and investigates how a low-cost WuRX can be built using an RF energy harvester available at the node.
Abstract: The existing passive wake-up receivers (WuRxs) are radio frequency identification (RFID) tag based, which incur high cost and complexity. In this paper, we study cost-effective and long-range WuRx solutions for range-based wake-up (RW) as well as directed wake-up (DW). In particular, we consider the case of an RF energy harvesting wireless sensor node and investigate how a low-cost WuRx can be built using an RF energy harvester available at the node. Experimental results show that our developed prototype can achieve a wake-up range of 1.16 m with +13 dBm transmit power. Furthermore, our empirical study shows that at +30 dBm transmit power the wake-up distance of our developed RW module is >9 m. High accuracy of DW is demonstrated by sending a 5-bit ID from a transmitter at a bit rate up to 33.33 kbps. Finally, we present optimized WuRx designs for RW and DW using Agilent advanced design system, which offer up to 5.69 times higher wake-up range for RW and energy savings per bit of about 0.41 mJ and 21.40 nJ, respectively, at the transmitter and the sensor node in DW.
31 citations