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: An optimal decentralized algorithm for sparse signal recovery via decentralized in-network processing is developed, based on a consensus optimization formulation and the alternating direction method of multipliers, and demonstrates its application in monitoring localized phenomena using energy-constrained large-scale wireless sensor networks.
Abstract: This paper develops an optimal decentralized algorithm for sparse signal recovery and demonstrates its application in monitoring localized phenomena using energy-constrained large-scale wireless sensor networks. Capitalizing on the spatial sparsity of localized phenomena, compressive data collection is enforced by turning off a fraction of sensors using a simple random node sleeping strategy, which conserves sensing energy and prolongs network lifetime. In the absence of a fusion center, sparse signal recovery via decentralized in-network processing is developed, based on a consensus optimization formulation and the alternating direction method of multipliers. In the proposed algorithm, each active sensor monitors and recovers its local region only, collaborates with its neighboring active sensors through low-power one-hop communication, and iteratively improves the local estimates until reaching the global optimum. Because each sensor monitors the local region rather than the entire large field, the iterative algorithm converges fast, in addition to being scalable in terms of transmission and computation costs. Further, through collaboration, the sensing performance is globally optimal and attains a high spatial resolution commensurate with the node density of the original network containing both active and inactive sensors. Simulations demonstrate the performance of the proposed approach.
181 citations
Posted Content•
TL;DR: In this article, the authors formulate the sensor selection problem as the design of a selection vector, which in its original form is a nonconvex l0-(quasi) norm optimization problem.
Abstract: Sensor selection is an important design problem in large-scale sensor networks. Sensor selection can be interpreted as the problem of selecting the best subset of sensors that guarantees a certain estimation performance. We focus on observations that are related to a general non-linear model. The proposed framework is valid as long as the observations are independent, and its likelihood satisfies the regularity conditions. We use several functions of the Cramer-Rao bound (CRB) as a performance measure. We formulate the sensor selection problem as the design of a selection vector, which in its original form is a nonconvex l0-(quasi) norm optimization problem. We present relaxed sensor selection solvers that can be efficiently solved in polynomial time. We also propose a projected subgradient algorithm that is attractive for large-scale problems and also show how the algorithm can be easily distributed. The proposed framework is illustrated with a number of examples related to sensor placement design for localization.
175 citations
TL;DR: The vision of exploiting MEC for s-health applications is envisioned and two main functionalities that can be implemented leveraging such an architecture to provide efficient data delivery are presented, namely, multimodal data compression and edge-based feature extraction for event detection.
Abstract: Improving the efficiency of healthcare systems is a top national interest worldwide. However, the need to deliver scalable healthcare services to patients while reducing costs is a challenging issue. Among the most promising approaches for enabling smart healthcare (s-health) are edge-computing capabilities and next-generation wireless networking technologies that can provide real-time and cost-effective patient remote monitoring. In this article, we present our vision of exploiting MEC for s-health applications. We envision a MEC-based architecture and discuss the benefits that it can bring to realize in-network and context-aware processing so that the s-health requirements are met. We then present two main functionalities that can be implemented leveraging such an architecture to provide efficient data delivery, namely, multimodal data compression and edge-based feature extraction for event detection. The former allows efficient and low distortion compression, while the latter ensures high-reliability and fast response in case of emergency applications. Finally, we discuss the main challenges and opportunities that edge computing could provide and possible directions for future research.
147 citations
TL;DR: The proposed methodology for lossy data compression in smart distribution systems using the singular value decomposition technique is capable of significantly reducing the volume of data to be transmitted through the communications network and accurately reconstructing the original data.
Abstract: Electrical distribution systems have been experiencing many changes in recent times. Advances in metering system infrastructure and the deployment of a large number of smart meters in the grid will produce a big volume of data that will be required for many different applications. Despite the significant investments taking place in the communications infrastructure, this remains a bottleneck for the implementation of some applications. This paper presents a methodology for lossy data compression in smart distribution systems using the singular value decomposition technique. The proposed method is capable of significantly reducing the volume of data to be transmitted through the communications network and accurately reconstructing the original data. These features are illustrated by results from tests carried out using real data collected from metering devices at many different substations.
140 citations
25 Oct 2004
TL;DR: The improvement in system lifetime, due to RAW, increases as the ratio of idle-to-sleep energy consumption increases, and as the density of the network increases.
Abstract: This paper presents a random asynchronous wakeup (RAW), a power saving technique for sensor networks that reduces energy consumption without significantly affecting the latency or connectivity of the network. RAW builds on the observation that when a region of a shared-channel wireless network has a sufficient density of nodes, only a small number of them need be active at any time to forward the traffic for active connections. RAW is a distributed, randomized algorithm where nodes make local decisions on whether to sleep, or to be active. Each node is awake for a randomly chosen fixed interval per time frame. High node density results in existence of several paths between two given nodes whose path length and delay characteristics are similar to the shortest path. Thus, a packet can be forwarded to any of several nodes in order to be delivered to the destination without affecting much the path length and delay experienced by the packet as compared to forwarding the packet through the shortest path. The improvement in system lifetime, due to RAW, increases as the ratio of idle-to-sleep energy consumption increases, and as the density of the network increases. Through analytical and experimental evaluations, we show that RAW improves communication latency and system lifetime compared to current schemes.
124 citations
"Green Sensing and Communication: A ..." refers background in this paper
...Random asynchronous wakeup (RAW) protocol developed in the work [46] is one such example....
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