Significance of Adaptive Sensing for Smart Building Monitoring: A Practical Study
01 Jan 2019-pp 684-689
TL;DR: This work tries to motivate the requirement of context adaptive sensing and in-network fusion of sensory data for smart building monitoring applications towards faster inference and thus efficient decision-making for automation.
Abstract: Exponential growth in the field of Internet of Things (IoT) in recent times has resulted in generation of huge amount of sensory data on daily basis. In the context of smart building monitoring applications, the inherent heterogeneity and dynamics of the system along with a wide range of quality of service requirement for different running applications make it challenging for efficient data management. An offline analysis on huge amount of data suffering from noise and redundancy for learning inference would not be a good idea considering the huge overhead. In this work, we try to motivate the requirement of context adaptive sensing and in-network fusion of sensory data for smart building monitoring applications towards faster inference and thus efficient decision-making for automation. To the best of our knowledge, this work is the first to exploit in-network fusion in smart building monitoring systems. We justify our proposed scheme by deploying a campus-scale sensor network and performing an in-network data fusion using Kalman Filter and making the system context-adaptive.
Citations
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TL;DR: This article comprehensively survey the sensing mechanism, collaboration and applications in the context of IoT, and highlights that sensing application is coupled with sensing mechanism and sensing collaboration, and discusses promising sensing applications.
Abstract: Collaborative sensing leverages the cooperation of a collection of sensors to complete a large-scale sensing task in Internet of Things (IoT). Although some previous studies have reviewed the literature of collaborative sensing in sensor networks, there still lacks a systematic and holistic overview with the consideration of practical application needs in IoT. In this article, we highlight that sensing application is coupled with sensing mechanism and sensing collaboration, and comprehensively survey the sensing mechanism, collaboration and applications in the context of IoT. Specifically, we first give an introduction of sensing technologies widely employed in IoT applications. Then, we systematically expatiate on more design issues for collaborative sensing, i.e., sensing models, deployment methods, sensing scheduling strategies and metrics for sensing quality. Next, the network-wide sensing collaboration is expatiated through three fundamental types of coverage problems as well as their solutions and insightful observations. Furthermore, we discuss promising sensing applications and elaborate how the sensing problem is established from the realistic application requirements and how the sensing quality impacts the application performance. Finally, we discuss several inspiring future research directions.
10 citations
TL;DR: In this article , a comprehensive survey of collaborative sensing in the context of the Internet of Things is presented, where the authors highlight that sensing application is coupled with sensing mechanism and sensing collaboration, and comprehensively survey the sensing mechanism, collaboration and applications.
Abstract: Collaborative sensing leverages the cooperation of a collection of sensors to complete a large-scale sensing task in Internet of Things (IoT). Although some previous studies have reviewed the literature of collaborative sensing in sensor networks, there still lacks a systematic and holistic overview with the consideration of practical application needs in IoT. In this article, we highlight that sensing application is coupled with sensing mechanism and sensing collaboration, and comprehensively survey the sensing mechanism, collaboration and applications in the context of IoT. Specifically, we first give an introduction of sensing technologies widely employed in IoT applications. Then, we systematically expatiate on more design issues for collaborative sensing, i.e., sensing models, deployment methods, sensing scheduling strategies and metrics for sensing quality. Next, the network-wide sensing collaboration is expatiated through three fundamental types of coverage problems as well as their solutions and insightful observations. Furthermore, we discuss promising sensing applications and elaborate how the sensing problem is established from the realistic application requirements and how the sensing quality impacts the application performance. Finally, we discuss several inspiring future research directions.
7 citations
TL;DR: A systematic literature review of the use of automation for quality of service (QoS) in computer networks is presented in this article , which summarizes the research trends, datasets, and methods used in the field and provides an overview of the current state of the art.
Abstract: The article is a systematic literature review of the use of automation for quality of service (QoS) in computer networks. It summarizes the research trends, datasets, and methods used in the field and provides an overview of the current state of the art. The focus of the review is on the use of automation for QoS management and improvement in computer networks, including the use of machine learning, artificial intelligence, and other computational techniques. The review highlights the need for further research and development in this area and provides insights into future directions for the field. The review covered a wide range of studies, including research papers and conference proceedings, and involved a comprehensive database search of the Scopus database covering journals and proceedings such as the Institute of Electrical and Electronics Engineers (IEEE) Xplore, Association for Computing Machinery (ACM) Digital Library, Springer, and ScienceDirect databases between 2017 and September 2022. From these databases, 1856 metadata were found, which eventually became seventy-three metadata after going through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol.
References
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TL;DR: This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.
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14,565 citations
"Significance of Adaptive Sensing fo..." refers background in this paper
...This work has been partially supported by SERB, DST, Government of India through project no....
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...Dempster Shafer’s theory (DST) [20] is a generalization of Bayesian probability theory and is popular for its capability for handling imprecision and uncertainty....
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...DST is computationally expensive as compared to Bayesian method but has other significant advantages....
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...Complexity in computation is a limitation of using DST on resourceconstrained IoT devices....
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TL;DR: This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the Fusion of spectral, spatial, and elevation information.
Abstract: The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Contest, a hyperspectral imagery and the corresponding LiDAR-derived digital surface model (DSM), were acquired by the NSF-funded Center for Airborne Laser Mapping over the University of Houston campus and its neighboring area in the summer of 2012. This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the fusion of spectral, spatial, and elevation information.
379 citations
"Significance of Adaptive Sensing fo..." refers methods in this paper
...fusing this type of data uses random forests [8]....
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07 Dec 2015
TL;DR: This paper revisits the joint probabilistic data association technique and proposes a novel solution based on recent developments in finding the m-best solutions to an integer linear program, which makes JPDA computationally tractable in applications with high target and/or clutter density.
Abstract: In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program. The key advantage of this approach is that it makes JPDA computationally tractable in applications with high target and/or clutter density, such as spot tracking in fluorescence microscopy sequences and pedestrian tracking in surveillance footage. We also show that our JPDA algorithm embedded in a simple tracking framework is surprisingly competitive with state-of-the-art global tracking methods in these two applications, while needing considerably less processing time.
330 citations
"Significance of Adaptive Sensing fo..." refers methods in this paper
...Probability based methods have been mostly used in target tracking problems [12]....
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TL;DR: In this article, the iterated unscented Kalman filter (IUKF) is proposed based on the analysis and comparison of conventional nonlinear tracking problem, which can obtain more accurate state and covariance estimation.
Abstract: It is of great importance to develop a robust and fast tracking algorithm in passive localization and tracking system because of its inherent disadvantages such as weak observability and large initial errors. In this correspondence, a new algorithm referred to as the iterated unscented Kalman filter (IUKF) is proposed based on the analysis and comparison of conventional nonlinear tracking problem. The algorithm is developed from UKF but it can obtain more accurate state and covariance estimation. Compared with the traditional approaches (e.g., extended Kalman filter (EKF) and UKF) used in passive localization, the proposed method has potential advantages in robustness, convergence speed, and tracking accuracy. The correctness as well as validity of the algorithm is demonstrated through numerical simulation and experiment results.
204 citations
"Significance of Adaptive Sensing fo..." refers methods in this paper
...EKF (Extended Kalman Filter) based approaches [23] are popular in non-linear IoT environment....
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TL;DR: The novelty of this study is to forecast the general trend of the incoming year by designing a data fusion algorithm through several neural networks by using a set of recent wind speed measurement samples from two meteorological stations in Malaysia.
Abstract: Long-term forecasting of wind speed has become a research hot spot in many different areas such as restructured electricity markets, energy management, and wind farm optimal design. However, wind energy with unstable and intermittent characteristics entails establishing accurate predicted data to avoid inefficient and less reliable results. The proposed study in this paper may provide a solution regarding the long-term wind speed forecast in order to solve the earlier-mentioned problems. For this purpose, two fundamentally different approaches, the statistical and the neural network-based approaches, have been developed to predict hourly wind speed data of the subsequent year. The novelty of this study is to forecast the general trend of the incoming year by designing a data fusion algorithm through several neural networks. A set of recent wind speed measurement samples from two meteorological stations in Malaysia, namely Kuala Terengganu and Mersing, are used to train and test the data set. The result obtained by the proposed method has given rather promising results in view of the very small mean absolute error (MAE).
193 citations
"Significance of Adaptive Sensing fo..." refers methods in this paper
...technique has been used for travel time prediction [4], wind speed forecasting [2] and indoor localization [1]....
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