Energy Efficient and Accurate Monitoring of Large-Scale Diffusive Objects in Internet of Things

Oh, Seungmin; Lee, Jeongcheol; Park, Soochang

doi:10.1109/lcomm.2016.2634526

Cited by 10 publications

(9 citation statements)

References 6 publications

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“…Oh et al [18] combined the two-layer grid model in the TGM-COT with a convex hull algorithm to track the boundary of continuous objects. Since convex hull methods cannot work on concave polygons, the authors proposed a recovery mechanism to detect the shape loss between the obtained convex hull and the actual boundary.…”

Section: Grid-based Boundary Trackingmentioning

confidence: 99%

Boundary Tracking of Continuous Objects Based on Binary Tree Structured SVM for Industrial Wireless Sensor Networks

Liu

Han

et al. 2022

IEEE Trans. on Mobile Comput.

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Due to the flammability, explosiveness and toxicity of continuous objects (e.g., chemical gas, oil spill, radioactive waste) in the petrochemical and nuclear industries, boundary tracking of continuous objects is a critical issue for industrial wireless sensor networks (IWSNs). In this article, we propose a continuous object boundary tracking algorithm for IWSNs-which fully exploits the collective intelligence and machine learning capability within the sensor nodes. The proposed algorithm first determines an upper bound of the event region covered by the continuous objects. A binary tree-based partition is performed within the event region, obtaining a coarse-grained boundary area mapping. To study the irregularity of continuous objects in detail, the boundary tracking problem is then transformed into a binary classification problem; a hierarchical soft margin support vector machine training strategy is designed to address the binary classification problem in a distributed fashion. Simulation results demonstrate that the proposed algorithm shows a reduction in the number of nodes required for boundary tracking by at least 50%. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to false sensor readings, even for high ratios of faulty nodes (≈ 9%).

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Section: Grid-based Boundary Trackingmentioning

confidence: 99%

Boundary Tracking of Continuous Objects Based on Binary Tree Structured SVM for Industrial Wireless Sensor Networks

Liu

Han

et al. 2022

IEEE Trans. on Mobile Comput.

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“…Therefore, TG-COD, TGM-COT, GAS, and COTS do not suffer from communication overhead for cluster construction and maintenance. Figure 4 and Table 2 TG-COD [30] CODA [34] GAS [38] COTS [39] PRECO [35] EUCOW [27] / PM-COT [32] COBOM [26] MCHD [40] BFA [37] FPOD [36] BTS-COT [33] / TPE-FTED [29] TGM-COT [31] DeGas [7] / GLDS [28] BRTCO [6] [25] COBOM [26], EUCOW [34], PRECO [35], FPOD [36], BTS-COT [33], PM-COT [32], TPE-FTED [29] CODA [34], GAS [38], COTS [39], BRTCO [6] TG-COD [30], MCHD [40], BFA [37], TGM-COT [31], DeGas [7], GLDS [28] # Dual prediction techniques that are proposed for abbreviation in IMO monitoring are not studied yet in LFO monitoring. Dual prediction for LFO monitoring is much more complex than that for IMO monitoring since it has to predict the next boundary shape not the next point.…”

Section: Phase Ii: Integrationmentioning

confidence: 99%

Large-Scale Object Monitoring in Internet-of-Things: Energy-Efficient Perspectives

Yim

Lee

2021

Electronics

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Recently, the demand for monitoring a certain object covering large and dynamic scopes such as wildfires, glaciers, and radioactive contaminations, called large-scale fluid objects (LFOs), is coming to the fore due to disasters and catastrophes that lately happened. This article provides an analytic comparison of such LFOs and typical individual mobile objects (IMOs), namely animals, humans, vehicles, etc., to figure out inherent characteristics of LFOs. Since energy-efficient monitoring of IMOs has been intensively researched so far, but such inherent properties of LFOs hinder the direct adaptation of legacy technologies for IMOs, this article surveys technological evolution and advances of LFOs along with ones of IMOs. Based on the communication cost perspective correlated to energy efficiency, three technological phases, namely concentration, integration, and abbreviation, are defined in this article. By reviewing various methods and strategies employed by existing works with the three phases, this article concludes that LFO monitoring should achieve not only decoupling from node density and network structure but also trading off quantitative reduction against qualitative loss as architectural principles of energy-efficient communication to break through inherent properties of LFOs. Future research challenges related to this topic are also discussed.

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“…(1) The single low-resolution image is converted into a corresponding high-resolution image by interpolation and enlargement [13].…”

Section: { }mentioning

confidence: 99%

Real-time Defogging of Single Image of IoTs-based Surveillance Video Based on MAP

Liu

2020

Teh. vjesn.

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Due to the atmospheric scattering phenomenon in fog weather, the current monitoring video image defogging method cannot estimate the fog density of the image. This paper proposes a real-time defogging algorithm for single images of IoTs surveillance video based on maximum a posteriori (MAP). Under the condition of single image sequence, the posterior probability of the high-resolution single image is set to the maximum, which improves the MAP design super-resolution image reconstruction. This paper introduces fuzzy classification to calculate atmospheric light intensity, and obtains a single image of IoTs surveillance video by the atmospheric dissipation function. The improved algorithm has the largest signal-to-noise ratio after defogging, and the maximum value is as high as 40.99 dB. The average time for defogging of 7 experimental surveillance video images is only 2.22 s, and the real-time performance is better. It can be concluded that the proposed algorithm has excellent defogging performance and strong applicability.

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Energy Efficient and Accurate Monitoring of Large-Scale Diffusive Objects in Internet of Things

Cited by 10 publications

References 6 publications

Boundary Tracking of Continuous Objects Based on Binary Tree Structured SVM for Industrial Wireless Sensor Networks

Boundary Tracking of Continuous Objects Based on Binary Tree Structured SVM for Industrial Wireless Sensor Networks

Large-Scale Object Monitoring in Internet-of-Things: Energy-Efficient Perspectives

Real-time Defogging of Single Image of IoTs-based Surveillance Video Based on MAP

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