A distributed deployment algorithm for communication coverage in wireless robotic networks

The distributed deployment of wireless sensor networks (WSNs) makes the network more convenient, but it also causes more hidden security hazards that are difficult to be solved. For example, the unprotected deployment of sensors makes distributed anomaly detection systems for WSNs more vulnerable to internal attacks, and the limited computing resources of WSNs hinder the construction of a trusted environment. In recent years, the widely observed blockchain technology has shown the potential to strengthen the security of the Internet of Things. Therefore, we propose a blockchain-based ensemble anomaly detection (BCEAD), which stores the model of a typical anomaly detection algorithm (isolated forest) in the blockchain for distributed anomaly detection in WSNs. By constructing a suitable block structure and consensus mechanism, the global model for detection can iteratively update to enhance detection performance. Moreover, the blockchain guarantees the trust environment of the network, making the detection algorithm resistant to internal attacks. Finally, compared with similar schemes, in terms of performance, cost, etc., the results prove that BCEAD performs better.

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GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during DRAGON-NE Asia 2012 campaign

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-8-9565-2015 ◽

2015 ◽

Vol 8 (9) ◽

pp. 9565-9609 ◽

Cited By ~ 6

Author(s):

M. Choi ◽

J. Kim ◽

J. Lee ◽

M. Kim ◽

Y. Je Park ◽

...

Keyword(s):

Optical Properties ◽

Land Surface ◽

Pearson Correlation ◽

Ocean Color ◽

Single Scattering ◽

Surface Reflectance ◽

Robotic Networks ◽

Deep Blue ◽

Aerosol Retrieval ◽

Highly Correlated

Abstract. The Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorology Satellites (COMS) is the first multi-channel ocean color imager in geostationary orbit. Hourly GOCI top-of-atmosphere radiance has been available for the retrieval of aerosol optical properties over East Asia since March 2011. This study presents improvements to the GOCI Yonsei Aerosol Retrieval (YAER) algorithm over ocean and land together with validation results during the DRAGON-NE Asia 2012 campaign. Optical properties of aerosol are retrieved from the GOCI YAER algorithm including aerosol optical depth (AOD) at 550 nm, fine-mode fraction (FMF) at 550 nm, single scattering albedo (SSA) at 440 nm, Angstrom exponent (AE) between 440 and 860 nm, and aerosol type from selected aerosol models in calculating AOD. Assumed aerosol models are compiled from global Aerosol Robotic Networks (AERONET) inversion data, and categorized according to AOD, FMF, and SSA. Nonsphericity is considered, and unified aerosol models are used over land and ocean. Different assumptions for surface reflectance are applied over ocean and land. Surface reflectance over the ocean varies with geometry and wind speed, while surface reflectance over land is obtained from the 1–3 % darkest pixels in a 6 km × 6 km area during 30 days. In the East China Sea and Yellow Sea, significant area is covered persistently by turbid waters, for which the land algorithm is used for aerosol retrieval. To detect turbid water pixels, TOA reflectance difference at 660 nm is used. GOCI YAER products are validated using other aerosol products from AERONET and the MODIS Collection 6 aerosol data from "Dark Target (DT)" and "Deep Blue (DB)" algorithms during the DRAGON-NE Asia 2012 campaign from March to May 2012. Comparison of AOD from GOCI and AERONET gives a Pearson correlation coefficient of 0.885 and a linear regression equation with GOCI AOD =1.086 × AERONET AOD – 0.041. GOCI and MODIS AODs are more highly correlated over ocean than land. Over land, especially, GOCI AOD shows better agreement with MODIS DB than MODIS DT because of the choice of surface reflectance assumptions. Other GOCI YAER products show lower correlation with AERONET than AOD, but are still qualitatively useful.

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Lithium-ion power battery grouping: A multisource data fusion based clustering approach and distributed deployment

Journal of Electrochemical Energy Conversion and Storage ◽

10.1115/1.4053307 ◽

2021 ◽

pp. 1-13

Author(s):

Yudong Wang ◽

Xiwei Bai ◽

Chengbao Liu ◽

Jie Tan

Keyword(s):

Data Fusion ◽

Lithium Ion ◽

Electrochemical Characteristics ◽

Static Characteristics ◽

Clustering Problem ◽

Power Battery ◽

Source Data ◽

Clustering Approach ◽

Effective Network ◽

Distributed Deployment

Abstract Consistence of lithium-ion power battery significantly affects the life and safety of battery modules and packs. To improve the consistence, battery grouping is employed, assembling batteries with similar electrochemical characteristics to make up modules and packs. Therefore, grouping process boils down to unsupervised clustering problem. Current used grouping approaches include two aspects, static characteristics based and dynamic based. However, there are three problems. First, the common problem is underutilization of multi-source data. Second, for the static characteristics based, there is grouping failure over time. Third, for the dynamic characteristics based, there is high computational complexity. To solve these problems, we propose a distributed multisource data fusion based battery grouping approach. The proposed approach designs an effective network structure for multisource data fusion, and a self supervised scheme for feature extraction from both static and dynamic multisource data. We apply our approach on real battery modules and test state of health (SOH) after charging-discharging cycles. Experimental results indicate that the proposed scheme can increase SOH of modules by 3.89%, and reduce the inconsistence by 68.4%. Meanwhile, with the distributed deployment the time cost is reduced by 87.9% than the centralized scheme.

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