Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis
Issue:
Volume 9, Issue 2, April 2020
Pages:
42-47
Received:
6 May 2020
Accepted:
26 May 2020
Published:
8 June 2020
Abstract: With the development of Internet of Things (IoT) technology and its vast applications in ship transportation systems, such as the Automatic Identification System (AIS), a large quantity of ship trajectory data have been recorded and stored. Nowadays ship transportation has also entered the age of big data, which can support IoT applications in Intelligent Transportation System (ITS), e.g. traffic monitoring, fleet management and traffic safety enhancement. However, the redundancy of ship trajectory data considerably reduces the effectiveness and efficiency of large scale traffic data storage, mining and visualization. Therefore, compression processing of the data becomes a very important issue for these applications. Because ship trajectory is a type of vector data, employing the vector data compression algorithms is an efficient way to solve the data redundancy problem. In this paper, the pseudo-code of five typical vector data compression algorithms for ship trajectory data compression is introduced. The performances of these algorithms were tested by the compression experiments of actual ship trajectories in the Qiongzhou Strait. The results show that ships’ speeds and rate of turns, the requirement of real time processing can affect the option of the most appropriate algorithm, and the algorithm selection in different applications is suggested. The results and conclusions lay the foundation for the future development of ship transportation intelligentization.
Abstract: With the development of Internet of Things (IoT) technology and its vast applications in ship transportation systems, such as the Automatic Identification System (AIS), a large quantity of ship trajectory data have been recorded and stored. Nowadays ship transportation has also entered the age of big data, which can support IoT applications in Inte...
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Kulfo River Stream Impact on the Sustainability of Aquatic Life in Chamo Lake at Arba Minch
Mosisa Teferi Timotewos,
Daniel Reddythota
Issue:
Volume 9, Issue 2, April 2020
Pages:
48-55
Received:
18 May 2020
Accepted:
5 June 2020
Published:
23 June 2020
Abstract: Over the last two decades, most of the water bodies in Ethiopia have become increasingly threatened due to pollution from different sources. Recently, many dead and floating fish on the surface of Chamo Lake at Arba Minch city indicated that lake water quality and ecosystem health had been deteriorated. The deteriorating quality of the lake or river systems is directly linked to the improper existing sewage and city waste disposal systems and untreated wastewater discharged from domestic, agricultural and industrial sources in Arba Minch, Ethiopia. This paper examined 23 water quality parameters to ascertain the water quality of Kulfo river stream as well as Chamo Lake and the impact of Kulfo river stream on Chamo Lake. Analysis of the data revealed that the concentration of Turbidity (21NTU), TDS (1070 mg/l), PO4-3 (1.1 mg/l), Iron (0.64 mg/l), Total Coliform bacteria (646), Ammonia (23.8 mg/l), pH (9.3) and Electrical Conductivity (1715µS/cm) are above the permissible limits in Kulfo river stream which is entering into the Chamo Lake. Besides, the dissolved oxygen levels were also very low as 5.2 mg/l. As per the field observations and laboratory analyses, the dissolved oxygen content in the lake was very low, whereas, the temperature was very high. The Ammonia concentration was very high which could be toxic for aquatic life, especially fish in the lake.
Abstract: Over the last two decades, most of the water bodies in Ethiopia have become increasingly threatened due to pollution from different sources. Recently, many dead and floating fish on the surface of Chamo Lake at Arba Minch city indicated that lake water quality and ecosystem health had been deteriorated. The deteriorating quality of the lake or rive...
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