Search Word(s) in Title, Keywords, Authors, and Abstract: Tier

A Tier-Based Duty-Cycling Scheme for Forest Monitoring
Fuquan Zhang, Deming Gao and In-Whee Joe
Page: 1320~1330, Vol. 13, No.5, 2017
10.3745/JIPS.04.0043

Keywords: Link Redundancy, Rechargeable Dynamic Duty Cycle, Tier, Wireless Sensor Networks
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Wireless sensor networks for forest monitoring are typically deployed in fields in which manual intervention cannot be easily accessed. An interesting approach to extending the lifetime of sensor nodes is the use of energy harvested from the environment. Design constraints are application-dependent and based on the monitored environment in which the energy harvesting takes place. To reduce energy consumption, we designed a power management scheme that combines dynamic duty cycle scheduling at the network layer to plan node duty time. The dynamic duty cycle scheduling is realized based on a tier structure in which the network is concentrically organized around the sink node. In addition, the multi-paths preserved in the tier structure can be used to deliver residual packets when a path failure occurs. Experimental results show that the proposed method has a better performance.


Femtocell Subband Selection Method for Managing Cross- and Co-tier Interference in a Femtocell Overlaid Cellular Network
Young Min Kwon, Hyunseung Choo, Tae-Jin Lee, Min Young Chung and Mihui Kim
Page: 384~394, Vol. 10, No.3, 2014
10.3745/JIPS.03.0008
Keywords: Clustering Method, Femtocell, Frequency Partition, Interference Management
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The femtocell overlaid cellular network (FOCN) has been used to enhance the capacity of existing cellular systems. To obtain the desired system performance, both cross-tier interference and co-tier interference in an FOCN need to be managed. This paper proposes an interference management scheme that adaptively constructs a femtocell cluster, which is a group of femtocell base stations that share the same frequency band. The performance evaluation shows that the proposed scheme can enhance the performance of the macrocell-tier and maintain a greater signal to interference-plus-noise ratio than the outage level can for about 99% of femtocell users.


A New Approach for Information Security using an Improved Steganography Technique
Mamta Juneja and Parvinder Singh Sandhu
Page: 405~424, Vol. 9, No.3, 2013
10.3745/JIPS.2013.9.3.405
Keywords: Adaptive LSB Steganography, AES; Hybrid Feature Detection, Random Pixel Embeddin g, Steganography, Two Component based LSB Steganography
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This research paper proposes a secured, robust approach of information security using steganography. It presents two component based LSB (Least Significant Bit) steganography methods for embedding secret data in the least significant bits of blue components and partial green components of random pixel locations in the edges of images. An adaptive LSB based steganography is proposed for embedding data based on the data available in MSB’s (Most Significant Bits) of red, green, and blue components of randomly selected pixels across smooth areas. A hybrid feature detection filter is also proposed that performs better to predict edge areas even in noisy conditions. AES (Advanced Encryption Standard) and random pixel embedding is incorporated to provide two-tier security. The experimental results of the proposed approach are better in terms of PSNR and capacity. The comparison analysis of output results with other existing techniques is giving the proposed approach an edge over others. It has been thoroughly tested for various steganalysis attacks like visual analysis, histogram analysis, chi-square, and RS analysis and could sustain all these attacks very well.


Two-Tier Storage DBMS for High-Performance Query Processing
Sang-Hun Eo, Yan Li, Ho-Seok Kim and Hae-Young Bae
Page: 9~16, Vol. 4, No.1, 2008
10.3745/JIPS.2008.4.1.009
Keywords: DBMS, Storage Management, Query Processing
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This paper describes the design and implementation of a two-tier DBMS for handling massive data and providing faster response time. In the present day, the main requirements of DBMS are figured out using two aspects. The first is handling large amounts of data. And the second is providing fast response time. But in fact, Traditional DBMS cannot fulfill both the requirements. The disk-oriented DBMS can handle massive data but the response time is relatively slower than the memory-resident DBMS. On the other hand, the memory-resident DBMS can provide fast response time but they have original restrictions of database size. In this paper, to meet the requirements of handling large volumes of data and providing fast response time, a two-tier DBMS is proposed. The cold-data which does not require fast response times are managed by disk storage manager, and the hot-data which require fast response time among the large volumes of data are handled by memory storage manager as snapshots. As a result, the proposed system performs significantly better than diskoriented DBMS with an added advantage to manage massive data at the same time.


A Knowledge Discovery Framework for Spatiotemporal Data Mining
Jun-Wook Lee and Yong-Joon Lee
Page: 124~129, Vol. 2, No.2, 2006
None
Keywords: spatiotemporal data mining, spatiotemporal knowledge discovery, spatiotemporal moving pattern, discovery framework
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With the explosive increase in the generation and utilization of spatiotemporal data sets, many research efforts have been focused on the efficient handling of the large volume of spatiotemporal sets. With the remarkable growth of ubiquitous computing technology, mining from the huge volume of spatiotemporal data sets is regarded as a core technology which can provide real world applications with intelligence. In this paper, we propose a 3-tier knowledge discovery framework for spatiotemporal data mining. This framework provides a foundation model not only to define the problem of spatiotemporal knowledge discovery but also to represent new knowledge and its relationships. Using the proposed knowledge discovery framework, we can easily formalize spatiotemporal data mining problems. The representation model is very useful in modeling the basic elements and the relationships between the objects in spatiotemporal data sets, information and knowledge.