• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1034-1051
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• 2016

Mobility models are invaluable for determining the performance of routing protocols in opportunistic networks. The movement of nodes has a significant influence on the topological structure and data transmission in networks. In this paper, we propose a new mobility model called the campus-based community mobility model (CBCNM) that closely reflects the daily life pattern of students on a real campus. Consequent on a discovery that the pause time of nodes in their community follows a power law distribution, instead of a classical exponential distribution, we abstract the semi-Markov model from the movement of the campus nodes and analyze its rationality. Then, using the semi-Markov algorithm to switch the movement of the nodes between communities, we infer the steady-state probability of node distribution at random time points. We verified the proposed CBCNM via numerical simulations and compared all the parameters with real data in several aspects, including the nodes' contact and inter-contact times. The results obtained indicate that the CBCNM is highly adaptive to an actual campus scenario. Further, the model is shown to have better data transmission network performance than conventional models under various routing strategies.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.50-54
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• 2010

A Container Security Device (CSD) which is different existing RFID Tag strengthens the physical security as mounted inside the container and the information security as encrypts doubly a data. CSD must use the resources efficiently in order to operate with the battery. Therefore, it needs low-power mechanism which repeats the sleep period and channel scan period. However, by adjusting these periods, the trade-off occurs between energy efficiency and network connectivity. In this paper, we implement low-power CSD and resolve this problem by adjusting beacon period and channel scan time. As a result, We guarantee the network connectivity 95% or more and maximum life up to 16 days using common AA batteries.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.404-412
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• 2022

The development in the field of the internet of things (IoT) have improved the quality of the life and also strengthened different areas in the society. All cities across the world are seeking to become smarter. The creation of a smart parking system is the essential use case in smart cities. In recent couple of years, the number of vehicles has increased significantly. As a result, it is critical to make the use of technology that enables hassle-free parking in both public and private spaces. In conventional parking systems, drivers are not able to find free parking space. Conventional systems requires more human interference in a parking lots. To manage these circumstances there is an intense need of IoT enabled parking solution that includes the well defined architecture that will contain the following components such as smart sensors, communication agreement and software solution. For implementing such a smart parking system in this paper we proposed a design of smart parking system and also compare it with convetional system. The proposed design utilizes sensors based on IoT and Data Mining techniques to handle real time management of the parking system. IoT enabled smart parking solution minimizes the human interference and also saves energy, money and time.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.119-130
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• 2022

Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.1B
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• pp.10-15
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• 2009

Recently, there are tendency that wireless sensor network is one of the important techniques for the future IT industry and thereby application areas in it are getting growing. Researches based on the hierarchical network topology are evaluated in good at energy efficiency in related protocols for wireless sensor network. LEACH is the best well known routing protocol for the hierarchical topology. However, there are problems in the range of message broadcasting, which should be expand into the overall network coverage, in LEACH related protocols. Thereby, this paper proposes a new clustering scheme to solve the co-shared problems in them. The basic idea of our scheme is using the inherent memory restrictions in sensor nodes. The results show that the proposed scheme could support the load balancing by distributing the clusters with a reasonable number of member nodes and thereby the network life time would be extended in about 1.8 times longer than LEACH.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.5
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• pp.61-76
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• 2018

Everything is coming into the world of network where modern people are living. The Internet of Things that attach sensors to objects allows real-time data transfer to and from the network. Mobile devices, essential for modern humans, play an important role in keeping all traces of everyday life in real time. Through the social network services, information acquisition activities and communication activities are left in a huge network in real time. From the business point of view, customer needs analysis begins with SNS data. In this research, we want to build an automatic collection system of SNS contents of web environment in real time using Python. We want to help customers' needs analysis through the typical data collection system of Instagram, Twitter, and YouTube, which has a large number of users worldwide. It is stored in database through the exploitation process and NLP process by using the virtual web browser in the Python web server environment. According to the results of this study, we want to conduct service through the site, the desired data is automatically collected by the search function and the netizen's response can be confirmed in real time. Through time series data analysis. Also, since the search was performed within 5 seconds of the execution result, the advantage of the proposed algorithm is confirmed.

• Journal of the Korea Society of Computer and Information
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• v.19 no.6
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• pp.49-59
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• 2014

Recently, wireless sensor networks(WSNs) are widely used for intrusion detection and ecology, environment, atmosphere, industry, traffic, fire monitoring. In this paper, an energy efficient clustering algorithm is proposed. The proposed algorithm forms clusters uniformly by selecting cluster head that optimally located based on receiving power. Besides, proposed algorithm can induce uniform energy consumption regardless of location of nodes by multi-hop transmission and MST formation with limited maximum depth. Through the above, proposed algorithm elongates network life time, reduces energy consumption of nodes and induces fair energy consumption compared to conventional LEACH and HEED. The results of simulation show that the proposed clustering algorithm elongates network life time through fair energy consumption.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• IMMUNE NETWORK
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• v.12 no.3
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• pp.71-83
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• 2012

T cell activation and function require physical contact with antigen presenting cells at a specialized junctional structure known as the immunological synapse. Once formed, the immunological synapse leads to sustained T cell receptor-mediated signalling and stabilized adhesion. High resolution microscopy indeed had a great impact in understanding the function and dynamic structure of immunological synapse. Trends of recent research are now moving towards understanding the mechanical part of immune system, expanding our knowledge in mechanosensitivity, force generation, and biophysics of cell-cell interaction. Actin cytoskeleton plays inevitable role in adaptive immune system, allowing it to bear dynamic and precise characteristics at the same time. The regulation of mechanical engine seems very complicated and overlapping, but it enables cells to be very sensitive to external signals such as surface rigidity. In this review, we focus on actin regulators and how immune cells regulate dynamic actin rearrangement process to drive the formation of immunological synapse.

• Journal of KIISE:Databases
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• v.36 no.2
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• pp.99-111
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• 2009

In wireless sensor networks, various schemes have been proposed to store and process sensed data efficiently. A Data-Centric Storage(DCS) scheme assigns distributed data regions to sensors and stores sensed data to the sensor which is responsible for the data region overlapping the data. The DCS schemes have been proposed to reduce the communication cost for transmitting data and process exact queries and range queries efficiently. Recently, KDDCS that readjusts the distributed data regions dynamically to sensors based on K-D tree was proposed to overcome the storage hot-spots. However, the existing DCS schemes including KDDCS suffer from Query Hot-Spots that are formed if the query regions are not uniformly distributed. As a result, it causes reducing the life time of the sensor network. In this paper, we propose a new DCS scheme, called TPDCS(Time-Parameterized DCS), that avoids the problems of storage hot-spots and query hot-spots. To decentralize the skewed. data and queries, the data regions are assigned by a time dimension as well as data dimensions in our proposed scheme. Therefore, TPDCS extends the life time of sensor networks. It is shown through various experiments that our scheme outperform the existing schemes.