• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.8
• /
• pp.2140-2156
• /
• 2023

The Wireless Sensor Network (WSN), is constructed out of teeny-tiny sensor nodes that are very low-cost, have a low impact on the environment in terms of the amount of power they consume, and are able to successfully transmit data to the base station. The primary challenges that are presented by WSN are those that are posed by the distance between nodes, the amount of energy that is consumed, and the delay in time. The sensor node's source of power supply is a battery, and this particular battery is not capable of being recharged. In this scenario, the amount of energy that is consumed rises in direct proportion to the distance that separates the nodes. Here, we present a Hybrid Firefly Glow-Worm Swarm Optimization (HF-GSO) guided routing strategy for preserving WSNs' low power footprint. An efficient fitness function based on firefly optimization is used to select the Cluster Head (CH) in this procedure. It aids in minimising power consumption and the occurrence of dead sensor nodes. After a cluster head (CH) has been chosen, the Glow-Worm Swarm Optimization (GSO) algorithm is used to figure out the best path for sending data to the sink node. Power consumption, throughput, packet delivery ratio, and network lifetime are just some of the metrics measured and compared between the proposed method and methods that are conceptually similar to those already in use. Simulation results showed that the proposed method significantly reduced energy consumption compared to the state-of-the-art methods, while simultaneously increasing the number of functioning sensor nodes by 2.4%. Proposed method produces superior outcomes compared to alternative optimization-based methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.3
• /
• pp.916-937
• /
• 2023

Most of the existing Distributed Denial-of-Service mitigation schemes in Software-Defined Networking are only implemented in the network domain managed by a single controller. In fact, the zombies for attackers to launch large-scale DDoS attacks are actually not in the same network domain. Therefore, abnormal traffic of DDoS attack will affect multiple paths and network domains. A single defense method is difficult to deal with large-scale DDoS attacks. The cooperative defense of multiple domains becomes an important means to effectively solve cross-domain DDoS attacks. We propose an efficient multi-domain DDoS cooperative defense mechanism by integrating blockchain and SDN architecture. It includes attack traceability, inter-domain information sharing and attack mitigation. In order to reduce the length of the marking path and shorten the traceability time, we propose an AS-level packet traceability method called ASPM. We propose an information sharing method across multiple domains based on blockchain and smart contract. It effectively solves the impact of DDoS illegal traffic on multiple domains. According to the traceability results, we designed a DDoS attack mitigation method by replacing the ACL list with the IP address black/gray list. The experimental results show that our ASPM traceability method requires less data packets, high traceability precision and low overhead. And blockchain-based inter-domain sharing scheme has low cost, high scalability and high security. Attack mitigation measures can prevent illegal data flow in a timely and efficient manner.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.7
• /
• pp.1873-1893
• /
• 2023

The development of lightweight, low energy and small-sized sensors incorporated with the wireless networks has brought about a phenomenal growth of Wireless Sensor Networks (WSNs) in its different fields of applications. Moreover, the routing of data is crucial in a wide number of critical applications that includes ecosystem monitoring, military and disaster management. However, the time-delay, energy imbalance and minimized network lifetime are considered as the key problems faced during the process of data transmission. Furthermore, only when the functionality of cluster head selection is available in WSNs, it is possible to improve energy and network lifetime. Besides that, the task of cluster head selection is regarded as an NP-hard optimization problem that can be effectively modelled using hybrid metaheuristic approaches. Due to this reason, an Improved Coyote Optimization Algorithm-based Clustering Technique (ICOACT) is proposed for extending the lifetime for making efficient choices for cluster heads while maintaining a consistent balance between exploitation and exploration. The issue of premature convergence and its tendency of being trapped into the local optima in the Improved Coyote Optimization Algorithm (ICOA) through the selection of center solution is used for replacing the best solution in the search space during the clustering functionality. The simulation results of the proposed ICOACT confirmed its efficiency by increasing the number of alive nodes, the total number of clusters formed with the least amount of end-to-end delay and mean packet loss rate.

• Journal of the Institute of Convergence Signal Processing
• /
• v.24 no.2
• /
• pp.112-118
• /
• 2023

Internet of Things(IoT) technology is developing to a stage where the Internet and objects are connected and objects themselves analyze and judge data to interconnect the real world and the virtual world in real time. This technology consists of sensors, actuators, devices, and networks, and it is being applied in various fields. As the number of IoT devices and applications increases, data traffic also increases. In this paper, a messaging system is designed and implemented in order to analyze the performance between an IoT device and MQTT broker. The experimental was performed to measure MQTT-based round-trip time and message transmission time between the IoT device and the broker. The result shows that there is no packet loss, and propagation delay affects round-trip time.

• Broadcasting and Media Magazine
• /
• v.11 no.4
• /
• pp.104-117
• /
• 2006

In this Paper, we analyze the application of Interactive Data Broadcasting in DMB(Digital Multimedia Broadcasting) in the accordance with convergence of service and technology. With the acceleration of digital convergence in the Ubiquitous period substantial development of digital media technology and convergence of broadcasting and telecommunication industry are being witnessed. Consequently these results gave rise to newly combined-products such as DMB(Digital Multimedia Broadcasting), WCDMA(Wide-band code division multiple access), Wibro(Wireless Broadband Internet), IP-TV (Internet protocol TV) and HSDPA(High speed downlink packet access). The preparatory stage for the implementation of Interactive Data Broadcasting Service will be reached by the end of December, 2006. DMB is the first result of a successful convergence service between Broadcasting and Telecommunication in new media era. Multimedia technology and services are the core elements of DMB. The Data Broadcasting will not only offer various services of interactive information such News, Weather, Broadcasting Program etc, but also be linked with characteristic function of mobile phone such as calling and SMS(Short Message Service) via Return Channel.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.2
• /
• pp.323-335
• /
• 2015

Mobile healthcare is a fusion of information technology and biotechnology and is a new type of health management service to keep people's health at anytime and anywhere without regard to time and space. The WBAN(Wireless Body Area Network) technology that collects bio signals and the data analysis and monitoring technology using mobile devices are essential for serving mobile healthcare. WBAN consisting of users with mobile devices meet another WBAN during movement, WBANs transmit data to the other media. Because of WBAN conflict, several nodes transmit data in same time slot so a collision will occur, resulting in the data transmission being failed and need more energy for re-transmission. In this thesis, we proposed a MAC protocol for WBAN with mobility to solve these problems. First, we proposed a superframe structure for WBAN. The proposed superframe consists of a TDMA(Time Division Muliple Access) based contention access phase with which a node can transmit data in its own time slot and a contention phase using CSMA/CA algorithm. Second, we proposed a network merging algorithm for conflicting WBAN based on the proposed MAC protocol. When a WBAN with mobility conflicts with other WBAN, data frame collision is reduced through network reestablishment. Simulations are performed using a Castalia based on the OMNeT++ network simulation framework to estimate the performance of the proposed superframe and algorithms. We estimated the performance of WBAN based on the proposed MAC protocol by comparing the performance of the WBAN based on IEEE 802.15.6. Performance evaluation results show that the packet transmission success rate and energy efficiency are improved by reducing the probability of collision using the proposed MAC protocol.

• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.175-186
• /
• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.03a
• /
• pp.175-186
• /
• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.4
• /
• pp.666-677
• /
• 2015

The importance of application traffic analysis for efficient network management has been emphasized continuously. Snort is a popular traffic analysis system which detects traffic matched to pre-defined signatures and perform various actions based on the rules. However, it is very difficult to get highly accurate signatures to meet various analysis purpose because it is very tedious and time-consuming work to search the entire traffic data manually or semi-automatically. In this paper, we propose a novel method to generate signatures in a fully automatic manner in the form of sort rule from raw packet data captured from network link or end-host. We use a sequence pattern algorithm to generate common substring satisfying the minimum support from traffic flow data. Also, we extract the location and header information of the signature which are the components of snort content rule. When we analyzed the proposed method to several application traffic data, the generated rule could detect more than 97 percentage of the traffic data.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.4
• /
• pp.83-90
• /
• 2010

In wireless sensor networks, the data transmitted from the sensor nodes are susceptible to corruption by errors which caused of noisy channels and other factors. In view of the severe energy constraint in Sensor Networks, it is important to use the error control scheme of the energy efficiently. In this paper, we presented RS (Reed-Solomon) codes in terms of their BER performance and power consumption. RS codes work by adding extra redundancy to the data. The encoded data can be stored or transmitted. It could have errors introduced, when the encoded data is recovered. The added redundancy allows a decoder to detect which parts of the received data is corrupted, and corrects them. The number of errors which are able to be corrected by RS code can determine by added redundancy. The results of experiment validate the performance of proposed method to provide high degree of reliability in low-power communication. We could predict the lifetime of RS codes which transmitted at 32 byte a 1 minutes. RS(15, 13), RS(31, 27), RS(63, 57), RS(127,115), and RS(255,239) can keep the days of 173.7, 169.1, 163.9, 150.7, and 149.7 respectively. The evaluation based on packet reception ratio (PRR) indicates that the RS(255,239) extends a sensor node's communication range by up about 3 miters.