Research on a Denial of Service (DoS) Detection System Based on Global Interdependent Behaviors in a Sensor Network Environment

This research suggests a Denial of Service (DoS) detection method based on the collection of interdependent behavior data in a sensor network environment. In order to collect the interdependent behavior data, we use a base station to analyze traffic and behaviors among nodes and introduce methods of detecting changes in the environment with precursor symptoms. The study presents a DoS Detection System based on Global Interdependent Behaviors and shows the result of detecting a sensor carrying out DoS attacks through the test-bed

A Study on Software-based Sensing Technology for Multiple Object Control in AR Video

Researches on Augmented Reality (AR) have recently received attention. With these, the Machine-to-Machine (M2M) market has started to be active and there are numerous efforts to apply this to real life in all sectors of society. To date, the M2M market has applied the existing marker-based AR technology in entertainment, business and other industries. With the existing marker-based AR technology, a designated object can only be loaded on the screen from one marker and a marker has to be added to load on the screen the same object again. This situation creates a problem where the relevant marker’should be extracted and printed in screen so that loading of the multiple objects is enabled. However, since the distance between markers will not be measured in the process of detecting and copying markers, the markers can be overlapped and thus the objects would not be augmented. To solve this problem, a circle having the longest radius needs to be created from a focal point of a marker to be copied, so that no object is copied within the confines of the circle. In this paper, software-based sensing technology for multiple object detection and loading using PPHT has been developed and overlapping marker control according to multiple object control has been studied using the Bresenham and Mean Shift algorithms

Influential Variables and Causal Relations Impact on Innovative Performance and Sustainable Growth of SMEs in Aspect of Industry 4.0 and Digital Transformation

Digital Transformation is essential in the global industry for survival and sustainable growth, and SMEs are mainly required to apply digital technology for sustainable growth. This study aims to verify the causal relationship between the variables significantly affecting Digital Transformation’s sustainable growth and innovative performance and suggest critical variables and strategies in which Digital Transformation’s constituent factors affect sustainable growth. Data were collected from an online survey of 303 CEOs of SMEs. Using Smart PLS, analyzed the factors affecting the sustainable growth of SMEs and verified the causal relationship. We found that applying Digital Transformation in SMEs is necessary because Digital Transformation affects innovation performance and ultimately impacts sustainable growth. By verifying the variables that affect the sustainable growth of 7 industrial sectors, we establish sustainable growth strategies suitable for each industry and provide the variables that affect sustainable growth. The findings imply that DT is essential for the sustainable growth of SMEs and that impact variables appropriate to the industry should be applied. The study results will be a new area of interest for future researchers

Network Modeling and Energy-Efficiency Optimization for Advanced Machine-to-Machine Sensor Networks

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Bloom Filter-Based Advanced Traceback Scheme in Wireless Sensor Networks

Many recent studies have focused on traceback schemes with the aim of finding the source of spoofed malicious packets and tracing the path of denial-of-service attacks. Although such schemes are academically well known, most exhibit some critical points in wireless sensor networks, which could become a significant issue as they are increasingly used for IP networks. This paper suggests an advanced traceback scheme based on existing Bloom filter methods. The proposed traceback scheme extends the basic Bloom filter design, enabling it to identify which entity added a given element, albeit with the incursion of false positives. However, our scheme allows only specific tracebacks, which can reduce the false positive rate of a node near a sink. Performance results show that the scheme can perform efficient tracebacks with very few false positives