Blockchain-based Perfect Sharing Project Platform based on the Proof of Atomicity Consensus Algorithm

The Korean government funded 12.8 billion USD to 652 research and development (R&D) projects supported by 20 ministries in 2019. Every year, various organizations are supported to conduct R&D projects focusing on selected core technologies by evaluating emerging technologies which industries are planning to develop. To manage the whole cycle of national R&D projects, information sharing on national R&D projects is very essential. The blockchain technology is considered as a core solution to share information reliably and prevent forgery in various fields. For efficient management of national R&D projects, we enhance and analyse the Perfect Sharing Project (PSP)-Platform based on a new blockchain-based platform for information sharing and forgery prevention. It is a shared platform for national ICT R&D projects management with excellent performance in preventing counterfeiting. As a consensus algorithm is very important to prevent forgery in blockchain, we survey not only architectural aspects and examples of the platform but also the consensus algorithms. Considering characteristics of the PSP-Platform, we adopt an atomic proof (POA) consensus algorithm as a new consensus algorithm in this paper. To prove the validity of the POA consensus algorithm, we have conducted experiments. The experiment results show the outstanding performance of the POA consensus algorithm used in the PSP-Platform in terms of block generation delay and block propagation time

Journalism Model Based on Blockchain with Sharing Space

The challenge that journalism is facing these days in the Internet mobile environment is greater than ever before. Journalism is losing its revenue structure to platform operators favoring a certain markets, and also the trust of its readers in light of fake news and infected news. To alleviate this situation, we propose a blockchain technology that is applicable to journalism in order to achieve decentralization as a reasonable alternative. The journalism model based on hybrid blockchain aims to achieve the following: the delivery of articles with sharing value, what we call proof of sharing; the distribution of roles of personalized agenda settings; and finally, the use of agora to collect public opinions. With all these, we attempt to resolve the issues with current journalism with our proposed model based on blockchain

Linked-Object Dynamic Offloading (LODO) for the Cooperation of Data and Tasks on Edge Computing Environment

With the evolution of the Internet of Things (IoT), edge computing technology is using to process data rapidly increasing from various IoT devices efficiently. Edge computing offloading reduces data processing time and bandwidth usage by processing data in real-time on the device where the data is generating or on a nearby server. Previous studies have proposed offloading between IoT devices through local-edge collaboration from resource-constrained edge servers. However, they did not consider nearby edge servers in the same layer with computing resources. Consequently, quality of service (QoS) degrade due to restricted resources of edge computing and higher execution latency due to congestion. To handle offloaded tasks in a rapidly changing dynamic environment, finding an optimal target server is still challenging. Therefore, a new cooperative offloading method to control edge computing resources is needed to allocate limited resources between distributed edges efficiently. This paper suggests the LODO (linked-object dynamic offloading) algorithm that provides an ideal balance between edges by considering the ready state or running state. LODO algorithm carries out tasks in the list in the order of high correlation between data and tasks through linked objects. Furthermore, dynamic offloading considers the running status of all cooperative terminals and decides to schedule task distribution. That can decrease the average delayed time and average power consumption of terminals. In addition, the resource shortage problem can settle by reducing task processing using its distributions

Design and Implementation of a Trust Information Management Platform for Social Internet of Things Environments

As the vast amount of data in social Internet of Things (IoT) environments considering interactions between IoT and people is accumulated and processed through cloud and big data technologies, the services that utilize them are applied in various fields. The trust between IoT devices and their data is recognized as the core of IoT ecosystem creation and growth. Connection with suspicious IoT devices may pose a risk to services and system operation. Therefore, it is essential to analyze and manage trust information for devices, services, and people, as well as to provide the trust information to the other devices or users that need it. This paper presents a trust information management framework which contains a generic IoT reference model with trust capabilities to achieve the goal of converged trust information management. Additionally, a trust information management platform (TIMP) consisting of trust agents, trust information brokers, and trust information management systems has been proposed, which aims to provide trustworthy and safe interactions among people, virtual objects, and physical things. Implementing and deploying a TIMP enables a trustworthy ecosystem to be built while activating social IoT businesses by reducing transaction costs, as well as by eliminating the uncertainties in the use of social IoT services and data transactions

Improving digital image watermarking by means of optimal channel selection

Supporting safe and resilient authentication and integrity of digital images is of critical importance in a time of enormous creation and sharing of these contents. This paper presents an improved digital image watermarking model based on a coefficient quantization technique that intelligently encodes the owner’s information for each color channel to improve imperceptibility and robustness of the hidden information. Concretely, a novel color channel selection mechanism automatically selects the optimal HL4 and LH4 wavelet coefficient blocks for embedding binary bits by adjusting block differences, calculated between LH and HL coefficients of the host image. The channel selection aims to minimize the visual difference between the original image and the embedded image. On the other hand, the strength of the watermark is controlled by a factor to achieve an acceptable tradeoff between robustness and imperceptibility. The arrangement of the watermark pixels before shuffling and the channel into which each pixel is embedded is ciphered in an associated key. This key is utterly required to recover the original watermark, which is extracted through an adaptive clustering thresholding mechanism based on the Otsu’s algorithm. Benchmark results prove the model to support imperceptible watermarking as well as high robustness against common attacks in image processing, including geometric, non-geometric transformations, and lossy JPEG compression. The proposed method enhances more than 4 dB in the watermarked image quality and significantly reduces Bit Error Rate in the comparison of state-of-the-art approaches

Traffic Behavior Recognition Using the Pachinko Allocation Model

CCTV-based behavior recognition systems have gained considerable attention in recent years in the transportation surveillance domain for identifying unusual patterns, such as traffic jams, accidents, dangerous driving and other abnormal behaviors. In this paper, a novel approach for traffic behavior modeling is presented for video-based road surveillance. The proposed system combines the pachinko allocation model (PAM) and support vector machine (SVM) for a hierarchical representation and identification of traffic behavior. A background subtraction technique using Gaussian mixture models (GMMs) and an object tracking mechanism based on Kalman filters are utilized to firstly construct the object trajectories. Then, the sparse features comprising the locations and directions of the moving objects are modeled by PAMinto traffic topics, namely activities and behaviors. As a key innovation, PAM captures not only the correlation among the activities, but also among the behaviors based on the arbitrary directed acyclic graph (DAG). The SVM classifier is then utilized on top to train and recognize the traffic activity and behavior. The proposed model shows more flexibility and greater expressive power than the commonly-used latent Dirichlet allocation (LDA) approach, leading to a higher recognition accuracy in the behavior classification