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In recent years, as IT technology has progressed, mobile devices have been created that enable various manual tasks to be automated and portable. A variety of mobile devices has computing, storage, and Internet capabilities and can handle many tasks. When miniaturized mobile devices perform tasks that require a large amount of computing resources due to limited computing and storage, there is a delay in operation and a non-operation state. Therefore, collaborative-based mobile cloud infrastructure (MCI) research is being conducted to provide computing services composed of mobile devices. Computation off-loading studies have been conducted for MCI’s high-performance computing, but it is difficult to build various mobile infrastructures and verify algorithm performance. In addition, performance verification is performed in a predetermined MCI environment or is carried out through small-scale test equipment. This causes waste of time, cost, and manpower for constructing the environment. Various studies have been conducted for this purpose, but there is a difficulty in performance verification and analysis since only the results are displayed or outputted in text form. In this paper, we propose a mobile cloud infrastructure simulator (MCIS) for computing off-loading, resource management, mobile deployment, and mobile information for MCI. MCIS enables user tasks, resource allocation methods, and various mobile device performance settings. In addition, visualization of the operating state makes it easy to analyze the performance of the user, and it is possible to grasp the problems that occur during operation.

The advancement of mobile Internet and ubiquitous computing is facilitating various crowdsourcing services in which individuals or organizations obtain goods and services with less time and monetary cost. Recently, crowdphotographing, an emerging self-service mode over the mobile Internet, is to recruit several users to take the pictures via incentive mechanism. Mobile users can earn the money by executing their requested sensing tasks. Thus, how to make an efficient pricing strategy is becoming a challenge issue in crowdphotographing. To this end, this paper mainly investigates the rationality and optimization of task pricing for crowdphotographing. First, we analyze the correlation among tasks pricing, location of members, and tasks. Then, a multivariable linear regression model is adopted for determining the task pricing strategy. Further, an improved pricing model is devised by considering the package of several tasks that can be packaged in terms of their locations distribution.

The security industry has recently paid attention to distributed ledger technology as the fundamental concept of blockchain, and it is utilizing the technology’s transparency and integrity features in a variety of security applications. This paper reviews the case of KT—a Korean company that implemented the blockchain-based IoT security solution platform to solve IoT security vulnerabilities—the case of Shh Korea, which applied blockchain technology to enhance the security of a messaging platform, and the case of Kakao, which applied blockchain technology to PKI for a payment authentication system. The current problems as well as the potential and limitations are then analyzed.

In recent years, building the cloud based on wireless mesh networks as well as wired networks is rapidly increased for processing the big data. However, existing scheduling and routing protocols cannot support processing the big data efficiently in the cloud, because the each flow path is determined before the data are transmitted based on some routing strategies in the wireless mesh networks. Currently, an important factor that should be considered is that the link capacity between mesh routers can be changed based on the current interference of the flow of other mesh routers. In general, network availability is also influenced by the interference related to the other layers in the protocol stack. In this paper, we study wireless mesh networks and propose JRS-S and JRS-M algorithms, which utilize both route discovery and resource allocation at the same time, in order to maximize capacity of the wireless mesh network in the cloud computing. Our algorithms for each flow use a cross-layer design method based on numerical modeling in order to adaptively control data scheduling at the link layer and find a high data rate path with minimum interference at the network layer. We analyze the optimal capacity of the wireless mesh networks for maximizing network utilization using a numerical solution tool. Through analysis, we also verify that our algorithms can improve system capacity by efficiently distributing a gateway load and that it can enhance the system availability.

Due to recent development in the IT industry, there has been an increase in the use of software in various fields and accordingly, the frequency of usage of open source software has increased and it is being used in a wide range. However, with the increase in the use of open source software, we can be exposed to the various problems based on the weakness of open source. The weakness of heartbleed ‘OpenSSL’ has actually brought about much damage world-wide. In addition, as the open source software makes a lot of increases in quantity and has the property of the unprofessional and similar development, it is not proper to apply the existing secure code checking system. It means that the checking system faster than the existing secure coding checking system is required to meet the demand of the fast growing open source software market. This requires the code based analysis which is proper for not the simple static analysis but for the cloud computing. In addition, to make up for the unprofessional and similar development, the secure code checking system based on the smart fuzzing like neuro fuzzy is required. Thus, in this paper, we have suggested a smart fuzzing system made in connection to the black box and white box test which can effectively detect/distinguish the weakness of software and also suggested a way to verify whether it is exploitable and a way to automatically produce exploit through this. Also mis-detection and un-detection was minimized and a weakness analysis method based on symbols was applied to accurately detect security weakness. The suggested system can secure reliability of the open source software by deducting the reason for security weakness of open source software which is used in various industries and can prevent weaknesses earlier on in open source software made afterwards. Also, it is expected to enhance the reliability of the open source software developer and the company using it.

Recently, cloud computing and cyber-physical system (CPS) are definitely basic elements in real industrial field. In particular, security is a mandatory factor for communications and operations in these environments. However, the existing CPS security mechanism is not suitable to the telecommunication framework provided by the standards. In addition, random number function of high entropy must be used to enhance security with encrypted communications and must support perfect secrecy. Random number functions supported by the devices instead of servers do not have sufficient entropy. Entropy injection and seed replacement are also impractical. In this paper, we propose a security scheme which provides light-weight secure CPS information transmission and device control scheme in integration of CPS and cloud computing. In this scheme, a light-weight security scheme can multicast event information to users who have heterogeneous device information access authorities based on oneM2M standards, and also be able to manage the control devices. This paper provides performance analysis of proposed scheme and confirms its security and efficiency.

With the widespread use of unmanned aerial vehicles (UAVs), there is an increasing demand for the development of their control technology. The key interaction technology between humans and UAVs needs to focus on the human body language, which comprises rich interactive information, as it is the most natural, intuitive, and easy to master approach of interpersonal communication for humans. Therefore, the research on human motion estimation for UAV control is of considerable practical significance. Recently, deep learning has made breakthroughs in speech, image recognition and, other fields, and has crushed the performance of traditional methods in many fields. However, in the field of human motion estimation, deep learning has been progressing slowly. To overcome the limitations of the traditional methods and explore the application of deep learning methods in the field of motion estimation, this study proposes a method to estimate human arm motion using deep learning networks. We proposed a bidirectional two-layer LSTM fusion network to estimate the forearms' motion according to the hand position measured by HTC Vive. The performance was verified using a real data set. The average Euclidean distance similarity can reach up to 56%. In comparison with the traditional methods, the proposed method demonstrated wider applicability and better performance.

The rapid development of Internet technology and the spread of various smart devices have enabled the creation of a convenient environment used by people all around the world. It has become increasingly popular, with the technology known as the Internet of Things (IoT). However, both the development and proliferation of IoT technology have caused various problems such as personal information leakage and privacy violations due to attacks by hackers. Furthermore, countless devices are connected to the network in the sense that all things are connected to the Internet, and network attacks that have thus far been exploited in the existing PC environment are now also occurring frequently in the IoT environment. In fact, there have been many security incidents such as DDoS attacks involving the hacking of IP cameras, which are typical IoT devices, leakages of personal information and the monitoring of numerous persons without their consent. While attacks in the existing Internet environment were PC-based, we have confirmed that various smart devices used in the IoT environment&mdash, such as IP cameras and tablets&mdash, can be utilized and exploited for attacks on the network. Even though it is necessary to apply security solutions to IoT devices in order to prevent potential problems in the IoT environment, it is difficult to install and execute security solutions due to the inherent features of small devices with limited memory space and computational power in this aforementioned IoT environment, and it is also difficult to protect certificates and encryption keys due to easy physical access. Accordingly, this paper examines potential security threats in the IoT environment and proposes a security design and the development of an intelligent security framework designed to prevent them. The results of the performance evaluation of this study confirm that the proposed protocol is able to cope with various security threats in the network. Furthermore, from the perspective of energy efficiency, it was also possible to confirm that the proposed protocol is superior to other cryptographic protocols. Thus, it is expected to be effective if applied to the IoT environment.