• International Journal of Computer Science & Network Security
• /
• v.21 no.9
• /
• pp.1-10
• /
• 2021

The Internet of things (IoT) is the main advancement in data processing and communication technologies. In IoT, intelligent devices play an exciting role in wireless communication. Although, sensor nodes are low-cost devices for communication and data gathering. However, sensor nodes are more vulnerable to different security threats because these nodes have continuous access to the internet. Therefore, the multiparty security credential-based key generation mechanism provides effective security against several attacks. The key generation-based methods are implemented at sensor nodes, edge nodes, and also at server nodes for secure communication. The main challenging issue in a collaborative key generation scheme is the extensive multiplication. When the number of parties increased the multiplications are more complex. Thus, the computational cost of batch key and multiparty key-based schemes is high. This paper presents a Secure Multipart Key Distribution scheme (SMKD) that provides secure communication among the nodes by generating a multiparty secure key for communication. In this paper, we provide node authentication and session key generation mechanism among mobile nodes, head nodes, and trusted servers. We analyzed the achievements of the SMKD scheme against SPPDA, PPDAS, and PFDA schemes. Thus, the simulation environment is established by employing an NS 2. Simulation results prove that the performance of SMKD is better in terms of communication cost, computational cost, and energy consumption.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.17 no.6
• /
• pp.375-382
• /
• 2022

This paper proposes a multi-core cooperative computing structure considering the heterogeneous features of automotive embedded on-chip software. The automotive embedded software has the heterogeneous execution flow properties for various hardware drives. Software developed with a homogeneous execution flow without considering these properties will incur inefficient overhead due to core latency and load. The proposed method was evaluated on an target board on which a automotive MCU (micro-controller unit) with built-in multi-cores was mounted. We demonstrate an overhead reduction when software including common embedded system tasks, such as ADC sampling, DSP operations, and communication interfaces, are implemented in a heterogeneous execution flow. When we used the proposed method, embedded software was able to take advantage of idle states that occur between heterogeneous tasks to make efficient use of the resources on the board. As a result of the experiments, the power consumption of the board decreased by 42.11% compared to the baseline. Furthermore, the time required to process the same amount of sampling data was reduced by 27.09%. Experimental results validate the efficiency of the proposed multi-core cooperative heterogeneous embedded software execution technique.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.3
• /
• pp.794-815
• /
• 2023

The rapid development of mobile communication not only has made the industry gradually diversified, but also has enhanced the service quality requirements of users. In this regard, it is imperative to consider jointly network slicing and mobile edge computing. The former mainly ensures the requirements of varied vertical services preferably, and the latter solves the conflict between the user's own energy and harsh latency. At present, the integration of the two faces many challenges and need to carry out at different levels. The main target of the paper is to minimize the energy consumption of the system, and introduce a multi-slice joint task offloading and resource allocation scheme for massive multiple input multiple output enabled heterogeneous networks. The problem is formulated by collaborative optimizing offloading ratios, user association, transmission power and resource slicing, while being limited by the dissimilar latency and rate of multi-slice. To solve it, assign the optimal problem to two sub-problems of offloading decision and resource allocation, then solve them separately by exploiting the alternative optimization technique and Karush-Kuhn-Tucker conditions. Finally, a novel slices task offloading and resource allocation algorithm is proposed to get the offloading and resource allocation strategies. Numerous simulation results manifest that the proposed scheme has certain feasibility and effectiveness, and its performance is better than the other baseline scheme.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.11
• /
• pp.1577-1585
• /
• 2022

As the interest in the 4th industrial revolution and metaverse increases, metaverse with multi edge structure is proposed and noted. Metaverse is a structure that can create digital doctor-like system through a large amount of image processing and data transmission in a multi edge system. Since metaverse application requires calculating performance, which can reconstruct 3-D space, edge hardware's insufficient calculating performance has been a problem. To provide streamable AI software in runtime, image processing, and data transmission, which is edge's loads, needs to be lightweight. Also lightweight at the edge leads to power consumption reduction of the entire metaverse application system. In this paper, we propose collaborative edge-cloud image processing with remote image processing method and Region of Interest (ROI) to overcome edge's power performance and build streamable and runtime executable AI software. The proposed structure was implemented using a PC and an embedded board, and the reduction of time, power, and network communications were verified.

• Toxicological Research
• /
• v.6 no.2
• /
• pp.167-182
• /
• 1990

Dietary restriction (caloric restriction) is the only intervention which has been reliably shown to extend the maximum life span of warm-blooded animals and delay the many phenomena associated with aging. It is also one of the most effective modulators of toxicity, especially cancer endpoints. In spite of the known modulator effects of caloric restriction, the biological mechanisms responsible for these effects had not been in vestigated until recently. The National Center for Toxicological Research (NCTR), in a collaborative effort with the National Institute of Aging (NIA), initiated a project whereby nine (9) combinations of rodent species/strains and diets were fed both restricted and ad libitum. The NIA's initiative was to identify biomarkers of aging whereas NCTR's initiative was to identify the biological effects associated with the profound effects caloric restriction has in protecting against both spontaneous (age-related) and chemically-induced toxic endpoints. Independent of sex or species, caloric restriction has similar effects on body temperature, oxygen consumption and $CO_2$production. Caloric restriction also decreased lipid glycolysis and metabolism in rats and mice, which suggest decreased production of metabolites which could lead to fatty acid epoxide formation. The age-associated loss of ciradian regulation of intermediate enzymes is also significantly reduced. Moreover, caloric restriction reduced the age-associated feminization of sexually dimorphic liver isozymes, increased several glucocorticoid responsive isozymes, elevated glucagon/insulin ratios, produced less microsomal superoxide and enhanced the capacity for utilzing detoxicating metabolic pathways. Calorically restricted rats have less than half the number of aflatoxin ($AFB_1$)-DNA adducts than ad libitum animals and urinary excretion of $AFB_1$ was increased significantly. Finally, DNA repair mechanisms are enhanced and oncogene expression is decreased in calorically restricted animals.

• STI Policy Review
• /
• v.7 no.2
• /
• pp.60-84
• /
• 2016

What impact does the sharing economy have on existing businesses? This paper empirically examines how Uber transformed the taxi industry in New York City. Using a regression model controlling various potential influencing factors, we find no direct evidence that daily trips or revenue per taxi driver decreased since Uber entered the taxi industry. However, a closer investigation into other dimensions of taxi trips reveals that taxi drivers were forced to change their way of doing businesses to retain existing daily trips and revenue. Since Uber crowded out yellow taxis from the central area of Manhattan, yellow taxis responded by serving customers outside of the Manhattan borough. From enlarging their geographical coverage and serving customers that were previously ignored, yellow taxis were able to retain their previous level of taxi trips and market share. We also find that yellow taxis responded by improving their service quality to better serve customers' needs. Our result suggests that incumbents actively responded to Uber's entry and provided substantial benefit to consumers. Combined with the incumbent's response, the sharing economy transformed the existing market in a welfare-enhancing way. This paper provides managerial and policy implication on how incumbents affected by the disruptions of the sharing economy should respond. Even though it might be yet premature to examine the impact of Uber, results suggest that incumbents have effectively defended against Uber's entry so far. We conclude that the sharing economy and the existing economy can create positive value in our society through well-intentioned competition, complementing each other's weaknesses and strengths.

• Knowledge Management Research
• /
• v.22 no.3
• /
• pp.55-73
• /
• 2021

The sharing economy is considered as a collaborative consumption which enables customers to share unused resources. This study investigated the key factors affecting consumer loyalty in the context of sharing accommodation. Emotions, perceived value and self-image consistency were posited as key antecedents of enhancing customer loyalty. Authentic experience, home amenities, and price fairness were also considered as Airbnb's selection attributes. Airbnb was selected a survey target because it is the largest company in the domain of shared accommodation market. The research model was analyzed for 294 Airbnb customer through structural equation models. Additionally, this paper examine Airbnb customers' experiences by topic modelling method posted on the Naver blog. Based on the understanding of the key factors affecting customer loyalty to sharing accommodation, the analysis results contribute to establish effective marketing and operation strategies by enhancing customer experience.

• The Journal of the Korea Contents Association
• /
• v.19 no.3
• /
• pp.171-182
• /
• 2019

As online shopping malls continue to grow in popularity, various chances of consumption are provided to customers. Customers decide the purchase by exploiting information provided by shopping malls such as the reviews of actual purchasing users, the detailed information of items, and so on. It is required to provide objective and reliable information because customers have to decide on their own whether the massive information is credible. In this paper, we propose a personalized recommendation method considering an item confidence to recommend reliable items. The proposed method determines user preferences based on various behaviors for personalized recommendation. We also propose an user preference measurement that considers time weights to apply the latest propensity to consume. Finally, we predict the preference score of items that have not been used or purchased before, and we recommend items that have highest scores in terms of both the predicted preference score and the item confidence score.

• Journal of Convergence for Information Technology
• /
• v.9 no.8
• /
• pp.110-122
• /
• 2019

IT innovation, cultural revolution based on smart and social networks diversified sharing economy services. Due to the rising of business utilizing the sharing economy concept, it is important to better understand the motivational factors that drive and deterrent sharing economy services in the marketplace. Based on responses from 809 adult users, 3 drivers and 2 deterrents affecting intention to use of sharing economy services were identified. Then this study categorized sharing economy services as three types of segments based on consumer perceptions and subjectivity, and analyzed differencies of perceptions on motivational factors between groups. As a result, redistribution market group has shown meaningful different average scores on economic benefit, sustainability and social risk with other groups. Based on the empirical evidence, this study suggests several propositions for future studies and implications for sharing economy businesses on how to formulate optical strategies and manage users.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.11
• /
• pp.3565-3583
• /
• 2022

The task scheduling problem has received a lot of attention in recent years as a crucial area for research in the cloud environment. However, due to the difference in objectives considered by service providers and users, it has become a major challenge to resolve the conflicting interests of service providers and users while both can still take into account their respective objectives. Therefore, the task scheduling problem as a bi-objective game problem is formulated first, and then a task scheduling model based on the bi-objective game (TSBOG) is constructed. In this model, energy consumption and resource utilization, which are of concern to the service provider, and cost and task completion rate, which are of concern to the user, are calculated simultaneously. Furthermore, a many-objective evolutionary algorithm based on a partitioned collaborative selection strategy (MaOEA-PCS) has been developed to solve the TSBOG. The MaOEA-PCS can find a balance between population convergence and diversity by partitioning the objective space and selecting the best converging individuals from each region into the next generation. To balance the players' multiple objectives, a crossover and mutation operator based on dynamic games is proposed and applied to MaPEA-PCS as a player's strategy update mechanism. Finally, through a series of experiments, not only the effectiveness of the model compared to a normal many-objective model is demonstrated, but also the performance of MaOEA-PCS and the validity of DGame.