• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.579-586
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• 2023

A wireless sensor network (WSN) has limited battery power because it is used wirelessly using low-cost small sensors. Since the battery cannot be replaced, the lifespan of the sensor node is directly related to the lifespan of the battery, so power must be used efficiently to maximize the lifespan of the network. In this study, based on PEGASIS, a representative energy-efficient routing protocol, we propose a protocol that classifies layers according to the distance from the sink node and configures multiple chains rather than one chain. The proposed protocol can increase network lifespan by reducing the transmission distance between nodes to prevent unnecessary energy consumption.

• Smart Structures and Systems
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• v.30 no.6
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• pp.583-599
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• 2022

In this paper, a multi-objective wireless sensor network configuration optimization method is proposed. The proposed method aims to determine the optimal information and lifespan wireless sensor network for structural health monitoring of large-scale infrastructures. In particular, cluster-based wireless sensor networks with multi-type of sensors are considered. To optimize the lifetime of the wireless sensor network, a cluster-based network optimization algorithm that optimizes the arrangement of cluster heads and base station is developed. On the other hand, based on the Bayesian inference, the uncertainty of the estimated parameters can be quantified. The coefficient of variance of the estimated parameters can be obtained, which is utilized as a holistic measure to evaluate the estimation accuracy of sensor configurations with multi-type of sensors. The proposed method provides the optimal wireless sensor network configuration that satisfies the required estimation accuracy with the longest lifetime. The proposed method is illustrated by designing the optimal wireless sensor network configuration of a cable-stayed bridge and a space truss.

• ETRI Journal
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• v.36 no.3
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• pp.418-428
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• 2014

In social network services, such as Facebook, Google+, Twitter, and certain postings attract more people than others. In this paper, we propose a novel method for predicting the lifespan and retweet times of tweets, the latter being a proxy for measuring the popularity of a tweet. We extract information from retweet graphs, such as posting times; and social, local, and content features, so as to construct prediction knowledge bases. Tweets with a similar topic, retweet pattern, and properties are sequentially extracted from the knowledge base and then used to make a prediction. To evaluate the performance of our model, we collected tweets on Twitter from June 2012 to October 2012. We compared our model with conventional models according to the prediction goal. For the lifespan prediction of a tweet, our model can reduce the time tolerance of a tweet lifespan by about four hours, compared with conventional models. In terms of prediction of the retweet times, our model achieved a significantly outstanding precision of about 50%, which is much higher than two of the conventional models showing a precision of around 30% and 20%, respectively.

• Journal of Sensor Science and Technology
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• v.28 no.1
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• pp.64-70
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• 2019

Wireless sensors have been developed in numerous ways for enhancing the convenience of installation, management and maintenance of sensors. Energy harvesting wireless sensors, which can collect energy from the external environment for permanent usage without the need of recharging and exchanging batteries, have been developed and employed used in Internet of Things and at various industrial sites. Energy harvesting wireless sensors are significantly affected by the sensor lifespan to sudden variation in the external environment. Furthermore, reduction in the sensor operating timespan can greatly affect the characteristics of the devices connected through a network. In this paper, a system performance index is proposed that can comprehensively evaluate the lifespan of a solar cell wireless sensor, determine the characteristics of devices connected to the associated network, and recommend dynamic power distribution control for improving the system performance index. Improvement in the system performance index was verified by applying the proposed dynamic power distribution control to an air conditioner network system using a solar cell wireless sensor. Obtained results corroborate that the dynamic power distribution control can extend the lifespan of the incorporated wireless sensor and reduce the air conditioner's power consumption.

• Asia Marketing Journal
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• v.17 no.1
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• pp.69-95
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• 2015

This paper examines a highly used social networking site (SNS) by studying the behavior of more than 11 million members over a 20 month period. The importance of the most highly active members to the overall network is demonstrated by the significant fraction of total visits by extremely active members in a given period but such members have surprisingly short lifespans (an average of only 2.5 months) as social hubs. We form and test a number of hypotheses concerning these social hubs and the determinants of their lifespan. We find that the speed of achieving social hub status increases the lifespan of a social hub. The norm of reciprocity is strongly confirmed to be present in the social hub population as visits are reciprocated. We also find that increasing local dominance in terms of activities over neighboring agents leads to a longer lifespan of a social hub. Contrary to expectations, local clustering in the vicinity of social hubs is smaller (rather than larger) than overall clustering. We discuss managerial implications in the paper.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.17-26
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• 2022

Sensor Nodes play a major role to monitor and sense the variations in physical space in various real-time application scenarios. These nodes are powered by limited battery resources and replacing those resource is highly tedious task along with this it increases implementation cost. Thus, maintaining a good network lifespan is amongst the utmost important challenge in this field of WSN. Currently, energy efficient routing techniques are considered as promising solution to prolong the network lifespan where multi-hop communications are performed by identifying the most energy efficient path. However, the existing scheme suffer from performance related issues. To solve the issues of existing techniques, a novel hybrid technique by merging particle swarm optimization and game theory model is presented. The PSO helps to obtain the efficient number of cluster and Cluster Head selection whereas game theory aids in finding the best optimized path from source to destination by utilizing a path selection probability approach. This probability is obtained by using conditional probability to compute payoff for agents. When compared to current strategies, the experimental study demonstrates that the proposed GTPSO strategy outperforms them.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.147-170
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• 2022

Although the number of patent which is one of the core outputs of technological innovation continues to increase, the number of low-value patents also hugely increased. Therefore, efficient evaluation of patents has become important. Estimation of patent lifespan which represents private value of a patent, has been studied for a long time, but in most cases it relied on a linear model. Even if machine learning methods were used, interpretation or explanation of the relationship between explanatory variables and patent lifespan was insufficient. In this study, patent lifespan (number of renewals) is predicted based on the idea that patent lifespan represents the value of the patent. For the research, 4,033,414 patents applied between 1996 and 2017 and finally granted were collected from USPTO (US Patent and Trademark Office). To predict the patent lifespan, we use variables that can reflect the characteristics of the patent, the patent owner's characteristics, and the inventor's characteristics. We build four different models (Ridge Regression, Random Forest, Feed Forward Neural Network, Gradient Boosting Models) and perform hyperparameter tuning through 5-fold Cross Validation. Then, the performance of the generated models are evaluated, and the relative importance of predictors is also presented. In addition, based on the Gradient Boosting Model which have excellent performance, Accumulated Local Effects Plot is presented to visualize the relationship between predictors and patent lifespan. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the evaluation reason of individual patents, and discuss applicability to the patent evaluation system. This study has academic significance in that it cumulatively contributes to the existing patent life estimation research and supplements the limitations of existing patent life estimation studies based on linearity. It is academically meaningful that this study contributes cumulatively to the existing studies which estimate patent lifespan, and that it supplements the limitations of linear models. Also, it is practically meaningful to suggest a method for deriving the evaluation basis for individual patent value and examine the applicability to patent evaluation systems.

• IMMUNE NETWORK
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• v.5 no.4
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• pp.252-257
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• 2005

Background: This study was designed to investigate the effect of exercise training on defense mechanism of chronic degenerative disease, aging, and memory impairments of senescence-accelerated mouse (SAM)P8 under the hypothesis that "Senile dementia may be prevented by regular exercises". Methods: To evaluate the effects of exercise training on the defense mechanism of aging and memory impairment, SAMP8 were divided into two groups, the control group and exercise training groups. the exercise training group were performed with low $(\dot{V}O_2max\;25{\sim}33%)$, middle ($\dot{V}O_2max$ 50%) and high $(\dot{V}O_2max\;66{\sim}75%)$ intensity exercise. All SAMP8 mice were fed experimental diet ad libitum until 4, 8 months, and dead period. Results: Median lifespan in middle exercise group resulted in a significantly increased (23.5% and 18.7%, respectively), whereas these lifespan in high exercise group resulted in an unexpectedly decreased (13.5% and 12.1%, respectively) compared with control group. Body fat levels in 4 and 8 months of age were significantly decreased 43% to 51% in middle exercise group, whereas were remarkably deceased to 57% in high exercise group compared with control group. It is believed that extended median and maximum lifespan may be effected by calory restriction through the exercise training. Acetylcholine (ACh) levels were significantly increased 6.7% and 8.5% in middle and high exercise groups, and also choline acetyltransfease (ChAT) activities were significantly increased 10.3% and 11.9% in middle and high exercise groups. Conclusion: These results suggest that proper and regular exercises such as middle group ($\dot{V}O_2max$ 50%) may play an effective role in attenuating an oxygen radicals and may play an important role in improving a learning and memory impairments of senile dementia.

• Journal of information and communication convergence engineering
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• v.19 no.2
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• pp.67-72
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• 2021

Wireless sensor networks (WSNs) require an enormous number of sensor nodes (SNs) to maintain processing, sensing, and communication capabilities for monitoring targeted sensing regions. SNs are generally operated by batteries and have a significantly restricted energy consumption; therefore, it is necessary to discover optimization techniques to enhance network lifetime by saving energy. The principal focus is on reducing the energy consumption of packet sharing (transmission and receiving) and improving the network lifespan. To achieve this objective, this paper presents a novel improved energy-efficient cluster-based routing protocol (IECRP) that aims to accomplish this by decreasing the energy consumption in data forwarding and receiving using a clustering technique. Doing so, we successfully increase node energy and network lifetime. In order to confirm the improvement of our algorithm, a simulation is done using matlab, in which analysis and simulation results show that the performance of the proposed algorithm is better than that of two well-known recent benchmarks.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.6-11
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• 2021

Routing protocols play a pivotal role in the energy management and lifespan of any Wireless Sensor Network. Lower network lifetime has been one of the biggest concerns in LEACH protocol due to dead nodes. The LEACH protocol suffers from uneven energy distribution problem due to random selection of a cluster head. The cluster head has much greater responsibility compared to other non- cluster head nodes and consumes greater energy for its roles. This results in early dead nodes due to energy lost for the role of cluster- head. This study proposes an approach to balance the energy consumption of the LEACH protocol by using a semi-deterministic opportunity coefficient to select the cluster head. This is calculated in each node with the battery energy level and node ID. Ultimately, based on the opportunity cost, cluster head will be selected and broadcasted for which other nodes with higher opportunity cost will agree. It minimizes the chances of nodes with lower battery level being elected as cluster head. Our simulation experiments demonstrate that cluster heads chosen using our proposed algorithm perform better than those using the legacy LEACH protocol.