• Journal of Information Processing Systems
• /
• 제18권3호
• /
• pp.389-401
• /
• 2022

The business of Internet of Vehicles (IoV) is growing rapidly, and the large amount of data exchange has caused problems of large mobile network communication delay and large energy loss. A strategy for resource allocation of IoV communication based on mobile edge computing (MEC) is thus proposed. First, a model of the cloud-side collaborative cache and resource allocation system for the IoV is designed. Vehicles can offload tasks to MEC servers or neighboring vehicles for communication. Then, the communication model and the calculation model of IoV system are comprehensively analyzed. The optimization objective of minimizing delay and energy consumption is constructed. Finally, the on-board computing task is coded, and the optimization problem is transformed into a knapsack problem. The optimal resource allocation strategy is obtained through genetic algorithm. The simulation results based on the MATLAB platform show that: The proposed strategy offloads tasks to the MEC server or neighboring vehicles, making full use of system resources. In different situations, the energy consumption does not exceed 300 J and 180 J, with an average delay of 210 ms, effectively reducing system overhead and improving response speed.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권6호
• /
• pp.2709-2734
• /
• 2020

The growing demand to make wireless data services 5G compatible has necessitated the development of an energy-efficient approach for an effective new wireless environment. In this paper, we first propose a cognitive sensor node (CSN) based game theory for deriving energy via a primary user-transmitted radio frequency signal. Cognitive users' time was segmented into three phases based on a time switching protocol: energy harvest, spectrum sensing and data transmission. The proposed model chooses the optimal energy-harvesting phase as the effected factor. We further propose a distributed energy-harvesting model as a utility function via pricing techniques. The model is a non-cooperative game where players can increase their net benefit in a selfish manner. Here, the price is described as a function pertaining to transmit power, which proves that the proposed energy harvest game includes Nash Equilibrium and is also unique. The best response algorithm is used to achieve the green connection between players. As a result, the results obtained from the proposed model and algorithm show the advantages as well as the effectiveness of the proposed study. Moreover, energy consumption was reduced significantly (12%) compared to the benchmark algorithm because the proposed algorithm succeeded in delivering energy in micro which is much better compared to previous studies. Considering the reduction and improvement in power consumption, we could say the proposed model is suitable for the next wireless environment represented in 5G.

• 전자공학회논문지CI
• /
• 제44권3호
• /
• pp.14-22
• /
• 2007

센서 네트워크를 구성하는 센서 노드들은 제한적인 에너지를 가지고 있으며, 한번 배치되면 더 이상 에너지의 추가 공급이 어렵다. 따라서 제한적인 에너지를 효율적으로 이용하는 기법이 중요하다. 일반적으로 인접한 센서 노드는 유사한 정보를 가지므로, 유사한 정보의 중복 전송으로 인한 에너지 낭비가 크다. 따라서 로컬 클러스터를 형성하고, 클러스터 헤드가 자신의 클러스터 멤버로부터 수집된 데이터를 집약(data aggregation)하는 클러스터링 기법이 유사한 정보의 중복 전송을 예방할 수 있어 저전력 구동에 효과적이다. 그러나 클러스터링 기법의 성능은 클러스터 헤드의 선출 방법, 클러스터의 크기 및 수 등에 따라 달라진다. 따라서 클러스터링 기법의 에너지 절감 효과를 최대화하기 위해 이러한 요인들을 최적화해야 한다. 본 논문에서는 대표적인 클러스터링 알고리즘인 LEACH의 에너지 소비량을 모델링하고, 이를 바탕으로 최적의 클러스터 수를 구한다. 본 논문에서 도출한 모델링 기법은 시뮬레이션을 통해 측정한 실제 네트워크의 에너지 소비량과 비교할 때 최소 80% 이상의 정확도를 보여 기존의 모델링과 비교하여 우수하다.

• ETRI Journal
• /
• 제43권3호
• /
• pp.436-446
• /
• 2021

In this paper, we propose an energy-efficient unmanned aerial vehicle (UAV) relaying network. In this network, the channels between UAVs and ground transceivers are model-free. A UAV acting as a flying relay explores better channels to assist in efficient data delivery between two ground nodes. The full-duplex relaying mode is applied for potential energy efficiency (EE) improvements. With the genetic algorithm, we manage to optimize the UAV trajectory for any arbitrary radio map scenario. Numerical results demonstrate that compared to other schemes (eg, fixed trajectory/speed policies), the proposed algorithm performs better in terms of EE. Additionally, the impact of self-interference on average EE is also investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권1호
• /
• pp.97-115
• /
• 2022

Smart power grid is a user friendly system that transforms the traditional electric grid to the one that operates in a co-operative and reliable manner. Demand Response (DR) is one of the important components of the smart grid. The DR programs enable the end user participation by which they can communicate with the electricity service provider and shape their daily energy consumption patterns and reduce their consumption costs. The increasing demands of electricity owing to growing population stresses the need for optimal usage of electricity and also to look out alternative and cheap renewable sources of electricity. The solar and wind energy are the promising sources of alternative energy at present because of renewable nature and low cost implementation. The proposed work models a smart home with renewable energy units. The random nature of the renewable sources like wind and solar energy brings an uncertainty to the model developed. A stochastic dual descent optimization method is used to bring optimality to the developed model. The proposed work is validated using the simulation results. From the results it is concluded that proposed work brings a balanced usage of the grid power and the renewable energy units. The work also optimizes the daily consumption pattern thereby reducing the consumption cost for the end users of electricity.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제4권4호
• /
• pp.560-574
• /
• 2010

In this paper, the relationship between the energy efficiency and spectrum efficiency in a two-cell cellular network is obtained, and the impact of multi-antenna on the energy efficiency of cellular network is analyzed and modeled based on two-state Markovian wireless channels. Then, the energy efficiency of multi-cell cellular networks with co-channel interference is investigated. Simulation results verify the proposed model and the energy-spectrum efficiency tradeoffs in cellular networks with multi-antenna and co-channel interference.

• 한국환경과학회지
• /
• 제24권11호
• /
• pp.1443-1450
• /
• 2015

Wastewater treatment plant(WWTP) has been recognized as a high energy consuming plant. Usually many WWTPs has been operated in the excessive operation conditions in order to maintain stable wastewater treatment. The energy required at WWTPs consists of various subparts such as pumping, aeration, and office maintenance. For management of energy comes from process operation, it can be useful to operators to provide some information about energy variations according to the adjustment of operational variables. In this study, multiple regression analysis was used to establish an energy estimation model. The independent variables for estimation energy were selected among operational variables. The $R^2$ value in the regression analysis appeared 0.68, and performance of the electric power prediction model had less than ${\pm}5%$ error.

• Asian Journal of Business Environment
• /
• 제14권3호
• /
• pp.23-35
• /
• 2024

Purpose: The study focuses on the impact of Foreign Direct Investment (FDI), trade openness, and energy consumption on carbon dioxide emissions in the Ivory Coast. It aims to quantitatively evaluate the effects of FDI, energy consumption, and trade openness on CO₂ emissions in Ivory Coast. Research design, data, and methodology: The research uses an econometric framework and the Autoregressive Distributed Lag (ARDL) model to analyze time-series data from 1980 to 2021 between these factors. Results: The analysis revealed that FDI significantly impacts the carbon dioxide emissions, FDI showed a negative impact on carbon emissions in the long-run equilibrium term. Also, energy consumption impacted CO₂ emissions in the long-run equilibrium term. Conclusion: To mitigate the upsurge of CO₂ emissions in the Ivorian context, concrete policy, including enactment and adherence to strict environmental regulations, adoption and prioritization of eco-friendly products and technologies, and investment in renewable energy infrastructure are recommended. The study contributes to the global discussion on sustainable development by offering a model for similar assessments in other emerging nations facing simultaneous economic growth and environmental conservation challenges.

• 한국컴퓨터정보학회논문지
• /
• 제20권10호
• /
• pp.107-112
• /
• 2015

There are numerous hazards and toxins have been produced in many forms along with life and working environments. Nevertheless, to remove theses hazards and toxins, there are many counteracting goods manufactured, but the result is limited in specific categories. Also it costs a lot of energy waste. In this paper, we propose a model that reduce wasting energy for detecting and getting rid of the harms. It adds a multi hazards auto-detection model for user friendly include the disable. It will be controlled the minimal sensed level of the harms by individuals through the proposed model. It can conduct detecting and eliminating the harms via eco-sensors pack which is adapted in different environments. As a result, the model works to produce only essential energy to clear the hazard and toxins as soon as the harms are generated and it leads to standby power.

• 한국태양에너지학회 논문집
• /
• 제39권5호
• /
• pp.41-52
• /
• 2019

The purpose of the work is to develop a simple solar irradiance prediction model using a deep learning method, the LSTM (long term short term memory). Other than existing prediction models, the proposed one uses only the cloudiness among the information forecasted from the national meterological forecast center. The future cloudiness is generally announced with four categories and for three-hour intervals. In this work, a daily irradiance pattern is used as an input vector to the LSTM together with that cloudiness information. The proposed model showed an error of 5% for learning and 30% for prediction. This level of error has lower influence on the load prediction in typical building cases.