• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.389-395
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• 2022

A Mobile Sensor Network is widely used in real time applications. A critical need in Mobile Sensor Network is to achieve energy efficiency during routing as the sensor nodes have scarce energy resource. The nodes' mobility in MWSN poses a challenge to design an energy efficient routing protocol. Clustering helps to achieve energy efficiency by reducing the organization complexity overhead of the network which is proportional to the number of nodes in the network. This paper proposes"EERA: Energy Efficient Routing Algorithm for Mobile Sensor Network" is divided into five phases. 1, Cluster Formation 2.Cluster head and Transmission head selection 3.Path Establishment / Route discovery and 4,Data Transmission. Experimental Analysis has been done and is found that the proposed method performs better than the existing method with respect to four parameters.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.178-184
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• 2022

Renewable energy sources based on solar radiation, wind energy, geothermal energy, and biomass energy have now reached the level of industrial application. A new way to generate electricity using low-potential heat is to install a hydro-steam turbine. In hydro-steam turbines, hot water is supplied directly to turbine rotor nozzles without prior separation into steam and water in separators, which significantly increases the efficiency of hot water energy use. Such turbines are suggested to be used as autonomous energy sources in geothermal heating systems, heating water boilers and cooling systems of chemical reactors, metallurgical furnaces, etc. The authors conclude that the installation of hydro-steam turbines in heating plants and process boiler plants can also be effective if the used exhaust steam-water mixture at the turbine outlet is used to heat the network water or as return water.

• Convergence Security Journal
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• v.22 no.1
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• pp.55-60
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• 2022

This study is to analyze trends and relational structures in the energy sector related to renewable energy. For this reason, in this study, we focused on big data including SNS data. SNS utilizes the Instagram platform to collect renewable energy hash tags and use them as a word embedding method for big data analysis and social network analysis, and based on the results derived from this research, it will be used for the development of the renewable energy industry. It is expected that it can be utilized.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.87-106
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• 2024

Vehicle-to-Home (V2H) and Home Centralized Photovoltaic (HCPV) systems can address various energy storage issues and enhance demand response programs. Renewable energy, such as solar energy and wind turbines, address the energy gap. However, no energy management system is currently available to regulate the uncertainty of renewable energy sources, electric vehicles, and appliance consumption within a smart microgrid. Therefore, this study investigated the impact of solar photovoltaic (PV) panels, electric vehicles, and Micro-Grid (MG) storage on maximum solar radiation hours. Several Deep Learning (DL) algorithms were applied to account for the uncertainty. Moreover, a Reinforcement Learning HCPV (RL-HCPV) algorithm was created for efficient real-time energy scheduling decisions. The proposed algorithm managed the energy demand between PV solar energy generation and vehicle energy storage. RL-HCPV was modeled according to several constraints to meet household electricity demands in sunny and cloudy weather. Simulations demonstrated how the proposed RL-HCPV system could efficiently handle the demand response and how V2H can help to smooth the appliance load profile and reduce power consumption costs with sustainable power generation. The results demonstrated the advantages of utilizing RL and V2H as potential storage technology for smart buildings.

• Carbon letters
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• v.26
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• pp.61-65
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• 2018

Comprehensive calculations of the Rh decoration effect on zigzag CNTs with n ranging from 7 to 12 were conducted in this work to understand the effect of Rh doping on geometric structures and electronic behaviors upon metallic and semiconducting CNTs. The obtained results indicated that Rh dopant not only contributes to the deformation of C-C bonds on the sidewall of CNTs, but also transforms the electron distribution of related complexes, thereby leading to a remarkable increase of the conductivity of pure CNTs given the emerged novel state within the energy gap for metallic CNTs and the narrowed energy gap for semiconducting CNTs. Our calculations will be meaningful for exploiting novel CNT-based materials with better sensitivity to electrons and higher electrical conductivity compared with pure CNTs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2874-2892
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• 2013

Since security plays an important role in several ZigBee applications, such as Smart Energy and medical sensor applications, ZigBee Specification includes various security mechanisms to protect ZigBee frames and infrastructure. Among them, the Join and Leave operations of ZigBee are investigated in this paper. The current Join-Leave operation is protected by the network key (a kind of group key). We claim it is not adequate to employ the network key for such purpose, and propose a new Join-Leave operation protected by the application link key (a kind of pairwise key), which is based on a more efficient key management scheme than that of ZigBee. Hence, the original Join operation consists of a total of 12 command frames, while the new Join operation consists of only 6 command frames. In particular, the security of the proposed Join-Leave operation is equivalent to or better than that of the original Join-Leave operation. The new Join-Leave operation is extensively analyzed in terms of security and efficiency, and compared with the original Join-Leave operation of ZigBee.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.3
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• pp.661-666
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• 2016

The Internet of Things (IoT) is an infrastructure of physical objects that could be connected to the Internet. Most of these are low performance to ensure a reasonable cost for the smart physical objects. Thus, these devices usually use a lightweight messaging protocol: message queue telemetry transport with SSL/TLS. Cipher suites in device are fixed by default and selected based on preference in SSL/TLS. However, the selected cipher suite provides high security level more than expected. This limitation causes energy waste and overhead of devices. In order to counter this problem, we proposed fuzzy logic based cipher suite decision method to improve energy efficiency. Our proposed method saved 36.03% energy.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.4
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• pp.41-49
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• 2008

Sensor networks are often deployed in unattended environments, thus leaving these networks vulnerable to false data injection attacks in which an adversary injects forged reports into the network through compromised nodes. Such attacks by compromised sensors can cause not only false alarms but also the depletion of the finite amount of energy in a battery powered network. In order to reduce damage from these attacks, several security solutions have been proposed. Researchers have also proposed some techniques to increase the energy-efficiency of such security solutions. In this paper, we propose a CH(Cluster Header) selection algorithm to choose low power delivery method in sensor networks. The CNP(Contract Net Protocol), which is an approach to solve distribution problems, is applied to choose CHs for event sensing. As a result of employing CNP, the proposed method can prevent dropping of sensing reports with an insufficient number of message authentication codes during the forwarding process, and is efficient in terms of energy saving.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2518-2533
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• 2020

In wireless sensor networks, adversaries may physically capture sensor nodes on the fields, and use them to launch false positive attacks (FPAs). FPAs could be conducted by injecting forged or old sensing reports, which would represent non-existent events on the fields, with the goal of disorientating the base stations and/or reducing the limited energy resources of sensor nodes on the fields. Researchers have proposed various mitigation methods against FPAs, including the statistical en-route filtering scheme (SEF). Most of these methods are based on key pre-distribution schemes and can efficiently filter injected false reports out at relay nodes through the verification of in-transit reports using the pre-distributed keys. However, their filtering power may decrease as time goes by since adversaries would attempt to capture additional nodes as many as possible. In this paper, we propose an adaptive key distribution method that could maintain the security power of SEF in WSNs under such circumstances. The proposed method makes, if necessary, BS update or re-distribute keys, which are used to endorse and verify reports, with the consideration of the filtering power and energy efficiency. Our experimental results show that the proposed method is more effective, compared to SEF, against FPAs in terms of security level and energy saving.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.453-463
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• 2013

The probabilistic nature of renewable energy, especially wind energy, increases the needs for new forms of planning and operating with electrical power. This paper presents a novel approach for determining the short-term generation schedule for optimal operations of wind energy-integrated power systems. The proposed probabilistic security-constrained optimal power flow (P-SCOPF) considers dispatch, network, and security constraints in pre- and post-contingency states. The method considers two sources of uncertainty: power demand and wind speed. The power demand is assumed to follow a normal distribution, while the correlated wind speed is modeled by the Weibull distribution. A Monte Carlo simulation is used to choose input variables of power demand and wind speed from their probability distribution functions. Then, P-SCOPF can be applied to the input variables. This approach was tested on a modified IEEE 30-bus system with two wind farms. The results show that the proposed approach provides information on power system economics, security, and environmental parameters to enable better decision-making by system operators.