• Journal of Communications and Networks
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• v.5 no.3
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• pp.266-274
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• 2003

One of the major issues in current on-demand routing protocols for ad hoc networks is the high resource consumed by route discovery traffic. In these protocols, flooding is typically used by the source to broadcast a route request (RREQ) packet in search of a route to the destination. Such network-wide flooding potentially disturbs many nodes unnecessarily by querying more nodes than is actually necessary, leading to rapid exhaustion of valuable network resources such as wireless bandwidth and battery power. In this paper, a simple optimization technique for efficient route discovery is proposed. The technique proposed herein is location-based and can be used in conjunction with the existing Location-Aided Routing (LAR) scheme to further reduce the route discovery overhead. A unique feature of our technique not found in LAR and most other protocols is the selective use of unicast instead of broadcast for route request/query transmission made possible by a novel reuse of routing and location information. We refer to this new optimization as the UNIQUE (UNIcast QUEry) technique. This paper studies the efficacy of UNIQUE by applying it to the route discovery of the Dynamic Source Routing (DSR) protocol. In addition, a comparative study is made with a DSR protocol optimized with only LAR. The results show that UNIQUE could further reduce the overall routing overhead by as much as 58% under highly mobile conditions. With less congestion caused by routing traffic, the data packet delivery performance also improves in terms of end-to-end delay and the number of data packets successfully delivered to their destinations.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.3
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• pp.293-303
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• 2015

Optimal design of the water supply pipe network aims to minimize construction cost while satisfying the required hydraulic constraints such as the minimum and maximum pressures, and velocity. Since considering one single design factor (i.e., cost) is very vulnerable for including future conditions and cannot satisfy operator's needs, various design factors should be considered. Hence, this study presents three kinds of design factors (i.e., minimizing construction cost, maximizing reliability, and surplus head) to perform multi-objective optimization design. Harmony Search (HS) Algorithm is used as an optimization technique. As well-known benchmark networks, Hanoi network and Gyeonggi-do P city real world network are used to verify the applicability of the proposed model. In addition, the proposed multi-objective model is also applied to a real water distribution networks and the optimization results were statistically analyzed. The results of the optimal design for the benchmark and real networks indicated much better performance compared to those of existing designs and the other approach (i.e., Genetic Algorithm) in terms of cost and reliability, cost, and surplus head. As a result, this study is expected to contribute for the efficient design of water distribution networks.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.211-218
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• 2010

Taguchi has used the signal-to-noise ratio (SN) to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. Many Statisticians criticize the Taguchi techniques of analysis, particularly those based on the SN. Moreover, there are difficulties in practical application, such as complexity and nonlinear relationships among quality characteristics and design (control) factors, and interactions occurred among control factors. Neural networks have a learning capability and model free characteristics. There characteristics support neural networks as a competitive tool in processing multivariable input-output implementation. In this paper we propose a substantially simpler optimization procedure for parameter design using neural network without resorting to SN. An example is illustrated to compare the difference between the Taguchi method and neural network method.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.12
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• pp.120-127
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• 2008

HMIPv6 draws lots of attentions in recent years for providing an efficient handover and reducing the signaling overhead. HMIPv6 employs MAP(Mobility Anchor Point) in order to minimize a signaling overhead and a local mobility management. MAP completes an efficient mobility management in HMIPv6 network environment with frequent handover. However, HMIPv6 causes load concentration at a paricular MAP and may have unnecessary latency between HN(Mobile Node) and CN(Correspondent Node) within the same network. A MAP may also disturb the route optimization in HMIPv6 network because all packets must be transmitted through a MAP. In this paper, we propose a scheme to optimize the route in HMIPv6 networks according to MAP load. We configure a threshold in order to support the better service into MAP domain. The packets do not pass through MAP and are directly transmitted to AR(Access Router) if the number of current MNs attached to the MAP exceed the desired threshold. We simulate the performance of the proposed scheme and compare with HMIPv6. Resultly, the proposed scheme reduces signaling costs and mitigates concentration of a paticular MAP as well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3834-3857
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• 2021

The design of cluster-based routing protocols is necessary for Wireless Sensor Networks (WSN). But, due to the lack of features, the traditional methods face issues, especially on unbalanced energy consumption of routing protocol. This work focuses on enhancing the security and energy efficiency of the system by proposing Energy Efficient Based Secure Routing Protocol (EESRP) which integrates trust management, optimization algorithm and key management. Initially, the locations of the deployed nodes are calculated along with their trust values. Here, packet transfer is maintained securely by compiling a Digital Signature Algorithm (DSA) and Elliptic Curve Cryptography (ECC) approach. Finally, trust, key, location and energy parameters are incorporated in Particle Swarm Optimization (PSO) and meta-heuristic based Harmony Search (HS) method to find the secure shortest path. Our results show that the energy consumption of the proposed approach is 1.06mJ during the transmission mode, and 8.69 mJ during the receive mode which is lower than the existing approaches. The average throughput and the average PDR for the attacks are also high with 72 and 62.5 respectively. The significance of the research is its ability to improve the performance metrics of existing work by combining the advantages of different approaches. After simulating the model, the results have been validated with conventional methods with respect to the number of live nodes, energy efficiency, network lifetime, packet loss rate, scalability, and energy consumption of routing protocol.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3494-3510
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• 2019

Wireless sensor networks encounter energy saving as a major issue as the sensor nodes having no rechargeable batteries and also the resources are limited. Clustering of sensors play a pivotal role in energy saving of the deployed sensor nodes. However, in the cluster based wireless sensor network, the cluster heads tend to consume more energy for additional functions such as reception of data, aggregation and transmission of the received data to the base station. So, careful selection of cluster head and formation of cluster plays vital role in energy conservation and enhancement of lifetime of the wireless sensor networks. This study proposes a new mutation chemical reaction optimization (MCRO) which is an algorithm based energy efficient clustering protocol termed as MCRO-ECP, for wireless sensor networks. The proposed protocol is extensively developed with effective methods such as potential energy function and molecular structure encoding for cluster head selection and cluster formation. While developing potential functions for energy conservation, the following parameters are taken into account: neighbor node distance, base station distance, ratio of energy, intra-cluster distance, and CH node degree to make the MCRO-ECP protocol to be potential energy conserver. The proposed protocol is studied extensively and tested elaborately on NS2.35 Simulator under various senarios like varying the number of sensor nodes and CHs. A comparative study between the simulation results derived from the proposed MCRO-ECP protocol and the results of the already existing protocol, shows that MCRO-ECP protocol produces significantly better results in energy conservation, increase network life time, packets received by the BS and the convergence rate.

• Smart Media Journal
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• v.11 no.2
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• pp.70-76
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• 2022

IoT application development using a cloud server causes problems such as data transmission and reception delay, network traffic, and cost for real-time processing support in network connected hardware. To solve this problem, edge cloud-based platforms can use neuromorphic hardware to enable fast data transfer. In this paper, we propose a model optimization method for supporting spiking neural networks on FPGA hardware. We focused on auto-adjusting network model parameters optimized for neuromorphic hardware. The proposed method performs optimization to show higher performance based on user requirements for accuracy. As a result of performance analysis, it satisfies all requirements of accuracy and showed higher performance in terms of expected execution time, unlike the naive method supported by the existing open source framework.

• The Journal of Bigdata
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• v.9 no.1
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• pp.115-126
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• 2024

The scale and cost of disasters are increasing globally, emphasizing the importance of logistics activities in disaster response. A disaster response logistics system must place logistics hub centers in regions relatively safe from disasters and ensure the stable supply of relief goods and emergency medicines to the affected areas. Therefore, this study focuses on locating drone delivery centers that minimize disaster vulnerability when designing a disaster response delivery network. To facilitate the transport of relief supplies via drones, the maximum delivery range of drones is considered and we employed a natural disaster vulnerability index to develop optimization models for selecting drone delivery center locations that minimize disaster vulnerability. The analysis indicates that while the optimization models to minimize disaster vulnerability increase the number of hub investments, these approaches mitigate disaster vulnerability and allows the safe and effective operation of a disaster response logistics system utilizing drone deliveries.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.9
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• pp.55-61
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• 2009

This paper presents a modified Phasor Discrete Particle Swarm Optimization (PDPSO) algorithm to configure Community Energy Systems(CESs) in the distribution system. The CES obtains electric power from its own Distributed Generations(DGs) and purchases insufficient power from the competitive power market, to supply power for customers contracted with the CES. When there are two or more CESs in a network, the CESs will continue the competitive expansion to reduce the total operation cost. The particles of the proposed PDPSO algorithm have magnitude and phase angle values, and move within a circle area. In the case study, the results by PDPSO algorithm was compared with that by the conventional DPSO algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1111-1130
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• 2016

Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle Swarm Ant Colony Optimization) based on PSACO (Particle Swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.