• Journal of Korea Multimedia Society
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• v.15 no.10
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• pp.1212-1220
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• 2012

In this paper, a collision-free resource reservation scheme for WUSB (Wireless Universal Serial Bus) networks with WiMedia D-MAC (Distributed Medium Access Control). Since distributed characteristic of the WiMedia D-MAC supporting DRP (Distributed Reservation Protocol) scheme may cause lots of conflicts, overall performances of the WUSB with WiMedia D-MAC can be deteriorated. In addition, when we consider multi-hop environment, "mobile" hidden node problem due to mobility of WUSB devices can be happened, and it is a critical problem to mobile WUSB devices transceiving real-time QoS (Quality of Service) traffic. To tackle the problem, we propose a new DRP reservation mechanism to prevent DRP conflicts for multi-hop mobility support in WUSB networks with WiMedia D-MAC and show its improved performance via simulation results.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.7-10
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• 2005

Ad hoc MAC protocols using directional antennas can be used to improve the network capacity. Directional antennas have a Deafness problem and decrease throughput of the network caused by increasing backoff duration. Dual channel protocols have been proposed to mitigate the Deafness and hidden node problems. In this paper, we propose a dual channel MAC protocol using a directional antenna to mitigate the Deafness problem and increase the network capacity. The performance of the proposed Ad hoc MAC protocol is confirmed by computer simulations using Qualnet ver. 3.8 simulator.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.6B
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• pp.875-887
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• 2010

The workgroup of IEEE 802 proposed the IEEE 802.16 standard, also known as WiMAX, to provide broadband wireless access (BWA). The standard specifies two operational modes, one is popular PMP mode, and the other is optional mesh mode. In the mesh mode, the network entry process-NetEntry is the pivotal procedure for mesh network topology formulation and thus, influences the accessibility of whole mesh network. Unfortunately, the NetEntry process suffers from the hidden neighbor problem, in which new neighborship emerges after a new node comes in and results in possible collisions. In this paper, we propose a new SN-protected NetEntry process to address the problem. Simulation results show that the new proposed NetEntry process is more stable compared with the standard-based NetEntry process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.10A
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• pp.830-838
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• 2011

Recently, studies for the network coding in the wireless network to achieve improvement of the network capacity are conducted. In this paper, we analysis considerations to apply RLNC in the wireless network. First of all, we verify whether the RLNC method in multicast is applied to distributed wireless network. In simulation results, the decoding failure can occur in the original manner of multicast. In RLNC which conducts encoding and decoding in X topology to gets rid of the decoding failure, the RLNC gain is insignificant. In this paper we also discuss considerations such as the hidden node problem, the occurrence of coding opportunity, and the RLNC overhead which are practical issues in the wireless network.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.511-516
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• 2002

The neural networks may have problem such that the amount of calculation for the network learning goes too big according to the dimension of the dimension. To overcome this problem, the wavelet neural networks(WNN) which use the orthogonal basis function in the hidden node are proposed. One can compose wavelet functions as activation functions in the WNN by determining the scale and center of wavelet function. In this paper, when we compose the WNN using wavelet functions, we set a single scale function as a node function together. We intend that one scale function approximates the target function roughly, the other wavelet functions approximate it finely During the determination of the parameters, the wavelet functions can be determined by the global search for solutions suitable for the suggested problem using the genetic algorithm and finally, we use the back-propagation algorithm in the learning of the weights.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.808-811
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• 2015

Cognitive radio has been proposed as a promising dynamic spectrum allocation paradigm. In cognitive radio, spectrum sensing is a fundamental procedure that enables secondary users (unlicensed) employing unused portion of spectrum of primary users (licensed) without causing harmful interference. However, the performance of single-user spectrum-sensing scheme was limited by fading, noise uncertainty shadowing and hidden node problem. Cooperative spectrum sensing was proposed to mitigate these problem. In this paper, we observe cooperative sensing scheme with energy detection using three adaptive thresholds for local decision, which can mitigate sensing failure problem and improve sensing performance at local node. In cooperative scheme we employed OR rules as decision combining at fusion center. We evaluate our scheme through computer simulation, and the results show that with OR combination rule our scheme can achieve best performance than other schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.141-156
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• 2011

In this paper we develop a link cost function for data delivery in sensor network. Usually most conventional methods determine the optimal coefficients in the cost function without considering the surrounding environment of the node such as the wireless propagation environment or the topological environment. Due to this reason, there are limitations to improve the quality of data delivery such as data delivery ratio and delay of data delivery. To solve this problem, we derive a new cost function using the concept of Partially Connected Neural Network (PCNN) which is modeled according to the input types whether inputs are correlated or uncorrelated. The correlated inputs are connected to the hidden layer of the PCNN in a coupled fashion but the uncoupled inputs are in an uncoupled fashion. We also propose the training technique for finding an optimal weight vector in the link cost function. The link cost function is trained to the direction that the packet transmission success ratio of each node maximizes. In the experimental section, we show that our method outperforms other conventional methods in terms of the quality of data delivery and the energy efficiency.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.189-199
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• 1999

This paper presents an optimization algorithm for a stable Self Dynamic Neural Network(SDNN) using genetic algorithm. Optimized SDNN is applied to a problem of controlling nonlinear dynamical systems. SDNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real-time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDW has considerably fewer weights than DNN. Since there is no interlink among the hidden layer. The object of proposed algorithm is that the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed optimized SDNN considering stability is demonstrated by case studies.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.419-427
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• 1999

We propose an algorithm for obtaining the optimal node number of hidden units in dynamic neural networks. The dynamic nerual networks comprise of dynamic neural units and neural processor consisting of two dynamic neural units; one functioning as an excitatory neuron and the other as an inhibitory neuron. Starting out with basic network structure to solve the problem of control, we find optimal neural structure by multiplication and combining dynamic neural unit. Numerical examples are presented for nonlinear systems. Those case studies showed that the proposed is useful is practical sense.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.29-35
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• 2024

TCP is considered as one of the major candidate transport protocols even for constrained IoT networks..In our previous work, we investigated the congestion control mechanism of the uIP TCP. Since the uIP TCP sets the window size to one segment by default, managing the retransmission timer is the primary approach to congestion control. However, the original uIP TCP sets the retransmission timer based on the fixed RTO, it performs poorly when a radio duty cycling mechanism is enabled and the hidden terminal problem is severe. In our previous work, we proposed a TCP retransmission timer adjustment scheme for uIP TCP which adopts the notion of weak RTT estimation of CoCoA, exponential backoffs with variable limits, and dithering. Although our previous work showed that the proposed retransmission timer adjustment scheme can improve performance, we observe that the scheme often causes a node to set the retransmission timer for an excessively too long time period. In this work, we show that slightly modifying the dithering mechanism of the previous scheme is effective for improving TCP fairness.