• Journal of Korean Institute of Industrial Engineers
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• v.34 no.2
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• pp.246-254
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• 2008

In this paper, a new efficient hidden node detection method is proposed to decide whether the RTS/CTS mechanism is necessary to resolve the hidden node problem for the data transmission of each node in infrastructure mode IEEE 802.11 wireless LANs. The nodes, for which the RTS/CTS mechanism is found to be not necessary by the hidden node detection method, can transmit their data frames without performing the RTS/CTS exchange. Only the nodes, for which the RTS/CTS mechanism is found to be necessary by the hidden node detection method, perform the RTS/CTS exchange before their data frame transmissions.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.12
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• pp.1165-1171
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• 2011

In this paper, we investigate hidden node problem to effectively utilize TV band devices(TVBDs) in the TV white space(TVWS), and also to protect digital television(DTV) service in Korea. Firstly, we classify the radio propagation environment into an urban area, a basin area, and a coastal area based on geographical characteristics. Thereafter, we measure and analyze local shape based hidden node attenuation at eight segmented positions in each geographic area. Because commercial buildings as well as residential and commercial buildings in Korea are located in closer proximity to each other than in other countries, hidden node margin should be more than 38 dB in order to safely protect DTV service in Korea.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.8A
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• pp.702-711
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• 2011

This paper proposes a HAG(Hidden-Node-Aware Grouping) algorithm for IEEE 802.15.4 networks to enhance the performance by eliminating collisions resulted from the hidden node problem without adopting the RTS/CTS packet exchanges. To solve the hidden node problem, the HAG algorithm organizes nodes into disjoint transmission groups by dynamically allocating hidden nodes into separate groups which take turns in a round robin way for their transmission. For dynamic group adjustment, it periodically evaluates the presence of hidden nodes based on subordinate nodes' receipt reports. To accurately measure its behavior, this paper also builds an analytical model to estimate its throughput fluctuation over various network topologies. The mathematical model along with simulation results confirmed that the HAG technique gracefully degraded the throughput of IEEE 802.15.4 networks whereas the standard IEEE 802.15.4 networks suffer severe throughput fallout as hidden nodes become populated.

• The Journal of Information Systems
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• v.9 no.1
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• pp.27-44
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• 2000

The structure of neural networks is represented by a weighted directed graph with nodes representing units and links representing connections. Each link is assigned a numerical value representing the weight of the connection. In learning process, the values of weights are adjusted by errors. Following experiment results, the interval of adjusting weights, that is, epoch size influenced neural networks＇ performance. As epoch size is larger than a certain size, neural networks＇performance decreased drastically. And the number of hidden layer＇s node also influenced neural networks＇performance. The networks＇performance decreased as hidden layers have more nodes and then increased at some number of hidden layer＇s node. So, in implementing of neural networks the epoch size and the number of hidden layer＇s node should be decided by systematic methods, not empirical or heuristic methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.1B
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• pp.8-15
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• 2008

Cognitive radio technology enables to share the spectrum dedicated to primary users. In CR network, it is of primary concern to protect the primary users. Thus, it is required to periodically sense the spectrums occupied by primary users and adapt the communication parameters used by CR users to protect the primary users. However, it is inevitable to experience the hidden node problem due to the primary users, that are not detected by spectrum sensing. To perfectly protect the primary users, it is essential to address the hidden node problem in CR network. In this paper, we propose a new approach to handle the hidden node problem and evaluate the performance of proposed scheme.

• Journal of the Korea Society of Computer and Information
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• v.5 no.2
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• pp.30-36
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• 2000

The hidden node plays a role of the functional units that classifies the features of input pattern in the given question. Therefore, a neural network that consists of the number of a suitable optimum hidden node has be on the rise as a factor that has an important effect upon a result. However there is a problem that decides the number of hidden nodes based on back-propagation learning algorithm. If the number of hidden nodes is designated very small perfect learning is not done because the input pattern given cannot be classified enough. On the other hand, if designated a lot, overfitting occurs due to the unnecessary execution of operation and extravagance of memory point. So, the recognition rate is been law and the generality is fallen. Therefore, this paper suggests a method that decides the number of neural network node with feature information consisted of the parameter of learning algorithm. It excludes a node in the Pruning target, that has a maximum value among the feature value obtained and compares the average of the rest of hidden node feature value with the feature value of each hidden node, and then would like to improve the learning speed of neural network deciding the optimum structure of the multi-layer neural network as pruning the hidden node that has the feature value smaller than the average.

• The Journal of Information Technology
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• v.5 no.2
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• pp.55-67
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• 2002

This paper presents an adaptive back propagation algorithm that update the learning parameter by the generated error, adaptively and varies the number of hidden layer node. This algorithm is expected to escaping from the local minimum and make the best environment for convergence to be change the number of hidden layer node. On the simulation tested this algorithm on two learning pattern. One was exclusive-OR learning and the other was $7{\times}5$ dot alphabetic font learning. In both examples, the probability of becoming trapped in local minimum was reduce. Furthermore, in alphabetic font learning, the neural network enhanced to learning efficient about 41.56%~58.28% for the conventional back propagation. and HNAD(Hidden Node Adding and Deleting) algorithm.

• The KIPS Transactions:PartC
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• v.16C no.1
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• pp.133-142
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• 2009

This paper proposes an analytical performance model of IEEE 802.15.4 in the presence of hidden nodes. Conventional 802.15.4 mathematical models assume ideal situations where every node can detect the transmission signal of every other nodes different from the realistic environments. Since nodes can be randomly located in real environments so that some nodes' presence is hidden from other ones, this assumption leads to wrong performance evaluation of 802.15.4. For solving this problem, we develop an extended performance model which combines the traditional 802.15.4 performance model with one for accounting the presence of hidden nodes. The extended model predicts the rapid performance degradation of 802.15.4 due to the small number of hidden nodes. The performance, for example, degrades by 62% at maximum when 5% of the total nodes are hidden. These predictions are confirmed to be equal to those of ns-2 simulations by less than 6% difference.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12B
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• pp.1066-1074
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• 2008

In this paper, we proposed an efficient algorithm using pulse signal based on group-node-contention in LR-WPAN. The purpose of IEEE 802.15.4 is low speed, low cost and low power consumption. Recently, as applications of LR-WPAN have been extended, there is a strong probability of collision as well and almost collision occurs because of hidden node problem. Moreover, if the collision continuously occurs due to hidden node collision, network performance could be decreased. Nowadays, although several papers focus on the hidden node collision, algorithms waste the channel resource if continuous collisions frequently occur. In this paper, we assume that PAN has been already formed groups, and by using pulse signal, coordinator allocates channel and orders, and then, nodes in the allocated group can compete each other. Hence, contention nodes are reduced significantly, channel wastage caused by collision is decreased, and data transmission rate is improving. Finally, this algorithm can protect the network from disruption caused by frequent collisions. Simulation shows that this algorithm can improve the performance.

• Journal of Communications and Networks
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• v.16 no.1
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• pp.81-91
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• 2014

This paper proposes hidden-node aware grouping (HAG) algorithm to enhance the performance of institute of electrical and electronics engineers (IEEE) 802.15.4 networks when they undergo either severe collisions or frequent interferences by hidden nodes. According to the degree of measured collisions and interferences, HAG algorithm dynamically transforms IEEE 802.15.4 protocol between a contention algorithm and a contention-limited one. As a way to reduce the degree of contentions, it organizes nodes into some number of groups and assigns each group an exclusive per-group time slot during which only its member nodes compete to grab the channel. To eliminate harmful disruptions by hidden nodes, especially, it identifies hidden nodes by analyzing the received signal powers that each node reports and then places them into distinct groups. For load balancing, finally it flexibly adapts each per-group time according to the periodic average collision rate of each group. This paper also extends a conventional Markov chain model of IEEE 802.15.4 by including the deferment technique and a traffic source to more accurately evaluate the throughput of HAG algorithm under both saturated and unsaturated environments. This mathematical model and corresponding simulations predict with 6%discrepancy that HAG algorithm can improve the performance of the legacy IEEE 802.15.4 protocol, for example, even by 95% in a network that contains two hidden nodes, resulting in creation of three groups.