• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.3
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• pp.477-493
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• 2011

The IEEE 802.16 mobile multi-hop relay (MMR) task group 'j' (TGj) has introduced the multi-hop relaying concept in the IEEE 802.16 Wireless MAN, wherein a relay station (RS) is employed to improve network coverage and capacity. Several RSs can be deployed between a base station and mobile stations, and configured to form a tree-like multi-hop topology. In such architecture, we consider the problem of a path selection through which the mobile station in and outside the coverage can communicate with the base station. In this paper, we propose a new path selection algorithm that ensures more efficient distribution of resources such as bandwidth among the relaying nodes for improving the overall performance of the network. Performance of our proposed scheme is compared with the path selection algorithms based on loss rate and the shortest path algorithm. Based on the simulation results using ns-2, we show our proposal significantly improves the performance on throughput, latency and bandwidth consumption.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.1
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• pp.30-35
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• 2015

The most important factor within the wireless sensor network is to have effective network usage and increase the lifetime of the individual nodes in order to operate the wireless network more efficiently. Therefore, many routing protocols have been developed. The LEACH protocol presented by Wendi Hein Zelman, especially well known as a simple and efficient clustering based routing protocol. However, because LEACH protocol in an irregular network is the total data throughput efficiency dropped, the stability of the cluster is declined. Therefore, to increase the stability of the cluster head, in this paper, it proposes a stochastic cluster head selection method for improving the LEACH protocol. To this end, it proposes a SH-LEACH (Stochastic Cluster Head Selection Method-LEACH) that it is combined to the HEED and LEACH protocol and the proposed algorithm is verified through the simulation.

• The KIPS Transactions:PartC
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• v.13C no.6 s.109
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• pp.741-748
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• 2006

Due to the continuous development of sensor technology, Wireless Sensor Networks are rapidly growing and are expected to be applied to various applications. One of the most important factors in Wireless Sensor Networks is energy-efficient management of network resources. For this purpose, a lot of researches have been ongoing in the development of energy-efficient routing protocol. In this paper, a cluster head selection algorithm considering node density in addition to the cluster head selection algorithm of LEACH-C is proposed and simulated. This algorithm gives nearly the same computational speed compared to that of LEACH-C and shows improvement of network lifetime about 11% better than LEACH-C. The simulation result shows that consideration of density as well as distance between nodes in cluster head selection can be more energy-efficient than considering only the distance between nodes as LEACH-C in energy usage of Wireless Sensor Networks.

• Journal of Communications and Networks
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• v.6 no.2
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• pp.147-155
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• 2004

In this paper, we propose an efficient sequencing technique, namely minimum Row time scheduling (MFTS), to manage variable-Iength message transmissions for single-hop passive starcoupled WDM optical networks. By considering not only the message length but also the state of the receivers and the tuning latency, the proposed protocol can reduce the average delay of the network greatly. This paper also introduces a new channel assignment technique latency minimizing scheduling (LMS), which aims to reduce the scheduling latency. We evaluate the proposed algorithm, using extensive discrete-event simulations, by comparing its performance with shortest job first (SJF) algorithm. We find that significant improvement in average delay could be achieved by MFTS algorithm. By combining the proposed message sequencing technique with the channel selection technique, the performance of the optical network could be further improved.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6B
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• pp.342-349
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• 2007

Clustering protocol of Wireless sensor networks(WSNs) not only reducing the volume of inter-node communication by the nodes's data aggreation but also extending the nodes's sleep times by cluster head's TDMA-schedule coordination. In order to extend network lifetime of WSNs, we propose ECS algorithm to select cluster-head using three variables. It consists of initial and current energy of nodes, round information and total numbers which have been selected as cluster head until current round.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.25-30
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• 2010

Wireless sensor networks is composed of many similar sensor nodes with limited resources. They are randomly scattered over a specific area and self-organize the network. For guarantee of network life time, load balancing and scalability in sensor networks, sensor networks needs the clustering algorithm which distribute the networks to a local cluster. In existing clustering algorithms, the cluster head selection method has two problems. One is additional communication cost for finding location and energy of nodes. Another is unequal clustering. To solve them, this paper proposes a novel cluster head selection algorithm revised previous clustering algorithm, LEACH. The simulation results show that the energy compared with the previous clustering method is reduced.

• Smart Media Journal
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• v.9 no.1
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• pp.23-29
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• 2020

This paper presents an approach for dynamic hand gesture recognition by using algorithm based on 3D Convolutional Neural Network (3D_CNN), which is later extended to 3D Residual Networks (3D_ResNet), and the neural network based key frame selection. Typically, 3D deep neural network is used to classify gestures from the input of image frames, randomly sampled from a video data. In this work, to improve the classification performance, we employ key frames which represent the overall video, as the input of the classification network. The key frames are extracted by SegNet instead of conventional clustering algorithms for video summarization (VSUMM) which require heavy computation. By using a deep neural network, key frame selection can be performed in a real-time system. Experiments are conducted using 3D convolutional kernels such as 3D_CNN, Inflated 3D_CNN (I3D) and 3D_ResNet for gesture classification. Our algorithm achieved up to 97.8% of classification accuracy on the Cambridge gesture dataset. The experimental results show that the proposed approach is efficient and outperforms existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2657-2675
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• 2013

A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3771-3788
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• 2015

This paper studies the distributed energy efficient resource management in the Internet of Things (IoT). Wireless communication networks support the IoT without limitation of distance and location, which significantly impels its development. We study the communication channel and energy management in the wireless communication network supported IoT to improve the ability of connection, communication, share and collaboration, by using the game theory and distributed learning algorithm. First, we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game. Second, we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner. We prove that the proposed algorithm will asymptotically converge to the global optimum with geometric speed. Finally, we make the simulations to verify the theoretic analysis and the performance of proposed algorithm.

• Journal of Information Processing Systems
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• v.7 no.2
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• pp.241-260
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• 2011

In earlier days, most of the data carried on communication networks was textual data requiring limited bandwidth. With the rise of multimedia and network technologies, the bandwidth requirements of data have increased considerably. If a network link at any time is not able to meet the minimum bandwidth requirement of data, data transmission at that path becomes difficult, which leads to network congestion. This causes delay in data transmission and might also lead to packet drops in the network. The retransmission of these lost packets would aggravate the situation and jam the network. In this paper, we aim at providing a solution to the problem of network congestion in mobile ad hoc networks [1, 2] by designing a protocol that performs routing intelligently and minimizes the delay in data transmission. Our Objective is to move the traffic away from the shortest path obtained by a suitable shortest path calculation algorithm to a less congested path so as to minimize the number of packet drops during data transmission and to avoid unnecessary delay. For this we have proposed a protocol named as Congestion Aware Selection Of Path With Efficient Routing (CASPER). Here, a router runs the shortest path algorithm after pruning those links that violate a given set of constraints. The proposed protocol has been compared with two link state protocols namely, OSPF [3, 4] and OLSR [5, 6, 7, 8].The results achieved show that our protocol performs better in terms of network throughput and transmission delay in case of bulky data transmission.