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Lee, Yong Chan
- Construction Maximum Lifetime Tree and Adaptation in Wireless Sensor Networks
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Authors
Affiliations
1 College of Information Science, Kim Il-Sung University, Democratic People's Republic of Korea, KP
2 Institute of Information Science, Kim Il-sung University, Democratic People's Republic of Korea, KP
1 College of Information Science, Kim Il-Sung University, Democratic People's Republic of Korea, KP
2 Institute of Information Science, Kim Il-sung University, Democratic People's Republic of Korea, KP
Source
ICTACT Journal on Communication Technology, Vol 8, No 3 (2017), Pagination: 1597-1603Abstract
The maximum lifetime problem in wireless sensor network is important to monitor a set of interesting target locations and route the collected information to a central base station. In this paper, first, we consider the method of construction maximum lifetime tree taking into account general type of data aggregation, exchange of control messages and packet transmission loss. Second, we consider the method increasing lifetime of tree and reducing complexity and latency combining optimization of energy consumption in entire network through quasioptimization of local nodes and adapting. Experiment results show that the proposed method is more robust and valid than the previous method.Keywords
Wireless Sensor Network (WSN), Maximum Lifetime, Data Gathering Tree.
References
- Kazem Sohraby, Daniel Minoli and Taieb Znati, “Wireless Sensor Networks: Technology, Protocols, and Applications”, John Wiley and Sons, 2007.
- Junbin Liang, Jianxin Wang, Jiannong Cao, Jianer Chen and Mingming Lu , “An Efficient Algorithm for Constructing Maximum Lifetime Tree for Data Gathering without Aggregation in Wireless Sensor Networks”, Proceedings of IEEE INFOCOM, pp. 1-5, 2010.
- B.A. Alyoubi and I.M. El Emary, “The Zigbee Wireless Sensor Network in Medical Applications: A Critical Analysis Study”, Journal of Current Research in Science, Vol. 4, No. 1, pp. 1-7, 2016.
- Y.F. Wen and F.Y.S. Lin, “Energy-Efficient Data Aggregation Routing and Duty-Cycle Scheduling in Cluster-based Sensor Networks”, Proceedings of 4th IEEE Consumer Communications and Networking Conference, pp. 95-99, 2007.
- L.A. Villas, A. Boukerche, H.S. Ramos, H.A.F. De Oliveira, R.B. De Araujo and A.A.F. Loureiro, “DRINA: A Lightweight and Reliable Routing Approach for in-Network Aggregation in Wireless Sensor Networks”, IEEE Transactions on Computers, Vol. 62, No. 4, pp. 676-689, 2013.
- H. Yetgin, K.T.K. Cheung, M. El-Hajjar and L.H. Hanzo, “A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks”, IEEE Communications Surveys and Tutorials, Vol. 19, No. 2, pp. 828-854, 2017.
- Energy Balanced and Efficient Clustering Method for Wireless Sensor Networks
Abstract Views :203 |
PDF Views:3
Authors
Affiliations
1 College of Information Science, Kim Il Sung University, KP
2 Institute of Information Science, Kim Il Sung University, KP
1 College of Information Science, Kim Il Sung University, KP
2 Institute of Information Science, Kim Il Sung University, KP
Source
ICTACT Journal on Communication Technology, Vol 8, No 4 (2017), Pagination: 1640-1649Abstract
In this paper the energy balanced and efficient clustering method based on balance of energy consumption of nodes in WSN is proposed, which may be applied to any WSN. The almost static centralized protocol that differs from previous methods is proposed, the main feature of which is that the sinks transmit most of control message and process most of data. First, EBEC method is proposed, which optimizes by considering energy consumption on transmitting and receiving data, energy consumption on the reclustering and hot-spot problem that be optimized individually in previous works. In order to implement this method, VW BAK-C algorithm is used by introducing the concept of variable weighted Euclid distance to k-clustering algorithm. Second, the previous clustering methods are classified into random method and the method based on QoS according to the characteristic of cluster head rotation, and average of total energy consumption of nodes is analyzed mathematically. The proposed method is compared and analyzed. Third, the performance of the proposed method is evaluated by comparing with other clustering methods through simulation.Keywords
WSN, EBEC, Clustering, QoS, Energy Consumption.References
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- Lorenzo A. Rossi and C.C. Jay Kuo, “Semi-Dynamic Approaches to Node Clustering for Sensor Networks”, Proceedings of Internet Quality of Service, Vol. 5245, pp. 54-65, 2003.
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- Indranil Gupta, Denis Riordan and Srinivas Sampalli, “Cluster-Head Election using Fuzzy Logic for Wireless Sensor Networks”, Proceedings of 3rd Annual Communication Networks and Services Research Conference, pp. 255-260, 2005.
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- Weike Chen, Wenfeng Li, Heng Shou and Bing Yuan, “A QoS-based Adaptive Clustering Algorithm for Wireless Sensor Networks”, Proceedings of IEEE International Conference on Mechatronics and Automation, pp. 1947-1952, 2006.
- Dawei Xia and Natalija Vlajic, “Near-Optimal Node Clustering in Wireless Sensor Networks for Environment Monitoring”, Proceedings of IEEE 21st International Conference on Advanced Information Networking and Applications, pp. 1825-1829, 2006.
- L.M.C. Arboleda and N. Nasser, “Comparison of Clustering Algorithms and Protocols for Wireless Sensor Networks”, Proceedings of Canadian Conference on Electrical and Computer Engineering, pp. 1787-1792, 2006.
- Wenfeng Li, Weike Chen and Xinzhu Ming, “A Local-centralized Adaptive Clustering Algorithm for Wireless Sensor Networks”, Proceedings of 15th International Conference on Computer Communications and Networks, pp. 149-154, 2006.
- Hang Su and Xi Zhang, “Energy-Efficient Clustering System Model and Reconfiguration Schemes for Wireless Sensor Networks”, Proceedings of 40th Annual Conference on Information Sciences and Systems, pp. 99-104, 2006.
- B. Huang, Fei Hao, Hui Zhu, Yuji Tanabe and B. Takaaki, “Low-Energy Static Clustering Scheme for Wireless Sensor Network”, Proceedings of 5th International Conference on ITS Telecommunications, pp. 925-930, 2006
- Yiping Yang, Chuan Lai and Lin Wang, “An Energy-Efficient clustering Algorithm for Wireless Sensor Networks”, Proceedings of 10th International Conference on Control and Automation, pp. 875-879, 2013.
- Adel Youssef, Mohamed Younis and Moustafa Youssef, “Distributed Formation of Overlapping Multi-hop Clusters in Wireless Sensor Networks”, Proceedings of IEEE Global Telecommunication Conferences, pp. 167-173, 2006.
- Na Yao and Laurie Cuthbert, “Reducing Congestion over Hotspot Clusters in WCDMA Networks”, Proceedings of IEEE Wireless Communications and Networking Conference, pp. 3731-3735, 2007
- Peter Hebden and Adrian R. Pearce, “Distributed Asynchronous Clustering for Self-Organisation of Wireless Sensor Networks”, Proceedings of 4th International Conference on Intelligent Sensing and Information Processing, pp. 37-42, 2006.
- Yongxuan Lai, Xiaobo Fan, Chen Hong and Ting Xie, “Optimization Framework for Distributed Clustering Scheme in Wireless Sensor Networks”, Proceedings of 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, pp. 26-31, 2007.
- Wei Zhou, Hui-Min Chen and Xue-Fan Zhang, “An Energy Efficient Strong Head Clustering Algorithm for Wireless Sensor Networks”, Proceedings of International Conference on Wireless Communications, Networking and Mobile Computing, pp. 2584-2587, 2007.
- Jing Deng, Yunghsiang S. Han, Wendi B. Heinzelman and Pramod K. Varshney,“Balanced-Energy Sleep Scheduling Scheme for High Density Cluster-based Sensor Networks”, Computer Communications, Vol. 28, No. 14, pp. 1631-1642, 2005.
- Xun Su, “A Combinatorial Algorithmic Approach to Energy-Efficient Information Collection in Wireless sensor Networks”, ACM Transactions on Sensor Networks, Vol. 3, No. 1, pp. 22-41, 2010.
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- Sankalpa Gamwarige and Chulantha Kulasekere, “Optimization of Cluster Head Rotation in Energy Constrained Wireless Sensor Networks”, Proceedings of International Conference on Wireless and Optical Communications, Networking, pp. 342-346, 2007.
- Rui Wang, Guozhi Liu and Cuie Zheng, “A Clustering Algorithm based on Virtual Area Partition for
- Heterogeneous Wireless Sensor Networks”, Proceedings of IEEE International Conference on Mechatronics and Automation, pp. 372-376, 2007.
- Changmin Duan and Hong Fan, “A Distributed Energy Balance Clustering Protocol for Heterogeneous Wireless Sensor Networks”, Proceedings of International Conference on Wireless and Optical Communications, Networking and Mobile Computing, pp. 2469-2473, 2007.
- Jing Deng, Yunghsiang S. Han, Wendi B. Heinzelman and Pramod K. Varshney, “Balanced-Energy Sleep Scheduling Scheme for High Density Cluster-based Sensor Networks”, Computer Communications, Vol. 28, No. 14, pp. 1631-1642, 2005.
- Jaime Lloret, Miguel Garcia, Diana Bri and Juan R. Diaz, “A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks”, Sensors, Vol. 9, No. 12, pp. 10513-10544, 2009.
- Yongxuan Lai, Xiaobo Fan, Hong Chen and Tingt Xie, “Optimization Framework for Distributed Clustering Scheme in Wireless Sensor Networks”, Proceedings of 8th International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, pp. 26-31, 2007.
- M. Dhanaraj and C.S.R. Murthy, “On Achieving Maximum Network Lifetime through Optimal Placement of Cluster-heads in Wireless Sensor”, Proceedings of IEEE International Conference on Communications, pp. 3142-3147, 2007.
- Dali Wei and H. Anthony Chan, “Clustering Algorithm to Balance and to Reduce Power Consumptions for Homogeneous Sensor Networks”, Proceedings of International Conference on Wireless and Optical Communications, Networking and Mobile Computing, pp. 2723-2726, 2007.
- Soheil Ghiasi, Ankur Srivastava, Xiaojian Yang and Majid Sarrafzadeh, “Optimal Energy Aware Clustering in Sensor Networks”, Sensors, Vol. 2, pp. 258-269, 2002.
- Y. Wu, Z. Chen, Q. Jing and Y.C. Wang, “LENO: Least Rotation Near-Optimal Cluster Head Rotation Strategy in Wireless Sensor Networks”, Proceedings of 21st International Conference on Advanced Networking and Applications, pp. 107-113, 2007
- Wei Zhou, Hui-Min Chen and Xue-Fan Zhang, “An Energy Efficient Strong Head Clustering Algorithm for Wireless Sensor Networks”, Proceedings of International Conference on Wireless and Optical Communications, Networking and Mobile Computing, pp. 2584-2587, 2007.
- The Method Estimating IQ Imbalance using Signal Power Constrained Condition in OFDM System
Abstract Views :184 |
PDF Views:1
Authors
Affiliations
1 College of Information Engineering, Wonsan Jogunsil University of Technology, KP
2 Institute of Information Science, Kim Il Sung University, KP
1 College of Information Engineering, Wonsan Jogunsil University of Technology, KP
2 Institute of Information Science, Kim Il Sung University, KP
Source
ICTACT Journal on Communication Technology, Vol 9, No 4 (2018), Pagination: 1904-1907Abstract
IQI (I/Q Imbalance) is caused by I, Q path gain disagreement and quadrature phase incomplete generation △φ. IQI is parasitic noise about one frequency in LO frequency band. IQI estimation and compensating method analyzed on slow frequency fading channel and all most previous works estimate and compensate FIIQI only and it has much complexity, as adaptive and Kalman filtering is done when estimating and compensating FSIQI. We propose the method estimating and compensating method using signal energy constrained condition. In this method, FI and FS IQI are compensated simultaneously and Kalman filtering is not used. Also, the robustness to frequency selective channel is high.Keywords
OFDM, IQ Imbalance, Frequency Fading Channel.References
- Tao Liu, “Joint Blind Estimation of Symbol Timing Offset and Carrier Frequency Offset for OFDM Systems with IQ Imbalance”, IEICE Electronics Express, Vol. 16, No. 8, pp. 443-448, 2011.
- Lopez Estrviz, “Optimal Training Sequences for Joint Channel and Frequency-Dependent IQ Imbalance Estimation in OFDM”, Proceedings of IEEE International Conference on Communications, pp. 11-15, 2006.
- J. Tubbax, “Compensation of IQ Imbalance and Phase Noise in OFDM systems”, IEEE Transactions on Wireless Communications, Vol. 4, No. 3, pp. 872-877, 2005.
- Qiyue Zou, “Joint Compensation of IQ Imbalance and Phase Noise in OFDM Systems”, IEEE Transactions on Wireless Communications, Vol. 57, No. 2, pp. 404-414, 2009.
- Qiyue Zou, “On the Joint Compensation of IQ Imbalances and Phase Noise in MIMO-OFDM Systems”, Proceedings of IEEE International Symposium on Circuits and Systems, pp. 1-6, 2007.
- Study on the Convergence of the Generalized Decision Feedback Equalizer Algorithm with Error Feedback Filter
Abstract Views :231 |
PDF Views:0
Authors
Affiliations
1 Kim Chaek University of Technology, KP
2 College of Information Engineering, Wonsan Jogunsil University of technology, KP
1 Kim Chaek University of Technology, KP
2 College of Information Engineering, Wonsan Jogunsil University of technology, KP
Source
ICTACT Journal on Communication Technology, Vol 10, No 2 (2019), Pagination: 2012-2017Abstract
In digital communications, a time-dispersive variation channel causes inter-symbol interference (ISI) and distorts the received signal, thus degrading the system performance. The distortion caused by ISI may be mitigated by passing the received signal through an adaptive equalizer such as linear equalizer, maximum likelihood sequence estimator (MLSE) and decision feedback equalizer (DFE). This paper demonstrates a new approach to improving the convergence of the DFE algorithm with error feedback filter in order to reduce the correlation of error signals and also to decrease the residual error variance. The paper theoretically analyzes the convergence of generalized DFE with error feedback filter, and proves that the minimum mean square error (MMSE) monotonically decreases and stably converges when the order of the error feedback filter increases. The simulation clearly shows that the proposed DFE algorithm results in better ERLE (Echo Return Loss Enhancement) than previous ones and particularly the significant improvement of BER performance in case that the number of taps in the error feedback filter increases under low SNR environment.Keywords
Decision Feedback Equalizer, Error Feedback Filter, ISI, Convergence, MMSE.References
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