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Kiani, Farzad
- A Method for Forecasting Weather Condition by using Artificial Neural Network Algorithm
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Authors
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1 Department of Computer Science and Engineering, Istanbul Sabahattin Zaim University, TR
1 Department of Computer Science and Engineering, Istanbul Sabahattin Zaim University, TR
Source
ICTACT Journal on Soft Computing, Vol 8, No 3 (2018), Pagination: 1696-1700Abstract
This article presents a method to forecast and make decision on weather condition. In most of the cities around the world, people try to decide on leisure activities on their spare time but weather condition would not be suitable for them. By this fact, we suggest a solution to solve this problem with ANN. Therefore, users of our proposed method can organize their daily life in accordance with weather condition. Artificial Neural Network (ANN) is one of the popular research subjects in computer science, thus, this paper aims to familiarize the reader with ANN. In our proposed method, at first, people can organize weather condition, and then the program suggest whether the time is suitable for them or not on chosen hour of day. In ANN, we discuss about neuron that have relation with performance. Mean Square Error (MSE) is the key issue for the performance of our method. At the end, the simulation results show that relation between Neuron and MSE is applicable for daily usage.Keywords
ANN, Neural Networks, Weather Conditions.
References
- R.W. Katz and A.H. Murphy, “Economic Value of Weather and Climate Forecasts”, Cambridge University Press, 1997.
- Kneale T. Marshall, “Decision Making and Forecasting: With Emphasis on Model Building and Policy Analysis”, McGraw-Hill, 1995.
- Abhishek Kumar, et al., “A Rainfall Prediction Model using Artificial Neural Network”, Proceedings of IEEE Control and System Graduate Research Colloquium, pp. 23-27, 2012.
- J. Kutsurelis, “Forecasting Financial Markets Using Neural Networks: An Analysis of Methods and Accuracy”, Master Thesis, Naval Postgraduate School, 1998.
- U. Bilge, “Enchanted Artificial Intelligence and Expert Systems”, Available at: https://turkmia.org/kongre2007/cd/pdf/113-118.pdf.
- Neural Network Toolbox, Available at: https://www.mathworks.com/products/neuralnetwork.html.
- Jr. Nascimento and L. Cairo, “Artificial Neural Networks in Control and Optimization”, PhD Dissertation, University of Manchester, 1994.
- Robert J. Schalkoff, “Artificial Neural Networks”, McGrawHill, 1997.
- Simon Haykin, “Neural Networks: A Comprehensive Foundation”, Macmillan Coll Div., 1994.
- Robert Hecht-Nielsen, “Neurocomputing”, AddisonWesley, 1990.
- Mikel Olazaran, “A Sociological Study of the Official History of the Perceptrons Controversy”, Social Studies of Science, Vol. 26, No. 3, pp. 611-659, 1996.
- Ciobanu Dumitru and Vasilescu Maria, “Advantages and Disadvantages of Using Neural Networks for Predictions”, Ovidius University Annals, Series Economic Sciences, Vol.1, pp. 444-449, 2013.
- S. Grossberg, “Adaptive Pattern Classification and Universal Recoding: II. Feedback, Expectation, Olfaction, Illusions”, Biological Cybernetics, Vol. 23, No. 4, pp. 187-202, 1976.
- David E. Rumelhart and David Zipser, “Feature Discovery by Competitive Learning”, Cognitive Science, Vol. 9, No. 1, pp. 75-112, 1985.
- Geoffrey E. Hinton, Terrence J. Sejnowski and David H. Ackley, “Boltzmann Machines: Constraint Satisfaction Networks that Learn”, PhD Dissertation, Department of Computer Science, Carnegie-Mellon University, 1984.
- John J. Hopfield, “Neural Networks and Physical Systems with Emergent Collective Computational Abilities”, Proceedings of the National Academy of Sciences, Vol. 79, No. 8, pp. 2554-2558, 1982.
- Teuvo Kohonen, “An Introduction to Neural Computing”, Neural Networks, Vol. 1, No. 1, pp. 3-16, 1988.
- Stephen Grossberg, “Competitive Learning: From Interactive Activation to Adaptive Resonance”, Cognitive Science, Vol. 11, No. 1, pp. 23-63, 1987.
- D.E. Rumelhart, G.E. Hinton and R.J. Williams. “Learning Internal Representation by Back Propagation”, Nature, Vol. 323, pp. 533-536, 1986.
- Meteorological Service Turkish State, Available at www.mgm.gov.tr.
- Target Tracking Based on base Station Node Using Prediction Method and Cluster Structure in Wireless Sensor Networks
Abstract Views :187 |
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Authors
Affiliations
1 Department of Computer Engineering, Istanbul Sabahttin Zaim University, TR
2 Department of Computer Engineering, Education Technology and Information in Zanjan Province, IR
1 Department of Computer Engineering, Istanbul Sabahttin Zaim University, TR
2 Department of Computer Engineering, Education Technology and Information in Zanjan Province, IR
Source
ICTACT Journal on Communication Technology, Vol 9, No 2 (2018), Pagination: 1779-1788Abstract
One of the most important and major challenging issues of wireless sensor networks is the tracking of mobile targets. The network continuously reports the spatial information of moving objects during specified periods to the base station. In this paper, by introducing new a protocol with two versions, of which, one of them is based on dynamic clustering with a focus on the base station, and the other is based on a predictive system for increasing the tracking accuracy of the objects movement and decreasing the energy consumption as well. In this paper, the task of clustering involves in determining the cluster heads, the number of cluster members, the selection of cluster members, and managing the activation of the nodes that is done by the base station. On the other hand, given that the base station is outside the field of wireless sensor networks and is connected to an unlimited power source. The second version of the proposed protocol is based on a predictive algorithm that it was inspired from the first proposed version in the role of the base station node by a prediction method. In this paper, three heuristic models are introduced to select the speed and direction in prediction models. They are instant, average and exponential-average models. These models can track the relevant targets more accurately and reduce the number of missing targets. The simulations are done in different scenarios in a custom developed tool. The results of simulation show a good performance of them in the network lifetime and target tracking applications.Keywords
Wireless Sensor Networks, Dynamic Clustering, Mobile Target Tracking, Network Lifetime, Target Prediction.References
- F. Kiani, A. Rad, M.K. Sis, A. Kut and A. Alpkocak, “EEAR: An Energy Effective-Accuracy Routing Algorithm for Wireless Sensor Networks”, Life Science Journal, Vol. 10, No. 2, pp. 39-45, 2013.
- X.Q. Yu, Z.L. Zhang and W.T. Han, “Evaluation of Communication in Wireless Underground Sensor Networks”, IOP Conference Series: Earth and Environmental Science, Vol. 69, pp. 1-8, 2017.
- M. Adam, M. Anisi, I. Ali, “Object Tracking Sensor Networks in Smart Cities: Taxonomy, Architecture, Applications, Research Challenges and Future Directions”, Future Generation Computer Systems, pp. 1-15, 2017.
- H. Jawad et al., “Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review”, Sensors, Vol. 17, No. 8, pp. 1-45, 2017.
- F. Kiani, “AR-RBFS: Aware-Routing Protocol Based on Recursive Best-First Search Algorithm for Wireless Sensor Networks”, Journal of Sensors, Vol. 2016, pp. 1-10, 2016.
- T.A. Alhmiedat and S.H. Yang, “A Survey: Localization and Tracking Mobile Targets through Wireless Sensors Network”, Proceedings of 8th Annual Network Symposium, pp. 2-9, 2007.
- M. Akter, “Energy-Efficient Tracking and Localization of Objects in Wireless Sensor Networks”, IEEE Access, Vol. 6, pp. 17165-17177, 2018.
- W. Zhang and G. Cao, “DCTC: Dynamic Convoy Tree-Based Collaboration for Target Tracking in Sensor Networks”, IEEE Transactions on Wireless Communications, Vol. 3, No. 5, pp. 1689-1701, 2004.
- H.T. Kung and D. Vlah, “Efficient Location Tracking using Sensor Networks”, Proceedings of IEEE Wireless Communications and Networking Conference, pp. 1-8, 2003.
- F. Kiyani, H. Chalangari and S. Yari, “DCSE: A Dynamic Clustering for Saving Energy in Wireless Sensor Network”, Proceedings of IEEE International Conference on Communication Software and Networks, pp. 13-17, 2010.
- H. Ahmadi, F. Viani and R. Bouallegue, “An Accurate Prediction Method for Moving Target Localization and Tracking in Wireless Sensor Networks”, Ad Hoc Networks, Vol. 70, pp. 14-22, 2018.
- F. Kiani, “Designing New Routing Algorithms Optimized for Wireless Sensor Network”, Academic Publishing, 2014.
- A. Ali, Y. Ming, S. Chakraborty and S. Irem, “A Comprehensive Survey on Real-Time Applications of WSN”, Future Internet, Vol. 9, No. 77, pp. 1-22, 2017.
- R. Shorey, A. Ananda, M. Chan and W. Ooi, “Mobile, Wireless and Sensor Networks”, Wiley-Blackwell, 2006.
- M. Navia, J. Campelo and A. Bonastre, “GTSO: Global Trace Synchronization and Ordering Mechanism for Wireless Sensor Network Monitoring Platforms”, Sensors, Vol. 18, No. 28, pp. 1-22, 2018.
- H. Karl and A. Willig, “Protocols and Architectures for Wireless Sensor Networks”, Wiley-Blackwell, 2005.
- O. Demigha, N. Badache, M. Aissani and A. Mellouk, “Fault-Tolerant Prediction-based Scheme for Target Tracking Application”, Proceedings of IEEE Global Telecommunications Conference, pp. 1-6, 2009.
- J. Tapeiro, H. Medeiros and R.H. Bishop, “Predicting Multiple Target Tracking Performance for Applications on Video Sequences”, Machine Vision and Applications, Vol. 28, No. 5, pp. 539-550, 2017.
- N. Liang, G. Wu and W. Kang, “Real-Time Long-Term Tracking with Prediction-Detection-Correction”, IEEE Transactions on Multimedia, pp. 1-14, 2018.
- D. Malan, T. F. Jones, M. Welsh and S. Moulton, “An Ad Hoc Sensor Network Infrastructure for Emergency Medical Care”, Proceedings of International Workshop on Wearable and Implantable Body Sensor Networks, pp. 1-5, 2004.
- D. Wajgi and N. Thakur, “Load Balancing Algorithms in Wireless Sensor Network: A Survey”, International Journal of Computer Networks and Wireless Communications, Vol. 2, No. 1, pp. 2250-3501, 2012.
- H. Tahmasebirad, “Target Tracking based on Dynamic Clustering with Energy Saving in Wireless Sensor Networks”, Master Thesis, Department of Computer Science, Islamic Azad University, 2012.
- A. Ghazi and A. Ahiod, “Impact of Random Waypoint Mobility Model on Ant-based Routing Protocol for Wireless Sensor Networks”, Proceedings of International Conference on Big Data and Advanced Wireless Technologies, pp. 1-7, 2016.
- M. Guo, E. Olule, G. Wang and S. Guo, “Designing Energy Efficient Target Tracking Protocol with Quality Monitoring in Wireless Sensor Networks”, Journal Supercomputer, Vol. 51, No. 2, pp. 131-148, 2009.
- W. Heinzelman, A. Chandrakasan and H. Balakrishnan, “An Application-Specific Protocol Architecture for Wireless Microsensor Networks”, IEEE Transactions on Wireless Communications, Vol. 1, No. 4, pp. 660-670, 2002.
- X. Ning and C.G. Cassandras, “Dynamic Sleep Time Control in Wireless Sensor Networks”, ACM Transactions on Sensor Networks, Vol. 6, No. 3, pp. 1-37, 2010.
- EER-Al:An Energy Efficient Routing Protocol Based on Automated Learning Method
Abstract Views :163 |
PDF Views:5
Authors
Affiliations
1 Department of Computer Engineering, Istanbul Sabahttin Zaim University, TR
1 Department of Computer Engineering, Istanbul Sabahttin Zaim University, TR
Source
ICTACT Journal on Communication Technology, Vol 9, No 2 (2018), Pagination: 1798-1803Abstract
The issue of energy in a wireless sensor network is one of the most important challenges for these networks. This issue is also being considered today in the new IoT topic. This paper studies the ability of the learning automata model to solve the problem in the sensor networks. Because they have capabilities such as low computational load, ability to use in distributed environments, and inaccurate information, require the least feedback from the environment, etc. One of the solutions to energy optimization is to provide routing protocols. In the routing area, a routing protocol based on learning automata has been proposed in which the network lifetime criterion is considered. The simulation results and the comparison of the proposed protocol with other protocols indicate that this protocol has better performance in the energy conversation and network lifetime.Keywords
Wireless Sensor Networks, Energy Efficiency, Routing Protocol, Fault Tolerance, Automated Learning.References
- Z. Mohammed and E. Ahmed, “Internet of Things Applications, Challenges and Related Future Technologies”, World Scientific News, Vol. 67, No. 2, pp. 126-148, 2017.
- F. Kiani, “AR-RBFS: Aware-Routing Protocol Based on Recursive Best-First Search Algorithm for Wireless Sensor Networks”, Journal of Sensors, Vol. 2016, pp. 1-10, 2016.
- L.B. Bhajantri and N. Nalini, “A Fault Tolerance Approach to Topology Control in Distributed Sensor Networks”, Proceedings of IEEE International Conference on Advanced Communication Control and Computing Technologies, pp. 208-212, 2012.
- H. Bagci, I. Korpeoglu and A. Yazici, “A Distributed Fault-Tolerant Topology Control Algorithm for Heterogeneous Wireless Sensor Networks”, IEEE Transactıons on Parallel and Dıstrıbuted Systems, Vol. 26, No. 4, pp. 914-923, 2015.
- F. Kiani, “Maximizing Wireless Sensor Network Lifetime Based on Linear Programming Method”, International Research Journal of Engineering and Technology, Vol. 3, No. 3, pp. 1354-1359, 2018.
- J.N. Al-Karaki and A.E. Kamal, “Routing Techniques in Wireless Sensor Networks: A Survey”, Proceedings of the IEEE International Conference on Wireless Communications, pp. 6-28, 2004.
- A. Sarkar and T. Murugan, “Routing Protocols for Wireless Sensor Networks: What the Literature Says?”, Alexandria Engineering Journal, Vol. 55, No. 4, pp. 3173-3183, 2016.
- A. Avizienis, J.C. Laprie and B. Randell, “Fundamental concepts of dependability”, Proceedings of the Technical report, UCLA CSD Report no. 010028, 2001.
- F. Koushanfar, M. Potkonjak and A. Sangiovanni, “Fault-Tolerance Techniques for Sensor Networks”, Proceedings of IEEE International Conference on Sensors, pp. 1491-1496, 2002.
- V. Saritha, P. V. Krishna, S. Misra and M.S. Obaidat, “Learning Automata based Optimized Multipath routing using Leapfrog Algorithm for VANETs”, Proceedings of International Conference on Mobile and Wireless Networking, pp. 1-5, 2017.
- K.S. Narendra and M.A.L. Thathachar, “Learning Automata: An Introduction”, Prentice Hall, 1989.
- Z. Shariat, A. Movaghar and, M. Hoseinzadeh, “A Learning Automata and Clustering-based Routing Protocol for Named Data Networking”, Telecommunication System, Vol. 65, No. 1, pp. 9-29, 2017.
- H. Ge and Sh. Li, “A Parameter-Free Learning Automaton Scheme”, Available at: https://pdfs.semanticscholar.org/4499/e181afb78f0ce8043accf2db37c3a90314c0.pdf
- F. Kiani, “Reinforcement Learning Based Routing Protocol for Wireless Body Sensor Networks”, Proceedings of IEEE 7th International Symposium on Cloud and Service Computing, pp. 71-78, 2017.
- K. Arulkuraman, M. Peter, M. Brundage and A. Bharath, “Deep Reinforcement Learning: A Brief Survey”, IEEE Signal Processing Magazine, Vol. 5, No. 3, pp. 26-38, 2017.
- C. S. Chasparis, “Stochastic Stability of Perturbed Learning Automata in Positive-Utility Games”, Available at: https://arxiv.org/pdf/1709.05859.pdf
- G. Barto and P. Anandan, “Pattern-Recognizing Stochastic Learning Automata”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 15, No. 3, pp. 360-375, 1985.
- S. Jabbar et al., “Analysis of Factors Affecting Energy Aware Routing in Wireless Sensor Network”, Wireless Communications and Mobile Computing, Vol. 2018, pp. 1-21, 2018.
- K. Arasu and R. Ganesan, “Effective Implementation of Energy Aware Routing for Wireless Sensor Network”, Materials Today: Proceedings, Vol. 5, No. 1, pp. 1186-1193, 2018.
- S. Aswale and V.R. Ghorbade, “LQEAR: Link Quality and Energy-Aware Routing for Wireless Multimedia Sensor Networks”, Wireless Personal Communications, Vol. 97, No. 1, pp. 1291-1304, 2017.
- M. Ilyas and I. Mahgoub, “Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems”, CRC Press, 2005.
- H. Zhoue et al., “A Multiple-Dimensional Tree Routing Protocol for Multi sink Wireless Sensor Networks based on Ant Colony Optimization”, International Journal of Distributed Sensor Networks, Vol. 2012, pp. 1-10, 2012.
- C. Intanagonwiwat et al., “Directed Diffusion for Wireless Sensor Networking”, IEEE/ACM Proceedings of the Transactions on Networking, Vol. 11, No. 1, pp. 2-16, 2003.
- M. Ankit et al., “TinyLAP: A Scalable Learning Automata-Based Energy Aware Routing Protocol for Sensor Networks”, Proceedings of International Conference on Wireless and Communications, pp. 1-8, 2006.
- D. De, W. Song and Sh. Tang, “EAR: An Energy and Activity-Aware Routing Protocol for Wireless Sensor Networks in Smart Environments”, The Computer Journal, Vol. 55, No. 12, pp. 1492-1506, 2012.
- T. Roosta, “Probabilistic Geographic Routing protocol for Ad Hoc and Sensor Networks”, Proceedings of International Workshop Wireless Ad Hoc Networks, pp. 1-8, 2006.