| Inter-Cluster Communication in Wireless Sensor Networks |
| Pages: 1-6 (6) | [Full Text] PDF (354 KB) |
| S ZAFAR, AH AKBAR |
| Department of Electrical Engineering, National University of Computer & Emerging Sciences, Lahore, PakistanDepartment of Computer Science, University of Engineering and Technology, Lahore, Pakistan |
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Abstract - Wireless Sensor Network (WSN) comprises of end devices or nodes equipped with sensors, microcontrollers and transceivers that communicate wirelessly to accomplish goals like surveillance, monitoring and control etc. Groups of nodes under the control of a single cluster head form clusters. Communication within WSN clusters is a widely researched area, however communication among multiple clusters located in close proximity is challenging due to clusters operating in distinct logical channels. Cluster merging and cluster diffusion are proposed for inter-cluster communication. In merging the two clusters combine to form a single network while in diffusion the two clusters are expected to retain their individuality while diffusing through edge nodes. This paper highlights the distinction between cluster merging and cluster diffusion, presents architecture for carrying out efficient diffusion and finally compares these two approaches under various network conditions. Simulations are carried out in Network Simulator (NS2) which shows that cluster diffusion can be rendered efficient when multiple edge nodes are used for inter-cluster communication. |
| Index Terms - Sensor Networks, Network Cluster, Cluster Merging, Cluster Diffusion, Sensor Nodes |
C itation - S ZAFAR, AH AKBAR. "Inter-Cluster Communication in Wireless Sensor Networks ." International Journal of Computer Networks and Communications Security 5, no. 1 (2017): 1-6. |
| Towards A New Architecture of Detecting Networks Intrusion Based on Neural Network |
| Pages: 7-18 (12) | [Full Text] PDF (695 KB) |
| BHL DJIONANG, G TINDO |
| University of Yaounde I, Faculty of science, Yaounde, Cameroon |
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Abstract - Networks intrusion detection systems appear nowadays as one of the most efficient solution for detecting illegal or suspicious activities in a network. Using neural networks to meet this objective has been studied by many authors. Most of the solutions provided in the literature face the problem of relevance and reliability. One of the major reasons of such a situation is the misconception of a correct profile. In this paper, a modular architecture is proposed, in which each module is dedicated to detecting a particular type of attack. Firstly, each module is trained with all attributes, then secondly some of the attributes used for training are pruned from the training set. This modularity allows us to know which of the systems module threaten the NIDS performance. The experimentation led us to observe that, depending on the type of the attack, some of the attributes have a marginal effect, although in the opposite, some other have a dominant effect. We have done a comparative study of the solution proposed and the others in the literature. It appears that our solution is more efficient on certain type of attacks. The NSL-KDD dataset has been used to train, test and evaluate our architecture. |
| Index Terms - NIDS, Neural networks, MLP, NSL-KDD Dataset |
C itation - BHL DJIONANG, G TINDO. "Towards A New Architecture of Detecting Networks Intrusion Based on Neural Network." International Journal of Computer Networks and Communications Security 5, no. 1 (2017): 7-18. |
| CEED: Computational and Energy Efficient, Dynamic Distributed Algorithm of Big Data |
| Pages: 19-23 (5) | [Full Text] PDF (Please wait) |
| MS Al-kahtani |
| Dept. of Computer Engg., Prince Sattam bin Abdulaziz University, Saudi Arabia |
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Abstract - As big data are emerging designing an efficient distributed algorithm is significantly important. While mostexisting distributed algorithms consider distributed processing only on the commodity computers this paperintroduces a Computational and Energy Efficient, Dynamic (CEED) distributed algorithm for big dataprocessing on a framework that comprises data processing both at the data collection end and data processingserver end. The proposed CEED algorithm works both in low powered nodes and high speed commoditycomputers as well performs sequential and parallel processing based on the amount of data received at thecentral server. Simulation results demonstrate that the CEED algorithm achieves processing efficiency interms of data processing time as compared to traditional distributed algorithms. |
| Index Terms - Big Data, Distributed Algorithms, MapReduce, Sensor, Commodity Hardware |
C itation - MS Al-kahtani. "CEED: Computational and Energy Efficient, Dynamic Distributed Algorithm of Big Data." International Journal of Computer Networks and Communications Security 5, no. 1 (2017): 19-23. |