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Editorial Board Bekim Fetaji South East European University, Republic of Macedonia Cesar Ortega-Sanchez Curtin University of Technology, Australia Charles Edward Notar Jacksonville State University, USA Deepak Garg Thapar Unviersity, India Hafizi Muhamad Ali Universiti Utara Malaysia, Malaysia Jose Manuel Cárdenas University of São Paulo, Brazil Katia Lida Kermanidis Ionian University, Greece Kim-Khoa Nguyen Concordia University & University of Sherbrooke, Canada Lenin Gopal Curtin University of Technology, Malaysia M-Iqbal Saripan Universiti Putra Malaysia, Malaysia Mohamad Noorman Masrek Mara University of Technology Malaysian, Malaysia N.Maheswari Kongu Arts and Science College, India Panagiotis Vlamos Ionian University, Greece Rajiv V. Dharaskar GH Raisoni College of Engineering, India Susan Sun Canadian Center of Science and Education, Canada Syed Faraz Hasan Ulster University, UK V.P.Kallimani University of Nottingham, Malaysia campus, Malaysia Wenwu Zhao Macrothink Institute, USA Computer and Information Science February, 2010 Contents Improvement in Network Lifetime for On-Demand Routing in Mobile Ad hoc Networks Using either 3 On-Demand Recharging or Transmission Power Control or Both Natarajan Meghanathan & Levon Paul Judon, Jr. Noise Removal of Spaceborne SAR Image Based on the FIR Digital Filter 12 Wei Zhang, Jinzhong Yang, Conghui Fang, Dongli Fan & Jianyong Wu Documenting Software Requirements Specification: A Revisit 17 Mingtao Shi Simulation Evaluation Algorithms of LSRP and DVRP in Bank Network 20 Feixue Huang, Yong Zhou & Zhijie Li Interacted Multiple Ant Colonies Optimization Approach to Enhance the Performance of Ant Colony 29 Optimization Algorithms Alaa Aljanaby, Ku Ruhana Ku-Mahamud & Norita Md. Norwawi A New Method of Reducing Pair-wise Combinatorial Test Suite 35 Lixin Wang & Renxia Wan Telecommunications Revolution: Implications on Criminality and Family Crisis in the South-South States 42 of Nigeria AGBA, A. M. OGABOH, IKOH, MOSES, USHIE, E.M. & BASSEY, A. O. Infer the Semantic Orientation of Words by Optimizing Modularity 52 Weifu Du & Songbo Tan Distributed University Registration Database System Using Oracle 9i 59 Sana J. Alyaseri Developments of the Research of the Formant Tracking Algorithm 68 Gang Lv & Heming Zhao Recovery of Software Architecture Using Partitioning Approach by Fiedler Vector and Clustering 72 Shaheda Akthar & Sk.MD.Rafi An Improved Wide-band Noise Signal Analysis Method 76 Wenyan Wang, Didi Liu & Xiaohua Wang A Framework of Collaborative Knowledge Management System in Open Source Software Development 81 Environment Modi Lakulu, Rusli Abdullah, Mohd Hasan Selamat, Hamidah Ibrahim & Mohd Zali Mohd Nor CLCL-A Clustering Algorithm Based on Lexical Chain for Large-Scale Documents 91 Ming Liu, Xiaolong Wang & Yuanchao Liu Utilizing Usability Model with Multi-agent Technology to Facilitate Knowledge Sharing of Software 101 Maintenance Process Knowledge Environment Amir Mohamed Talib & Rusli Abdullah Comparative Research on Particle Swarm Optimization and Genetic Algorithm 120 Zhijie Li, Xiangdong Liu, Xiaodong Duan & Feixue Huang 1 Vol. 3, No. 1 Computer and Information Science Contents Collaborative Supply Chain in the Digital Age: A Case Study of its Extent of Adoption by Indigenous 128 Organizations in Building Inter-and Intra-firm Alignments Hart O. Awa, Nsobiari F. Awara & Bartholomew C. Emecheta Study on the Route Optimization of Military Logistics Distribution in Wartime Based on the Ant Colony 139 Algorithm Hongtao Shi, Yucai Dong, Lianghai Yi, Dongyun Zheng, Hong Ju, Erchang Ma & Weidong Li Exposure of Computer Games among IHL Students in Malaysia: Case Study of Computer Science Students 144 in UiTM Terengganu Hasiah Mohamed @ Omar, Nora Yanti Che Jan & Nik Marsyahariani Nik Daud A Knowledge Innovation Algorithm Based on Granularity 152 Taishan Yan & Duwu Cui E-Community System towards First Class Mentality Development: An Infrastructure Requirements Analysis 160 Rusli Abdullah, Musa Abu Hasan, Siti Zobidah Omar, Narimah Ismail & Jusang Bolong Automatic Recognition of Focus and Interrogative Word in Chinese Question for Classification 168 Zhichang Zhang, Yu Zhang, Ting Liu & Sheng Li Simulation of Radiation Characteristics for Cylindrical Conformal Phased Array 175 Rongcang Han Analysis of Particle Swarm Optimization Algorithm 180 Qinghai Bai Ontologies Acquisition from Relational Databases 185 Shufeng Zhou, Guangwu Meng & Haiyun Ling Synthesis of Shaped Beam for Base-station Antennas in Cellular Mobile Communications 188 Ruying Sun Call Admission Control for Next Generation Wireless Networks Using Higher Order Markov Model 192 Ramesh Babu H.S, Gowrishankar & Satyanarayana P.S 2 Computer and Information Science February, 2010 Improvement in Network Lifetime for On-Demand Routing in Mobile Ad hoc Networks Using either On-Demand Recharging or Transmission Power Control or Both Natarajan Meghanathan (Corresponding Author) Department of Computer Science, Jackson State University P. O. Box 18839, 1400 John R. Lynch Street, Jackson, MS 39217, USA Tel: 01-601-979-3661 E-mail: [email protected] Levon Paul Judon, Jr. Department of Computer Science, Jackson State University E-mail: [email protected] Abstract Given a fixed energy budget for the operation of a mobile ad hoc network (MANET), on-demand recharging is the technique of charging the nodes initially with identical, but reduced energy level called the recharge quantum, and then recharging the nodes with the recharge quantum of energy whenever the energy level at a node goes below a threshold level. Transmission power control is the technique of adjusting the transmission power at a sender node depending on the distance to the receiver node. The high-level contribution of this paper is a simulation-based analysis of the network lifetime obtained for each of the following four scenarios: [a] No power control, No on-demand recharging; [b] Power control, but no on-demand recharging; [c] On-demand recharging, but no power control and [d] Both power control and on-demand recharging. Network lifetime is defined as the time of first node failure due to the exhaustion of energy level at the node and the inability to further charge the node. The on-demand routing protocols studied are: Dynamic Source Routing (DSR), Flow-Oriented Routing Protocol (FORP) and the Min-Max Battery Cost Routing (MMBCR) algorithm run on the top of DSR. We illustrate the improvement obtained in network lifetime as we transition from scenarios [a] through [d]. Simulation results illustrate that scenarios involving on-demand recharging ([c] and [d]) yield a higher network lifetime than scenarios [a] and [b]. When we operate the network with both on-demand recharging and power control, we obtain the maximum improvement in network lifetime. The percentage of the supplied energy that has been consumed in the network at the time of first node failure for each of the four scenarios and the three routing protocols is also measured to illustrate the effectiveness of on-demand recharging in maximizing the usage of the available energy budget. Keywords: On-demand Recharging, Mobile Ad hoc Networks, Transmission Power Control, Network Lifetime, Routing Protocols 1. Introduction A mobile ad hoc network (MANET) is a dynamic distributed system of wireless nodes where in the nodes move independent of each other. MANETs have several operating constraints such as: limited battery charge per node, limited transmission range per node and limited bandwidth. Routes in MANETs are often multi-hop in nature. Packet transmission or reception consumes the battery charge at a node. Nodes forward packets for their peers in addition to their own. In other words, nodes are forced to expend their battery charge for receiving and transmitting packets that are not intended for them. Given the limited energy budget for MANETs, inadvertent over usage of the energy resources of a small set of nodes at the cost of others can have an adverse impact on the node lifetime. There exist two classes of MANET routing protocols: proactive and reactive. The reactive (also called on-demand) routing protocols tend to be more economical in terms of energy and bandwidth usage (Broch, Maltz, Johnson, Hu & Jetcheva, 1998; Johansson, Larsson, Hedman, Mielczarek & Degermark, 1999) in dynamically changing scenarios, characteristic of MANETs. Hence, in this paper, we restrict ourselves to the class of on-demand routing protocols. We use three on-demand routing protocols: Dynamic Source Routing (DSR – Johnson, Maltz & Broch, 2001), Flow-Oriented Routing Protocol (FORP – Su & Gerla, 1999) and Min-Max Battery Cost Routing (MMBCR – Toh, 3 Vol. 3, No. 1 Computer and Information Science 2001). DSR, FORP and MMBCR are respectively selected as representatives of the minimum-hop, stable path and power-aware routing strategies. In an earlier work (Meghanathan, 2008), it has been observed that FORP discovers the sequence of most stable (long-living) routes among the currently available MANET stable path routing protocols. DSR discovers minimum-hop routes with a lower routing overhead (Broch, Maltz, Johnson, Hu & Jetcheva, 1998). MMBCR is a power-aware routing algorithm that attempts to equally distribute energy consumption to all the nodes in an ad hoc network and takes into consideration the available energy level at the nodes before route selection. The residual energy of a path is the minimum of the energy levels of the intermediate nodes on the path and MMBCR chooses the path with the maximum residual