JPN1413 An Energy-Balanced Routing Method Based on Forward-Aware Factor for Wireless Sensor Networks

JPN1413 An Energy-Balanced Routing Method Based on Forward-Aware Factor for Wireless Sensor Networks

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  An Energy-Balanced RoutingMethod Based on Forward- Aware Factor for Wireless Sensor Networks ABSTRACT: As an important part of industrial application (IA), the wireless sensor network (WSN) has been an active research area over the past few years. Due to the limited energy and com-munication ability of sensor nodes, it seems especially importantto design a routing protocol for WSNs so that sensing data can be transmitted to the receiver effectively. An energy-balanced routing method based on forward-aware factor (FAF-EBRM) is proposed in this paper. In FAF-EBRM, the next-hop node is selected according to the awareness of link weight and forward energy density. Furthermore, a spontaneous reconstruction mechanism for local topology is designed additionally. In the experiments, FAF-EBRM is compared with LEACH and EEUC, experimental results show that FAF-EBRM outperforms LEACH and EEUC, which balances the energy consumption, prolongs the function lifetime and guarantees high QoS of WSN. EXISTING SYSTEM: BBV model is widely used to analyze the real complex networks such as scientist collaboration network (SCN) and worldwide airport network (WWAN). Similar to
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  SCN and WWAN,there are numerous nodes and community structures (clusters) in WSN, important nodes (cluster heads) have more connections than common nodes. Many researches on “energy ho le” show that the data flow on each connection varies considerably in WSN because of these different distances to the sink node. Thus, it is not suitable to represent a connection as connected (“1”) or connectionless (“0”). Furthermore, global information is limited in WSN of IA sensors exchange information in their “local-world”. Overall, weighted network and local-world theory is appropriate to model WSN of IA. DISADVANTAGES OF EXISTING SYSTEM:  Each sensor node is both a sensor and a router, and its computing ability, storage capacity, communication ability, and power supply are limited.  Due to the limited energy and communication ability of sensor nodes, it seems especially important to design a routing protocol for WSNs so that sensing data can be transmitted to the receiver effectively.
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  PROPOSED SYSTEM: Inthis paper, we propose an approach to the problem of routing around connectivity holes that works in any connected topology without the overhead and inaccuracies incurred by methods based on topology planarization. Specifically, we define a cross-layer protocol, named ALBA for Adaptive Load-Balancing Algorithm, whose main ingredients (geographic routing, load balancing, contention-based relay selection) are blended with a mechanism to route packets out and around dead ends, the Rainbow protocol. The combination of the two protocols, called ALBA-R, results in an integrated solution for convergecasting in WSNs that, although connected, can be sparse and with connectivity holes. ADVANTAGES OF PROPOSED SYSTEM:  Achieves performance superior to existing protocols in terms of energy efficiency, packet delivery ratio (PDR), and latency.  The Rainbow mechanism allows guarantee packet delivery in realistic deployment.
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   Simulation resultsalso show better performance than that of two recent proposals for routing around dead ends. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS:  System : Pentium IV 2.4 GHz.  Hard Disk : 40 GB.  Floppy Drive : 1.44 Mb.  Monitor : 15 VGA Colour.  Mouse : Logitech.  Ram : 512 Mb. SOFTWARE REQUIREMENTS:  Operating system : Windows XP/7/LINUX.  Implementation : NS2  NS2 Version : NS2.2.28  Front End : OTCL (Object Oriented Tool Command Language)
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   Tool :Cygwin (To simulate in Windows OS) REFERENCE: Degan Zhang, Member, IEEE, Guang Li, Member, IEEE, Ke Zheng, Member, IEEE, Xuechao Ming, Member, IEEE, and Zhao-Hua Pan, Member, IEEE, “An Energy-Balanced Routing Method Based on Forward-Aware Factor for Wireless Sensor Networks”, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 10, NO. 1, FEBRUARY 2014.