TRAP: A New Transmission Range Adjustment Protocol for MANET
Dara Singh1, Rohit Mahajan2

1Mr. Dara Singh, Department of Computer Science & Engineering, Golden College of Engineering and Technology, Gurdaspur, India
2Prof. Rohit Mahajan, Department of Computer Science & Engineering, Golden College of Engineering and Technology, Gurdaspur, India.
Manuscript received on 12 May 2018 | Revised Manuscript received on 21 May 2018 | Manuscript published on 30 May 2018 | PP: 1-5 | Volume-7 Issue-8, May 2018 | Retrieval Number: G2501047718/18©BEIESP
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Abstract: MANET is combination of mobile nodes which uses multi hop transmission for communication. Due to highly dynamic topology, routing in MANET is challenging task, moreover presence of malicious nodes makes the overall network very insecure. We study both the availability and the duration probability of a routing path that is subject to link failures caused by node mobility. In this paper we studied of Energy Efficient Cluster based routing Protocol and also proposed a Transmission Range Adjustment Protocol (TRAP) algorithmic approach towards energy efficient clustering. The aim of this algorithm is to build energy efficient cluster formation, so that the re- election of cluster head can avoid. TRAP algorithmic approach effectively uses the knowledge of neighborhood to calculate mobility, energy and signal strength of the nodes. The high value of node is elected as cluster head to keep the head alive for long time that further lead to enhance the overall performance of network and reduce the network overhead. The performance of proposed TRAP (Transmission Range Adjustment Protocol) algorithmic approach is analysis under five metrics such as a Network life time, Energy consumption, throughput, delay, packet delivery ratio. In this algorithm, we will find that node which has highest weight and make it as cluster head node for base station and best efficient path. Index Terms: 
Keyword: MANET, Clustering, Mobility, Efficiency, TRAP, Routing Protocols, Cluster Head
Scope of the Article: Clustering