RSS Based Energy Efficient Scheme for the Reduction of Overhearing and Rebroadcast for MANET

Abstract In MANET, reducing the amount of overhearing and rebroadcast based on Received Signal Strength (RSS) value can reduce the energy consumption. A cross layer framework is designed by combining the physical, MAC and network layer. In order to reduce the energy consumption, 802.11 PSM is integrated with DSR. Overhearing in DSR will improve the routing efficiency by expending some amount of energy. The main causes for energy consumption are unconditional overhearing and unnecessary rebroadcast of RREQ to the nodes which are having less Received Signal Strength (RSS). Here, RSS is used to predict the mobility of nodes. The less value of RSS indicates that the nodes are far away from the sender and this may lead to many path breakages. Probability of overhearing reduction (POR) is determined in order to limit the amount of hearing for the unicast packets. The proposed mechanism R-ROR avoids unnecessary overhearing and rebroadcast using cross layer design aiming to achieve energy consumption. Rebroadcast based on the RSS can reduce the number of path breakages, energy consumption and overhead. Simulation results are compared for Packet Delivery Ratio (PDR), energy consumption and delay. The analysis shows that R-ROR is energy efficient compared to 802.11, 802.11PSM, ODPM and RandomCast.

[1]  Tomasz Imielinski,et al.  Mobile Computing , 1996 .

[2]  Daniel P. Siewiorek,et al.  802.11 Power Management Extensions to Monarch ns , 2004 .

[3]  David A. Maltz,et al.  Dynamic Source Routing in Ad Hoc Wireless Networks , 1994, Mobidata.

[4]  Lifei Huang,et al.  On the scalability of IEEE 802.11 ad hoc networks , 2002, MobiHoc '02.

[5]  Zongpeng Li,et al.  Probabilistic Power Management for Wireless Ad Hoc Networks , 2005, Mob. Networks Appl..

[6]  Ashish Shukla.  Ensuring Cache Freshness in On-Demand Routing Protocols for Mobile Ad Hoc Network: A Cross-layer Framework , 2007, 2007 4th IEEE Consumer Communications and Networking Conference.

[7]  R. Li,et al.  CCS-MAC: Exploiting the overheard data for compression in wireless sensor networks , 2011, Comput. Commun..

[8]  Hannes Hartenstein,et al.  Stochastic Properties of the Random Waypoint Mobility Model , 2004, Wirel. Networks.

[9]  Rong Zheng,et al.  On-demand power management for ad hoc networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[10]  Wenjing Lou,et al.  The effects of cache organizations on the performance of on-demand routing protocols in ad hoc networks , 2002, MILCOM 2002. Proceedings.

[11]  Chita R. Das,et al.  RandomCast: An Energy-Efficient Communication Scheme for Mobile Ad Hoc Networks , 2009, IEEE Transactions on Mobile Computing.

[12]  Jennifer C. Hou,et al.  LISP: a link-indexed statistical traffic prediction approach to improving IEEE 802.11 PSM , 2004, 24th International Conference on Distributed Computing Systems, 2004. Proceedings..

[13]  Ahmed Helmy,et al.  IMPORTANT: a framework to systematically analyze the Impact of Mobility on Performance of Routing Protocols for Adhoc Networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[14]  Charles E. Perkins,et al.  Ad-hoc on-demand distance vector routing , 1999, Proceedings WMCSA'99. Second IEEE Workshop on Mobile Computing Systems and Applications.

[15]  David B. Johnson,et al.  The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks , 2003 .

[16]  Ivan Stojmenovic,et al.  Power-Aware Localized Routing in Wireless Networks , 2001, IEEE Trans. Parallel Distributed Syst..

[17]  Yu-Chee Tseng,et al.  The Broadcast Storm Problem in a Mobile Ad Hoc Network , 1999, Wirel. Networks.