EAAC: Energy-Aware Admission Control Scheme for Ad Hoc Networks

Authors: Dilip Kumar S.M, Vijaya Kumar B.P.

Abstract:

The decisions made by admission control algorithms are based on the availability of network resources viz. bandwidth, energy, memory buffers, etc., without degrading the Quality-of-Service (QoS) requirement of applications that are admitted. In this paper, we present an energy-aware admission control (EAAC) scheme which provides admission control for flows in an ad hoc network based on the knowledge of the present and future residual energy of the intermediate nodes along the routing path. The aim of EAAC is to quantify the energy that the new flow will consume so that it can be decided whether the future residual energy of the nodes along the routing path can satisfy the energy requirement. In other words, this energy-aware routing admits a new flow iff any node in the routing path does not run out of its energy during the transmission of packets. The future residual energy of a node is predicted using the Multi-layer Neural Network (MNN) model. Simulation results shows that the proposed scheme increases the network lifetime. Also the performance of the MNN model is presented.

Keywords: Ad hoc networks, admission control, energy-aware routing, Quality-of-Service, future residual energy, neural network.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056803

References:

[1] D.B. Johnson and D.A. Maltz, "Dynamic Source Routing in Ad-Hoc Wireless Networks", Mobile Computing, Kluwer Academic Publishers, ch. 5, pp. 153-181, 1996.
[2] C. Perkins and E. Royer, "Ad hoc On-Demand Distance Vector Routing", Proc. 2nd IEEE Workshop on Mobile Computing Systems & Applications, pp. 90-100, Feb. 1999.
[3] R. Asokan and A. M. Natarajan, "An approach for reducing the Endto- end Delay and increasing the Network Lifetime in Mobile Ad hoc Networks", Intr-l Jr. of Info. Tech., vol. 4, no. 2, pp. 121-127, 2008.
[4] Q. Sun and H. Langendoerfer, "Multicast Routing in Multimedia Communication", Proc. 2nd Intr-l Workshop on Protocols for Multimedia Systems (PROMS -95), pp. 452-458, 1995.
[5] C.P. Low and X. Song, "On Finding Feasible Solutions for the Delay Constrained Group Multicast Routing Problem", IEEE Trans. on Computers, vol. 51, pp. 581-588, 2002.
[6] V. P. Kompella, J. C. Pasquale and G.C Polyzos, "Multicast Routing in Multimedia Communication", IEEE Trans. on Computers, vol. 51, pp. 581-588, 2002.
[7] Y. Yang and R. Kravets, "Contention-aware Admission control for Ad Hoc Networks", IEEE Trans. on Mobile Computing, vol. 4, no. 4, 2005.
[8] K. Scott and N. Bambos, "Routing & Channel Assignment for Low Power Transmission in PCS", Proc. IEEE Int-l Conf. on Universal Personal Comm. (ICUPC-96), pp. 498-502, 1996.
[9] S. Singh, M. Woo and C.S. Raghavendra, "Power-aware with Routing in Mobile Ad Hoc Networks", Proc. IV Annual ACM/IEEE Int-l Conf. on Mobile Computing & Networking, 1998.
[10] C.-K Toh, "Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks", IEEE Comm. Magazine, Jun 2001.
[11] Z. Guo and B. Malakooti, "Energy Aware Proactive MANET Routing with Prediction on Energy Consumption" Proc. IEEE Int-l Conf. on Wireless Algorithms, Systems and Applications, pp. 287-292, 2007.
[12] Nen-Chung W. and Yu-Li S., "A Power-Aware Multicast Routing Protocol for Mobile Ad Hoc Networks with Mobility Prediction", Proc. IEEE Conf. on Local Computer Networks (LCN-05), 2005.
[13] Dilip Kumar S.M. and Vijaya Kumar B.P., "Energy-Aware Multicast Routing in MANETs based on Genetic Algorithms", Proc. XVI IEEE Intr-l Conf. on Networks (ICON- 08), New Delhi, 2008.
[14] D. Kim, J.J. Garcia L-A, K. Obraczka, K-C Cano, and P. Manzoni, "Routing Mechanisms for Mobile Ad Hoc Networks based on the Energy Drain Rate", IEEE Trans. on Mobile Computing, vol 2., no. 2, 2003.
[15] K. Murugan and S. Shanmugavel, "Traffic-Dependent and Energy-Based Time Delay Routing Algorithms for improving Energy Efficiency in Mobile Ad Hoc Networks", EURASIP Jr. on Wireless Communications and Networking, no. 5, pp. 625-634, 2005.
[16] Koushik K., M. Kodialam, T.V. Lakshman and L. Tassiulas, "Routing for Network Capacity Maximization in Energy-constrained Ad-hoc Networks", Proc. IEEE INFOCOM, 2003.
[17] Tragoudas S., and Dimitrova S., "Routing with Energy considerations in Mobile Ad Hoc Networks", IEEE Wireless Communications and Networking Conf. (WCNC-00), vol. 3, pp. 1258-1261, 2000.
[18] M. B. Pursley, H. B. Russell, and J. S. Wysocarski, "Energy-efficient Transmission and Routing Protocols for Wireless Multiple-hop Networks and Spread Spectrum Radios", Proc. EUROCOMM Conf., pp. 1-5, 2000.
[19] I-Shyan Hwang and Wen-Hsin Pang, "Energy Efficient Clustering Technique for Multicast Routing Protocol in Wireless Ad Hoc Networks", Int-l Jr. of Computer Science and Network Security, vol. 7, no. 8, Aug. 2007.
[20] L. Lin, N. B. Shroff, and R. Srikant, "Asymptotically Optimal Poweraware Routing for Multihop networks with Renewable Energy Sources", Proc. IEEE INFOCOM-05. 24th Annual Joint Conf. of IEEE Computer and Comm. Societies, FL Mar. 2005.
[21] Zhihao Guo and B. Malakooti, "Energy Aware Proactive MANET Routing with Prediction on Energy Consumption", Proc. Intr-l Conf. on Wireless Algorithms, Systems and Applications, pp. 287-292, 2007.
[22] Box. G.E. and Jenkins G.M., Time Series Analysis, Holden-day, San Francisco. 1970.
[23] G. Peter Zhang, B. Eddy Patumo and M.Y. Hu, "A Simulation Study of Artificial Neural Networks for Nonlinear Time-Series Forecasting", Computers and operations research, vol. 28, pp.381-396, 2001.
[24] Ruelhart, D.E. and McClelland, J. L, eds., Parallel Distributed Processing: Explorations in the Microstructure Cognition, Cambridge, MA: The MIT press, vol. 1, 318-362, 1986.
[25] Simon Haykin, Neural Networks: A Comprehensive Foundation, Macmillan college publishing company, New york, 1995.
[26] , Feng Shu-hu and Guan Xiao-ji, "Energy Output Prediction Model on Time Series Analysis and Neural Network" ÔÇöÔÇö-, 2007.
[27] Mozer N. Neural Net for Temporal Sequence Processing, Time Series Prediction: Forecasting the future and understanding the past, Addison- Wesley, Reading, MIT.
[28] Koskela T., Lehtokangas M., Saarinen J. and Kaski K., "Time Series Prediction with Multilayer Perceptron, FIR and Elman neural networks", Proc. of the World Congress on Neural Networks, INNS Press, pp. 491- 496.
[29] A. Cichocki and R. Unbehauen, Neural Networks for Optimization and Signal Processing, John-Wiley and sons, Stuttgart, 1993.
[30] K. Fall and K. Varadhan, ns Notes and Documents, The VINT Project, UC Berkeley, LBL, USC/ISI, and Xerox PARC, Feb. 2000.