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Figure 14.19 ESRT protocol state model and transitions. (Reproduced by permission of IEEE [27].)
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current state Si , and the values of f i and i , ESRT then calculates the updated reporting frequency f i+1 to be broadcast to the source nodes. At the end of the next decision interval, the sink derives a new reliability indicator i+1 corresponding to the updated reporting frequency f i+1 of source nodes. In conjunction with any congestion reports, ESRT then determines the new network state Si+1 . This process is repeated until the optimal operating region (state OOR) is reached. The state model of the ESRT protocol and state transitions are shown in Figure 14.19. The following reporting rate updating rules are used [140]: (NC,LR) (NC,HR) (C,HR) (C,LR) f i+1 = f i+1 f i+1 fi i fi = 2 fi = i
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where k denotes the number of successive decision intervals for which the network has remained in state (C,LR) including the current decision interval OOR f i+1 = f i
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In order to determine the current network state Si in ESRT, the sink must be able to detect congestion in the network. ESRT uses a congestion detection mechanism based on local buffer level monitoring in sensor nodes. Any sensor node whose routing buffer over ows due to excessive incoming packets is said to be congested and it informs the sink of the same. For more on system performance see Sankarasubramaniam et al. [140].
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REFERENCES [1] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, A survey on sensor networks, Comput. Networks, 2002, pp. 393 422. [2] G.J. Pottie and W.J. Kaiser, Wireless integrated network sensors, Commun. ACM, vol. 43, no. 5, 2000, pp. 51 58. [3] J. Rabaey, M.J. Ammer, J.L. da Silva Jr, D. Patel and S. Roundy, Picoradio supports ad hoc ultra-low power wireless networking, Comput. Mag., July 2000, pp. 42 48. [4] S. Tilak, N. Abu-Ghazaleh and W. Heinzelman, A taxonomy of wireless micro-sensor network models, ACM Mobile Comput. Commun. Rev. (MC2R), vol. 6, no. 2, April 2002, pp. 28 36. [5] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler and J. Anderson, Wireless sensor networks for habitat monitoring, in 1st Workshop on Sensor Networks and Applications, Atlanta, GA, October 2002, pp. 88 97. [6] G.D. Abowd and J.P.G. Sterbenz, Final report on the interagency workshop on research issues for smart environments, IEEE Person. Commun., October 2000, pp. 36 40. [7] J. Agre and L. Clare, An integrated architecture for cooperative sensing networks, IEEE Comput. Mag., May 2000, pp. 106 108.
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[8] A. Bakre and B.R. Badrinath, I-TCP: indirect TCP for mobile hosts, in Proc. 15th Int. Conf. Distributed Computing Systems, Vancouver, BC, May 1995, pp. 136 143. [9] P. Bonnet, J. Gehrke and P. Seshadri, Querying the physical world, IEEE Person. Commun., October 2000, pp. 10 15. [10] B.G. Celler, T. Hesketh, W. Earnshaw and E. Ilsar, An instrumentation system for the remote monitoring of changes in functional health status of the elderly, in Int. Conf. IEEE-EMBS, NewYork, 1994, pp. 908 909. [11] A. Chandrakasan, R. Amirtharajah, S. Cho, J. Goodman, G. Konduri, J. Kulik, W. Rabiner and A. Wang, Design considerations for distributed micro-sensor systems, in Proc. IEEE 1999 Custom Integrated Circuits Conf., San Diego, CA, May 1999, pp. 279 286. [12] S. Cho and A. Chandrakasan, Energy-ef cient protocols for low duty cycle wireless microsensor, in Proc. 33rd Annual Hawaii Int. Conf. System Sciences, Maui, HI, vol. 2, 2000, p. 10. [13] G. Coyle et al., Home telecare for the elderly, J. Telemed. Telecare, vol. 1, 1995, pp. 183 184. [14] I.A. Essa, Ubiquitous sensing for smart and aware environments, IEEE Person. Commun., October 2000, pp. 47 49. [15] D. Estrin, R. Govindan, J. Heidemann and S. Kumar, Next century challenges: scalable coordination in sensor networks, in ACM MobiCom 99, Washingtion, DC, 1999, pp. 263 270. [16] P. Favre, N. Joehl, A. Vouilloz, P. Deval, C. Dehollain and M. J. Declerz, A 2 V, 600 A, 1 GHz BiCMOS super regenerative receiver for ISM applications, IEEE J. Solid St. Circuits, vol. 33, 1998, pp. 2186 2196. [17] K. Govil, E. Chan and H. Wasserman, Comparing algorithms for dynamic speedsetting of a low-power CPU, in Proc. ACM MobiCom 95, Berkeley, CA, November 1995, pp. 13 25. [18] M.P. Hamilton and M. Flaxman, Scienti c data visualization and biological diversity: new tools for spatializing multimedia observations of species and ecosystems, Landscape Urban Plann., vol. 21, 1992, pp. 285 297. [19] M.P. Hamilton and Hummercams, robots, and the virtual reserve, Directors Notebook, 6 February 2000; available from www.jamesreserve.edu/news.html [20] B. Halweil, Study nds modern farming is costly, World Watch, vol. 14, no. 1, 2001, pp. 9 10. [21] S. Hedetniemi and A. Liestman, A survey of gossiping and broadcasting in communication networks, Networks, vol. 18, no. 4, 1988, pp. 319 349. [22] W.R. Heinzelman, A. Chandrakasan and H. Balakrishnan, Energy-ef cient communication protocol for wireless microsensor networks, in IEEE Proc. Hawaii Int. Conf. System Sciences, January 2000, pp. 1 10. [23] W.R. Heinzelman, J. Kulik and H. Balakrishnan, Adaptive protocols for information dissemination in wireless sensor networks, Proc. ACM MobiCom 99, Seattle, WA, 1999, pp. 174 185. [24] C. Herring and S. Kaplan, Component-based software systems for smart environments, IEEE Person. Commun., October 2000, pp. 60 61. [25] G. Hoblos, M. Staroswiecki and A. Aitouche, Optimal design of fault tolerant sensor networks, IEEE Int. Conf. Control Applications, Anchorage, AK, September 2000, pp. 467 472.
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