ISSN 000-985, CODEN RUXUEW E-mail: jos@iscasaccn Journal of Software, Vol7, No4, April 006, pp860 867 http://wwwjosorgcn DOI: 0360/jos70860 Tel/Fax: +86-0-656563 006 by Journal of Software All rights reserved, +,,3, 3, (,0087 (, 4008 3 (Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, USA An Algorithm of Data Aggregation Based on Data Compression for Sensor Networks XIE Zhi-Jun, WANG Lei +, LIN Ya-Ping,3, CHEN Hong, LIU Yong-He 3 (College of Information, Renmin University of China, Beijing 0087, China (College of Software, Hu nan University, Changsha 4008, China 3 (Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, USA + Corresponding author: Phn: +86-73-8893, E-mail: wanglei_hn@hn65com Xie ZJ, Wang L, Lin YP, Chen H, Liu YH An algorithm of data aggregation based on data compression for sensor networks Journal of Software, 006,7(4:860 867 http://wwwjosorgcn/000-985/7/860htm Abstract: Considering the characteristics and location information of nodes in sensor networks, a modified directed transfer model of sensor networks and a new distributed data aggregation model based on area are proposed On the basis of these new models, a novel mixed entropy data compression algorithm based on interval wavelet transforming is proposed for sensor network, according to the characteristics of data in sensor networks and the good performances of wavelet transforming in compression of the data stream Theoretical analyses and simulation results show that, the above new methods can compress the data stream and reduce the energy costs of nodes in data transferring efficiently for sensor networks So, it can prolong the lifetime of the whole networks to a greater degree when the above new methods are deployed with those traditional DC (data centric routing algorithms such as DD (directed diffusion protocol for sensor networks Key words: : sensor network; location information; data aggregation; interval wavelet transforming; entropy,, DDAM(distributed data aggregation modelddam,, ;,, : DC -DD,, Supported by the National Natural Science Foundation of China under Grant No607307 ( ; the National High-Tech Research and Development Plan of China under Grant No00AA4Z340 ( (863; the Key Project of Chinese Ministry of Education under Grant No0036 ( Received 004--9; Accepted 005-09-06
: 86, : ; ; ; : TP393 : A GPS(global positioning system [ 5],, ( (sensor network,,, :(, ;(, ;(3,,, (address-centric, AC (data centric protocol, DC AC SAR(sequential assignment routing [7] AC, Sink :, [8], DC DC :DD(directed diffusion [9],GITDC(greedy incremental tree data centric protocol [0],LEACH(low energy adaptive clustering hierarchy [],GEAR(geographic and energy aware routing [],DDCH(distributed data centric hierarchical routing algorithm [3] DD DC hop, DC AC,,,,,,,, :,,,,, DC,, [4],, DDAM(distributed data aggregation model,ddam,, ;,,,, DC : DC,, ( G=(V,E,G, G : ( G ;( G ( G S V, S : p V p S p S q S, G S 3( G=(V,E, G S V : S, S G, S S, S [6]
86 Journal of Software Vol7, No4, April 006,,, G=(V,E,,V,E,,, (core node;,, (sink node;sink, Internet, ID, Sink,, ( (, Sink, Sink, Sink,, :,,,,,,, ;, Sink, Sink Core Gateway Member Fig Connected dominant set based directed transfer model 4( G C V, C : p V p C p C q 5( G=(V,E, G C V : C, C G, C 6( p, Sink R (x,y,sink R r r r, p (m,n, : m = x, n = y,, [] : r=5,a (40,40, A x =4,A y =4, A (4,4; B (35,37, B x =4, B y =4, B (4,4 6 : Sink R r, 6
: 863 r : 5,, r r + =r,, p, (x,y Sink R r (communication radius, p 6,, DDAM( G: DDAM p GPS, 6 ; p :(i p S p ( 3 ;(ii p E p ;(iii p G p ;(iv p, ; 3, G Sink, p S p E p G p, : p G p, G p E p ;, p, p, p ; 4, :DDAM,, 3 : 3 G=(V,E, DDAM ψ={p p p V} G :, DDAM 3 : G,, ψ G, ψ p,q ψ, p,q ψ,,, p,q p,q, d(p,q G, d(p,q<r(r, d(p,q (i d(p,q= p,q, ψ ; (ii d(p,q=, G (p,r,q d(p,q=,, p,q ; r p,q, DDAM 3, r, r ; r ψ ψ ; (iii d(p,q=3, G (p,r,r,q d(p,r =, p,q,,p,r ; r p,r, DDAM 3 : r, r, r ψ d(r,q=, r ψ ψ d(p,q=m(m>3,ψ ; 3 d(p,q=m+,g (p,r,r,,r m,q ψ G r ψ r r d(p,r 3 : p,r ψ, d(r,q m, r,q ψ ψ 3 : DDAM ψ,
864 Journal of Software Vol7, No4, April 006, (MRA, φ ψ, Mallat : : c j, k = am kc j, m m Z ( d j, k = bm kc j, m m Z ( = + (3 c j, m hm kc j, k g m kd j, k k Z k Z,,j ;c j,m d j,m j ( (,{h k } {g k }, {a k } {b k } Mallat, ( ( c j,m ; c j,k d j,k, (3,, 3 c j+,k c j,k c j,k c j n,k c j+,k c j,k c j,k c j n,k d j,k d j,k d j n,k d j,k d j,k d j n,k Fig Decomposition process of sensing data Fig3 Reconstruction process of sensing data Mallat 3-4 3 L H H H 0 Fig4 Space-Frequency structure graph of decomposed sensing data 4-4,,H 0,H,H,L, L,, R, K, L (R, L (RL (K,,, : L (K, L (R, ; [5,6], L (K, L ([0,],, 8( (entropy, I(a j, : m j= H ( X = p I( a = p log p, j j m j= bit/, p j a j, -, : type=four-legged animal instance=elephant ( ( j j
: 865 location=[5,0] intensity=06 confidence=085 timestamp=0:0:40 ( ( ( (, elephant,,,,,,,, 5 Sensed data Fig5 Interval wavelet transforming Quantization Mixed entropy coding Code bit stream Procedure graph of mixed entropy coding based on interval wavelet 5 :, 0 -,,,,,, 0,, ;,, 0,,,, Huffman Huffman,,, 3, DD, DD SENSE(sensor network simulator and emulator [7] SENSE, 4 : (application (networking MAC (physical, AODV(ad-hoc on demand distance vector routing DSR(dynamic source routing CR (communication radius, 00 00, 00, 50, : 0 ( Sink,9 Source ; Sink, 0 (0 0 Source, DDAM, ;,, DD, DD 000, 6~ 9
866 Journal of Software Vol7, No4, April 006 Core nodes (% 00 80 60 40 0 0 5 0 5 30 35 40 45 Communication radius Fig6 Node proportion in connected Core nodes (% 00 80 60 40 0 0 40 50 60 70 80 90 Total nodes Fig7 Node proportion in connected core of DDAM in case core of DDAM in case 6 DDAM 7 DDAM 5 45 4 35 7 6 5 3 4 5 3 5 Modified DD algorithm Modified DD algorithm 05 Traditional DD algorithm Traditional DD algorithm 0 0 30 40 50 60 70 80 90 00 0 0 30 40 50 60 70 80 90 00 Total nodes Total nodes Fig8 The comparision of energy comsuming case Fig9 The comparision of energy comsuming case Average energy consumed Average energy consumed 8 DD 9 DD 6 7 CR=35, DDAM, 6 7, CR, 0%~80%, 8 9, DD, 4,,,,,, DC -DD : DC -DD, DC References: [] Xie ZJ, Wang L, Chen H Subnets based distributed data-centric hierarchical ant routing for sensor networks In: Liu JN, Conners A, eds IEEE Int l Conf on Wireless Communications, Networking and Mobile Computing Wuhan: IEEE Computer Society, 005 895 900 [] Agre J, Clare L An integrated architecture for cooperative sensing networks IEEE Computer, 000,33(5:06 08 [3] Ren FY, Huang HN, Lin C Wireless sensor networks Journal of Software, 003,4(7:8 9 (in Chinese with English abstract http://wwwjosorgcn/000-985/4/8htm [4] Li JZ, Li JB, Shi SF Concepts, issues and advance of sensor networks and data management of sensor networks Journal of Software, 003,4(0:77 77 (in Chinese with English abstract http://wwwjosorgcn/000-985/4/77htm [5] Srinivasan V, Chiasserini CF, Nuggehalli PS, Rao RR Optimal rate allocation for energy-efficent multipath roting in wireless ad hoc network IEEE Trans on Wireless Communications, 004,3(3:89 899 [6] Estrin D, Govindan R, Heideman J, Kumar S Next century challenges: Scalable coordination in sensor networks In: Heidemann J, ed Proc of the 5th Annual ACM/IEEE Int l Conf on Mobile Computing and Networking Seattle: ACM Press, 999 63 70
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