CCABC: Cyclic Cellular Automata Based Clustering For Energy Conservation in Sensor Networks

Indrajit Banerj ee # , Prasenjit Chanak * , Hafizur Rahaman # # Department o f Information Techn ology * Purabi Das School of Information Tech nology Bengal Engin eering and Science Universit y, Shibpur, Ho wrah, India. 1 ibanerjee @it.becs.ac.in , 2 prasenjit.chanak@gmai l.com , 3 rahaman_h @it.becs.ac.in Abstract — Senso r networ k has been recognized as the most significant t echnology for next century. Despites of its potential application, wireless sensor network encounters resource restriction such as low power, reduced band width and sp ecially limited power sources. Thi s work pr oposes an efficient techni que for the conser vation of energ y i n a wireles s s ensor network (WSN) by f orming an effect ive cluster of th e network nodes distributed over a wide r ange of geogra phical area. The cl ustering s cheme is deve loped a round a specified cl ass o f cell ular automata (CA) r eferr ed to as t he modified cyclic cell ular a utomata (mCCA). It sets a nu mber of nod es in st and-by mode at an instan ce of time without compromising t he area of netwo rk co verage and t hereby conserves t he battery power . Th e propo sed scheme also d etermines an effe ctive clust er size where th e inter-clust er and intra-cluster communi cation cost is minimum . The simulation results establish that the cycli c cellular automata based clusteri ng for energy conservati on in sensor networks ( CCABC) i s m ore relia ble than th e existi ng s chemes wher e clust ering and CA based en ergy saving tec hnique is used. Keywords — Modi fied cyclic cel lular automata (mCCA), clust ering, wireles s se nsor n etw ork (WS N), bas e st ation (BS) I.I NTRODUCTION Wireless sensor network (WSN), consisting of thousands of wireless sensors, is bounded du e to the limited computational capability , batter y power and memory capabilit y of its components. The sen sor nodes are deployed in a monitoring area and communicate among themselves following the multi-hop wireless communication. The information received at a node is com puted and communicated to the CCABC: Cy c lic Cellular Automata Ba sed Clustering F or Energ y Conser v ation in Sensor Netw or ks nearest base station [1]. In homo g e n e o u s n e t w o r k s , a l l t h e s e n s o r n o d e s a r e i d e n t energy /power and hardware com p l e x i t y . T h e e n e r g y s a v i n g n e t w o r k d e s i g n i s t h e m a j o r i s s u e i n W S N t o increase the life time of netw ork n o d e s . Fig.1: Clus t e r b a s e c o m m u n i c a t i o n i n w i r e l e s s s e n s o r n e t w o r k In WSN , a base station (BS) is s t a t i o n a r y a n d t h e s e n s o r n o d e s m a y b e m o v a b l e [ 2 ] . T h e e n e r g y l o s s i n a node is very high w hen a node d i r e c t l y c o m m u n i c a t e s w i t h t h e b a s e s t a t i o n ( F i g . 1 ) . O n t h e o t h e r h a n d , in a cluster based node managem e n t s c h e m e , a n o d e c o m m u n i c a t e s w i t h t h e B cluster head (CH) (Fig. 1). I n the c l u s t e r e d s c h e m e p r o p o s e d s o f a r [ 2 ] , [ 3 ] a n d [ 4 ] , e a c h a n d e v e r y n o d e i s in active s tate, theref ore, a pa rtic u l a r a r e a i s m o n i t o r e d b y t h e t w o o r m o r e n o d e s . T h e s c h e m e s L E A C H [2], EEPSC [3] , LEACH- C [4] , U C C P [ 5 ] , E E C S [ 6 ] , E E D U C [ 7 ] a n d D D C [ 8 ] c a n n o t p r o t e c t t h e network nodes from early ene rg y d i s s i p a t i o n l e a d t o s h o r t s p a n o f l i f e . A s a n o d e e x p i r e s w i t h i n a s h o r t time, the new sets of clusters a r e f o r m e d v e r y f r e q u e n t l y . T h i s d e m a n d s m a s s i v among nodes and, therefore, ca u s e s t h e u n c o n t r o l l e d p o w e r d i s s i p a t i o n . techniques [1], [9] and [ 10] pro p o s e d s o f a r r e c o v e r t h e c o m m o n r e g i o n s e n s i n g p r o b l e m , w h i c h e n s u r e that the number of nodes in activ e s station leading to unw anted ener g y l o s s i n t h e n e t w o r k . In this context, the CCABC t e c h n i q u e , p r o p o s e d i n t h i s w o r k , d e v e l o p s a c l u s t e r b a s e d n e t w o r k management s y st em th at ensure s f u l l c node. If any acti ve nodes fail to s e n s e , t r a n s m i t o r r e c e i v e d a t a t h e n t h i s n o d e i s d e c l a r e d a s a d e a d n o d e and a neighbouring stand- by no d e i s s e l e c t e d t h r o u g h C C A B C s c h e m e f o r e f f i c i e n t r e p l a c e m CCABC scheme, t he clusters ge n e r a t e d a r e o f o p t i m a l s i z e , i n w h i c h t h e d a t a a r e a g g r e g a t e d p r o p e r l y f o r n e a r e s t b a s e s t a t i o n [ 1 ] . I n h o m ogeneous network s, all the sensor nodes are iden t e n e r g y / p o w e r a n d h a r d w a r e c o mplexity . T he energy s aving network design is th e m a j o r i s s u e i n W S N t o i n c r e a s e t h e l i f e t i m e o f n e t w o r k nodes. C l u ster base commu n ication in wireless sensor netwo r k ( B S ) i s stationary a nd the sensor nod es may be movabl e [ 2 ] . T h e e n e r g y l o s s i n a n o d e i s v e r y h i g h w h e n a n o d e directly communicates with the base station (F i g . 1 ) . O n t h e o t h e r h a n d , i n a c l u s t e r b a s e d n o d e m a n a g e men t scheme, a node communicates with the B S t h r o u g h a l e a d e r , c a l l e d c l u s t e r h e a d ( C H ) ( F i g . 1 ) . I n t h e clustered scheme proposed so far [2], [3] and [4 ] , e a c h a n d e v e r y n o d e i s i n a c t i v e s t a t e , t h e r e f o r e , a p a r t i cular area is monitored by the two or more node s . T h e s c h e m e s L E A C H C [ 4 ], UCCP [5], EECS [6], E EDUC [7] and DDC [ 8 ] c a n n o t p r o t e c t t h e n e t w o r k n o d e s f r o m e a r l y e n e r gy dissipati on lead to short span of li fe. As a nod e e x p i r e s w i t h i n a s h o r t t i m e , t h e n e w s e t s o f c l u s t e r s are for med ver y frequently. This demands mas s i v a m o n g n o d e s a n d , t h e r e f o r e , c auses the uncontro lled power dissipation. The c e l l u l a r a u t o m a t a b a s e d t e c h n i q u e s [ 1 ] , [ 9 ] a n d [ 1 0 ] p r o posed s o far recover the common region sensing p r o b l e m , w h i c h e n s u r e t h a t t h e n u m b e r o f n o d e s i n a c t i ve s tate is minimum. The active nodes com munic a t e d i r e c t l t o u n w a n t e d e n e r gy loss in the network. I n t h i s c o n t e x t , t h e C C A B C technique, proposed in this work, develops a c l u s t e r b a s e d n e t w o r k m a n a g e m e n t s y s t e m t h a t e n s u r es full c overage of the sensor netw ork with min i m u m n u m b e r o f a c t i v e n o d e . I f a n y a c t i v e n o d e s f a i l t o sense, transmit or receive data then this node is d e c l a r e d a s a d e a d n o d e b y n o de is sel ected t hrough CCABC s cheme for e ffic i e n t r e p l a c e m C C A B C s c h e m e , t h e c l u s t e r s g e nerated a re of optimal size, in which the data are a g g r e g a t e d p r o p e r l y f o r n e a r e s t b a s e s t a t i o n [ 1 ] . I n h o m o g e n e o u s n e t w o r k s , a l l t h e s e n s o r n o d e s a r e i d e nt ical in t erms of batter y e n e r g y / p o w e r a n d h a r d w a r e c o m p l e x i t y . T h e e n e r g y s a v i n g n e t w o r k d e s i g n i s t h e major issue in WSN to C l u s t e r b a s e c o m m u n i c a t i o n i n w i r e l e s s s e n s o r n e t w ork ( B S ) i s s t a t i o n a r y a n d t h e s e n s o r n o d e s m a y b e m o v a b le [2]. The energy loss in a n o d e i s v e r y h i g h w h e n a n o d e d i r e c t l y c o m m u n i c a t e s w i t h t h e b a s e s t a t i o n ( F ig.1). On t he other hand , S throug h a lea der, call ed c l u s t e r h e a d ( C H ) ( F i g . 1 ) . I n t h e c l u s t e r e d s c h e m e p r o p o s e d s o f a r [ 2 ] , [ 3 ] a n d [ 4 ] , each and every node is i n a c t i v e s t a t e , t h e r e f o r e , a p a r t i c u l a r a r e a i s m o n i t o r e d b y t h e t w o o r m o r e n o d es. The schemes LEACH C [ 4 ] , U C C P [ 5 ] , E E C S [ 6 ] , E E D U C [ 7 ] a n d D D C [8] c annot protect t he n e t w o r k n o d e s f r o m e a r l y e n e r g y d i s s i p a t i o n l e a d t o s h o r t s p a n o f l i f e . A s a n o de expires within a short t i m e , t h e n e w s e t s o f c l u s t e r s a r e f o r m e d v e r y f r e q u e n t l y . T h i s d e m a n d s m a s siv e message exchanges T h e cellular automata based t e c h n i q u e s [ 1 ] , [ 9 ] a n d [ 1 0 ] p r o p o s e d s o f a r r e c o v e r t h e c o m m o n r e g i o n s e n s i n g p roblem, which ensure t a t e i s m i n i m u m . T h e a c t i v e n o d e s c o m m u n i c ate directl y with the bas e I n t h i s c o n t e x t , t h e C C A B C t e c h n i q u e , p r o p o s e d i n t h i s w o r k , d e v e l o p s a cluster based network o v e r a g e o f t h e s e n s o r n e t w o r k w i t h m i nimum number of active n o d e . I f a n y a c t i v e n o d e s f a i l t o s e n s e , t r a n s m i t o r r e c e i v e d a t a t h e n t h i s n o d e i s d eclared as a dead node b y n o d e i s s e l e c t e d t h r o u g h C C A B C s c h e m e f o r e f f i cient replacem ent. In t he C C A B C s c h e m e , t h e c l u s t e r s g e n e r a t e d a r e o f o p t i m a l s i z e , i n w h i c h t h e d a t a a r e aggregated properly for further reduction of overhead in data p rocessing . The member nodes of that optimal cl uster size can send their data to the cluster head with minimum energy loss (Fig.2). The proposed scheme also determines the position of the cluster head where each node of the cluster sends their data with mini mum energy l oss. The CCABC selects a c luster head from the nodes in an energy efficient manner. The organization of this paper is as follows. Modified cyclic cellular automata (mCCA) are elucidated in Section 2. The mathematical model of the proposed scheme is described in Section 3. In Section 4, we have introduced t he proposed algorithms, developed over mCCA. The simulation results are reported in Section 5. Finally we conclude our paper in Section 6. II. C YCLIC C ELLULAR A UTOM A T A The c y clic cellular a utomata (CCA) follow a l ocal rule which is sa me for all states S. E ach cell in CCA contents d ifferent states from st ate range S= {0, 1 , 2...k-1} [11], [12], [13] and [14]. T he integer k is the maximum number of state. I n the exiting field   all cells chang e their states w ithin S.              (1) The CCA genera tes a spi ral structure when cell s are changing the ir states from zero to k-1 as equation number 1. T he    represents the present state of a cell P    at integer time t. If and only if for so me given threshold value  at Von Neumann neighbour set N(x) a cell P changes its state    to another state      at time t+1 is show n in equation 2.              ( 2) The threshold value  re presents the y number of neighboring cell’s condition in set N (x) within the field, y  N(x). T he neighboring cells set N (x) a re als o in k-1 state. The initial state of automaton is said to be primordial s oup [11]. A classical model of excitable media w as introduced in 1978 by Green berg and Hastings (GH) [13] and [14] described next. Fig.2: Cluster head connected w ith cluster nodes within a cluster. Greenberg-Hastings model (GHM) The Greenberg – Has tings model [12 ] is a simplified cellular automaton that is r un in ex citable medium. In GHM according to state change rule each cells of the automata changes their state and produced a special ty pe n ode pattern . The state change rules of the CCA in G HM are described bellow 1. If γ       then γ       2. If γ       and at l east n neighb ours are in state 1 then γ     ; otherwise the current state (0) is continued. Where      is cell’s condition (or s tate) at time t and     is the next state of cel l at time t+1. I n our proposed modified c y clic cellular automata based EERI H [19] we have modified the G SM rules. The EERIH generate special t ype of nodes p attern and arrange the active nodes i nto some cluster. This pattern also helps us for routing the data from clu ster head to base s tation in energy ef ficient manner. Proposed modified CCA In our modified cyclic cellular automata (m CCA) based scheme ever y cell c hang es it s tate according to the nine neighbour s’ cells state condition . The state change rules of the cells are defined below [19]: 1. If cell’s present state is δ               , then next state of the cells is δ         2. If cell’s present sta te is  δ            , then next state of the cells is  δ       . 3. If cell’s present s tate is δ       then they ch eck th eir ne ighbour cells stat e and i f θ numbers (threshold value) of nodes are present in nonzero state then ne xt state is δ       ot her wise they are not changing the ir state i.e. δ     . Fig.3: Atom ic structure of no de pattern genera ted by CCA Where      is the state of the cells at time t and     is the state of cells at t+1 time. Number of state is {0, 1…I-1} . With the help of this state chang e rule we are arranging every sensor node in a pattern of a tomic structure. In thi s scheme ever y n ode is cha nging th eir state in a time interval an d nodes pattern are controlled from the primordial soup . The nodes pattern of the proposed cy c lic cellular automata show s in Fig.3 where w ith time every node is changing their state i .e. spirals are propagated. III. M A THEM A T ICA L A NAL YSIS In this section we have analy sed the mathemat ical model of our proposed scheme en ergy efficient clustering scheme for wireless sensor networks. Here we ca lculate effective cl uster size where each nod e sends their data with minimum ener gy loss. In energ y efficient clustering sc heme for wireless se nsor networks, two t y pes o f data communication c an take pl ace among nodes in the network. T hese are intra- cluster and inter-cluster data c ommunicatio n. The inter-cluster d ata communication is the data transfer between cluster head and base station as shown in Fig. 1. In the proposed scheme the active nodes are self organized into an atomic structure to form clusters (see Section IV). The intra-cluster communication cost is the energy spent b y all orbital nod es to send their dat a to the clust er head. It involves intra-orbit and inter-orbital data communication cost. In intra -orb ital data communication, t he sub- orbital node transmits their data to the nucleus of the orbit (Fig.2). In inter-orbital data communication, orbits are sending their data t o the nucleus of nearest upper la y er orb it. If we consider t he total sensor network as a single cluster, then inter-cluster communication cost is zero but t he intra-cluster communication c ost is very high. On the other hand if cluster size is zero, then intra-cluste r communication cost is z ero but in this case inter- cluster communication cost is ver y hi gh. With the help of inter-cluster commun ication cost and i ntra- cluster comm unication cost we can formulate an efficient cluster siz e in cluster based energy effic ient wireless sensor networks management scheme, where each node sends their data with minimum energy loss. We have find out the position of the cluster head within a cluster where the communication en ergy loss is minimal. In w ireless sensor netw ork different se nsor nodes send their data to base st ation from different location s. H ence the transmission ra nge of every sensor node i s diff erent as well as energy loss of the sensor nod es is also different. In th e proposed mode l e very sensor n ode sends their data with minimum energy loss. T hese ar e calculated by energy loss e rrors in t he sensor n etw ork. We are also introducing an efficien t data ag gregation formula of the propo sed technique . Definition 1: Let X be the monitoring field, covered by the set of sensor n ode and   i s the i nter- cluster communica tion cost that is dep ending on trans mission distance and nodes dens ity. Therefore,                              ( 3) The siz e of data is    , in cluster c having s number of nodes. The nodes’ density is µ . The ε is the energy consumed in the transmitter circuit, ω is the di ssipated energy for data aggregation and γ is the dissipated energ y in the transmitter op-amp. The   is the cluster head that collects all data of a single cluster and transmits i t to base station. Transmission distance between two c luster head   is d i and n is a path-loss exponent. M c is the number of membe r node in a cluster. Definition 2: The intra-cluster communication cost of t he node i s f(I) that depends on the c luster size and transmission distan ce between cluster head and cluster mem ber nodes. Therefore,                      (4 ) In above equation   is the total b i t s t r a n s m i t t e d a l o n g e d g e used t o collect data from e ach or b i t i n a c l u s t e r c . T h e d i s t a n c e b e t w e e n t h e t r a n s m i t t e r a n d r e c e i v e r n o d e in a cluster is d j . Theorem 1: I nter- cluster com m u n i c a t i o n c o s t with respect to  at all points o f a W S N r e g i o n R ( i n W S N e a c h n o d e s a r e c o n n e c t e d b y m u l t i communication) is called an anal y t i c o r a r e g u l a r f u n c t i o n o f function ceases to possess a deriv a t i o n i s c a l l e d a s i n g u l a r p o i n t o Fig.4: Close R e g i o n R i n W S N , w h e r e P a n d Q a r e a n y n o d e p o s i t i o n Proof - L et     be a si n g l e  (Fig. 4 ). T hen the derivative o f transmission betw e en the cluster h e a d , provided the limit exists and has t h e s a m e v a l u e f o r a l l t h e d i f f e r e n t w a y s i n w h i c h In sensor n etw ork ever y node is v i r t u a l l y c o n n e c t e d t o e a c h o t h e r , e a c h o r b i t a l i s v i r t u a l l y c o n n e c t e d t o upper orbital and cluster heads t r a n s m i t Suppose   is fixed node pos i t i o n w i t h i n a r e g i o n R a n d (Fig. 4 ). The node Q may appro a c h e s t o w a r d s P a l o n g a n y s t r a i g h t o r c u r v e d p a t h i n t h e g i i.e.  may tend to zero in any m a n n e r a n d Theorem 2: Th e inter- cluster c o m m u n i c a t i o n c o s t simple close cluste rs X and X1, t h e n t o t a l bits transmitted along edge   .   is n umber of n o d e s i n c l u s t e r , w h i c h a r e o c o l l e c t d a t a f r o m e a c h o rbit i n a cluster c. T he distance between the trans m i t t e r a n d r e c e i v e r n o d e c l u s t e r c o mmunication cost  is sing le- valued an d posse s s e s a u n i q u e d e r i v a t i o n a t a l l p o i n t s of a WSN region R (in WS N each nodes are c o n n e c t e d b y m u l t i c o m m u n i c a t i o n ) i s c a l l e d a n a n a ly tic or a regular function of  in that region. A p o i n t a t w h i c h a n a n a l y t i c f u n c t i o n c e a s e s t o p o s s e s s a d e r i v ation is called a singular point o f the function. C l o s e Region R in WSN, where P and Q are any node p o s i t i o n b e a s i ngle - valu ed function in a re gion w ithin the WSN o f t h e v a r i a b l e ) . T h e n t h e d e r i v a t i v e of      is defined the inter-cluster co mm u n i c a t i o n c o s t f o r d a t a t r a n s m i s s i o n b e t w e e n t h e c l u s t e r head,                    p r o v i d e d t h e l i m i t e x i s t s a n d h a s the same value for all the differen t ways in whic h I n s e n s o r n e t w o r k e v e r y n o d e i s virtually connected to each other, each o rbital i s v i r t u a l l y c o n n e c t e d t o u p p e r o r b i t a l a n d c l u s t e r h e a d s tr ansmit data to base station w ith the help of oth e r c l u s t e r h e a d s ( F i g . i s f i x e d n o d e p o sition within a region R and       is a nei g h b o u r i n g n o d e p o s i t i o n ) . T h e n o d e Q m a y a p p r o ache s towards P along an y strai ght or c urved p a t h i n t h e g i m a y t e n d t o z e r o i n a n y manner and   to exist. c l u s t e r communication cost  is analytic in t he clust e r r e g i o n D b e t w e e n t w o s i m p l e c l o s e c l u s t e r s X a n d X 1 , then i s n u m b e r o f n odes i n cluster, which are o c o l l e c t d a t a f r o m e a c h o r b i t i n a c l u s t e r c . T h e d i s t a n c e b e t w e e n t h e t r a n smitter and receiver node v a l u e d a n d p o s s esses a unique d erivation a t a l l p o i n t s o f a W S N r e g i o n R ( i n W S N e a c h n o d e s a r e connected by multi -hop i n t h a t r e g i o n . A point at which an analyti c C l o s e R e g i o n R i n W S N , w h e r e P a n d Q a r e a n y n o d e position v a l u e d f u n c t i o n i n a r e g i o n w i t h i n t h e W S N of the variab le     m munication cost for data (5 ) p r o v i d e d t h e l i m i t e x i s t s a n d h a s t h e s a m e v a l u e f o r a l l t h e d i f f e r e n t w a y s i n w h i c h  approaches to zero. I n s e n s o r n e t w o r k e v e r y n o d e i s v i r t u a l l y c o n n e c t e d t o e a c h o t h e r , e a c h o r b i t a l is virtually connected to d a t a t o b a s e s t a t i o n w i t h t h e h e l p o f o t h er cluster heads ( Fig. 2). i s a n e i ghbouring node position ) . T h e n o d e Q m a y a p p r o a c h e s t o w a r d s P a l o n g a n y s t r a i g h t o r c u r v e d p a th in the gi ven region R, i s a n a l y t i c i n t h e c l u s ter region D between two Where X represents the whole s e n s o r n e t w o r k a s a c l u s t e r a n d t h e X 1 i s t h e o t h e r c l u s t e r w cluster X. Fig. Proof- We int roduce the cross by arrows in Fig . 5 ; i.e. alon g AB , X 1 i n c l o c k w i s e s e n s e & a l o n g B A , X i n Therefore,                and BA cancel each other, it foll o w s t h a t integral around X1 and transposi n g , w e g e t the anti- clockwise sense . If X1, X 2 , X 3 . . . . . . b e a n y n u m b e r o f c l o s e c l u s t e r w i t h i n c l o s e c l u s t e r X t h e n ,       Theorem 2 proves that th e total i n t e r are same as that of whole networ k . Theorem 3: Th e inter- cluster c o m m u n i c a t i o n c o s is any node position within X, th e n e n e r g y l o s s a t t h a t n o d e i s Proof - Let us consider the fu n c t i o n    node position with the no d e p o s i t i o n draw a small circle cluster l y in g e n t i r e l y w i t h i n X . N o w                   W h e r e X r e p r e s e n t s t h e w h o l e se nsor n etwo rk as a cluster and the X1 i s the o t h e r c l u s t e r w F i g . 5: X1 is a close cluster under a close cluster X. W e i n t r o d u c e t h e c r o s s -cut AB in reg ion X. Then         Where t h e p a t h i s a s i n d i c a t e d ; i . e . a l o n g A B, X1 in clockw ise sense & along BA, X in anti- c l o c k w i s e s e n s e .                         But, since th e i n t e g r a t i o n a l o n g A B a n d B A c a n c e l e a c h o t h e r , i t f o l l ows that                    . Reversing t h e d i r e c t i o n o f t h e i n t e g r a l a r o u n d X 1 a n d t r a n s p o s ing, we get                  here each in t e g r a t i o n b e i n g t a k e n i n c l o c k w i s e s e n s e . I f X 1 , X2, X3 ...... be any number of close cluste r within c l o s e c l u s t e r X t h e n ,                                 p r o v e s t h a t t h e t o t a l inter -cluste r c ommun ication cost   of an y nu m b e r o f i n t e r n a l c l u s t e r s a r e s a m e a s t h a t o f w h o l e n e t w o rk. c l u s t e r communication cos t  is a naly tic within a clo s e c l u s t e r a n d i f p o i n t i s a n y n o d e p o s i t i o n w i t h i n X , t hen energy loss at that node is                 L e t u s c o n s i d e r t h e f unction   which i s analytic at all nodes pos i t i o n w i t h i n X e x c e p t a t n ode position  as ce ntre o f cl uster a nd r i s t he ra d i u s o f c l u s t e r a r e a . W e d r a w a s m a l l c i r c l e c l u s t e r l y i ng entirely within X. Now       bein g a n a l y t i c i n t h e r e g i o n (6 ) W h e r e X r e p r e s e n t s t h e w h o l e s e n s o r n e t w o r k a s a c l u s t e r a n d t h e X 1 i s t h e other cluster w ithin the W h e r e the path is as ind icated clockwise sense. B u t , s i n c e t he integrat ion along AB R e v e r s i n g the di rection of the h e r e e a c h i ntegration being taken in c l o c k w i s e s e n s e . I f X 1 , X 2 , X 3 . . . . . . b e a n y n u m b e r o f c l o s e c l u s t e r w i t h i n close cluster X then, (7 ) o f a n y n umber of in ternal clusters i s a n a l y t i c w i t h i n a c l ose cluster and if p oint  (8 ) w h i c h i s a n a l y t i c a t a l l n o d e s p o sition within X except at a s c e n t r e o f c l u s t e r a n d r i s t h e r adius of cluster area. We b e i n g analy ti c in the region enclosed by X and X1.                                             for any nodes on the network,            . In the limiting form, as th e ci rcle cluster X1 shrinks to the node position  , as    we consider e very se nsor node as a point. The integral approaches to                                  I n general,                       X is a larg e monitoring area, and data is travelling among the nodes in sensor network then        . In this reason  is unevenly distributed w i thin the close cluster X.            (9) Where M is the maxim um va lue of     on cluster X. In-order to find out the actual position of the cluster he ad in the network, we ha ve divided the whole network i nto equal size of clusters . The s uitab le position of the c luster head can b e determined according to the following theorem. Theorem 4: T he analytic function inter-cluster commun ication c ost  within the close cluster region is average at the centre position of a cluster region. In th is r egion f(I) is ver y s mall. T he centre position, where inter-clus ter communication cost  is average and intra-cluster communication cost f (I) is small, is the cluster head location. Proof - When we conside r the cluster of sensor n ode as a circle then inter-cluster c ommun ication cost of t he cluster node   is average in the centre point of the cluster. Bec ause inter-cluster co mmunicat ion cost   depends mainly on t he distance between c luster he ad a nd base station. On the other h a nd intra- cluster communication cost    also depe nds o n the mem ber nodes to cluster head distance. If we select cluster head as a nearest node of the base s tation then the inter-cluster communication cost is minimum but within the cluster member nodes and cluster head, distance is increased ( so the value o f f(I) is high ), therefore, total energy loss by the cluster          is increased. Within cluster long distance cluster member n odes lose more energy . So we are going to select cluster head’s po sitio n a t the median of the cluster. Theorem 1 describes inter-cluster communication c ost  , which is an anal y tic function, because this function follows th e necessary a nd sufficient condition of an analy tic function. We are selecting some nodes position of whole n etwork according t o Theorem 2 and s tart t he CCA spiral propagation for the selection of nodes’ position. With t ime the CCA spiral propagation cover the entir e net work and therefore  value is increased a nd f(I) value is decreased . When the inter-cluster comm unication cost and i ntra- cluster communication cost is equal i.e.       , t he spiral propagation (see Section I V) will stop at that point. This i s the optim al size of the cluster where data are aggregated properly according to the equation no. 11. The member no des of that optimal cluster si ze can send their data with minimum energy loss. With the help of Theorem 3 we are going to ca lculate inter-cluster communication cost i n each sensor node and determine the position of the nodes from w here CCA spiral start to propagate. Theorem 4 determines th e position of the cluster head where each node of t he cluster sends th eir data with minimum energy loss. Data Aggregation Model Data aggreg ation on a s ens or network depends o n data correlation. With the help of data correlation we can a gg regate eff icient amount of da ta from the nodes. The aggregated data is then transmitted to cl uste r head (CH). A large amount of energy is wasting due to transmission of same type o f information to the base station. When density o f the active nodes increases to provide fault tolerant feature in the sensor network [16], large number of number of nodes cover a particular area wh ich sense similar information. I f density of the nodes is very h igh, then a l arge amount of energy i s w asted for t ransformation o f s ame information t hrough multi-hop sensor ne twork. Different types of approach es have been proposed to model the correlation of data; one of th em, entropy-base model is very popul ar. T he entropy -base data correlation and compression algorithm is described in [17].                         (10 ) Here d o is the inter-node distance and b o is the number of bits generated b y e ach source, the c onstant parameter c characterizes the spatial data correlation. B s (d 0 ) i s the n umber of compressed bit messages generated by the c luster head in a s-no de cluster. The entrop y -base model is applied in t he CC ABC scheme, as t he model aggregates d ata accurately and efficiently. In CCABC, we have modified th e equation no. 18 in-ord er to decrease the data aggreg ation error rate.                           (11 )  represents t he minimum size of th e cluster. Other parameters h ave t he same meaning as i n equation no 18. The correlation error r educes in our proposed equation. If the cluster size increases the data correlation error increases. So we consider here the optimal clus ter size. IV. CCABC A LGORITHM The modified CCA (see Section II) is used to create clusters in sensor network. During the change of state the sp irals are decomposed randomly in a time i nterval and generate wave pattern. In th is wa y nodes are self-organized into an atomic structure (AS). In an ato mic structure the nodes are d istributed into orbits and nucleus. The orbit is also divided i nto sub-orbits . The proposed cluster formation algorithm is as follows: CCABC (Algorithm 1 ): Cluster Generation Insert all nodes into S (array of nodes) CS (Cluster Set) = Nu ll (empty set) WHILE S! = Null D O Calculate Nodes energy from Algorithm 3 Set CC = Si (single Clus ter) Set Inter cluster Com munication Cost f (  ) = 0 Calculate Intra Cluster Communication Co st f (I) Orbital to orbital dis tance from Algorithm 2 when mC CA is starting Take some nodes position Pi where mCCA s tarts to spiral propag ation within Si Check f(  ) and f (I ) after some specific time slot IF f (  ) = f (I ) THEN Stop spiral propag ation Insert into CS (cluster set) ELSE Carry on spiral propagat ion END IF Start data ag gregation and data transmi ssion END WHILE CCABC (Algorithm 2 ): Orbital to Orbita l distance Calcula tion Set   is current transmission range Set r ca = r max Finding neighbour (   , i, j ) Calculate D (Density ) Calculate   Calculate    Set the orbital distant    CCABC (Algorithm 3 ): Verification Algorithm IF nodes Energy i s less than threshold TH EN Nodes is dead IF Ti = 0 THEN CCA Is rotated ELSE Decrement time END IF END IF In proposed scheme all nodes have two states; the active state a nd the stand-by s ta te. In active state a node senses t he data and tr ansmits it to clus ter head. The stand-by nodes are in sl eeping mode. Any one node from the nucleus i s acting as cluster head. Each sub-orbit tr ansmits t heir data to the upper sub-orbit within an orbit (Fig. 2). In each orbit, t he sub-orbit, which is nearest to the nucleus, collects other sub- orbits data. Then the data is agg r egated and transmitted to next or bital. N earest orbit of nucleus transmits data to the cluster head in nu cleus. Cluster head collects data an d agg regates and tra nsmits it to the base station. The position of the base station is f ixed. Orbital nodes are c hanging their st ates ra ndomly and repeatedly. The whole network e nergy as well a s the e nergy utilization of the nodes is divided i nto two parts; one is data sensing and another i s data transmission. In i nitial condition the network is divided in to some clusters of actual size depending on intra-cluster communicatio n cost f(I) and inter-cluster communication cost   as discussed in Theorem 2 . The cluster nodes are arranged like atomic structure with the help of modified CCA, c luster hea d (CH) is selected from nuc leus. At the nucleus few nodes are in active s tate, one out of t hem w ill act as a CH and rest will behave a s normal node. At the nucleon the f(I) and    is minimum at this point according to Theorem 4. These nodes are also changing their state from active to stand-by. When CH changes i ts state or its energy reaches to a threshold value, an y ot her active node from the nucleus may be designated as a CH for next round. The transition phase is di vided into two parts; on e is data collection part and a nother is data transmission part. In the data collection p art active no des are sensing d ata from their monitoring area an d in transmission phase sel ected sub-orbital n odes and c luster heads are collecting ot her cl uster member data, agg regating an d tr ansm itting t he same. In nucleus, one of the nu cleo n nodes is selected as a cluster head by CCABC. A sufficient amount of area is covered by the cluster head. Inter- cluster co mmunica tion costs also minimum in CCABC scheme as show n in Theorem 2 . In CCABC, data bits a re aggregated in different orbits . In CCABC model the dat a are propagating form lower orbit to the upp e r orbit. Finally cluster head gets the aggreg a ted data from nearest orbit. Hence cluster head’s data aggregation and receiving loads are distributed i n different orbital n ode , therefore, receiving energy loss in CCABC cluster head is less. The transmitting, receiv ing energy losses calculating formula and CCABC orbital to orbital dis tance calculation formula are defined below . V. S IMULA TIO N R ESUL T The whole simulations are don e i n M A T L A B . T h e w h o l e the c luster formation throu gh sp i r a l p r o p a g a t i o n a n d a n o t h e r i s d a t a t r a n s m i s s i o n w i t h e n e r g y c a l c u l a t i o n phase. In data tr ansmissio n and e n e r g y c a l c u l a t i o n p h a s e e v e r y n o d e c h e c k s t h e i r p r e s e n t e n e r g y a n d co mpares with its threshold valu e . I f p r e s e n t e n e r g y i s l e s s t h a n t h e t h r e s h o l d , t h e n t h e n o d e i s d e c l a r e d a s a dead node. The cluster format i o n i s b a s e d o n i n t e r n a l k n o w l e d g e o f e a c h n o d e a n d m a s s a g e p a s s i n g i s not r equired. The spira l prop a ga t i o n w i l l b e s t o p p e d w h e n t h e c l u s t e r s a r e f o r m e d w h e r e t h e i n t e r and intra- cluster comm unicatio n c o s t i s m i n i m u m . T h e n o d e s a r e s e n s i n g d a t a , a g g r e g a t i n g a n d transmitting it to the cluster head . I n e a c h r o u n d , c l u s t e r m e m b e r n o d e s t r a n s m i times to t he cluster head. The CH c o l l e c t s t h i s d a t a a n d t h e n i t i s t r a n s m i t t e d a f t e r a g g r e g a t i o n t o t h e b a s e station. After d ata trans missio n r o u n d e a c h n o d e c a l c u l a t e s t h e i r transmission and rece iving ener g y l o s s running continuously for the enti r e l i f e s p a n o f t h e n e t w o r k . F i g . We h ave taken 150 ×150 matri x a n d n u m b e r o f s t a t e CCABC nodes are self- organize d . T h e F i g . network. In the C CABC , sensor n o d e s u r v i v e s l o n g e r t i m e t h a n t h e o t h e r a u t o m a t a b a s e d a l g o r i t h m a n d cluster based algorithms e.g . L E A C H , U C C P , M generated without message tr ans m i t t i n g a n d c l u s t e r h e a d s a r e s e l e c t e d o n t h e b a s i s o f l o T h e w h o l e s i m u l a t i o n s a r e d o ne in MATLAB. Th e whole simulations are di vid e d i n t o t w o p a r t s , o n e i s t h e c l u s t e r f o r m a t i o n t h r o u g h s p ira l propag ation and another is data transmission w i t h e n e r g y c a l c u l a t i o n p h a s e . I n d a t a t r a n s m i s s i o n a n d energy calc ulation phase every node checks t h e i r p r e s e n t e n e r g y a n d m p a r e s w i t h i t s t h r e s h o l d v a l u e. If present energy is less than t he threshold, the n t h e n o d e i s d e c l a r e d a s a d e a d n o d e . T h e c l u s t e r f o r m a t ion is ba sed on internal knowledg e of each node a n d m a s s a g e p a s s i n g i s l p r o p a g a tion will be stopped when t he clusters are forme d w h e r e t h e i n t e r c l u s t e r c o m m u n i c a t i on cost i s minimum. The nodes a re sensing d a t a , a g g r e g a t i n g a n d t r a n s m i t t i n g i t t o t h e c l u s t e r h e a d. In each round, cluster member nodes transmi t t h e i r s e n s e d d a t a f o r f i v e t i m e s t o t h e c l u s t e r h e a d . T h e C H collects this data and then it is transmitted afte r a g g r e g a t i o n t o t h e b a s e s t a t i o n . A f t e r d a t a t r a n s m i s s i on round each node ca lculates their remainin g e n e r g y i v i n g e n e rgy loss equations which is describe in [19] . T h r u n n i n g c o n t i n u o u s l y f o r t h e e n t ire life span of the netw ork. F ig. 6: Number of Active Nodes i n CCABC W e h a v e t a k e n 1 5 0 × 1 5 0 m a t r ix and number of state k=15. I n thi s matri x we h a v e a p p l i e d o r g a n i z ed. T he Fig . 6 shows t he total number of activ e n o d e s p e r r o u n d i n a , s e n s o r node survives longer time t han the other autom a t a b a s e d a l g o r i t h m a n d c l u s t e r b a s e d a l g o r i t h m s e . g . LEACH, UCCP, M - LEACH and EECS. I n thi s g e n e r a t e d w i t h o u t m e s s a g e t r a n smitting and cluster heads are selected on the ba s i s o f l o s i m u l a t i o n s a r e d i v i ded into two parts, one is t h e c l u s t e r f o r m a t i o n t h r o u g h s p i r a l p r o p a g a t i o n a n d a n o t h e r i s d a t a t r a n s m i s s i o n with energy calculation p h a s e . I n d a t a t r a n s m i s s i o n a n d e n e r g y c a l c u l a t i o n p h a s e e v e r y n o d e c h e c k s t heir present energy and m p a r e s w i t h i t s t h r e s h o l d v a l u e . I f p r e s e n t e n e r g y i s l e s s t h a n t h e t h r e s h o l d , t h en the node is declared as a d e a d n o d e . T h e c l u s t e r f o r m a t i o n i s b a s e d o n i n t e r n a l k n o w l e d g e o f e a c h n o d e and massage passing is l p r o p a g a t i o n w i l l b e s t o p p e d w h e n t h e c l u s t e r s a r e f o r m ed where th e inter -cluster c l u s t e r c o m m u n i c a t i o n c o s t i s m i n i m u m . T h e n o d e s a r e s e n s i n g data, agg regating and t their sensed data for five t i m e s t o t h e c l u s t e r h e a d . T h e C H c o l l e c t s t h i s d a t a a n d t h e n i t i s t r a n s m i t t e d a f t e r aggregation to the base r e m a i n i n g energy acc ording to . Th ese two processes are k = 1 5 . I n t h i s m a t r i x w e have ap plied mCCA. T he s h o w s t h e t o t a l n u m b e r o f a c t i ve nodes p er round in a , s e n s o r n o d e s u r v i v e s l o n g e r t i m e t h a n t h e o t h e r a u t o mata based algorithm and L E A C H a n d E E C S . I n t h is CCABC, clusters are g e n e r a t e d w i t h o u t m e s s a g e t r a n s m i t t i n g a n d c l u s t e r h e a d s a r e s e l e c t e d o n t h e b asis of lo cal in formati on. Whereas, the other popular clust e r i n g t e c h n i q u e l i k e L E A C H , U C C P , E E C C P s o m e a m o u n t o f e n e r g y a r e spent by the message passing fo r c l u s t e r g e n e r a t i o n . I n recovered, which is present in L E A C H c l u s t used in simulation . Sensor Deploym ent Area Base Station Loca tion Number of node Data Packet Size Initial Energy Stand- by state energy loss E nergy per bit spent by the tra n s m i t t e r Amplifier energy (e d ) In CCABC simulation techni q u e , i n i t i a l e n e r g y o f a n o d e i s 0 . 5 J a n d p a c k e t s i z e o f e a c h m e s s a g e i s 100 bytes is fixed in whole sim u l a t i o n . T h e b a s e s t a t i o n p o s i t i o n i s f i x e d . I n e a c h r o u n d o r b i t a l n o d e s collect data and send message w i t h t h e h e l p o f T D M A ( T i m e d i v i s i o n m u l t i p l e [18]. In each ro und , nodes are s e n d i n g f i v e d a t a m e s s a g e t o t h e c l u s t e r h e a d . C l u s t e r h e a d c o l l e c t t h e i r data and send data m essage to th e b a s e s t a t i o n . F o r d a t a a g g r e g a t i o n , When the clusters are genera t e d b y number. These active nodes cov e r w h o l e n e t w o r k f i e l d , a n d n u m b e r o f s t a n d W h e r e a s , t h e o t h e r p o p u l a r c l u s t ering technique like LEACH, UCCP, EECC P som e a m o u n t o f e n e r g y a r e s p e n t b y t h e m e s s a g e p a s s i n g f or cluster generation. In CCABC , message over h e a d i n g p r o b l e m c a n b e r e c o v e r e d , w h i c h i s p r e s e n t i n L EACH clust ering technique. T able 1 show t he p a r a m e t e r v a l u e s w h i c h i s Table 1 Simulation paramet ers B a s e S t a t i o n L o c a t i o n N u m b e r o f n o d e D a t a P a c k e t S i z e I n i t i a l E n e r g y b y s t a t e e n e r g y l o s s n e r g y p e r b i t s p e n t b y t h e t r ansmitter circuit ( e t ) s i m u l a t i o n t e c h n ique, initial energy of a node is 0.5J and packet s i z e o f e a c h m e s s a g e i s 1 0 0 b y t e s i s f i x e d i n w h o l e s i mul ation. T he base st ation position is fixed. In e a c h r o u n d o r b i t a l n o d e s s e n d m e s s a g e with the help of T DMA (Time division multipl e [ 1 8 ] . I n e a c h r o u n d , n o d e s a r e sending five d ata message to t he cluster head. C l u s t e r h e a d c o l l e c t t h e i r d a t a a n d s e n d d a t a m e s s a g e t o t h e base station. For data agg regation, energy spent i s 5 n J / b i t / m e s s a g e [ 2 ] . Fig.7 : Life time of the node s in CCABC W h e n t h e c l u s t e r s a r e g e n e r ated by CCABC scheme at the beginning, t he a c t i v e n o d e s a r e 1 0 6 5 2 i n n u m b e r . T h e s e a c t i v e n o d e s c o ver whole network field, and number of stand - b y n o d W h e r e a s , t h e o t h e r p o p u l a r c l u s t e r i n g t e c h n i q u e l i k e L E A C H , U C C P , E E C C P s o me amount of energy are , m e s s a g e o v e r heading problem can be e r i n g t e c h n i q u e . T a b l e 1 s h o w t h e p a rameter values which is 150×150 (50,175) 22500 800bit 0.5J 0.00006J 50 nJ/bit 10 pJ/bit/m 2 s i m u l a t i o n t e c h n i q u e , i n i t i a l e n e r g y o f a n o d e i s 0 . 5 J a n d p a c k e t size of ea ch message is 1 0 0 b y t e s i s f i x e d i n w h o l e s i m u l a t i o n . T h e b a s e s t a t i o n p o s i t i o n i s f i x e d . I n each round orbital nodes s e n d m e s s a g e w i t h t h e h e l p o f T D M A ( T i m e d i v i s i o n m u l t i p l e -access) MAC protocol [ 1 8 ] . I n e a c h r o u n d , n o d e s a r e s e n d i n g f i v e d a t a m e s s a g e t o t h e c l u s t e r h e a d . Cluster head c ollect their e n e r g y s p e n t is 5nJ/bit/message [2] . s c h e m e a t t h e b e g i n n i n g , t h e a ctive nodes a re 106 52 in by nod es are 11848. We compare CCABC with othe r c l u s t e r i n g a l g o r i t h m L E A C H , U C C P ( U n i f i e d C l u s t e r i n g a n d Communication Protocol [5] ), E E C S ( E n e r g y E f f i c i e n t C l u s t e r i n g S c h e m e [ 6 ] , [ 1 4 ] . T h e r e s u l t s h o w s t h a t (Fig. 7.) in C CABC , death of t h e f i r s t n o d e o c c u r s a f t algorithm. T he CCABC extend n e t w o r k EECS. F i g . 8 The Fig. 8 represents the ave r a g e e n e r g y c o n s u m p t i o n p e r r o u n d f o r t h r e e d i f f e r e n t n e t w o r k s i z e s . These s tatistics are collected usin g 1 5 0 0 i n d e p e n d e n t r o u n d s w i t h n o d e a d n o d e s i n t h e n e t w o r k . I t c a n b e observed that C CABC outperfor m s o v e r a l l o t h e r p r o t o c o l b e c a u s cyclic cellular au tomata and a l a r g e n u m b e r o f n o d e s a r e i n s t a n d reduces message ove rheads com p a r e d t o o t h e r t e c h n i q u e . Fig. 9: Tota l a m o u n t o f e n e r g y o f t h e n e t w o r k i n e n e r g y w i t h o t h e r clustering al gorithm LEACH, UCCP (U n i f i e d C l u s t e r i n g a n d C o m m u n i c a t i o n P r o t o c o l [ 5 ] ) , E ECS (Energy Efficient Clustering Scheme [6], [1 4 ] . T h e r e s u l t s h o w s t h a t , d e a t h o f t he first node occurs aft er 1560 round which is b e t t e r t h a n o t h e r e x i s t i n g e x t e n d network life time 41% over UC CP, 64% over L E A C H a n d 5 4 . 5 5 Fig.8 : Averag e energy consumed per round r e p r e s e n t s t h e a v erage ener gy consumption per r ound for three d i f f e r e n t n e t w o r k s i z e s . T h e s e s t a t i s t i c s a r e c o l l e c t e d u s i ng 1500 independent rounds with no dead nodes i n t h e n e t w o r k . I t c a n b e o u t p e r f o r ms over al l other protocol be caus e it generates cl u s t e r i n g w i t h t h e h e l p o f c y c l i c c e l l u l a r a u t o m a t a a n d a l arge n umber of nodes are i n stand - by st ate. O n t h e o t h e r h a n d r e d u c e s m e s s a g e o v e r h e a d s c o m pared to other technique . T o t a l amount of ene rgy of the network in energy CCA B C w i t h o t h e r c l u s t e r i n g a l g o r i t h m L E A C H , U C C P ( Unified Clustering an d C o m m u n i c a t i o n P r o t o c o l [ 5 ] ) , E E C S ( E n e r g y E f f i c i e n t C l u s t e r i n g S c h e m e [ 6 ] , [ 14]. The result shows that e r 1 5 6 0 r o u n d w h i c h i s better than other existing t i m e 4 1 % o v e r U C C P , 6 4 % o v e r L EAC H and 54.55 % over r e p r e s e n t s t h e a v e r a g e e n e r g y c o n s u m p t i o n p e r r o u n d f o r t h r e e different network s izes. T h e s e s t a t i s t i c s a r e c o l l e c t e d u s i n g 1 5 0 0 i n d e p e n d e n t r o u n d s w i t h n o d e a d n o d e s in the network. It can be e i t g e n e r a t e s c lustering with the he lp of b y s t a t e . O n the other hand CCABC C C ABC Total energy spent by the s e n s o r n e t w o r k i n compared in Fig. 9. Th e CCABC and 13.898(J) energy pe r round c o m p a r e d t o L E A C H . T h e b e t t e r e n increases net work’s lifetime 81 . 8 3 % compared to LEA CH. Fig. 10: The network c o v e r a g e i n The area which is monitored b y t h e s e n s i n g r a n g e o f a l l a c t i v e n o d e s i s c a l l e d c o v e r a g e 10 we have co mpared percentag e o f c o v e r a g e b e t w e e n better result compared to three coverage is approximate l y 40 % i n t h r e e CCABC achieves up to 80% of n e t w o r k c o v e r a g e . Fig . T o t a l e n e r g y s p e n t b y t h e sensor n etwork i n CCABC, three- phased algo r i t h m a n d L E A C H a r e C C A B C saves 60 .5305(J) energy per round c ompared to t h r e e a n d 1 3 . 8 9 8 ( J ) e n e r g y p e r r o u n d compared to LEACH. The better e n ergy util iza t i o n o f 8 1.83 % more compared t o three-phased alg o r i t h m a n d T h e n e t w o r k coverag e in CCABC vs. Three- phased algorithm T h e a r e a w h i c h i s m o n i t o r e d by the sensing range of all activ e nodes is called c o v e r a g e p e r c e n t a g e of coverage between CCABC and three- phased a l g o r i t h m . W e h a v e g o t b e t t e r r e s u l t c o m p a r e d t o t h r e e -phased algorithm. Th e experimental results co n f i r m t h a t t h e n e t w o r k y 4 0 % in three -phased algorithm and 20% i n ECCA [9] a l g o r i t h m , a c h i e v e s u p t o 8 0 % o f network cov erage. F i g. 11: Energy ut ilization of the sensor network p h a s e d a l g orithm and LEACH are s a v e s 6 0 . 5 3 0 5 ( J ) e n e r g y p e r r o u n d c o m p a r e d t o three -phased algorithm e r g y u t i l i z ation of CCABC scheme a l gorithm and 56% more p h a s e d a l g o r i t h m and ECCA T h e a r e a w h i c h i s m o n i t o r e d b y t h e s e n s i n g r a n g e o f a l l a c t i v e n o d e s i s c a l l e d coverage area. In the Fig. p h a s e d algorithm. We have got e e x p e r i m e n t a l r e s u l t s c onfirm that the network 2 0 % i n E C C A [ 9 ] algorithm, whereas, the In CCABC, sensor nodes send their data w ith minimum ene rgy loss compared to other ex isting algorithm LEACH, U CCP. In CC ABC 5 2.82% of nodes are in stand-by state, loses min imum amount o f energy . 47.34% nodes are in active state. Th e energy uti lization for topological management in different network is shown in Fig 11 . The energy utiliz ation for topological management in L EACH and UC CP is 83% and 77.9% respectively . Whereas, in c luster based energy ef ficient wireless sensor networks management scheme 60.9% energy uses for topological managem ent. VI. C ONCLUSION In this paper we have d esigned an ene rgy efficient se nsor n etwork with modified cy cli c cellular automata based clustering. Modified cyclic cellular automata ( mC CA) splits th e whole network into some effective size clusters. T he mCCA also ensure maximum coverage in the network with minimum active nodes. In CCABC w e have det ermined a n optimal cluster size wh ere the node s ends their data with minimum energy loss. An e fficient cluster head position is also determined in CCABC. Here we have proposed a new effective data aggregation model which is used b y the cluster heads before data propagation. The simulation r esults established that the p roposed scheme is better compared to other popular clustering al gorithms. The mobile network model works with CCABC, ma y sh ow better performance in r eal application. The CCABC is s uited for VLSI implementation an d, therefore, the proposed management scheme can be implemented w ith low cost hardware . R EFERENCES [1] S . Adabi, A.K.Zadeh, A.Dana, S.Adabi, “Cellular Automata Base M ethod for Energy Conservation Solution in W ireless Sensor Net work ”, IEEE Wirel ess Communicat ions, Netw o rking and Mobile Computing 4th Internat ional Conference on, 2008. [2] W. R.Heinzelman , A nantha C handrakasan , and H. B alakrishnan, “E nergy -Efficient Co mmunica tion Protocol for Wireless Micro sensor Networks”, IEEE 33th Hawaii International Conference on Sy stem Scie nces, 2000. [3] A. S. Zahmati, B. Abolhassani, A. A. B. Shirazi , and A. S. Bakhtiar i, “An E nergy -Efficient Protocol With Stat ic Clustering for W ireless Sensor N etworks”, World A cademy o f Science, Engineering And Technology 28 2007. [4] W. Heinzelman, A. Chandrakasan, H. 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Greenberg and S. H asting s, “Spatial patterns for discrete models of diffusion i n excitable media” SIAM Journal of Applied Mathematic s”, 34515-523 , 1978. [14] R.Fisch , J. G ravner, and D. Griffeath. “Metastability i n th e Greenberg-Hastings model”, The An nals of Applied Probability , 3(4):935-967, 1993. [15] S. Liao , “ On the general Taylor theorem and its applications in solv ing non-linear problems”, Communications in Nonli near Science and Numerical Simulat ion, Volume 2, Issue 3, September 1997, Pages 135-14. [16] Hui- Ching Hs ieh, Jenq-Shiou Leu, Wei-Kuan Shih, “A fault-tolerant scheme for an autonom ous local wireless sen sor net work ”, Computer Standards & Interfaces, Volume 32, Issue 4, June 2010, Pages215-221. [17] S. Pattem , B. Krishnamachari , and R. Govvinadan, “The impact of spatial correlation on routing with compression in wirele ss sensor network”, in Proc . I nt. Symp. Information Processing in sensor Network , Apr.2004, pp.28-35. [18] Wei Ye, Jo hn Hei demann, Deborah Estrin, “Medium Access Control with Coordinated Adaptive Selling for Wireless Sensor Network”, I EEE Transation on Netw orking, 2004. [19] I ndraj it Ba nerjee, Prasenjit Chanak, Biplab k. sik dar, Hafijur Rahaman, “EERIH: Energy Effic ient Routing via Information Highway in Sensor Net work ” IEEE International co nference on emerging trends in Electrica l and Computer technology , March 23rd and 24th 2011, kany a kumari, India. Indrajit Banerjee is a n assistant prof essor in the Information Technology Department at Bengal Engineering and Science University, Shibpur, India. He got the bachelor degree in mechanical engineering from Institute of Engineers, I ndia. H e received his masters in Information Technology from Bengal En gineering an d Sc ience University in 2004. He is currently pursuing his Ph. D. in Information Tec hn ology in Be ngal E ngineer ing & Science University . His main research in terests are cellular automata, wireless ad hoc and sen sor network. Prasenjit Chanak received his B.Tech de gree in Information Technology from Institute of Engineering and Technology, U.P., India in 2007. He received his masters degree in Info rmation Technology from B engal Engineering an d Sc ience University in 2011. His main research interest is wireless ad hoc sensor netw ork. Hafizur Rahaman received the B.E. degree in electrical engineering from Bengal Engineering College, Calcutta U niversity , Calcutta, India, in 1982 and the M.E. degree in electrical engineering an d Ph.D. degree in computer science and engineering from Jadavpur U niversity , C alcutta, in 1988 an d 2 003, respectively. Dur ing 2006–2007, he visited the D epartment of C omputer Science, B risto l Universit y , Bri stol, U.K., as Postdoctoral Research Fellow . He is currentl y chai ring the D epartment of Information Technology , Bengal Engineering and Sci ence University, H owrah, India. His research interests include logic s y nthesis and testing of V LSI c ircuits, fault-tolerant computing, G alois-f ield-based arithmetic circuits, and quantum computing. Dr. Rahaman has served on the Organizing Committees of the International C onference on VLSI Desi gn i n 2000 a nd 200 5 and as t he Re gistration Chair f or the 2005 Asian Test Sy mposium, which was held in Calcutta.