Energy Efficient Algorithm for Wireless Sensor Network using Fuzzy C-Means Clustering

Transcription

1 nergy ffcent Algorm for Wreless Sensor Network usng Fuzzy C-Means Clusterng Abhlasha Jan CS Department GZS Campus College of ngneerng and Technology Maharaja anjeet Sngh, PTU, Punjab, Inda Ashok Kumar Goel C Department GZS Campus College of ngneerng and Technology Maharaja anjeet Sngh, PTU, Punjab, Inda Abstract nergy effcency s a vtal ssue n wreless sensor networks. In s paper, an energy effcent routng algorm has been proposed w an am to enhance lfetme of network. In s paper, Fuzzy C-Means clusterng has been used to form optmum number of statc clusters. A concept of coherence s used to elmnate redundant data generaton and transmsson whch avods undue loss of energy. Intra-cluster and nter-cluster gateways are used to avod nodes from transmttng data rough long dstances. A new strategy has been proposed to select robust nodes near snk for drect data transmssons. The proposed algorm s compared w LACH, M-LACH, MH-LACH and OCM-FCM based upon lfetme, average energy consumpton and roughput. From e results, t s confrmed at e performance of e proposed algorm s much better an oer algorms and s more sutable for mplementaton n wreless sensor networks. Keywords WSN; clusterng; sleep-awake; vrtual grds; multhop; routng I. INTODUCTION Wreless Sensor Networks (WSNs) consst of ousands of mcro-szed low power Sensor Nodes (SNs) randomly deployed n e Sensor Feld (SF). These SNs sense local envronmental statstcs, aggregate and communcate sensed nformaton to snk. For each operaton SN consumes ts battery power. As SFs are hostle n most of e applcatons of WSN, t s not possble to replace batteres of SNs [1]. In order to enhance lfetme of network, e routng algorms n WSN manly focus on energy effcency. Clusterng s one of e most effectve technques n routng to preserve e energy of e network. The whole network s organzed nto small groups of SNs called clusters. In each cluster, one node s elected as Cluster Head (CH) whch performs e task of collectng data from all Cluster Member (CMs) nodes, aggregaton of data and forwardng t to e snk drectly or n mult-hop manner [2], [3]. The aggregaton may or may not be perfect dependng upon e relaton between e sensed data. Perfect aggregaton means many k-bt data packets are compressed to a sngle k-bt packet. Perfect-fuson, a smple technque has attracted many researchers nterest [2], [4]. LACH (Low nergy Adaptve Clusterng Herarchy) [2], UC (nergy ffcent Unequal Clusterng) [3], IB-LACH (Intra-Balanced LACH) [5], M-LACH (Mult-hop outng LACH) [6], MH-LACH (Mult-Hop LACH) [7], MP (nergy effcent mult-hop routng protocol) [8], ACC [9], OCM-FCM (Optmal Clusterng Mechansm Fuzzy-C Means) [10], SCP (Stable nergy ffcent Clusterng Protocol) [11] and MLC (Mult- Level oute-aware Clusterng) [12] are some of e popular clusterng protocols. In ese protocols, all nodes n e network actvely sense from e envronment and contnuously generate data. The nodes whch are placed close to each oer tend to generate redundant data due to phenomenon of coherence. A major amount of network energy s wasted n transmttng redundant data to snk whch drectly affects e network lfetme. Protocols lke Span [13] and LACH-SM (LACH Spare Management) [14] keep a small subset of SNs actve n such a way at ese nodes cover e whole network whle oer nodes n e network are kept n sleep mode. In [15] an energy effcent sleep schedulng has been proposed n order to mantan network coverage and connectvty. Consequently, e energy consumed per round s reduced, us, ncreasng e WSN lfetme. Major part of energy s consumed by SNs n transmttng data whch s proportonal to e dstance between sensor nodes rased to power ( 2). Therefore, long-dstance transmssons should be mnmzed for optmzng e usage of network energy [16]. Mult-hop data transmsson between Gate Way nodes (GWs), CHs and snk can be used to reduce energy consumpton, but SNs (may be CHs or GWs) near snk are more probable to be selected for transmttng e data to snk, resultng n er early dea and us, effectng e overall performance of e network. In many clusterng algorms, number of clusters n each round s not fxed and employs poor CH electon and cluster formaton technques. As a result, total nter and ntra cluster dstance becomes large, resultng n hgh energy consumpton of e network. Therefore, soft computng technques can be benefcally employed n cluster based routng protocols [16], [17]. Fuzzy-C means clusterng groups e SNs based upon er degree of membershp n each cluster. The am s to mnmze e sum of dstances between e SNs and e centrod of er cluster. OCM-FCM uses Fuzzy-C means clusterng to form more unform and correlated clusters n order to mprove e lfetme of WSN [10]. In s paper, a mult-hop effcent routng algorm for WSN has been proposed. The algorm emphaszes to optmze e energy usage n e network n order to enhance e network lfetme. It elmnates redundant data generaton n e network by keepng only one node actve from e set of nodes at are sendng redundant data and also renders full network coverage. Intally, e whole network s dvded nto optmal number of statc clusters usng FCM. CH n each P a g e

2 cluster changes determnstcally durng e network operaton. In order to reduce long dstance transmssons and balance load, mult-hop routes are establshed usng gate way nodes. In mult-hop routng, e nodes whch le near e snk are more probable to be selected for establshng drect lnk to e snk despte of havng lower dual energy. Therefore, SNs near e snk wll de soon and create energy hole n e network. To avod t, a strategy s desgned whch selects e nearest elgble SN for transmttng data to snk, erefore consumng least amount of energy. It also ensures at s SN has suffcent dual energy so at t may not become dead. The remander of e paper s organzed as follows: Secton 2 gves bref revew of e related work. Network model and assumpton made for e proposed work has been presented n Secton 3. Secton 4 descrbes e proposed algorm for an energy effcent mult-hop adaptve routng usng FCM clusterng. The results and er comparson w oer algorms have been dscussed n Secton 5. Fnally concludng remarks are gven n Secton 6. II. LATD SACH Many clusterng protocols have been explored n lterature for WSN n e last few years to ncrease network lfetme. LACH s forerunner decentralzed clusterng protocol uses random rotaton of CH n each round to evenly dstrbute e load n e network. CHs are responsble to transmt cluster nformaton to e snk [2]. CHs far away from e snk consume lot of e energy to transmt data to snk. andomzed selecton of CHs n each round adds extra overhead to e network. The algorm does not assure even dstrbuton of CHs n e network whch may result nto uneven energy consumpton n dfferent parts of e network. Moreover, e clusters formed n each round may or may not be correlated whch ncreases e ntra cluster dstances. Therefore, LACH s not sutable for large scale networks. IB-LACH s an mprovement over LACH [5]. CH selecton of meod s complex and nvolves lot of computatons. Some nodes n e network are overburdened. MH-LACH and M-LACH are e varants of LACH, whch follow e same CH selecton meods as proposed n LACH [6], [7]. These algorms establsh e mult-hop routes to transmt data to e snk and reduce number of long dstance transmssons n e network. CH nodes whch are near e snk (nner CH) are responsble for transmttng data to e snk and oer CHs (outer CH) wll transmt er data to e nearby CH. When snk les outsde e sensor feld, nner CH wll consume hgher energy an outer CHs and may de soon. UC and MP dvde e network nto unequal szed clusters [3], [8]. The clusters at are formed near e snk are smaller n sze as compared to clusters far away from e snk. There s an addtonal overhead nvolved for CH selecton n each round. Nodes near snk are responsble to transmt data drectly to snk even f ey have low energy. ACC and THCC [9] are e centralzed routng algorms whch results n better network lfetme as compared to e conventonal dstrbuted routng protocols lke LACH. The algorm dvdes e whole network n regons whch act as statc clusters for WSN. The snk fnds CH n each regon and effcent pa to route regon data to snk. Ths centralzed scheme s helpful n plannng poweraware, well-organzed routes n e network. Network status needs to transmt to e snk n each round whch ncreases energy consumpton n all SNs and overall performance of network degrades. Soft computng meods lke Fuzzy C-Means clusterng (FCM), K-means clusterng, Genetc Algorms (GA) and Partcle Swarm Optmzaton (PSO) are recently used to overcome problems of non-correlated clusters [10], [17]. It helps to prolong e lfe of WSN. However, all ese meods run at centralzed node.e. at snk and need physcal locaton of SNs. In OCM-FCM reduces e cluster formaton overhead on SNs as snk dvdes WSN n K optmal number of statc clusters. After frst round, e current CH selects a hghest energy node n e cluster and elects t as CH for e next round. Ths algorm uses mult-hop technque by fndng Secondary Cluster Heads (SCHs) from already chosen CHs f snk s far away from e sensor feld [18], [19]. It wll result n reducton of long dstance transmssons. The drawbacks of e algorm are: 1) Poor CH selecton; 2) CHs are overburdened as compared to oer SNs; 3) SCHs consume more energy as compared to prmary CHs. In all e algorms dscussed, all nodes are regularly sensng and transmttng data to snk. Thus, large amount of network energy s wasted n redundant data transmssons whch need to be reduced. III. NTWOK MODL AND ASSUMPTIONS Total N sensor nodes n WSN are randomly deployed n of X m by Y m rectangular sensor feld. The assumptons made for e mplementaton of e proposed work are as gven under: 1) All nodes are statonary and have unque ID. 2) It s assumed at all SNs fnd er locatons eer rough GPS system or usng some localzaton algorm. 3) All nodes are statc and have same ntal energy nt 4) All e actve sensor nodes regularly generate nformaton from e envronment. 5) All SNs are capable to perform data aggregatons n perfect fuson mode. 6) In e cluster formaton process, each SN can be a member of only one cluster and can act as CH for e same cluster. 7) The snk has unlmted energy and computatonal resources. 8) Frst order rado energy model descrbed n [2] s utlzed for sensor nodes durng er communcatons procedure. IV. POPOSD NGY FFICINT ALGOITHM The paper proposes an energy effcent algorm for WSN usng mult-hop routng. Intally t runs a network dmensonng phase where snk dvdes e whole network nto desred number of statc clusters usng FCM technque and turns off all SNs producng redundant data. Thereafter, t organzes e network operaton nto a seres of rounds where each round s dvded nto network setup phase and network communcaton phase. In network set up phase, new CHs are elected n statc clusters f requred and energy effcent multhop pas are establshed from each SN to e snk. The network communcaton phase deals w ntra-cluster and P a g e

3 nter-cluster mult-hop routng and data transmssons. In network communcaton phase every CH allocates TDMA slots to CMs n each cluster and for nter-cluster communcaton s acheved rough CDMA codes. A. Network Dmensonng Phase a) Dvson of SF n granules: WSN s ntally dvded unformly nto granules. The sze of granule may vary accordng to degree of coherence n sensed data. In a SF, f nformaton to be sensed s more coherent, en sze of granule may be taken large and vce-versa. The entre SF s dvded ng n n nto granules where x granules are along x-axs and y x granules along y-axs. The sze of granule s w y by w subject x to constrant at w y and w are nteger multple of wd and heght of SF respectvely. Any SN w co-ordnates x, y under: fnds ts assocated granule ID usng formula gven as x y GranID round 0.5 round y.(1) n x w y w All SNs save s nformaton for furer use. b) Intal Actve-Sleep Meodology: Once SNs fnd er assocated granule ID, each SN wll broadcast a message S( NodeID, GranID) to all SNs n ts radus. Where com com s equal to e dagonal of a granule and s gven by followng equaton: x y. (2) 2 2 com w w Intally, SNs do not need to broadcast er dual energy as all SNs have same nt. ach SN saves NodeID of e receved message f t belongs to ts granule node and dscards oerwse. In s way, all SNs mantans NodeID and of ts granule SNs. Once, all SNs save nformaton of er assocated GranID, granule SNs. SN w hghest node ID n a granule wll actvate tself and broadcast ts locaton and ID nformaton to e snk. emanng nodes n e granule shall be n sleep mode. c) Clusters Formaton: At e end of actve-sleep process, e snk wll receve locaton nformaton of all n actve nodes. The snk frst calculates er assocated GranIDs usng (1), en dvdes whole WSN nto K optmal number of clusters usng FCM clusterng algorm. The SN s assocated w e cluster n whch t has e hghest membershp value based upon ts dstance from e centrods of e clusters. The snk broadcasts messages S ClusterID, Lst of assocated GranIDs for each cluster to all SNs. SNs belongng to GranID specfed n e message wll save s message n ts cluster table. d) Intal Selecton of Cluster Heads: The snk dvdes e whole network nto clusters. Thereafter, e snk evaluates e ftness of all SNs n each cluster on e bass of an objectve functon gven by (3). The SN w hghest ftness value wll be elected as CH n each cluster. The snk broadcasts s nformaton to each cluster. 1 (3) f ( ), j c cluster, j,1 c K ds(, j) j where f() s e ftness of member of c cluster. ds(, j) s e ucldean dstance between and member of c cluster. The value of s taken as 2 for ds(, j) less an reshold dstance, oerwse 4. e) Logcal egon Dvson: In many mult-hop routng algorms proposed by varous researchers, SNs nearer to snk carry heaver traffc loads and are responsble for routng data to e snk. Therefore, SNs around snk deplete er energy at faster rate and may create energy hole near e snk. To avod e problem, e SF under consderaton s dvded nto ree logcal regons as Low nergy Area (LA), Medum nergy Area (MA) and Hgh nergy Area (HA) dependng upon e energy requred by SNs n ese regons to transmt data drectly to e snk. The snk assgns regon IDs to all SNs. The logcal regon dvson has been shown n Fg. 1. The SNs from e regon specfed by e snk wll only establsh drect communcaton to t. Intally, LA becomes regon of nterest. Fg. 1. Logcal egon Dvson of SF n LA, MA and HA (s fgure s created for s research work). Fg. 2. Mult-hop topology for proposed system (s fgure s created for s research work). j P a g e

4 B. Network Setup Phase After ntal set up of WSN by snk, mult-hop energy effcent routes are establshed for each SN n dstrbutve manner. e-electon of CHs may start from next round at each cluster level. a) CH lecton: A CH of a cluster contnues to be CH for e next rounds provded ts dual energy s greater C an reshold energy of ts cluster,. The technque Th elmnates e overhead of formng CHs n each round and saves energy. For ts mplementaton, reshold energy of each C cluster Th begns w 60% of nt and decreased by 20% when energy of all nodes n e cluster falls below e reshold level. When of CH goes below C Th, t prompts new CH electon process and actvates e actve-sleep process n e cluster. ach actve SN n e cluster scans ts granule to fnds a SN (n sleep mode) w hghest. Make specfed SN actve and goes tself n sleep mode. Before gong to sleep mode t transmts all ts routng nformaton to newly selected actve node. Then, present CH fnds canddate CH nodes, havng dual energy above C Th. Canddate CH w maxmum ftness calculated accordng to (3) s selected as new CH. If ere are two or more nodes w same ftness value, en, a node w hgher ID number s chosen. b) Mult-hop route establshment: CH n a cluster receves nformaton from ts CMs, aggregates e receved data w ts own sensed data and forwards t to snk eer n sngle hop or n mult-hop. CMs are accountable to sense envronmental data and transmt sensed nformaton to CH. Therefore, CH n a cluster does more work an oer nodes and consumes much hgher energy. To reduce ts work, e task of nter cluster communcaton s shfted to anoer node known as Intra Cluster GW (IntraCGW). The cluster CH wll choose IntraCGW n e drecton of logcal regon announced by snk. If e selected IntraCGW s n e announced logcal regon, lnk s establshed drectly to snk dependng upon a probablty functon descrbed n (4), oerwse nter cluster pa s devsed. Then, IntraCGW fnds a nearest nter cluster SN n a cluster, currently not connected n order to avod loops n communcaton pa. The selected nter cluster node called Inter Cluster GW (InterGW) forms e lnk between e two clusters. The mult-hop topology formed by e proposed algorm s shown n Fg. 2. P avg, f c 1 Tc () 1 P ( rmod ) (4) avg P 0, oerwse where P s e desred percentage of drect lnks to e snk, r s e current round, s e average energy of CMs avg of e cluster n announced regon, avg s e average energy of e actve nodes n e network. ach CH n c cluster n announced regon generates a random number between 0 and 1. The CH wll establsh a lnk to e snk rough selected IntraGW, f generated random number s less an reshold value of e cluster as calculated n (4). The proposed algorm to establsh mult-hop routes s gven n Fg. 3. c) egon Announcement by Snk: In order to attan nearly balanced energy depleton and to ncrease e relablty of WSN, t s desrable at all nodes reman alve for larger tme perod. SNs n LA regon consume lowest energy n drect data transmssons an MA and HA regon nodes. Therefore, nodes from LA regon are desrable for establshng drect lnk to snk, but f ese nodes contnue to transmt nformaton drectly to snk, ey wll consume energy contnuously and ultmately become dead. To avod such stuaton, a shftng strategy between ree logcal regons has been proposed n s paper. A reshold parameter s taken to decde s shftng strategy. Intally, s set to 30% of nt energy. Dependng upon ts value, e snk announces e regon of nterest n each round. If dual energy of all SNs n e SF goes below en s decreased to 10% of nt and announce LA as regon of nterest. 1. For each CH check CH C Th Algorm: Network Setup Phase 2. Broadcast a query What s e hghest dual energy of e granule? to all CMs and actvate actve sleep process. 3. ach actve SN n e cluster fnds oer SN n ts granule w hghest. Actvate newly selected SN. Transfer ts routng nformaton and goes n sleep mode. 4. All e actvated nodes send reply message to S NodeID, GranID, current CH. 5. Current CH fnds a set CCH of canddate CHs such at er C Th 6. Check f no CCH s selected CH s decreased by 20% and current CH remans CH for next e rounds. Oerwse Select a canddate CH w maxmum ftness value and broadcast t to all SNs n e cluster. 7. Mult-Hop oute stablshment to Snk by Selected CH a. CH chooses GW wn ts cluster n e drecton of announced regon of nterest w hghest. b. Check selected GW s n egon of Interest and CH generates a random number <1; f t s less an reshold of ts cluster en establsh connecton to e snk. Oerwse Check f all oer clusters n e network are connected drectly or ndrectly to t. stablsh connecton to e snk Oerwse selects anoer closest SN from oer clusters not connected to t and stablsh connecton to t. C. Network Communcaton Phase In communcaton phase, ere are two types of communcaton takes place n e network: ntra-cluster P a g e

5 communcaton and nter cluster communcaton. For ntra cluster communcaton CH node assgns TDMA schedule to ts entre CMs. ach CM n a cluster transmts ts sensed data to CH n ts allotted slot. W TDMA scheme lot of energy of CMs s saved as CM goes n sleep mode when t s not transmttng any data. CH of each cluster receves data packets from CMs and aggregated e receved nformaton w ts own sensed data and forwards t to IntraGW. It wll en aggregate e receved data packet w ts own and forward t to InterGW usng CDMA codes. In each round, actve SN ose go below transmt ts energy status to e snk. Dependng upon nformaton receved, t announces e regon of nterest. V. SULTS AND DISCUSSIONS The proposed meodology has been mplemented w MATLAB and executed on 100 randomly deployed wreless sensor networks n order to evaluate e performance parameters: network lfetme, energy consumpton and roughput. Network lfetme s consdered as e number of rounds untl e complete network s dead. nergy consumpton n each SN of e network s measured as e sum of energy consumed n recevng, aggregatng and forwardng e data to oer ntermedate nodes or to e snk. Throughput s defned as sum of unque data packets generated n each round. For example, four data packets are generated from an actve granule by four SNs n at granule. We consder t as one because oer ree carres same data as at of frst packet. For smplcty, t s assumed at ere s no data collson and packet loss n e wreless channel. Lkewse, control packets have also been gnored for e evaluaton of performance parameters. In s smulaton setup, one hundred statc sensor nodes are randomly dstrbuted n a SF of area 200m X 200m and consdered as one WSN deployment. The snk s placed at locaton (100,100) and s mmoble. Intal battery power for all SN s taken as 0.5J. Furer, to ensure e generalsaton of results, e smulaton experments were conducted for one hundred WSN deployments. ach smulaton s run untl all sensor nodes n e network become dead. The energy consumed n e network s calculated for transmttng, recevng and aggregatng data packets. The energy consumed n aggregatng and transmttng 1 bt data over dstance d s calculated usng (5). equal to square root of rato fs. Total energy consumed n mp communcaton from SN to SN j over dstance s gven by (7). ( l, d) l *( e e ) (7) j t r Where s e leng of data packet taken as 4000 bts. To decde optmal number of clusters K opt for e proposed algorm, e algorm s executed on all 100 smulatons for varyng K,.e. number of clusters, from 1 to 20. The average energy consumpton per round for each value of K s calculated. The mnmum average energy consumpton per round s found at K =10. Hence, e value of K opt s taken as10. In order to generalze e results, all e fve algorms (LACH, M-LACH, MH-LACH, OCM-FCM and A- FCM) were mplemented on e same hundred deployments and averaged results obtaned from em have been used to present e comparatve account of e algorms. The results are shown n Fg. 4, 5, 6, 7 and 8. Fg. 3. Graph of number of operatonal nodes verses rounds n WSN for N= elec DA fsd, d do et d, d d 2 elec DA mp o..(5) nergy consumed n recevng 1 bt data s gven as under: e r (6) elec where elec s energy dsspated per bt to run transmtter or recever crcut, taken as 50nJ/bt. DA s energy consumed n aggregatng one bt data and set to 5nJ/bt/sgnal. fs and mp are energy consumed n free space model and energy used n multpa fadng model taken as 10 pj/bt/m 2 and pj/bt/m 4 respectvely. The reshold dstance do s Fg. 4. nergy consumpton n WSN for N= P a g e

6 Fg. 5. FND, HND and LND for WSN for N=100. Fg. 6. Standard devaton of average energy consumpton per SN per ound n WSN for N=100. Fg. 7. Comparatve plot of roughput for N=100, 400 and The graph of number of operatonal nodes verses e number of rounds s shown n Fg. 4. It s clearly observed from e fgure at A-FCM remans operatonal for longer perod of tme as compared to LACH, M-LACH, MH- LACH and OCM-FCM algorms. At 900 round, e dead node percentage ncreases to 7, 10 and 14 for MH-LACH, M-LACH and OCM-FCM algorms respectvely whle t s 1% for A-FCM. Smlarly, when A-FCM lost ts 10% network operatonal ablty (.e. 10% nodes are dead), network n OCM-FCM and MH-LACH almost ceases to exst. Frst 10% nodes n M-LACH ded more quckly as compared to MH-LACH, OCM-FCM and A-FCM. Network n M- LACH stablzes to some extent after losng 20% of ts operatonal ablty. Ths shows A-FCM algorm performs far better an e oer algorms under test at each pont of network operaton. Percentage of total energy consumed n all e fve protocols has been presented n Fg. 5. It s observed from e fgure at e rate of energy consumpton n LACH s much hgher as compared to A-FCM, OCM-FCM, M-LACH and MH-LACH. nergy consumpton n OCM-FCM and MH-LACH s almost same. It s wor notng at OCM- FCM s transmttng cluster data to e snk n sngle hop whereas MH-LACH transmts n multple hops. Therefore, OCM-FCM clusterng technque s much better as compared to MH-LACH. At e pont of network operaton where A-FCM consumed ffty percent of ts network energy, OCM-FCM and M-LACH consumed 65% and 60% respectvely. Hence, energy usage of e network s more economcal n A-FCM as compared to OCM-FCM and M-LACH. From Fg. 4 and 5, t s clearly observable at dstrbuton of network load amongst all e SNs s more balanced whch wll enhance e network lfetme. ound value for FND (Frst Node Dead), HND (Half Node Dead) and LND (Last Node Dead) has great sgnfcance to evaluate e performance of network. The round value of FND ndcates e relablty and stablty of e network operaton. The round value of HND specfes rate of decay of performance of network after frst node become dead. The round value for LND ndcates e duraton for whch e network remans operatonal. Fg. 6 shows at FND, HND and LND for A-FCM s much hgher as compared to compared to OCM-FCM, MH-LACH, M-LACH and LACH algorms. In e proposed A- FCM algorm, data collecton, aggregaton and data forwardng s dvded amongst e CH and GW nodes of e cluster whch results n healer CH node for long perod of tme. Due to ese factors, energy usage n e network and decay n relablty of WSN operaton s reduced. The standard devaton of node s average consumpton per SN per round s shown n Fg. 7. Standard devaton value of average energy consumpton n a SN at any round ndcates load dstrbuton amongst all SNs n e network. Large value of standard devaton means at ere s large fluctuaton from mean of average energy consumpton n a SN. It ndcates at some nodes n e network consume large amount of energy as compared to oer nodes. OCM-FCM shows hgher value of standard devaton, when less number of nodes n e network are alve. It s wor notng e standard devaton of energy consumpton per SN n A s very small as compared to LACH, M-LACH, MH- LACH and OCM-FCM P a g e

7 Fg. 8. Percentage of dead nodes verses rounds at number of nodes s 400. e four hundred nodes for A-FCM are stll alve. When 15% nodes of A-FCM are dead, 90% nodes n M-LACH has already become dead. Furer, e slops of fve curves n Fg. 6 have been observed. It s clearly seen at e slop of curve for A-FCM s much less an oer two curves. Ths ndcates at e rate of nodes becomng dead s much slower n A-FCM. Lfetme of WSN for e proposed algorm s almost double an LACH, MH-LACH and OCM-FCM algorms. Furer, when number of nodes n WSN ncreased from 400 to 1000, smlar behavour of algorms has been observed. There s no mprovement n network lfetme of LACH, MH-LACH, M-LACH and OCM-FCM. Network lfetme of A-FCM s far mproved over oer two algorms. It s approxmately 4.5 tmes at of LACH, MH- LACH and OCM-FCM. Fg. 9. Percentage of dead nodes verses rounds at number of nodes s Therefore, network load s unformly dstrbuted amongst all SNs n A-FCM and consume almost equal amount of energy whch leads to more stable and relable network operaton. A comparatve graph of roughput for all fve algorms s presented n Fg. 8. OCM-FCM and M-LACH acqure less amount data from e SF as er nodes have lesser lfetme as compared to A-FCM. In A-FCM, SNs are not generatng redundant data from SF and reman alve for longer perod. Therefore, roughput of e network s mproved. Total roughput n case of AA-FCM s 7.6% hgher an M-LACH and 28% more an OCM-FCM. Thence, e proposed algorm shows a sgnfcant mprovement n roughput. Node densty of WSN may greatly affect SF coverage, network connectvty and network lfe tme. Therefore, t s an mportant factor at needs to be consdered whle evaluatng e performance of WSN. To verfy e effect of node densty on e performance of e network, addtonal smulaton experments are conducted by varyng e number of nodes, N, n e WSN deployments as N=100, 400 and Please note at N=400 n SF of 200m X 200m s equvalent 100 nodes n an area of 100m X100m. The percentage of dead nodes for varyng number of rounds has been shown n Fg. 4 (N=100) and Fg. 10 (N=400) and Fg. 11 (N=1000). Comparson for all ree algorm for N=100 has already been dscussed earler. Takng e dscusson furer, t s seen at when network node densty ncreased to 400, wn frst 1100 rounds, all nodes for LACH, MH-LACH and OCM-FCM become dead, but all Fg. 10. FND, HND and LND for WSN for Varyng Number of Nodes s 400. The network roughput obtaned n LACH, M- LACH, MH-LACH, OCM-FCM and A-FCM w varyng N s shown n Fg. 9 and Table I. It s observed, for N=100, OCM-FCM, M-LACH and A-FCM generate 96641, and data packets n ts lfetme, respectvely. There s a 63% mprovement n roughput of A-FCM over OCM-FCM for N=400. By varyng nodes from 400 to 1000, roughput obtaned n A-FCM s almost ree tmes as compared to OCM-FCM. The roughput of A-FCM s mproved n many folds compare to LACH and OCM-FCM w e ncrease n node densty. Fg. 11. FND, HND and LND for WSN for Varyng Number of Nodes s P a g e

8 The value of rounds for Frst Node Dead (FND), Half Node Dead (HND) and Last Node Dead (LND) for all algorms under examnaton at N=100, 400 and 1000 are shown n Fg. 10 and 11. For N=100, OCM-FCM mproves e round number for FND by 4% over MH-LACH. A- FCM mproves FND of MH-LACH furer by anoer 11%. The round number for HND s 694, 1171, 984, 977 and 1220 round n LACH, M-LACH, MH-LACH, OCM- FCM and A-FCM respectvely.m-lach mproves e round number for HND for OCM-FCM by 19%. A-FCM mproves t furer by 5%. The LND round number s almost same for LACH and OCM-FCM. The network lfetme for (IJACSA) Internatonal Journal of Advanced Computer Scence and Applcatons, A-FCM s mproved 58% over LACH. It s also observed at by changng N from 100 to 400, FND values of all ree algorms are mproved. A-FCM mproves HND value of OCM-FCM by 63%. Network lfetme for A-FCM s almost double as compared to e oer two algorms. For N=1000, ere s not much mprovement n LACH and OCM-FCM. The round number for FND, HND and LND n A-FCM s delayed by 182, 1396 and 2063 rounds respectvely as compared to er values at N=400. Therefore, network operaton got mproved n A-FCM on ncreasng e node densty. TABL I. LIFTIM AND THOUGHPUT FO VAYING NOD DNSITY N=100, 400 AND 1000 Algorm Lfetme Throughput Lfetme Throughput Lfetme Throughput Node Densty N=100 Node Densty N=400 Node Densty N=1000 LACH M-LACH MH-LACH OCM-FCM AA-FCM VI. CONCLUSION In s paper, an energy effcent algorm for WSN s presented. The results have been compared w LACH, MH-LACH, M-LACH and OCM-FCM algorms. From e outcome, t s concluded at lfetme and roughput of proposed algorm s 58% and 28% more as compared to e oer algorms under examnaton. Network load amongst all SNs s optmally dstrbuted n e proposed algorm. The rate of decay.e. e nodes becomng dead s also mnmum for e proposed algorm as compared to oer algorms. Therefore, t s concluded at A-FCM acheves better energy effcency and enhanced lfetme. 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