Broadcast Time Synchronization Algorithm for Wireless Sensor Networks Chaonong Xu 1)2)3), Lei Zhao 1)2), Yongjun Xu 1)2) and Xiaowei Li 1)2)
|
|
- Osborn Collins
- 5 years ago
- Views:
Transcription
1 Broadcast Tme Synchronzaton Algorthm for Wreless Sensor Networs Chaonong Xu )2)3), Le Zhao )2), Yongun Xu )2) and Xaowe L )2) ) Key Laboratory of Comuter Archtecture, Insttute of Comutng Technology Chnese Academy of Scences, Beng PR Chna, 8 2) Advanced Test Technology Lab, Insttute of Comutng Technolog Chnese Academy of Scences, Beng PR Chna, 8 3) Deartment of comuter scence, Hefe Unversty of Technology, Hefe PR Chna, 239 xu_chaonong@ct.ac.cn Abstract - We roosed a tme synchronzaton algorthm based on acet exchange mechansm for wreless sensor networs. A sngle-ho synchronzaton scheme was resented where ggybac technque and the least-squares lnear regresson technque were emloyed. Mult-ho synchronzaton scheme conssts of toology constructon algorthm, sngle-ho synchronzaton scheme and tme transformaton algorthm. Toology constructon algorthm constructs a breadth-frst sannng-tree. Sngle-ho synchronzaton scheme s emloyed on every sngle-ho area of the sannng-tree, and tme transformaton algorthm s to relate the tme of nodes n dfferent sngle-ho area. Smulaton results ndcate that n an 8-ho networ where every node has 8 neghbor nodes, the sngle ho synchronzaton error s about 6.86 mcroseconds on Mca2. Average synchronzaton error ncreases lnearly at the rate of 9.2 mcroseconds for one addtonal ho. To synchronze a mult-ho networ, 2n acets are needed n one synchronzaton cycle where n s the number of non-leaf node n the sannng tree. I. INTRODUCTION Recently, research nterest has been drawn towards wreless sensor networs (WSN). Tme synchronzaton [] s one of the basc servces of WSN. It can be used n many occasons such as tme-stamng sensor event, localzaton, energy-savng slee mode, data fuson and tme-dvson multlexng access to the shared wreless meda(tdma), etc. Many tme synchronzaton algorthms for WSN have been roosed over the ast few years [2][3][4][5][6][7]. They wll be dscussed n secton II. In ths aer, we resent BTS(broadcast tme synchronzaton) for wreless sensor networs. Based on acet exchange mechansm, t combnes the sender-recever synchronzaton scheme wth the recever-recever synchronzaton scheme. In sngle-ho networ, we conclude that ts synchronzaton error s the sum of two arts. One s the synchronzaton error caused by the sender-recever synchronzaton scheme, the other s the synchronzaton error caused by the recever-recever synchronzaton scheme. We mlement BTS and other two related algorthms on Smsync[8], a tme synchronzaton smulator whch smulates the tme character of Mca2 [9][]. The smulated results are also resented. We also extend BTS algorthm from sngle-ho to mult-ho networ. Mult-ho synchronzaton scheme conssts of three arts whch are the toology constructon algorthm, the sngle-ho synchronzaton algorthm and the tme transformaton algorthm whch s the most contrbuton of ths aer. The toology constructon algorthm constructs a breadth-frst sannng-tree. The sngle-ho synchronzaton algorthm s erformed n every sngle-ho area of the sannng-tree, and the tme transformaton algorthm s emloyed on the sannng-tree to relate the tme of nodes n dfferent sngle-ho area. BTS s ft for wreless sensor networs. Frst, only 2 acets are needed to synchronze a sngle-ho area n one synchronzaton cycle. So the energy consumton for tme synchronzaton s small. Second, BTS s a contnuous tme synchronzaton algorthm whch means that the local tme of node wll never be modfed. In many occasons such as energy-savng slee [], t s mortant to mae sure that the local tme of node s ndeendent wth the tme synchronzaton algorthm. Thrd, all algorthms n BTS are lghtweght n comlexty, even an eght-bt mcrocontroller wll suffce. The organzaton of ths aer s as follows: In secton II, we ntroduce the related wors. Secton III detals the BTS algorthm n a sngle-ho networ. Secton IV analyzes the synchronzaton error of BTS and some of ts features. Secton V exands BTS from sngle-ho to mult-ho networ. A toology constructon algorthm and a tme transformaton algorthm are also resented n ths secton. Secton VI resents the smulated results. The last secton s the conclusons of ths aer and future wors. II. RELATED WORKS Elson and Estrn roose RBS(reference broadcast synchronzaton) [3]. As shown by Fg., RBS s a tycal recever-recever synchronzaton algorthm [3], t excludes the tme uncertanty on sender. Fg. llustrates the crtcal ath of the sender-recever synchronzaton scheme and the receverrecever synchronzaton scheme. RBS(Reference Broadcast Synchronzaton) s a ar-wse synchronzaton algorthm n essence. So f all nodes n networ need to be synchronzed, ts energy consumton s tremendous. Furthermore, the reference node can not synchronze wth any other nodes. But the dea of recever-recever synchronzaton scheme s revolutonary. Many latter tme synchronzaton algorthms adot the dea. TPSN(Tmng-sync Protocol for Sensor Networs) [4] s a tycal sender-recever synchronzaton algorthm. It ndcates that synchronzaton error can be greatly decreased by stamng tme at MAC layer. HRTS(Herarchy Referencng Tme Synchronzaton Protocol) s not a ar-wse synchronzaton algorthm. Its synchronzaton scheme n sngle-ho networ s smlar to our BTS algorthm. So t wll be ntroduced n secton III.A. III. BTS IN SINGLE-HOP NETWORK
2 In ths secton, we ntroduce BTS algorthm n sngle-ho networ. Snce the synchronzaton scheme of BTS s derved from HRTS, we frst have an overvew of HRTS. Then we ntroduce ther dfference brefly. At last, BTS s ntroduced. Sender Recever Tme NIC Crtcal Path Sender Recever Recever2 Crtcal Path Fg.. Sender-recever and recever-recever synchronzaton Two terms have to be exlaned frstly, whch s the local tme and the estmated tme. Fg. 2 llustrates ther dfference. Counter ulse Local tme from other nodes Local tme Counter Local tme from other nodes Instantaneous Tme Synchronzaton Algorthm Contnuous Tme Synchronzaton Algorthm A. Contnuous synchronzaton Counter ulse Counter B. Instantaneous synchronzaton Fg. 2. Local tme and estmated tme The local tme s actually the value of tme counter whch ncreases wth counter ulse. If we loo tme synchronzaton algorthm as a system and the local tme as system nut, the estmated tme can be looed on as the system outut. Fg. 2.A llustrates ther relatonsh n contnuous tme synchronzaton algorthm. The estmated tme never nfluences the local tme. But for an nstantaneous tme synchronzaton algorthm as n the lower art of Fg. 2.B, at the begnnng of a synchronzaton cycle, the value of tme counter s relaced wth the estmated tme once the estmated tme s wored out by the nstantaneous tme synchronzaton algorthm. Snce then, the estmated tme s same to the local tme. Both of them wll ncrease wth counter ulse untl the next synchronzaton cycle. Its estmated tme s returned when a node s nqured about ts tme. So the duraton of synchronzaton cycle wll nfluence more heavly on nstantaneous tme synchronzaton algorthm than contnuous tme synchronzaton algorthm. A. HRTS n Sngle-ho Networ Fg. 3 llustrates the scheme of HRTS algorthm. When t s tme to synchronze, the tme reference node randomly aonts one of ts neghbours as the reler. Frst, t broadcasts a acet whch s referred as M, every of ts neghbours ncludng the reler records the arrval tme of M wth ther local tme and save t nto buffer. For examle, the reler records t as t2 and nodek records t as t2. Then, the reler wll return a acet to the tme reference node whch s referred as M2. In Fg. 3, t s the tme when M s ust transmtted, t4 s the tme when M2 s ust receved. Both of them are recorded NIC Estmated tme Estmated Tme = Local tme wth the local tme of the tme reference node. t2 s the tme when M s ust receved, t3 s the tme when M2 s ust transmtted. Both of them are recorded wth the local tme of the reler. t2 and t3 are contaned n M2 whch s send to the tme reference node by the reler. We assume that the roagaton delay of M and M2 are same and the tme offset between them s fxed durng the tme from t to t4. If we refer the roagaton delay of M as d, and the tme offset between the tme reference node and the reler s referred as d2. Snce the tme reference node has nown about the t, t2, t3 and t4 when t receves M2, so: Node The tme t t4 reference node The reler Node Node M M2 M3 t2 t2 t2 Fg. 3. Synchronzaton scheme of HRTS algorthm Tme t 2 = t+ d+ d2 t 4 = t3 + d d 2 d, d2 can be wored out as follows: d = ( t2 t + t4 t3) / 2 d 2 = ( t2 t + t3 t4) / 2 () If we refer the local tme of the tme reference node as T r, the local tme of the reler as T and the local tme of other nodes such as nodek as T. We can now that: T r = T - d2 (2) At last, the tme reference node broadcasts a message whch s referred as M3 wth t2 and d2 contaned n t. If we assume that all nodes receve M at the same tme, we can now that: T - T = t2 - t2 Combned wth equaton (2), t can be now that: T r = T + t2 - t2 - d2 (3) t2 - t2 - d2 s called comensated tme of nodek. NodeK can now adust ts local tme by addng comensated tme onto ts local tme. If K s equal to P, then t2 s same as t2, n that case, equaton (3) s equvalent to equaton (2). After adustment, the local tme of every node n the networ s equal to that of the tme reference node under assumton. We can thn that the whole networ s synchronzed at that nstant. B. BTS n Sngle-ho Networ In sngle-ho networ, BTS modfes HRTS n two asects. Frst, the t2 and d2 can be ggybaced n M of the next cycle. There are only two acets n one synchronzaton cycle. So when a node receves M, t not only records tme nformaton for ths cycle but also comutes the comensated tme for last cycle. Second, when the comensated tme s wored out, every node wll save t nto buffer nstead of adustng ts local tme mmedately. BTS then emloys the least-square lnear regresson technque to smooth the tter of synchronzaton error. So a lnear equaton whch descrbes the t3
3 tme relatonsh between tself and the tme reference node s setu. To descrbe the scheme of BTS n more formal language, we have some notatons: we refer the local tme of nodek as t ] [ when t sends M, [ t 2] when t receves M, [ t 3] when t sends M2, [ t 4] when t receves M2 n the th synchronzaton cycle. The cloc offset n the th synchronzaton cycle between nodek and the tme reference node s referred as[ d 2]. For convenence, we assume that the tme reference node s node. Stes n the th synchronzaton cycle are as follows: Ste: The tme reference node broadcasts M. It randomly aonts a neghbour node (assume t s nodek) as the reler for ths cycle, [ t2] and [ d 2] m of the (-) th synchronzaton cycle are also ggybaced n M (assume nodem s aonted as the reler n the (-) th synchronzaton cycle). At the same tme, the tme reference node records ts tme as [ t]. Ste 2: When one node(assume t s nodep) receves M, t records the arrval tme of M as [ t 2] wth ts local tme, t also comutes the comensated tme of the last cycle accordng to equaton (3), the value of the comensated tme s [ t 2] m [ t2] [ d2] m, for smlcty, we refer t as [ tc ]. Then the ont ( [ t 2], [ tc ] ) s saved nto ts buffer. The meanng of the ont s that when the local tme of nodep s [ t 2], t needs to add [ tc ] on ts local tme so as to synchronze wth the tme reference node. Ste 3: the nodek who s aonted as the reler for ths cycle sends M2 bac to the tme reference node wth [ t 2] and [ 3 contaned n t. t ] Ste 4: the tme reference node records the arrval tme of M2 as [ t 4]. It now wors out [ d 2] accordng to equaton (). When the nodep s nqured about the tme, ts estmated tme s returned. Stes are as follows: Ste : fnd the ont whose x-coordnate s the smallest n buffer. Notate the ont as ([ t 2],[ tc] ). Ste 2: construct a ont set {([ t 2] [ t2],[ tc ] [ tc] )} (for all n buffer) and do the least-squares lnear regresson on the constructed ont set. So lnear equaton y = x + b s constructed. We can now that b must be. Ste 3: f we refer the local tme of nodep as RT, then: = RT + ( RT [ t 2 ] ) + [ tc = ( + ) RT + [ tc ] [ t 2 ] ET ] = RT d = f RT ) P + ( (4) That s, nodep can estmate the local tme of node accordng to equaton (4). ET stands for the estmated value. Accordng to Fg. 2, t s also the estmated tme of nodep. We ont out here that ET may be unequal to the local tme of node n real crcumstance. Ther dfference s the synchronzaton error between nodep and node. We call and d as local sew and local offset of nodep resectvely. Equaton (4) means that the estmated tme of a node can be wored out based on ts local tme, local sew and local offset. Ths s the bass of the tme transformaton algorthm whch wll be dscussed latter. IV. ANALYSIS FOR BTS A. Error Analyss We now try to fnd what nfluences the synchronzaton error of BTS. For convenence, we assume that there are only three nodes n a sngle ho networ, ther ID number are, and 2 resectvely. We also assume that node s the tme reference node and the three nodes starts runnng at the same nstant and ther frequences are same at any nstant. So ther local tmes are comletely equal at any nstant. We now consder the nstant when M2 has ust been receved by node n th synchronzaton cycle. Assume that node s aonted as the reler n the cycle. It s obvous that the synchronzaton scheme between node and node s the sender-recever synchronzaton. It s same wth TPSN algorthm. There, the author declares that roagaton tme, receton tme are man erroneous factors. Of course, TPSN stams tme at MAC layer whle BTS does not, so the crtcal ath of BTS s from the alcaton layer of sender to that of recever. We refer the synchronzaton error caused by all comonents n the crtcal ath to beδ, (t can be ether ostve or negatve). So: RT = ET + δ (5), The synchronzaton scheme between node2 and node s the recever-recever synchronzaton. It s same wth RBS algorthm. There, the author declared that the receve tme on recever s man erroneous factor. We refer the synchronzaton error caused by t to be, 2 (t can be ether ostve or negatve). So: 2 ET = ET + (6),2 Accordng to equaton (5) and (6), So: 2 RT = ET + δ, + (7), 2 If e stands for the synchronzaton error between node2 and node, abs(x) s the functon whch gets bac the absolute value of the varable x, so: abs ( abs ( δ,) abs (,2 )) abs ( e) abs ( abs ( δ,) + abs (, 2 )) (8) From equaton (8), we can see that BTS s equvalent to RBS f δ, s zero. Furthermore, BTS s equvalent to TPSN f,2 s zero and tme-stamng at MAC layer. There exsts the nequalty between the reler and the non-reler n one synchronzaton cycle. For the reler, ts synchronzaton error s only nfluenced by the sender-recever synchronzaton scheme whle the synchronzaton error of the non-reler s nfluenced by both the sender-recever synchronzaton scheme and the recever-recever synchronzaton scheme. So n a sngle-ho networ, the average synchronzaton error wll ee ncreasng wth the number of node and ee aroachng a lmt whch t never reaches for ever. B. Other Analyss Both tme comlexty and sace comlexty of BTS algorthm are low. Both of them are O(n) where n s the sze of buffer. Furthermore, comutaton s no more than oeraton of
4 addton and multlcaton on nteger. Any 8-bt mcrocontroller suffces. The energy consumton of BTS s very low. Only 2 acets are transmtted for tme synchronzaton n each synchronzaton cycle. The oeraton of least-square lnear regresson wll not be done untl the node s nqured about ts tme. Snce the reler s randomly aonted n every cycle, the energy consumton wll be dstrbuted unformly n the networ. One fault of BTS s that the energy consumton of the tme reference node s tremendous. But n many alcatons, the sn node has suffcent energy suly. If the sn node s also the tme reference node, the fault can be overcome. Snce the synchronzaton error can not be decreased by the sze of buffer, buffer sze s not an mortant arameter. But t can smooth the tter of synchronzaton error n some degree. So f external envronment change fercely, the buffer sze should be set larger. In our exerment, t s set as 3. V. BTS IN MULTI-HOP NETWORK In ths secton, we extend BTS from sngle-ho to multho networ. Frst, a toology constructon algorthm s emloyed to construct a breadth-frst sannng-tree. Sngle-ho synchronzaton s erformed on every sngle-ho area n the sannng-tree. Second, a tme transformaton algorthm s ntroduced whch s emloyed to relate the tme of dfferent nodes whch belong to the dfferent sngle-ho area. A. Toology Constructon Algorthm It s obvous that the synchronzaton error of a node s hghly related to ts ho number to the tme reference node. So the am of the toology constructon algorthm s to construct a breadth-frst sannng-tree. Every node has a varable level to ndcate ts ho number to the tme reference node. Intally, ts value of the tme reference node s, and the value of other nodes are n+ where n s the number of node n networ. There s a feld named acet_level n M. When a node transmts M, t wll coy ts level nto the acet_level feld n M. When a node receves M, t wll test f the acet_level contaned n M s no less than the varable level. If yes, t wll gnore the message, otherwse, t wll udate ts level to be (acet_level+) and transmt another M. So, a tree herarchy toology s constructed wth the tme reference node as the root of the tree. B. Tme Transformaton Algorthm To clearly exlan the rncle of the tme transformaton algorthm, we tae an examle to descrbe ts dea. We tae Fg. 4 as examle. The sannng-tree s constructed by the toology constructon algorthm dscussed above. Assume that Node s the tme reference node. So: RT ET = (9) It s obvous that any node can drectly communcate wth ts son nodes n the sannng-tree. That s, a node and all of ts son nodes n the sannng-tree form a sngle-ho area. Based on t, we dvde the sannng-tree nto many sngle-ho areas where sngle-ho BTS algorthm can be drectly aled. Fg. 4 llustrates that there are 4 sngle-ho areas n the sannng-tree. Here, we focus on the tme of node, node, node2 and node3 to see how the tme transformaton algorthm wors. When we aly sngle-ho BTS algorthm on Area, node and node wll synchronze soon. If we don t consder synchronzaton error, we can now: ET = RT () Assume that s the local sew of node, d s the local offset of node. Accordng to (4), we can get the followng: ET = f ( RT) = d () By analogy, when we aly sngle-ho BTS algorthm on Area2 and Area4, we can get the followng: Area2 Area4 3 Fg. 4. A sannng-tree constructed by the toology constructon algorthm 2 RT ET = (2) 2 ET = f ( RT2 ) = 2 RT 2+ d 2 (3), 3 ET 2 = RT 2 (4) 3 ET 2 = f ( RT 3 ) = 3 RT 3+ d 3 (5) Accordng to (), (), we can get the followng: RT = d (6) Accordng to (), (), (2) and (3), we can get the followng: RT = 2 RT 2 + d 2 + d (7) Accordng to () to (5), we can get the followng: RT = 2 3 RT3 + 2 d 3 + d 2 + d (8) We recursvely defne and d as follows: = d = d + d = = = d = d (9) We have already referred to and as local sew and d local offset resectvely. Here, we refer to and d as global sew and global offset resectvely. Accordng to the recursve defnton, We now rewrte equaton (9),(6),(7) and (8) as follows: = RT + = RT d = d = RT + d + d = RT d = 2 RT2 + d 2 + d = 2 RT2 + d 2 + d = 2 RT2 d2 = 32 RT3 + 2d3 + d 2 + d = 32 RT3 + 2d3 d2 = 3 RT 3 + d 3 5 Area Area3 By analogy, for node, we can get the followng: RT = RT + d (2) So, f a node nows ts global sew, global offset, t can estmate the local tme of the tme reference node accordng to the local tme of ts own. Of course, global sew and global offset can be get recursvely. We now consder how to realze the algorthm. Every tme a node s about to send an M, t ggybacs ts global_sew and global_offset nto the M so that every of ts son nodes can construct ts own global_sew and global_offset based on equaton (9). The global_sew and global_offset of the tme reference node s and resectvely. We can see that the tme transformaton algorthm needs no any addtonal message. When a node s nqured about ts tme, t wors out
5 ts estmated tme of the tme reference node accordng to equaton (2) and returns t as results. VI. SIMULATION To test the erformance of BTS algorthm, we have develoed a tme synchronzaton smulator named Smsync. Secfc nformaton about Smsync can be got from [8]. In ths secton, we frst smulate BTS, RBS and HRTS algorthm n sngle-ho networ. Then, we smulate BTS algorthm n mult-ho networ. A. Smulaton n Sngle-ho Networ We frst realze BTS, RBS and HRTS n a sngle-ho networ on Smsync. 3 nodes named node, node and node2 are deloyed n a sngle-ho networ. Node s the tme reference node n BTS and HRTS. In RBS, node s the reference node. The synchronzaton cycle s set as seconds, the buffer sze s set as 3. For every algorthm, we smulate t for 5 mnutes. At the begnnng of every synchronzaton cycle, we nqure and record the tme of every node n sequence. At the end of smulaton, all these tme s exorted as a fle used for further analyss. So there are 3 tme tems for each node. For BTS and HRTS algorthm, based on the exorted fle, we wor out the dfference between node and node for every tme tem, the dfference between node2 and node for every tme tem s also wored out. The synchronzaton error s the mean of them. For RBS algorthm, we wor out the dfference between node and node2. The dfference s actually the synchronzaton error of RBS algorthm. The smulated results are lsted n Table I, there, µ s the mean of the synchronzaton error, σ s the standard devaton of the synchronzaton error. Ther unts are mcrosecond(µs).. s the number of messages used for tme synchronzaton n one synchronzaton cycle. N s the number of node n the networ. TABLE I SIMULATED RESULTS OF BTS, RBS AND HRTS Algorthm µ(unt: µs) σ(unt: µs) BTS RBS N(N-)+ HRTS Accordng to Table I, from the vew of synchronzaton error, RBS s the best, BTS taes the second lace and HRTS s the worst. The result s consstent wth our revous analyss. In secton IV, we have stated that the synchronzaton error of BTS must be larger than that of RBS. Furthermore, the synchronzaton scheme of BTS and HRTS are very ale, but HRTS s an nstantaneous synchronzaton algorthm whle BTS s a contnuous tme synchronzaton algorthm, so BTS wll not be nfluenced so heavly wth the duraton of synchronzaton cycle than HRTS. As to the message number n one synchronzaton cycle, RBS s the worst and BTS s the best. Based on the result, we can conclude that the ower consumton of BTS s the least. Table II s coed from [7], we can see that ts results of HRTS have great gas wth our smulated results, but ts results of RBS and ours are very ale. The reason s analyzed as follows: n [7], besdes a ublc wreless channel, every node has a secal wreless channel to drectly communcate wth the tme reference node. As n Fg. 3, the tme reference node broadcasts M and M3 usng the ublc channel so that all of ts neghbours can receve them, but the aonted reler reles to the tme reference node usng ts secal channel, so there s no wreless collson at all. But Smsync has already bult the wreless collson nto ts model. We have nown that wreless collson s the man cause of synchronzaton loss, so the results of HRTS from [7] must be better than our smulated results. As to the results of RBS, n [7], although every node has a secal channel, all messages have to be broadcasted usng the ublc channel, there must be some wreless collsons. So ts results of RBS and ours must be very ale. In a word, the results from [7] defend our results. TABLE II EXPERIMENT RESULTS FROM [7] Algorthm µ(unt: µs) σ(unt: µs) RBS HRTS B. Smulaton n Mult-ho Networ We also smulate the mult-ho BTS on Smsync. 2 nodes are deloyed alone a straght lne. Every node can only communcate wth ts last and next neghbour. The smulaton arameters and statstcal method are same to that of sngleho smulaton. We lst the detaled smulated results from ho to 8 hos n Table III. Fg. 5 llustrates the relaton between the mean of synchronzaton error and ho number. It also llustrates the relaton between the standard devaton of synchronzaton error and ho number. We can see that both the mean and the standard devaton of the synchronzaton error ncrease lnearly wth the ho number. Synchronzaton error ncreases at the rate of about 9.6 mcroseconds for one addtonal ho. TABLE III SIMULATED RESULTS OF MULTI-HOP BTS FOR LINEAR TOPOLOGY Ho number µ(unt: µs) σ(unt: µs) The lnear toology s not reresentatve. Snce every node has only one son node n ths toology, so n every sngle-ho area, the reler s fxed durng the smulaton. Accordng to, the synchronzaton error s only related to the sender-recever synchronzaton scheme and has no relaton wth the recever-recever synchronzaton scheme under ths toology. Another toology s smulated, the smulaton scenaro nvolves 8 nodes deloyed n a 9ⅹ9 grd n such way that each note can drectly communcated wth ts 8 neghbours. So t s a networ of 8 hos. The smulaton arameters and statstcal method are same to that of revous smulaton. We lst the detaled smulated results from ho to 8 hos n Table IV. Fg. 6 llustrates the relaton between the mean of synchronzaton error and ho number. It also llustrates the relaton between the standard devaton of synchronzaton
6 error and ho number. We can see that both the mean and the standard devaton of the synchronzaton error ncrease lnearly wth the ho number. Average synchronzaton error ncreases at the rate of about 9.2 mcroseconds for one addtonal ho. Accordng to Table IV, we can see that the synchronzaton error of BTS s about 6.86 mcroseconds n sngle-ho networ. Both Fg. 5 and Fg. 6 show that the mean and the standard devaton of the synchronzaton error ncrease lnearly wth the ho number. Comarng Table III wth Table IV, we can see that every tem n Table III s smaller than the corresondng tem n Table IV. Ths s caused by the nequalty between the reler and the nonreler n one synchronzaton cycle. For the lnear toology, every node s the reler. Synchronzaton Error(µs) Ho number Fg. 5. The relaton between synchronzaton error and ho number for lnear toology TABLE IV SIMULATED RESULTS OF MULTI-HOP BTS FOR NON-LINEAR TOPOLOGY Ho number µ(unt: µs) σ (unt: µs) Synchronzaton Error(µs) µ σ µ σ Ho number Fg. 6. The relaton between synchronzaton error and ho number for nonlnear toology VII. CONCLUSION AND FUTURE WORKS We have mlemented a lghtweght tme synchronzaton algorthm named BTS. Based on acet exchange mechansm, t combnes the sender-recever synchronzaton scheme wth the recever-recever synchronzaton scheme together. In sngle-ho networ, t emloys ggybac technque to reduce the number of acets necessary for tme synchronzaton. It buffers tme nformaton of acets and emloys the leastsquares lnear regresson technque on them to smooth tter of synchronzaton error. The sngle ho synchronzaton error s about 6.86 mcroseconds n an 8-ho networ where every node has 8 neghbour nodes. Average synchronzaton error ncreases at the rate of about 9.2 mcroseconds for one addtonal ho. In a sngle-ho networ, only 2 acets are transmtted n one synchronzaton cycle to synchronze all nodes. To synchronze a mult-ho networ n one synchronzaton cycle, 2n acets are transmtted where n s the number of sngle-ho cell n the sannng tree. n s also the number of non-leaf node n the sannng tree. It overcomes the demerts of both RBS and HRTS, and t s a retty trade-off between the synchronzaton recson and the energy consumton used for tme synchronzaton. We conclude that ts synchronzaton error s the sum of two arts. One art s caused by the sender-recever synchronzaton scheme. The other art s caused by the recever-recever synchronzaton scheme. To rove our conclusons, we mlement BTS and other two related algorthms on Smsync. The smulated result testfes our conclusons. To extent BTS from sngle-ho to mult-ho networ, we roose a tme transformaton algorthm. It needs no addtonal acets. We also fnd that both the mean and the standard devaton of a node s synchronzaton error ncrease lnearly wth ts ho number to the tme reference node. Our future wors are to realze BTS on Mca2, we beleve that n real envronment, some unexected henomena may occur when the networ s large enough. After all, to mantan the robustness and scalablty of BTS s an mortant tas. REFERENCES [] J. Elson and K. Römer, Wreless Sensor Networs: A New Regme for Tme Synchronzaton, ACM SIGCOMM Comuter Communcaton Revew, vol. 33, no., , Jan 23. [2] M. L. Schtu and C. Veerartthan, Smle, Accurate Tme Synchronzaton for Wreless Sensor Networs, n Proceedngs of IEEE Wreless Communcaton and Networng Conference (WCNC 23), New Orleans, LA, vol. 2,. 6-2, Mar 23. [3] J. Elson, L. Grod, and D. Estrn, Fne-Graned Tme Synchronzaton usng Reference Broadcasts, n Proceedngs of the ffth Symosum Oeraton System Desgn and Imlementaton (OSDI22), Boston, MA, , Dec 22. [4] S. Ganerwal, R. Kumar, and M. Srvastava, Tmng-Sync Protocol for Sensor Networs. n Proceedngs of the Frst ACM Conference on Embedded Networed Sensor Systems (SENSYS 23), Los Angeles, CA, ACM Press, , Nov 23 [5] J. V. Greunen and J. Rabaey, Lghtweght Tme Synchronzaton for Sensor Networs, n Proceedngs of the Second ACM Internatonal Worsho on Wreless Sensor Networs and Alcatons (WSNA 23), San Dego, CA,. -9, Set 23 [6] Mlos Marot, Branslav Kusy, Gyula Smon, and Aos Ledecz, The Floodng Tme Synchronzaton Protocol, n Proceedngs of the Second ACM Conference on Embedded Networed Sensor Systems (SenSys), Baltmore, MD, ACM Press, , Nov 24 [7] Hu Da, Rchard Han, TSync: A Lghtweght Bdrectonal Tme Synchronzaton Servce for Wreless Sensor Networs, ACM Moble Comutng and Communcatons Revew, vol. 8, no., , Jan 24 [8] Chaonong Xu, Le Zhao, Yongun Xu, Xaowe L, SmSync: An effectve Tme Synchronzaton Smulator for Sensor Networs, n Proceedngs of the Frst Internatonal Worsho on Sensor Networs and Alcatons (SNA 5), Beng, Chna,. 2-5, Oct.25. Avalable at: htt:// /subages/smsync.df. [9] Hll, J., and Culler, D, Mca: A Wreless Platform for Deely Embedded Networs, IEEE Mcro archve, vol. 22, no. 6,. 2-24, Nov 22 [] Mca2 and Mca2Dot: htt:// Networs. htm [] Fret Svraya and Bulent Yener, Tme Synchronzaton n Sensor Networs: A Survey, IEEE Networ vol. 8, no. 4,. 45-5, 24.
A Scheduling Algorithm of Periodic Messages for Hard Real-time Communications on a Switched Ethernet
IJCSNS Internatonal Journal of Comuter Scence and Networ Securty VOL.6 No.5B May 26 A Schedulng Algorthm of Perodc Messages for Hard eal-tme Communcatons on a Swtched Ethernet Hee Chan Lee and Myung Kyun
More informationFor instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1)
Secton 1.2 Subsets and the Boolean operatons on sets If every element of the set A s an element of the set B, we say that A s a subset of B, or that A s contaned n B, or that B contans A, and we wrte A
More informationParallelism for Nested Loops with Non-uniform and Flow Dependences
Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr
More informationTHE CONDENSED FUZZY K-NEAREST NEIGHBOR RULE BASED ON SAMPLE FUZZY ENTROPY
Proceedngs of the 20 Internatonal Conference on Machne Learnng and Cybernetcs, Guln, 0-3 July, 20 THE CONDENSED FUZZY K-NEAREST NEIGHBOR RULE BASED ON SAMPLE FUZZY ENTROPY JUN-HAI ZHAI, NA LI, MENG-YAO
More informationAdvanced LEACH: A Static Clustering-based Heteroneous Routing Protocol for WSNs
Advanced LEACH: A Statc Clusterng-based Heteroneous Routng Protocol for WSNs A. Iqbal 1, M. Akbar 1, N. Javad 1, S. H. Bouk 1, M. Ilah 1, R. D. Khan 2 1 COMSATS Insttute of Informaton Technology, Islamabad,
More informationE-DEEC- Enhanced Distributed Energy Efficient Clustering Scheme for heterogeneous WSN
21 1st Internatonal Conference on Parallel, Dstrbuted and Grd Comutng (PDGC - 21) E-DEEC- Enhanced Dstrbuted Energy Effcent Clusterng Scheme for heterogeneous WSN Parul San Deartment of Comuter Scence
More informationSimulation Based Analysis of FAST TCP using OMNET++
Smulaton Based Analyss of FAST TCP usng OMNET++ Umar ul Hassan 04030038@lums.edu.pk Md Term Report CS678 Topcs n Internet Research Sprng, 2006 Introducton Internet traffc s doublng roughly every 3 months
More informationLecture Note 08 EECS 4101/5101 Instructor: Andy Mirzaian. All Nearest Neighbors: The Lifting Method
Lecture Note 08 EECS 4101/5101 Instructor: Andy Mrzaan Introducton All Nearest Neghbors: The Lftng Method Suose we are gven aset P ={ 1, 2,..., n }of n onts n the lane. The gven coordnates of the -th ont
More informationComplex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following.
Complex Numbers The last topc n ths secton s not really related to most of what we ve done n ths chapter, although t s somewhat related to the radcals secton as we wll see. We also won t need the materal
More informationWireless Sensor Network Localization Research
Sensors & Transducers 014 by IFSA Publshng, S L http://wwwsensorsportalcom Wreless Sensor Network Localzaton Research Lang Xn School of Informaton Scence and Engneerng, Hunan Internatonal Economcs Unversty,
More informationLoad Balancing for Hex-Cell Interconnection Network
Int. J. Communcatons, Network and System Scences,,, - Publshed Onlne Aprl n ScRes. http://www.scrp.org/journal/jcns http://dx.do.org/./jcns.. Load Balancng for Hex-Cell Interconnecton Network Saher Manaseer,
More informationAlignment Results of SOBOM for OAEI 2010
Algnment Results of SOBOM for OAEI 2010 Pegang Xu, Yadong Wang, Lang Cheng, Tany Zang School of Computer Scence and Technology Harbn Insttute of Technology, Harbn, Chna pegang.xu@gmal.com, ydwang@ht.edu.cn,
More informationX- Chart Using ANOM Approach
ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are
More informationMaking Name-Based Content Routing More Efficient than Link-State Routing
Makng Name-Based Content Routng More Effcent than Lnk-State Routng Ehsan Hemmat and J.J. Garca-Luna-Aceves, Comuter Engneerng Deartment, UC Santa Cruz, Santa Cruz, CA 95064 PARC, Palo Alto, CA 94304 {
More informationAn Optimal Algorithm for Prufer Codes *
J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,
More informationOverview. CSC 2400: Computer Systems. Pointers in C. Pointers - Variables that hold memory addresses - Using pointers to do call-by-reference in C
CSC 2400: Comuter Systems Ponters n C Overvew Ponters - Varables that hold memory addresses - Usng onters to do call-by-reference n C Ponters vs. Arrays - Array names are constant onters Ponters and Strngs
More informationAssignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.
Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton
More informationSpeed of price adjustment with price conjectures
Seed of rce adustment wh rce conectures Mchael Olve Macquare Unversy, Sydney, Australa Emal: molve@efs.mq.edu.au Abstract We derve a measure of frm seed of rce adustment that s drectly nversely related
More informationTIME-EFFICIENT NURBS CURVE EVALUATION ALGORITHMS
TIME-EFFICIENT NURBS CURVE EVALUATION ALGORITHMS Kestuts Jankauskas Kaunas Unversty of Technology, Deartment of Multmeda Engneerng, Studentu st. 5, LT-5368 Kaunas, Lthuana, kestuts.jankauskas@ktu.lt Abstract:
More informationy and the total sum of
Lnear regresson Testng for non-lnearty In analytcal chemstry, lnear regresson s commonly used n the constructon of calbraton functons requred for analytcal technques such as gas chromatography, atomc absorpton
More informationModule Management Tool in Software Development Organizations
Journal of Computer Scence (5): 8-, 7 ISSN 59-66 7 Scence Publcatons Management Tool n Software Development Organzatons Ahmad A. Al-Rababah and Mohammad A. Al-Rababah Faculty of IT, Al-Ahlyyah Amman Unversty,
More informationS1 Note. Basis functions.
S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type
More information6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour
6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the
More informationCluster Analysis of Electrical Behavior
Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School
More informationPositive Semi-definite Programming Localization in Wireless Sensor Networks
Postve Sem-defnte Programmng Localzaton n Wreless Sensor etworks Shengdong Xe 1,, Jn Wang, Aqun Hu 1, Yunl Gu, Jang Xu, 1 School of Informaton Scence and Engneerng, Southeast Unversty, 10096, anjng Computer
More informationRelated-Mode Attacks on CTR Encryption Mode
Internatonal Journal of Network Securty, Vol.4, No.3, PP.282 287, May 2007 282 Related-Mode Attacks on CTR Encrypton Mode Dayn Wang, Dongda Ln, and Wenlng Wu (Correspondng author: Dayn Wang) Key Laboratory
More informationAnalytical Performance Analysis of Network- Processor-Based Application Designs
Analytcal Performance Analyss of Networ- Processor-Based Alcaton Desgns e Lu BMC Software Inc. Waltham, MA e Wang Unversty of Massachusetts Lowell, MA Abstract Networ rocessors (NP) are desgned to rovde
More informationThe Codesign Challenge
ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.
More informationRegion Segmentation Readings: Chapter 10: 10.1 Additional Materials Provided
Regon Segmentaton Readngs: hater 10: 10.1 Addtonal Materals Provded K-means lusterng tet EM lusterng aer Grah Parttonng tet Mean-Shft lusterng aer 1 Image Segmentaton Image segmentaton s the oeraton of
More informationSLAM Summer School 2006 Practical 2: SLAM using Monocular Vision
SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,
More informationFAHP and Modified GRA Based Network Selection in Heterogeneous Wireless Networks
2017 2nd Internatonal Semnar on Appled Physcs, Optoelectroncs and Photoncs (APOP 2017) ISBN: 978-1-60595-522-3 FAHP and Modfed GRA Based Network Selecton n Heterogeneous Wreless Networks Xaohan DU, Zhqng
More informationMachine Learning 9. week
Machne Learnng 9. week Mappng Concept Radal Bass Functons (RBF) RBF Networks 1 Mappng It s probably the best scenaro for the classfcaton of two dataset s to separate them lnearly. As you see n the below
More informationVirtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory
Background EECS. Operatng System Fundamentals No. Vrtual Memory Prof. Hu Jang Department of Electrcal Engneerng and Computer Scence, York Unversty Memory-management methods normally requres the entre process
More informationSkew Estimation in Document Images Based on an Energy Minimization Framework
Skew Estmaton n Document Images Based on an Energy Mnmzaton Framework Youbao Tang 1, Xangqan u 1, e Bu 2, and Hongyang ang 3 1 School of Comuter Scence and Technology, Harbn Insttute of Technology, Harbn,
More informationRational Ruled surfaces construction by interpolating dual unit vectors representing lines
Ratonal Ruled surfaces constructon by nterolatng dual unt vectors reresentng lnes Stavros G. Paageorgou Robotcs Grou, Deartment of Mechancal and Aeronautcal Engneerng, Unversty of Patras 265 Patra, Greece
More informationIndex Terms-Software effort estimation, principle component analysis, datasets, neural networks, and radial basis functions.
ISO 9001:2008 Certfed Internatonal Journal of Engneerng and Innovatve Technology (IJEIT The Effect of Dmensonalty Reducton on the Performance of Software Cost Estmaton Models Ryadh A.K. Mehd College of
More informationBayesian Networks: Independencies and Inference. What Independencies does a Bayes Net Model?
Bayesan Networks: Indeendences and Inference Scott Daves and Andrew Moore Note to other teachers and users of these sldes. Andrew and Scott would be delghted f you found ths source materal useful n gvng
More informationCompiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz
Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster
More informationA new Algorithm for Lossless Compression applied to two-dimensional Static Images
A new Algorthm for Lossless Comresson aled to two-dmensonal Statc Images JUAN IGNACIO LARRAURI Deartment of Technology Industral Unversty of Deusto Avda. Unversdades, 4. 48007 Blbao SPAIN larrau@deusto.es
More informationPerformance Evaluation of Information Retrieval Systems
Why System Evaluaton? Performance Evaluaton of Informaton Retreval Systems Many sldes n ths secton are adapted from Prof. Joydeep Ghosh (UT ECE) who n turn adapted them from Prof. Dk Lee (Unv. of Scence
More informationCourse Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms
Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques
More informationVideo Proxy System for a Large-scale VOD System (DINA)
Vdeo Proxy System for a Large-scale VOD System (DINA) KWUN-CHUNG CHAN #, KWOK-WAI CHEUNG *# #Department of Informaton Engneerng *Centre of Innovaton and Technology The Chnese Unversty of Hong Kong SHATIN,
More informationTraffic Classification Method Based On Data Stream Fingerprint
5th nternatonal Conference on Advanced Materals and Comuter Scence (CAMCS 6) Traffc Classfcaton Method Based On Data Stream Fngerrnt Kefe Cheng, a, Guohu We,b and Xangjun Ma3,c College of Comuter Scence
More informationPrivate Information Retrieval (PIR)
2 Levente Buttyán Problem formulaton Alce wants to obtan nformaton from a database, but she does not want the database to learn whch nformaton she wanted e.g., Alce s an nvestor queryng a stock-market
More informationAdaptive Energy and Location Aware Routing in Wireless Sensor Network
Adaptve Energy and Locaton Aware Routng n Wreless Sensor Network Hong Fu 1,1, Xaomng Wang 1, Yngshu L 1 Department of Computer Scence, Shaanx Normal Unversty, X an, Chna, 71006 fuhong433@gmal.com {wangxmsnnu@hotmal.cn}
More informationA Binarization Algorithm specialized on Document Images and Photos
A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a
More informationBase Station Location Protection in Wireless Sensor Networks: Attacks and Defense
Base Staton Locaton Protecton n Wreless Sensor Networks: Attacks and Defense Juan Chen, Hongl Zhang, Xaojang Du 2, Bnxng Fang, Yan Lu 3, Hanng Yu Research Center of Computer Network and Informaton Securty
More informationWishing you all a Total Quality New Year!
Total Qualty Management and Sx Sgma Post Graduate Program 214-15 Sesson 4 Vnay Kumar Kalakband Assstant Professor Operatons & Systems Area 1 Wshng you all a Total Qualty New Year! Hope you acheve Sx sgma
More informationProblem Set 3 Solutions
Introducton to Algorthms October 4, 2002 Massachusetts Insttute of Technology 6046J/18410J Professors Erk Demane and Shaf Goldwasser Handout 14 Problem Set 3 Solutons (Exercses were not to be turned n,
More informationSupport Vector Machines
/9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.
More informationCMPS 10 Introduction to Computer Science Lecture Notes
CPS 0 Introducton to Computer Scence Lecture Notes Chapter : Algorthm Desgn How should we present algorthms? Natural languages lke Englsh, Spansh, or French whch are rch n nterpretaton and meanng are not
More informationLife Tables (Times) Summary. Sample StatFolio: lifetable times.sgp
Lfe Tables (Tmes) Summary... 1 Data Input... 2 Analyss Summary... 3 Survval Functon... 5 Log Survval Functon... 6 Cumulatve Hazard Functon... 7 Percentles... 7 Group Comparsons... 8 Summary The Lfe Tables
More informationReal-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input
Real-tme Jont Tracng of a Hand Manpulatng an Object from RGB-D Input Srnath Srdhar 1 Franzsa Mueller 1 Mchael Zollhöfer 1 Dan Casas 1 Antt Oulasvrta 2 Chrstan Theobalt 1 1 Max Planc Insttute for Informatcs
More informationA Fast Visual Tracking Algorithm Based on Circle Pixels Matching
A Fast Vsual Trackng Algorthm Based on Crcle Pxels Matchng Zhqang Hou hou_zhq@sohu.com Chongzhao Han czhan@mal.xjtu.edu.cn Ln Zheng Abstract: A fast vsual trackng algorthm based on crcle pxels matchng
More informationA New Transaction Processing Model Based on Optimistic Concurrency Control
A New Transacton Processng Model Based on Optmstc Concurrency Control Wang Pedong,Duan Xpng,Jr. Abstract-- In ths paper, to support moblty and dsconnecton of moble clents effectvely n moble computng envronment,
More informationEfficient Content Distribution in Wireless P2P Networks
Effcent Content Dstrbuton n Wreless P2P Networs Qong Sun, Vctor O. K. L, and Ka-Cheong Leung Department of Electrcal and Electronc Engneerng The Unversty of Hong Kong Pofulam Road, Hong Kong, Chna {oansun,
More informationOptimum Number of RLC Retransmissions for Best TCP Performance in UTRAN
Optmum Number of RLC Retransmssons for Best TCP Performance n UTRAN Olver De Mey, Laurent Schumacher, Xaver Dubos {ode,lsc,xdubos}@nfo.fundp.ac.be Computer Scence Insttute, The Unversty of Namur (FUNDP)
More informationPriority-Based Scheduling Algorithm for Downlink Traffics in IEEE Networks
Prorty-Based Schedulng Algorthm for Downlnk Traffcs n IEEE 80.6 Networks Ja-Mng Lang, Jen-Jee Chen, You-Chun Wang, Yu-Chee Tseng, and Bao-Shuh P. Ln Department of Computer Scence Natonal Chao-Tung Unversty,
More informationHermite Splines in Lie Groups as Products of Geodesics
Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the
More informationCSCI 104 Sorting Algorithms. Mark Redekopp David Kempe
CSCI 104 Sortng Algorthms Mark Redekopp Davd Kempe Algorthm Effcency SORTING 2 Sortng If we have an unordered lst, sequental search becomes our only choce If we wll perform a lot of searches t may be benefcal
More informationI 2 C: A Holistic Approach to Prolong the Sensor Network Lifetime
: A Holstc Aroach to Prolong the Sensor Network Lfetme Yang Peng, Z L, Da Qao, and Wensheng Zhang Iowa State Unversty, Ames, IA 511 {yangeng, zl, da, wzhang}@astate.edu Abstract We resent a novel holstc
More informationConstructing Minimum Connected Dominating Set: Algorithmic approach
Constructng Mnmum Connected Domnatng Set: Algorthmc approach G.N. Puroht and Usha Sharma Centre for Mathematcal Scences, Banasthal Unversty, Rajasthan 304022 usha.sharma94@yahoo.com Abstract: Connected
More informationOn the Exact Analysis of Bluetooth Scheduling Algorithms
On the Exact Analyss of Bluetooth Schedulng Algorth Gl Zussman Dept. of Electrcal Engneerng Technon IIT Hafa 3000, Israel glz@tx.technon.ac.l Ur Yechal Dept. of Statstcs and Operatons Research School of
More informationII. RELATED WORK AND BACKGROUND
Comarson of Bluetooth Interconnecton Methods usng BlueProbe Sewook Jung, Alexander Chang, and Maro Gerla Deartment of Comuter Scence Unversty of Calforna, Los Angeles {sewookj, acmchang, gerla}@cs.ucla.edu
More informationSteps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices
Steps for Computng the Dssmlarty, Entropy, Herfndahl-Hrschman and Accessblty (Gravty wth Competton) Indces I. Dssmlarty Index Measurement: The followng formula can be used to measure the evenness between
More informationGSLM Operations Research II Fall 13/14
GSLM 58 Operatons Research II Fall /4 6. Separable Programmng Consder a general NLP mn f(x) s.t. g j (x) b j j =. m. Defnton 6.. The NLP s a separable program f ts objectve functon and all constrants are
More informationOverview. Basic Setup [9] Motivation and Tasks. Modularization 2008/2/20 IMPROVED COVERAGE CONTROL USING ONLY LOCAL INFORMATION
Overvew 2 IMPROVED COVERAGE CONTROL USING ONLY LOCAL INFORMATION Introducton Mult- Smulator MASIM Theoretcal Work and Smulaton Results Concluson Jay Wagenpfel, Adran Trachte Motvaton and Tasks Basc Setup
More informationA Low-Overhead Routing Protocol for Ad Hoc Networks with selfish nodes
A Low-Oerhead Routng Protocol for Ad Hoc Networks wth selfsh nodes Dongbn Wang 1, Xaofeng Wang 2, Xangzhan Yu 3, Kacheng Q 1, Zhbn Xa 1 1 School of Software Engneerng, Bejng Unersty of Posts and Telecommuncatons,100876,
More informationEmpirical Distributions of Parameter Estimates. in Binary Logistic Regression Using Bootstrap
Int. Journal of Math. Analyss, Vol. 8, 4, no. 5, 7-7 HIKARI Ltd, www.m-hkar.com http://dx.do.org/.988/jma.4.494 Emprcal Dstrbutons of Parameter Estmates n Bnary Logstc Regresson Usng Bootstrap Anwar Ftranto*
More informationA Base Station-Coordinated Contention Resolution for IEEE PMP Networks
A Base Staton-Coordnated Contenton Resoluton for IEEE 802.6 PMP Networks Wenyan Lu, Weja Ja,2, Wenfeng Du, and Ldong Ln 2 School of Informaton Scence & Engneerng, Central South Unversty, Changsha, 40083,
More informationDESIGNING TRANSMISSION SCHEDULES FOR WIRELESS AD HOC NETWORKS TO MAXIMIZE NETWORK THROUGHPUT
DESIGNING TRANSMISSION SCHEDULES FOR WIRELESS AD HOC NETWORKS TO MAXIMIZE NETWORK THROUGHPUT Bran J. Wolf, Joseph L. Hammond, and Harlan B. Russell Dept. of Electrcal and Computer Engneerng, Clemson Unversty,
More informationA Variable Threats Based Self-Organization Scheme for Wireless Sensor Networks
009 Thrd Internatonal Conference on Sensor Technologes and Applcatons A Varable Threats Based Self-Organzaton Scheme for Wreless Sensor Networks Jan Zhong School of Computer Scence and Informaton Technology
More informationProblem Definitions and Evaluation Criteria for Computational Expensive Optimization
Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty
More informationEvaluation of an Enhanced Scheme for High-level Nested Network Mobility
IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.15 No.10, October 2015 1 Evaluaton of an Enhanced Scheme for Hgh-level Nested Network Moblty Mohammed Babker Al Mohammed, Asha Hassan.
More informationA note on Schema Equivalence
note on Schema Equvalence.H.M. ter Hofstede and H.. Proer and Th.P. van der Wede E.Proer@acm.org PUBLISHED S:.H.M. ter Hofstede, H.. Proer, and Th.P. van der Wede. Note on Schema Equvalence. Techncal Reort
More informationUSING GRAPHING SKILLS
Name: BOLOGY: Date: _ Class: USNG GRAPHNG SKLLS NTRODUCTON: Recorded data can be plotted on a graph. A graph s a pctoral representaton of nformaton recorded n a data table. t s used to show a relatonshp
More informationConvolutional interleaver for unequal error protection of turbo codes
Convolutonal nterleaver for unequal error protecton of turbo codes Sna Vaf, Tadeusz Wysock, Ian Burnett Unversty of Wollongong, SW 2522, Australa E-mal:{sv39,wysock,an_burnett}@uow.edu.au Abstract: Ths
More informationResource and Virtual Function Status Monitoring in Network Function Virtualization Environment
Journal of Physcs: Conference Seres PAPER OPEN ACCESS Resource and Vrtual Functon Status Montorng n Network Functon Vrtualzaton Envronment To cte ths artcle: MS Ha et al 2018 J. Phys.: Conf. Ser. 1087
More informationTerm Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task
Proceedngs of NTCIR-6 Workshop Meetng, May 15-18, 2007, Tokyo, Japan Term Weghtng Classfcaton System Usng the Ch-square Statstc for the Classfcaton Subtask at NTCIR-6 Patent Retreval Task Kotaro Hashmoto
More informationACCURATE BIT ALLOCATION AND RATE CONTROL FOR DCT DOMAIN VIDEO TRANSCODING
ACCUATE BIT ALLOCATION AND ATE CONTOL FO DCT DOMAIN VIDEO TANSCODING Zhjun Le, Ncolas D. Georganas Multmeda Communcatons esearch Laboratory Unversty of Ottawa, Ottawa, Canada {lezj, georganas}@ mcrlab.uottawa.ca
More informationOn Some Entertaining Applications of the Concept of Set in Computer Science Course
On Some Entertanng Applcatons of the Concept of Set n Computer Scence Course Krasmr Yordzhev *, Hrstna Kostadnova ** * Assocate Professor Krasmr Yordzhev, Ph.D., Faculty of Mathematcs and Natural Scences,
More informationEfficient Caching of Video Content to an Architecture of Proxies according to a Frequency-Based Cache Management Policy
Effcent Cachng of Vdeo Content to an Archtecture of Proxes accordng to a Frequency-Based Cache Management Polcy Anna Satsou, Mchael Pateraks Laboratory of Informaton and Comuter Networks Deartment of Electronc
More informationMinimum Cost Optimization of Multicast Wireless Networks with Network Coding
Mnmum Cost Optmzaton of Multcast Wreless Networks wth Network Codng Chengyu Xong and Xaohua L Department of ECE, State Unversty of New York at Bnghamton, Bnghamton, NY 13902 Emal: {cxong1, xl}@bnghamton.edu
More informationDesign of Structure Optimization with APDL
Desgn of Structure Optmzaton wth APDL Yanyun School of Cvl Engneerng and Archtecture, East Chna Jaotong Unversty Nanchang 330013 Chna Abstract In ths paper, the desgn process of structure optmzaton wth
More informationView-Dependent Multiresolution Representation for a Height Map
Internatonal Journal of Innovaton, Management and echnology, Vol. 4, No. 1, February 013 Vew-Deendent Multresoluton Reresentaton for a Heght Ma Yong H. Chung, Won K. Hwam, Dae S. Chang, Jung-Ju Cho, and
More informationTransaction-Consistent Global Checkpoints in a Distributed Database System
Proceedngs of the World Congress on Engneerng 2008 Vol I Transacton-Consstent Global Checkponts n a Dstrbuted Database System Jang Wu, D. Manvannan and Bhavan Thurasngham Abstract Checkpontng and rollback
More informationOptimized Query Planning of Continuous Aggregation Queries in Dynamic Data Dissemination Networks
WWW 007 / Trac: Performance and Scalablty Sesson: Scalable Systems for Dynamc Content Otmzed Query Plannng of Contnuous Aggregaton Queres n Dynamc Data Dssemnaton Networs Rajeev Guta IBM Inda Research
More informationParallel matrix-vector multiplication
Appendx A Parallel matrx-vector multplcaton The reduced transton matrx of the three-dmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more
More informationOn the Two-level Hybrid Clustering Algorithm
On the Two-level Clusterng Algorthm ng Yeow Cheu, Chee Keong Kwoh, Zongln Zhou Bonformatcs Research Centre, School of Comuter ngneerng, Nanyang Technologcal Unversty, Sngaore 639798 ezlzhou@ntu.edu.sg
More informationVISUAL SELECTION OF SURFACE FEATURES DURING THEIR GEOMETRIC SIMULATION WITH THE HELP OF COMPUTER TECHNOLOGIES
UbCC 2011, Volume 6, 5002981-x manuscrpts OPEN ACCES UbCC Journal ISSN 1992-8424 www.ubcc.org VISUAL SELECTION OF SURFACE FEATURES DURING THEIR GEOMETRIC SIMULATION WITH THE HELP OF COMPUTER TECHNOLOGIES
More informationQoS-Based Service Provision Schemes and Plan Durability in Service Composition
QoS-Based Servce Provson Schemes and Plan Durablty n Servce Comoston Koramt Pchanaharee and Twtte Senvongse Deartment of Comuter Engneerng, Faculty of Engneerng, Chulalongkorn Unversty Phyatha Road, Pathumwan,
More informationReducing Frame Rate for Object Tracking
Reducng Frame Rate for Object Trackng Pavel Korshunov 1 and We Tsang Oo 2 1 Natonal Unversty of Sngapore, Sngapore 11977, pavelkor@comp.nus.edu.sg 2 Natonal Unversty of Sngapore, Sngapore 11977, oowt@comp.nus.edu.sg
More informationON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE
Yordzhev K., Kostadnova H. Інформаційні технології в освіті ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Some aspects of programmng educaton
More informationNAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics
Introducton G10 NAG Fortran Lbrary Chapter Introducton G10 Smoothng n Statstcs Contents 1 Scope of the Chapter... 2 2 Background to the Problems... 2 2.1 Smoothng Methods... 2 2.2 Smoothng Splnes and Regresson
More informationSome Advanced SPC Tools 1. Cumulative Sum Control (Cusum) Chart For the data shown in Table 9-1, the x chart can be generated.
Some Advanced SP Tools 1. umulatve Sum ontrol (usum) hart For the data shown n Table 9-1, the x chart can be generated. However, the shft taken place at sample #21 s not apparent. 92 For ths set samples,
More informationMaintaining temporal validity of real-time data on non-continuously executing resources
Mantanng temporal valdty of real-tme data on non-contnuously executng resources Tan Ba, Hong Lu and Juan Yang Hunan Insttute of Scence and Technology, College of Computer Scence, 44, Yueyang, Chna Wuhan
More informationOntology based data warehouses federation management system
Ontolog based data warehouses federaton management sstem Naoual MOUHNI 1, Abderrafaa EL KALAY 2 1 Deartment of mathematcs and comuter scences, Unverst Cad Aad, Facult of scences and technologes Marrakesh,
More informationA Geometric Approach for Multi-Degree Spline
L X, Huang ZJ, Lu Z. A geometrc approach for mult-degree splne. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 27(4): 84 850 July 202. DOI 0.007/s390-02-268-2 A Geometrc Approach for Mult-Degree Splne Xn L
More informationImprovement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration
Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,
More informationAn Approach for Congestion Control in Sensor Network Using Priority Based Application*
An Aroach for Congeston Control n Sensor Network Usng Prorty Based Alcaton* Md. Obadur Rahman Muhammad Mostafa Monowar Byung Goo Cho Choong Seon Hong Deartment of Comuter Engneerng, Kyung Hee Unversty
More information