Wreless Communcatons and Moble Computng Volume 2018, Artcle ID 1571974, 9 pages https://do.org/10.1155/2018/1571974 Research Artcle Informaton Transmsson Probablty and Cache Management Method n Opportunstc Networks Ja Wu, 1,2 Zhgang Chen, 2,3 and Mng Zhao 2,3 1 School of Informaton Scence and Engneerng, Central South Unversty, Changsha 410083, Chna 2 Moble Health Mnstry of Educaton-Chna Moble Jont Laboratory, Changsha 410083, Chna 3 School of Software, Central South Unversty, Changsha 410075, Chna Correspondence should be addressed to Ja Wu; jawu5110@163.com Receved 19 July 2017; Revsed 11 January 2018; Accepted 31 January 2018; Publshed 5 March 2018 Academc Edtor: Sabrna Gato Copyrght 2018 Ja Wu et al. Ths s an open access artcle dstrbuted under the Creatve Commons Attrbuton Lcense, whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly cted. In real network envronment, nodes may acqure the communcaton destnaton durng data transmsson and fnd a sutable neghbor node to perform effectve data classfcaton transmsson. Ths s smlar to fndng certan transmsson targets durng data transmsson wth moble devces. However, the node cache space n networks s lmted, and watng for the destnaton node can also cause end-to-end delay. To mprove the transmsson envronment, ths study establshed Data Transmsson Probablty and Cache Management method. Accordng to selecton of hgh meetng probablty node, cache space s reconstructed by node. It s good for nodes to mprove delvery rato and reduce delay. Through experments and the comparson of opportunstc network algorthms, ths method mproves the cache utlzaton rate of nodes, reduces data transmsson delay, and mproves the overall network effcency. 1. Introducton Opportunstc network s a type of multhop wreless network. It has emerged n recent years [1]. The key for dstngushng the features of opportunstc networks from ad hoc [2 4] s that an end-to-end path wll never occur. However, the unon of networks may present an end-toend path at snapshots over tme. The applcaton areas of opportunstc networks nclude mltary communcatons [5], nterplanetary networks [6], networks n underdeveloped areas [7], feld trackng [8], and dsaster rescue [9]. In opportunstc networks, the tradtonal algorthm paradgm for the Internet and ad hoc networks, where routng algorthms are computed based on topologcal nformaton, becomes nadequate. The frst approach to routng n opportunstc networks s a varaton of controlled floodng. All messages are flooded and are lmted by tme to lve, and then messages are delvered to ther destnaton. Ths approach contacts the node that s recevng the message durng floodng. Several advanced proposals have replaced topologcal nformaton wth hgher-level nformaton whle attemptng to lmt floodng cost. For socal network applcaton scenaros, data take up sgnfcant cache space devce because people use portable moble devces durng data transmsson and no sutable transmsson range target s respondng, whch eventually causes transmsson delay. On the one hand, a number of peces of awatng nformaton are stored n the devce. Some nformaton may be stored for a long tme wthout user acceptance and response status. On the other hand, new data s receved and emergency nformaton s released n real tme, because the cache space s bg, resultng n the storage of new data [10, 11]. To solve these problems, ths work presents an Informaton Transmsson Probablty and Cache Management (ITPCM) method based on node data nformaton cache. The algorthm s based on the node that can dentfy surroundng neghbors to evaluate nodes between the meetng probabltes, whch wll cache data dstrbuton adjustment, ensure the hgh probablty of node preferental access to data, and acheve the objectves of cache adjustment. Meanwhle, to avod deletng cached data, the cache task of the node s shared through the neghbor node wrtng method, and the effectve data shunt s performed.
2 Wreless Communcatons and Moble Computng The contrbutons of ths study are as follows: (1) By analyzng the relatonshp between nodes, the probablty of meetng the neghbor s evaluated. (2) The lst of nodes s sorted after evaluaton and node cache reconstructon. (3) Through the effectve cachng adjustment method, ths algorthm can mprove delvery rato, and then the delay of end-to-end data transmsson s reduced. 2. Related Works Research on opportunstc networks currently focuses on routng algorthms. Exstng routng algorthms can be used n dfferent areas through mprovement. Some methods adopted n opportunstc networks are as follows. Grossglauser and Tse [12] suggested a store-and-forward mechansm Epdemc algorthm that smulated the transmsson mechansm of nfectous dseases. In ths algorthm, two nodes exchange a message that s not stored by the other when they meet. Ths method s smlar to exclusve or transmsson (EX-OR) and allows the nodes to obtan addtonal nformaton. The route where a node reaches the destnaton node and transmts the message can be guaranteed to be the shortest by ncreasng network bandwdth and bufferng memory space. In real applcatons, however, congeston can occur n the message transmsson network as the number of nodes ncluded n the transmsson ncreases, gven that related resourcesnrealnetworksarelmted.inactualapplcatons, ths method cannot obtan good result due to the lmtaton of resources. Wang et al. [13] proposed the spray and wat algorthm based on the Epdemc algorthm. Ths algorthm conssts of two phases, namely, sprayng and watng. The source node ntally counts the avalable nodes around t for message transmsson and then transmts ts message to the nodes through sprayng. In the watng phase, the message s transmtted to the destnaton node through drect delvery to fulfll the transmsson process f no avalable node can be found durng the sprayng phase. Ths method s a modfed algorthm that mproves the flood transmsson feature of the Epdemc algorthm. Furthermore, the sprayng phase may waste source nodes f a huge number of neghbor nodes that consume consderable space exst n the source nodes. Hence, ths algorthm can cause the death of nodes by randomly oversprayng source nodes n several networks. Spyropoulos et al. [14] recommended the PRoPHET Algorthm. Ths algorthm mproves the utlzaton of a network by frst countng the avalable message transmsson nodes and then calculatng the approprate transmsson nodes to form message groups. Leguay et al. [15] establshed the MV algorthm based on the probablty algorthm. Ths algorthm calculates transmsson probablty based on records and statstcs n the meetng and area vstng processes of the nodes. Burgess et al. [16] presented the MaxProp algorthm based on array settng prorty. Ths algorthm features determnng the transmsson sequence accordng to a settled array prorty when two nodes meet. Ths method decreases the consumpton of resources and the effcency of the algorthm s mproved by arrangng a reasonable sequence for message transmsson. Leguay et al. [17] suggested the MobySpace algorthm. In ths algorthm, node groups or pars wth hgh relevance form nto a self-organzng transmsson area to realze optmal communcaton among nodes. Burns et al. [18] recommended the context-aware routng algorthm based on calculatng the transmsson probablty of the source nodes reachng the target nodes. Ths algorthm obtans the mddle node by calculatng the cyclc exchange transmsson probablty and then collects and groups messages to gude the mddle node n transmttng messages drectly to the node wth hgher transmsson probablty. Kavtha and Altman [19] presented the message ferry algorthm, whch refers to the groupng and transmsson of messages. Ths algorthm classfes and groups the messages collected by the source nodes that are gong to be transmtted and then counts the exstng transmsson traces for each ferry node n the network. The movement rule of ferry nodes can be acheved. The source node wll move to the ferry node automatcally durng message transmsson. Transmsson effects can be mproved by predctng the node movng trace n the algorthm. Ths paper dscusses and demonstrates the applcaton of opportunstc networks to socal networks based on the analyss and summary of related works. 3. System Model Desgn 3.1. Analyss Model of Node Connecton Status. The capablty toforwardandcachethemessagesconveyedbytheencounter node becomes robust when the node establshes a strong connecton. Accordng to the connectons, the runnng tme of the nodes n the network can be dvded nto connecton nterval tme and duraton. The followng analyses can be obtaned by examnng the state of the node. (1) It s assumed that the nodes n the network exhbt ndependent and dstrbutve propertes, and the moton state of the nodes s unaffected by the moton state of other nodes. Therefore, the connecton events n the nonoverlappng tme doman are ndependent of each other; the constrant condtons are expressed n P(C,j (t 1 )C,j (t 2 )) = P (C,j (t 1 )) P (C,j (t 2 )). (1) Among the constrants, P(C,j (t 1 )) and P(C,j (t 2 )) represent the connecton probablty between nodes and j at tmes t 1 and t 2,respectvely. (2) Gven that the nodes that meet other nodes can be descrbed as the average for Posson dstrbuton, the node of the connecton state s related to ts connecton strength and tme nterval montorng. In the tme nterval [t, t + Δt], the connecton probablty of node can be expressed n P (C (t, t + Δt)) =λδt+o(δt). (2) Among the constrants, C (t, t + Δt) ndcates that node has establshed a connecton n [t, t + Δt]. λ s the connecton strength, and o(δt) s the hgh-order nfntesmal of Δt.
Wreless Communcatons and Moble Computng 3 (3) The nodes contnuously establsh two or more connectonswthamnmalprobabltyeventatabreftmenterval, as shown n n=2 P n (C (t, t + Δt)) =o(δt). (3) In (1), (2), and (3), the connecton s establshed among the nodes for a random event at a gven perod, whch s equvalent to the number of Posson processes; then, unknown nodes establsh two connecton ntervals by exponental dstrbuton [5]. In addton, relevant studes show that the duraton of node connecton s subdvded [8]. Node connecton s establshed by the state analyss model. The node can be analyzed by the specfed message node to transfer servce capablty to estmate the message transmsson probablty and to provde the decson bass for forwardng and deletng a message. 3.2. Calculaton Method for Data Transmsson Probablty. The arrval strength of the node connecton reflects the strength of the node servce capablty. The duraton of node connecton demonstrates a robust randomness and s lmted by meda-sharng characterstcs. In addton, a lnk conflct s observed among the nodes. Therefore, the servce capablty of the node should fully consder the probablty of connecton arrval strength and flud connecton avalablty. The nodes n the network can be analyzed by the establshed analyss model of dstrbuted connecton status. The average connecton of node s determned at nterval tme t and can be establshed by the local record of connectons between N and the current system runnng tme T,asshown n t = T N. (4) Furthermore, the connecton of node can be obtaned from strength λ,asshownn λ = 1 t. (5) For node, connecton tme n s determned by three parameters, namely, the connecton setup tme T Cd (n), connecton broken tme T Cu (n), and connecton tme T (n), as expressed n T (n) =T Cd (n) T Cu (n). (6) Accordng to the hstorcal nformaton of local recordng, the average connecton duraton of node can be obtaned, as presented n T = N n=1 T (n) N = N n=1 (TCd (n) T Cu (n)). (7) N Apparently, the servce nodes wthn a gven tme nterval rate are drectly related to servce node number. The fast rate of servce nodes wth the same connecton strength can servce a consderable number of nodes, can cache a consderable number of messages, and can show the robust capablty of servce nodes. The servce rate of node can be connected to the average duraton of obtaned T,asprovded n μ = 1 T = N n=1 (TCd N (n) T Cu (n)). (8) The node connecton at tme t n the establshed state probabltes P 0 (t) and P 1 (t) n the off-state probablty through nature and dfferental equaton of K n the node queue model satsfes the followng: P 0 (t+1) = λ P 0 (t) +μ P 1 (t), P 1 (t+1) = μ P 1 (t) +λ P 0 (t). The connecton state of the node at any tme must have one establshed connecton and dsconnecton, namely, P 0 (t) and P 1 (t), respectvely, to satsfy the regularty, as expressed n 1 n=0 (9) P n (t) =1. (10) The densty of the network node dstrbuton s relatvely sparse, and the ntal tme node n the system changes from statc to a crtcal state. Hence, any node and other nodes are connected to a mnmal probablty event, and the avalablty s shown n P 0 (t) =P 1 (t) =0. (11) Accordng to the nodes that meet other nodes whch can be descrbed as the average for Posson dstrbuton, the node of the connecton state s related to ts connecton strength and tme nterval montorng [5]. Equatons (9), (10), and (11) dsplay the connecton probablty P 0 (t) of node connected at tme t,aspresentedn P 0 (t) = μ μ + e (λ +μ )t. (12) λ +μ λ +μ The operatng state of the node transton set S={0,1} ntegrates the elements wth the nteroperablty; thus, the statonary dstrbuton of P 0 (t) exsts. So, P 0 (t) = lm t P 0 (t) = lm t ( μ λ +μ + μ λ +μ e (λ +μ )t ). (13) Accordng to (3) (μ /(λ +μ ))e (λ +μ )t s hgh-order nfntesmal [8], from {1, }, (μ /(λ +μ ))e (λ +μ )t =0.So, the network at a steady state tme pont connecton may be
4 Wreless Communcatons and Moble Computng Send nformaton Cache n locaton Z R Informaton n locaton Cache for other nodes Z C Informaton for other nodes Receve nformaton Fgure 1: Cache for node. revealedntheoff-stateprobabltyp 0 (t),ncludngδ =λ /μ for the load ntensty of node,asshownn P 0 (t) = lm t P 0 (t) = lm = t ( 1 1+δ. μ λ +μ + μ λ +μ e (λ +μ )t )= μ λ +μ (14) A new node connecton at any tme should satsfy the basc condton that the node connecton s n a dsconnected state. Therefore, the connecton probablty of node P 0 at any tme n a dsconnected state s equvalent to the flud avalablty of connecton P 0,aspresentedn P 0 =P 0. (15) The servce force Se of node can be obtaned, as expressed n Se =λ P 0 = λ 1+δ. (16) The message delvery status s related to the relay node that transmts the message. The servce capablty of the relay node that has been stored n the message should be consdered when estmatng the delvery probablty of the message. In ths study, the relatve servce capablty of each node n the network can be obtaned accordng to the proposed method of node servce capablty, as provded n P d = n =1 (Se /Se max ). (17) n Se max denotes the maxmum node servce capablty, whch can be obtaned through nformaton nteracton among the nodes. Consderng the relatve servce capablty of relay nodes n the message, the delvery probablty P d can be obtaned, where n=n path, and path s the number of paths stored n the message transmsson. The large P d equates toahghprobabltyofmessagedelveryandtocontnuously reduced forwardng and storng of the message. In ths study, the basc prncple of network news transmsson s used by usng node communcaton opportuntes, nteracton n the news propagaton path among the nodes, and node nformaton servce capablty. After a bref convergencetme,thenodecanapproxmatelyobtanthe transmsson path of local news and the servce capablty of each node when the network s stable. 3.3. Informaton Delvery and Cache Management n Algorthm. The node n opportunstc network has a strong socal attrbute, whch not only needs to cache the nformaton t generates but also provdes cache and forwardng servces [20]. However, to ensure that the messages they generate are delvered successfully, nodes usually show a certan degree of selfshness; that s, the messages generated for themselves are gven hgher cachng prorty. When many news networks exst, the node easly obtans news of storage, carryng, and forwardng, and other nodes produce a fewer cache spaces for news dstrbuton, whch causes serous cache competton problem. Therefore, when desgnng the cache management strategy, you should consder the source of the message and cache the resource allocaton for the messages t and ts nodes generate. Generally, the news source node cache s not lost. The node cache s dvded nto local and cooperatve cachng areas, and the news of ther local communty produces storage node and other nodes generated message. Its structure s shownnfgure1. To mprove the robustness of the storage and forwardng processes and effectvely reduce the load, n vew of the news from dfferent parttons, the cooperatve cache replacement
Wreless Communcatons and Moble Computng 5 Node j Node Informaton n locaton j Copy Z R Copy Z R Informaton n locaton Transmt Z j (P d (t) <P jd (t)) Informaton for other nodes Transmt Z (P d (t) <P jd (t)) Informaton for other nodes Z C j Copy and transmt Z C Fgure 2: Cache storage structure dagram when nodes meet. source percepton of partton method s proposed for dfferent messages utlzng dfferent cache area replacement methods. The basc process of ths method s as follows: (1) In the ntal state, a hgher cache replacement and forward prorty s set for the local cache area messages, and lower prorty s set for the message n the cooperatve cache area. (2) When nodes meet, they perform cache replacement or message forwardng. The messages are coped or transferred from the local cache to each other on the bass of the mportance of the message. The structure s shown n Fgure 2. Nodes, j replcate the local cache area message sets Z R and Z j R andthenextractthecooperatvecacheareamessages Z C and Z j C f cache space s avalable. Then, the probablty of the target node nteracton of both sdes reaches news P d (t) and P jd (t), respectvely, and hgher probablty of messages s found n each other, that s, from Z R, Z j R, Z C,andZ j C, and separately extracted Z (P d (t) < P jd (t)) and Z j (P d (t) < P jd (t)). Fnally, the node copes the message n the local cache node to the other party n the buffer. If the other party stll has cache space, then node collaboraton wll cache the message drectly to the other party n the cooperatve cache area. Accordng to the above method, we propose a dstrbuted cache management method for nodes n the opportunstc network, and processes are as follows. Step 1. If the meetng node s the destnaton node of the message, then send the message drectly to the other node. Step 2. If the meetng node s not the destnaton node of themessageandhasfreecachespace,thenthecaches replaced accordng to the aforementoned cache substtuton method.peopleonbothsdesofthenodesonthebassofthe estmated acquston and message between the destnaton nodes encounter probablty. Combned wth the mportance of the news, prorty wll be on the nodes n the local cache wth hgher message coped to the other party on the mportance of the cooperatve cache area. If a cache space s stll avalable, then the two nodes n the cooperatve cache area wll contnue to be transferred to each other s cooperatve cache areas. Step 3. If the destnaton nodes s not news, then the node wll use the proposed dstrbuted cooperatve cachng transfer method for nodes wthn the local cache nformaton transfer whenthecachesfull.thenodeselectondynamccollaboraton node set frst determnes the collaboraton n the neghbor node. Then, on the bass of the message s mportance, mportantnewssbroadcastedtothecollaboratonnodeand the message from the local deleton. Step 4. In the dstrbuted cooperatve cachng transfer method, f the node s not wthn the scope of communcaton to fnd the rght collaboraton node, then the mportance of the message prorty deletes nodes wth low levels of local cache area mportant message. Step 5. Collaboratvely cache the return of messages. The nodes wth the collaboraton of the message are cached to store the node after meetng. If surplus cache space s avalable, then the local node at ths tme wll be the two node sdes wth the message meetng probablty of the destnaton node. If the other person has much tme, then the message s redrected to the local node, where the node deletes the message from ts cache. Otherwse, do nothng. From above, we may establsh an algorthm to explan ths method. In Algorthm 1, tme complexty s O(n). Becausemessages transmsson s the lst whch can be defned from 1 to n, we may compare wth spray and wat algorthm. In spray step, t must select neghbor sprayng and then store messages. The tme complexty s O(n log n). In Epdemc algorthm, messages can transmt drectly when they meet. So the tme complexty s O(n). 4. Smulaton 4.1. Smulaton Envronment. The smulaton adopts the tool Opportunstc Network Envronment smulator verson 1.56
6 Wreless Communcatons and Moble Computng Algorthm: Informaton Cache Management and Transmsson Input: node, node j, cache, cache j Output: message Lst, probablty P d Begn Create meetng lst K Whle node and node j meet If (node j. s TargetNode()) send message to nodej; Else f (!node j.stargetnode() and!cache sfull()and!cache j. sfull()) Accordng to evaluate P d and message mportance news; Copy message from cache to node j End else f End f If (!node j.sfull()) If (P (t) > P j (t)) Then transfer cache.getmessage(p (t))tocachej End f Else f (!node j.stargetnode() and node j.sfull()) then set locaton s node.selectcooperatonnodeset() broadcast some locaton of message to locaton.sneghbornode() cache.deletebroadcastmessage() If (locaton.sempty()) Then cache.deletesomemessage(p (t)) When node meet node j of transfer message End else f End f If (!cache.sfull()) Probablty P (t) =node.meetmessagetargetnode() Probablty P j (t) =nodej.meetmessagetargetnode() End f If (P (t) > P j (t)) Transfer cache j.getmessage(p j (t))tocache cache j. deletetransfermessage() Output Message End If End Algorthm 1: Informaton Transmsson Probablty and Cache Management (ITPCM) algorthm. totextntherealenvronmentandtoanalyzetheperformance n the ITPCM. Ths tool adopts dfferent moble models to descrbe the moble locus and to record data transmsson groupng and then accordngly adopts model of SPMBM (Shortest Path Map-Based Movement) n smulaton.inthsstudy,theitpcmscomparedwththefollowng classcal algorthms: (1) Epdemc-TTL routng algorthm (TTL = 60 mn). (2) Spray and wat routng algorthm (copy = 10). (3) Spray and wat routng algorthm (copy = 30). In addton, the smulaton adopts an open street map to edt cty maps. Dfferent parks, streets, and shops are establshed n the map. They can exhbt a real envronment. Fgure 3 s smulaton map. It adopts real map n Helsnk. The parameters can be settled based on the random models socal networks. The parameters adopted n the experment are set as follows. The smulaton tme s 1 hour to 6 hours, and the smulaton area s 4500 m 3400 m n the map. The nvolved nodes are 350. The transmsson pattern s broadcast, the maxmum transmsson area of each node s 10 m 2, and the sendng frequency of a data packet s 25 s to 35 s. The data packet type s random array. Moreover, a node consumesoneuntenergywhentsendsadatapacket.each node carres 10 data packets, and the transmsson pattern of nodes s a socal model. Furthermore, the transmsson speed of the node s 0.5 1.5 m/s, and the cache of each node s 5 MB. 4.2. Parameter Analyss. In opportunstc networks, delvery rato, overhead, and delay are very mportant parameters n research, because those parameters can decde network performance. Moreover, energy consumpton can judge how manydatacanbetransmttedbynodes.so,weselectthose parameters n smulaton. ITPCM s compared wth types of classcal algorthm mentoned n Secton 4.1 to verfy ts performance. Ths study focuses on the followng parameters:
Wreless Communcatons and Moble Computng 7 Fgure 3: Smulaton map n Helsnk. (1) Delvery rato: ths parameter refers to the probablty ofselectngarelevancenodenthetransmsson process. (2) Overhead on average: ths parameter shows the overhead between two nodes when nformaton s transmtted. (3) Energy consumpton: ths parameter records energy consumpton wth nodes n the transmsson process. (4) Average end-to-end delay: ths parameter concludes the delay of route seekng, watng delay n the data classfcaton queue, transmsson delay, and redelverng n MAC. 4.3. Smulaton Results. Fgure 4 shows the relatonshp between the delvery rato and the cache. As shown n Fgure 4, the ITPCM algorthm has the hghest delvery rato among all algorthms, reachng 0.72 0.95. Ths s because the ITPCM algorthm uses the node cache to share the task transmsson method. When ncreasng the node cache, the delvery rato of the algorthm s obvous. Ths method can ncrease the cooperaton between nodes. The spray and wat routng algorthm (copy = 30) has the lowest transmsson delvery rato, wth only 0.22 0.47. Ths s because the algorthm uses the flood form to carry out nformaton transmssontothenodeofthecommunty,resultngnthelossof a lot of nformaton. The page-ttl routng algorthm (TTL = 60 mn) and the spray and wat routng algorthms (copy = 10) mprove the delvery rato of the orgnal algorthm by ncreasng the condton of the nformaton transfer, makng thedelveryratomorethan50%.asshownnfgure4, the ITPCM algorthm greatly mproves the delvery rato of nformaton under the condton of ncreasng the cache. Fgure 5 shows the relatonshp between routng overhead and cache. In Fgure 5, the four algorthms receve a cache ncrease,andtheroutngoverheadsgreatlyreduced.inthe ITPCM algorthm, the routng overhead dropped from 210 to 23. Because enough cache by cooperaton nodes affords Energy consumpton (J) 100 95 90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0 10 15 20 25 Cache (Mb) 30 35 40 ICMT Epdemc-TTL routng algorthm (44, = 60 GCH) Spray and wat routng algorthm (=IJS = 10) Spray and wat routng algorthm (=IJS = 30) Fgure 4: Delvery rato and cache. data transmsson, then nformaton can be delvered by nodesverysoon.theroutngoverheadofthesprayand wat routng algorthm (copy = 30) s reduced from 260 to 150. The routng overhead of the spray and wat routng algorthm (copy = 10) decreased from 280 to 140. Because of overtransmsson, the reducton of the three mproved algorthms s extremely obvous, ndcatng that the larger the node cache, the smaller the node cost. The pn-ttl routng algorthm (TTL = 60 mn) s less than spray and wat routng algorthm,buttsalsoaffectedbythecache.asaresult,the node cache can effectvely mprove the routng overhead n the communty. Fgure 6 shows the relatonshp between end-to-end delay on average and cache. All nodes transport delays decrease as the cache ncreases. The pn-ttl routng algorthm (TTL = 60 mn) controls the tme nterval of transmsson nformaton from 254 to 76, and the spray and wat routng algorthm s (copy = 30) transmsson s bascally the same as the pn- TTL routng algorthm (TTL = 60 mn). The spray and wat routng algorthm (copy = 10) s reduced from 256 to 117 because of the fast packet tme frequency and low delay. The ITPCM algorthm reduces latency from 251 to 48, whch shows that the ncreasng node cache can effectvely mprove thenodetransmssondelay,especallyntheprocessofdata transmsson, by usng dynamc allocaton and adjustng the cache ITPCM, whch helps control nformaton transmsson delay further. Fgure 7 shows the relatonshp between energy consumpton and cache. Wth the ncrease of the cache, the reducton of energy consumpton of the ITPCM algorthm s gradually reduced, and the energy consumpton of the ITPCM algorthm s retaned by 45% durng the 6-hour communcaton tme. Because cooperaton can afford data transmsson, some nodes can exchange data and keep
8 Wreless Communcatons and Moble Computng End-to-end delay on average (s) 600 550 500 450 400 350 300 250 200 150 100 50 0 100 150 200 250 Tme (s) EWDCR Epdemc PRoPHET 300 350 400 Spray and wat Fgure 5: Overhead on average and cache. End-to-end delay on average (s) 325 300 275 250 225 200 175 150 125 100 75 50 25 0 10 15 20 25 30 35 40 Cache (Mb) ICMT Epdemc-TTL routng algorthm (44, = 60 GCH) Spray and wat routng algorthm (=IJS = 10) Spray and wat routng algorthm (=IJS = 30) Fgure 7: Energy consumpton and cache. Delvery rato 0.95 0.90 0.85 0.80 0.75 0.70 0.65 0.60 0.55 0.50 0.45 0.40 0.35 0.30 0.25 0.20 100 150 200 250 Tme (s) EWDCR Epdemc PRoPHET 300 350 400 Spray and wat Fgure 6: End-to-end delay on average and cache. watng status. So, those nodes may save energy and awat next msson. The spray and wat routng algorthm has the largest energy consumpton, because every node n ths algorthm has to transmt nformaton to all neghbors n the communty through sprayng, resultng n hgh energy consumpton. Specfcally, copy = 30 consumes more than 90 when the cache reaches 40 MB. The algorthm adopts the meetng and passng method and copes nformaton from a sngle copy, whch s better than the spray and wat routng algorthm n terms of energy optmzaton. 5. Concluson Ths work presents an Informaton Cache Management and Transmsson (ITPCM) algorthm based on node data nformaton cache. The algorthm s based on the node that can dentfy surroundng neghbors to evaluate nodes between the project probablty, whch wll cache data dstrbuton adjustment, ensure the hgh project probablty of node preferental access to nformaton, and acheve the objectves of cache adjustment. Meanwhle, to avod deletng cached data, the cache task of the node s shared through the neghbor node wrtng method, and the effectve data shunt s performed. In the future work, ths method can adapt to bg data envronment to solve the problem n transmsson. Parameter Symbols P(C,j (t)): The connecton probablty between nodes and j at tme t λ: The connecton strength T Cd (n): Connecton setup tme T Cu (n): Connecton broken tme T (n): Connecton tme K: Node queue lst P 0 (t): Node connected at tme t μ: Average value of locaton Se max : The maxmum node servce capablty Z R Z j R : Localcachearea Z C Z j C : Cooperatvecachearea. Conflcts of Interest The authors declare that they have no conflcts of nterest.
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