An Enhanced Link Availability Model for Supporting Multimedia Streaming in MANETs

Transcription

1 An Enhanced Link Availability Model for Supporting Multiedia Streaing in MANETs Min Qin and Roger Zierann Abstract As wireless connectivity is integrated into any handheld devices, streaing ultiedia content aong obile ad-hoc peers is becoing a popular application. One of the ajor challenges in obile streaing is the requireent that the link ust be continuously available for a period of tie to enable uninterrupted data transission and a sooth edia perforance. Hence, an accurate prediction of future link availability is very desirable. In our previous work, we provided an iterative algorith for estiating future link availability by using the rando walk odel. However, this algorith assues that all nodes current speeds and directions are uniforly distributed between [, v ax ] and [, π], respectively. With the help of GPS equipent, it is possible for obile hosts to accurately calculate their current velocity. In this paper, we provide an enhanced link availability odel based on our previous approach by utilizing the current velocity inforation of obile nodes. To deonstrate the advantage of our odel, we copared it with two other link availability odels in selecting the best choice aong a nuber of sources. Siulation results show that our odel can reduce the nuber of link breaks and achieve sooth streaing experiences aong obile peers. Index Ters Link availability, Mobile ad-hoc networks, Mobility odels, Stationary regie, Streaing W I. INTRODUCTION ITH the widespread use of PDAs and other handhelds, streaing ultiedia content between obile ad-hoc peers is becoing a popular application. Songs, video clips and pictures can all be streaed fro one user to another if they get close to each other. Exeplar systes that ipleent such ideas include Bubbles [1], Mercora [16] and MStrea [13, 1]. Most of the recent handheld devices can operate via 8.11 wireless networks, which provide adequate bandwidth and counication range of ore than 1 eters. Therefore, streaing aong obile ad-hoc peers is possible in any situations, for exaple between two patrons visiting a useu. One challenge in sharing ultiedia content aong obile peers is to deliver the content, usually large in size, over a dynaic peer-to-peer based network where link availability is constantly changing. To achieve a sooth edia perforance, link reliability is of crucial iportance to edia streaing in obile ad-hoc networks. Since wireless streaing applications require two peers to be continuously Manuscript received January 31, 6 connected for a period of tie, it is very helpful if we can provide an accurate analysis on future link availability. When a obile user finds that another user in her vicinity has a song she is interested in, the probability that she can strea that song before the link breaks up is of special interest to her. For exaple, a typical MP3 usic file is 3 MB in size. If the available bandwidth is 56 kbps, a link has to be continuously available for 1 seconds for a song to be successfully streaed. If the file is available fro ultiple peers, the user should select the source that has the highest probability to stay connected for 1 seconds. A Content holder C Requester B Content holder Fig 1. Multiedia content sharing aong PDAs. Current node oveent As shown in Fig. 1, the link between A and C and that between B and C are constrained by C s counication range. If both peer A and B have the file that C requested, it is better to choose peer A as the source since A and C are oving in a siilar direction. Therefore, providing an accurate prediction of continuous link availability in the future can help obile ad-hoc peers deterine hand-offs or select the best choice aong a nuber of sources. It is also helpful to any areas in obile ad-hoc networks, for exaple: 1) audio/video streaing between obile peers, ) file sharing in wireless pp networks, 3) serving as a basis for existing clustering protocols in wireless ad-hoc networks, and 4) iproving existing ad-hoc routing protocols such as DSR[1] and AODV[17]. Due to the unpredictability of huan oveent, coputing future link availability is a very challenging proble. In our previous work [1], we introduced an iterative ethod for predicting future link availability based on the rando walk obility odel [14, 15]. This algorith assues that all obile nodes current speeds and directions are uniforly

2 distributed between [, v ax ] and [, π], respectively. However, the result is unintuitive, naely that that all links have the sae future link availability as long as v ax and the initial distance between the two nodes reains unchanged. The current velocity of two obile hosts does have a great ipact on future connectivity between the. Therefore, if either one or both nodes current velocity is known, this algorith ay not be very accurate in predicting the future link status. With the availability of inexpensive global positioning syste (GPS) [6] receivers, ore and ore obile clients are able to estiate their velocity accurately. For exaple, the Dell Axi X51v PDA includes a GPS receiver and sells for $5 dollars. In addition to GPS, Received Signal Strength (RSS) [5, 18], Tie of Arrival (ToA) [19, 4] and acoustic sensing [7] can all be used to estiate the location and velocity of a obile host. Hence it is very helpful if we can take such inforation into consideration to copute a ore accurate estiation of future link availability. In this paper, we extend the result of our previous work to the scenario when either one or both nodes current velocities are known. Copared with the original algorith, this new odel proves to be ore accurate in predicting future link availability. Like our previous iterative algorith, our new link availability odel is based on the rando walk obility odel. In reality, people do walk soewhat unpredictably in environents such as useus, crowded areas, etc. However, our work can be easily extended to other obility odels. To the best of our knowledge, there have been few studies on accurately predicting future link availability over any period of tie. Furtherore, our study can be extended to analyze path availability in obile ad-hoc networks. The paper is organized as follows. In Section II, related work is introduced. Section III describes the assuptions we ade for our study. Details of our link availability odel are introduced in Section IV and Section V presents its perforance. Section VI copares our work with other link availability odels. Finally, we suarize our contributions and provide suggestions for further research in Section VII. II. RELATED WORK There are nuerous studies on link and path durations in obile ad-hoc networks (MANET). Many of the are based on the rando waypoint odel []. According to [7], the rando waypoint odel is considered harful because it suffers fro speed decay and non-unifor node distribution. In [3], the authors presented a nuerical study on link and path duration with different obility odels. In [6], the authors validated the results of [3] by using the pal calculus [3]. Both studies analyze the behavior of the network when it has reached the stationary regie, which eans the network has run for a long tie and node distribution is likely to be fixed [11]. Therefore, these approaches analyze the average link or path duration. Also, none of these works predict the future link availability by taking the present node location and velocity into consideration. Therefore, it is inappropriate to apply these odels to edia streaing applications that often last for a certain period of tie. A siilar analysis on path availability is given in [4, 8]. Iproveents for existing ad-hoc network protocols by utilizing obility inforation of obile hosts are introduced in [1,, 5]. The authors predict the disconnection tie by utilizing the current location and velocity inforation of the nodes. All of these techniques assue that when two nodes becoe neighbors, they will not change their current velocity before the link breaks up. However, this is unrealistic since obile nodes do change their velocity frequently in any situations. In [14, 15], the authors introduced a probabilistic odel for predicting the future status of a link. Their work is based on the rando walk obility odel. In their odel, link availability is defined as the probability that a link is available at tie t +t given that the link is available at t. This definition considers the link as available even if it has experienced disconnections during the interval [t, t +t]. Consequently, their work is not very useful for streaing applications, which require the link to be continuously available over a period of tie. An analysis of continuous link availability is given in [8, 9]. In [9], the author first estiates a tie period T p that the current link will last if both nodes of the link keep their current oveent. Then a statistical ethod for estiating the possibility L(T p ) that the link can last fro t to t +T p is given. In [8], the authors iprove their ethod in [9] by separating the estiation of two statistical constants. Because a link with high L(T p ) ay have a very sall T p value, the authors use T p L(T p ) as a etric to evaluate the reliability of a link. However, for a given t that is not equal to T p, this approach cannot copute the continuous link availability fro t to t +t. In [1], we introduced an iterative algorith to predict L(d,t), which is of special interest to streaing applications. For exaple, if a song requires seconds to strea, it is ore inforative to know the probability that the link can last for seconds. This algorith akes it possible to use L(d,t) directly as a etric in evaluating the reliability of a link. The algorith assues that all obile nodes are unable to estiate their current velocity. Therefore, each node s current speed and direction are randoly distributed between [, v ax ] and [, π], respectively. Experiental results show that our algorith can accurately estiate the future link status with an error argin lower than 7%. Based on this rando initial velocity odel, we extended it to the case that the current relative velocity between the two nodes can be estiated. However, there are any possible cobinations for both obile nodes velocities given the relative velocity between the. As a result, our odel shows the average link availability given the current relative velocity. Siulation results show that this relative velocity odel works well only when the initial relative speed is low. In this paper, we introduce an algorith that can predict future link availability given the current velocity of either or both of the nodes. Copared to the results of [1], our new algorith is ore accurate if obile nodes are able to calculate their velocity through signal strength or GPS systes.

3 III. PROBLEM STATEMENT For siplicity, we assue that the two obile peers of a given link have the sae radio coverage, which is a perfect circle of radius R. The initial distance between the two obile nodes is approxiated by ( R). An accurate estiation of can be obtained through Received Signal Strength (RSS) [5, 18], Tie of Arrival (ToA) [19, 4] and acoustic sensing [7] or GPS, if applicable. As shown in Fig., let v 1 and v represent the current velocities of node n and. In [1], we provided an iterative algorith for predicting the probability L(,t) that the link will be continuously available fro t to t +t given that v1 and v are unknown. For a given t, our siulation in [1] shows that L(,t) does not differ a lot for different values. This is due to the fact that nodes can either ove towards or ove away fro each other. In this paper, we extend our calculation of L(, t) to L( d, t, v 1) and L( d, t, v1, v ), which are the link availability if either or both nodes current velocities can be estiated. Since R, we have Ld (,, v1) = 1 and Ld (,, v1, v ) = 1. λ does not change its velocity within t seconds is n t λ e or t e, respectively. According to [8], the link duration between n and can be divided into a nuber of link epochs. Each link epoch is a tie period during which both n and do not change their velocities. The distribution of each link epoch is also exponential with ean λ -1, where λ =λ n + λ. Therefore, the possibility that there is no change in velocities to this link λt within t seconds is given by e. IV. CALCULATION OF CONTINUOUS LINK AVAILABILITY A Known Velocity of Single Peer We begin with the case that one node s current velocity is known. In Fig. 3, for exaple, node n has GPS equipent and is able to estiate its current speed v 1 and direction. Therefore, our goal is to give an accurate estiation of Ld (, tv, 1), which is the probability that both nodes will be continuously connected during [t,t +t] given that the current velocity of node n is v 1. n V 1 r d(t) R n V Fig.. The current link status between node n and. We assue the following properties between two obile ad-hoc peers: 1) Battery power can support the wireless counication for a long tie. ) Two nodes can counicate with each other as long as they are within each other s transission range. 3) The wireless bandwidth is enough to support streaing applications, regardless of signal attenuation. To odel the oveent of a obile node, we use the rando walk odel as introduced in [14]. Copared with the rando waypoint odel [], the rando walk odel does not suffer fro speed decay and non-unifor node distribution. However, our odel can be easily extended to other obility odels. According to [14], a node's oveent is divided into a sequence of intervals called obility epochs. Each epoch is a rando period of tie that is exponentially distributed with ean λ n -1. During each obility epoch, a node oves with a constant speed and direction. The speed is a rando variable uniforly distributed between [, v ax ] and the direction is uniforly distributed over [, π]. Thus given a large ean epoch length, a node is likely to go straight for a long period of tie. How to calculate v ax and λ n -1 is beyond the scope of this paper. Let λ n -1 and λ -1 denote the ean epoch lengths of two independent obile nodes n and. The possibility that n or ω(t) r n Fig. 3. Link availability if the current velocity of node n is known. Let v and θ denote the current speed and direction of node. Because s current velocity is unknown, v and θ are randoly distributed between [, v ax ] and [, π], respectively. Let Ln_variablev ( d, t, v 1 ) and Ln _ fixedv( d, t, v 1) denote the probability that and n will be continuously connected for t seconds given that n changes or does not change its current velocity during [t, t +t], respectively. As we have discussed in Section 3, the probability that n does not change its velocity within t seconds is given by e λ n t. Therefore, the continuous link availability L( d, t, v 1) is given by: λ (,, ) nt λ (,, ) (1 nt Ld tv = e L d tv + e ) L ( d, tv, ) (1) 1 n _ fixedv 1 n_variablev 1 1) Estiation of Ln_ fixedv( d, t, v1) Siilar to the calculation of L(, t, v 1 ), we divide the calculation of L _ () t into two cases: a) keeps its n fixedv velocity during [t, t +t] and b) changes its current velocity during [t, t +t]. Since n does not change its current velocity, as shown in Fig. 3, it will ove to n after t seconds. The distance between the current position of and n is given by v 1 dt () = d + vt 1 + dvt 1 cosθ1 ()

4 a) keeps its current velocity during [t,t +t]. In order for and n to be continuously connected, has to be within the shaded region shown in Fig. 3 at t +t. Therefore, we have ( d + vt 1 cosθ1 vtcos θ) + ( vt 1 sinθ1 vtsin θ) R Fro the above equation, we get () + sin( θ + α) d t v t R vtdt ( ) where α is deterined by sin α = vt 1 sin dt () (3) and + 1 cosθ cosα = d vt 1. Since θ is randoly distributed between dt () [, π], for a given v, the probability that Equation (3) can be d ( t) v t R satisfied is arcsin(ax(in(,1), 1)). π vtdt ( ) Therefore, the chance that and n are continuously connected in this case is given by vax d () t + v arcsin(ax(in( t R,1), 1)) dv 1 ( ) 1() v vtdt L t = = πvax b) changes its velocity during [t, t +t]. We use the following ethod to approxiate the probability in this case. Let the rando obility vector R () t represent the distance and direction of node s position at tie t +t with respect to its position at tie t. According to [15], R () t is approxiately Raleigh distributed with a phase uniforly dispersed between [, π]. Because the axiu distance node can travel in t seconds is v ax t, we extend the result of [15] by conditionally liiting the distance range. Thus we have Where r r α f( R ( ) = ax ) = t r r v t e αη() t ( t / )( ) α = λ δ + µ and η (4) (5) r vaxt r () t = e α dr. r= α Here δ and µ are the ean and variance of node s speed. As shown in Fig. 3, has to be within the shaded region at t +t in order to be connected with n. Therefore, we have ax( dt ( ) R,) R ( t) dt ( ) + R. Given R(t)=r, the probability that is within the shaded region is constrained by the angle ω(t) shown in Fig. 3. We have 1 r + d () t R cos, dt ( ) R < r rd( t) ω() t = π,< r < R d( t),< r < d( t) R As a result, the probability that is within n s range at t +t is (6) r ax( dt ( ) + Rv, axt) 1 L () t = re α ω() t dr (7) παη () t r= Here the calculation of L () t includes the possibility that the link is broken at t +s (s t) and is recovered afterwards. However, when t is sall, (1 e λ t ) is close to ; when t is ( r / α ) large, e is close to. As a result, we can regard the chance that leaves the counication range of n and then coes back as sli. Because the probability that does not change its velocity within t seconds is given by e λ t, we have: L ( d, t, v ) e L ( t) (1 e ) L ( t) λt λt n _ fixedv 1 = 1 + ) Calculation of Ln_variablev( d, t, v1) Now we consider the case that n changes its velocity during [t, t +t]. Suppose node n first changes its velocity at tie t +s(s t). Because both n and s velocities are rando after t +s, we can use our previous result in [1] to calculate the link availability during [t +s, t +t]. As indicated in [1], when the current velocities of both peers are rando, different initial distances do not have a big ipact on future link availability. This is due to the fact that nodes can either ove towards or away fro each other. As a result, we can calculate the expectations of the probability that the link is continuously available for t-s seconds. We have 1 prob() t = rl( r,) t dr R R r= where L(r,t) is the result of our iterative algorith in [1]. Because n does not change its velocity before t +s, the probability that the link is continuously available during [t, t +s] is given by Ln _ fixedv( d, s, v 1). Therefore, we have n n_variablev 1 t s s λn λ = n _ fixedv 1 L ( d, t, v ) = e L ( d, s, v ) prob( t s) ds (1) B Known Velocity for Both Nodes When both v 1 and v are known, we can extend our result fro Section A to derive a ore accurate prediction of future link availability. If neither node changes its velocity during [t, t +t], the distance between n and at t +t will be: D() t = ( d + v1tcosθ1 vtcos θ) + ( v1sinθ1 vsin θ) t (11) Let T p denote the tie at which the link between and n will break if both of the keep their current velocities. T p can be easily obtained by solving the equation D(T p )=R. Let L (t) represent the probability that both peers are continuously connected and there is no velocity change to the link during [t, t +t]. The possibility that there is no velocity change (8) (9)

5 (λ + λ ) within t seconds is e n. Therefore, L(t) is equal to (λ n + λ ) t e when t Tp or when t>t p. If there is at least one velocity change to the link during [t, t +t], we can divide t into two parts s(s t) and t-s so that the first velocity change takes place at t +s. According to the property of a Poisson process [], the probability that or n changes its velocity first is given by λλ and λn λ, respectively. Because the link has to be continuously available fro t to t +t, we have s T p. At t +s, one node keeps its velocity and the other node randoly changes its velocity. As a result, the situation at t +s becoes the sae as what we have discussed in the Section 4. Therefore, we have 1 t in( tt, p ) λs λn n n s= in( tt, p ) λs e L( D( s), t s, v( s)) ds s= L(, tdv,, v) = L() t + e L( Ds (), t sv, ()) s ds + λ (1) Because we use the location of as the reference point in Fig. 3, here n () v s and v () s are the velocities of node n and after adjusting the coordination syste to ake the position of and n at t+s the reference point. V. SIMULATION RESULTS To verify the accuracy of our algorith, we copared the analytical results of our new link availability odel with the actual siulation results. The siulation environent is a two diensional space. For each siulation, we placed two obile peers a certain distance apart and conducted 1, independent experients. Then we statistically calculated the probability that the link was continuously available for t seconds based on all the siulation results. For each experient, we fixed node n s initial velocity and let node start with a rando velocity. The counication radius R was set to 1 for both nodes. The ajor issue of our enhanced link availability odel is the coputational coplexity in calculating Ln_variablev ( d, t, v 1 ). As we have discussed in [1], prob(t) can be calculated offline. However, we need to calculate Ln _ fixedv( d, s, v 1) for all s<t. It takes about 4.8 seconds to copute Equation (1) when t on a Pentiu 4 1.6GHz laptop. To reduce the coputation coplexity, we calculate Ln _ fixedv( d, s, v 1) at the following intervals: t, t/, t/4, and so on. The reaining values are approxiated through linear ethods. This reduces the coputation tie to less than 1 illiseconds when t and the axiu error ratio easured between the two ethods is within %. Fig. 4 shows the results of 14 independent siulations with two different initial speed settings for node n. In Fig. 4, solid lines represent the siulation results and the corresponding dotted lines illustrate the results by using Equation (1). In each figure, different solid lines represent different directions of the initial velocity of n. The ean epoch length is set to 3 seconds in our siulation. In Fig. 4(a) to (c), we set the current speed of node n to /s. In Fig. 4(d) to (f), the current velocity of n is set to 4/s. The axiu velocity of both nodes is set to 5/s. When the initial distance is equal to (Fig. 4(a,d)), different values produce the sae result. As a result, we only show one pair of lines when.. Siulation results Predicted results d o / = / = (a) v 1 = /s, = (b) v 1 = /s, = 5 (c) v 1 = /s, = / = / = (d) v 1 =4 /s, = (e) v 1 =4 /s, = 5 (f) v 1 =4 /s, = 8 Fig. 4. with known current velocity of node n, v ax =5 /s, λ n -1 = λ -1 =3 sec, R=1.

6 (a) t= sec (b) t= 7 sec Fig. 5. Distribution of continuous link availability L(t) as a function of and, v ax =5 /s, λ -1 n = λ -1 =3 sec, R=1, v 1 = /s. When the current velocity of node n increases fro /s to 4/s, there is a big change on continuous link availability for sall t values. However, link availabilities are getting close to each other when t>5. Since the ean epoch length is 3 seconds, node n is likely to ove with a rando velocity after that period of tie. As a result, both nodes can either walk towards or away fro each other when t is large. According to our siulation results in [1], a different initial distance between the two nodes does not have a big ipact on future link availability. Therefore, the current velocity of node n has a liited ipact on continuous link availability in the far future. Fig. 5 shows the distribution of Ld (, tv, 1) for two given t values. We use the sae siulation settings as those used in Figs. 4(a)-(c). As illustrated in Figs. 4 and 5, θ1 does not have a big ipact on future link availabilities when is sall. However, when increases fro 5 to 1, we can see a big difference on future link availability for different values. This is due to the fact that node n will keep its current velocity during a certain period of tie. When increases, different values have a large ipact on how far node n can ove in one direction before it reaches the boundary of s counication range. Fig. 6 shows the average error between the predicted results and the siulation results for all possible values. As we have discussed in Section 4, the calculation of L () t incorporates the rando obility vector R () t fro [15]. However, as discussed in [15], R () t works best for large t and norally distributed speed values. As a result, the predicted results have a bigger error argin for sall t values. The error reaches its axiu when t is equal to the ean epoch length. After that, n is ore likely to walk with a rando velocity and L () t becoe a less doinant factor in calculating Equation (1). The axiu prediction error is less than 1%, and for ost t values, the error is less than %. This deonstrates that our algorith is very accurate in predicting continuous link availability. Absolute prediction error =5 =8 Absolute prediction error (a) v 1 = /s (b) v 1 = 4/s Fig. 6. Absolute prediction errors with known current velocity of node n, v ax =5 /s, λ -1 n = λ -1 =3 sec, = 5, R=1.

7 . Predicted results Siulation results Relative velocity odel[1] v 1 =1/s,v =3/s,,θ v. 1 =v =/s,,θ 5 Duration (sec) Fig. 7. with known initial velocities for both nodes, v ax =5 /s, λ 1-1 = λ -1 =3 sec, = 5, R=1. Fig. 7 shows the siulation results when the initial velocities of both nodes can be estiated. We copared the results of our enhanced link availability odel with the previous odel for known relative velocity in [1]. Given the current relative velocity, there are any possible cobinations of both nodes velocities. Different cobinations ay result in different future link availabilities. Therefore, our previous relative velocity odel ay not be very accurate if both nodes velocities are known. In Fig. 7, the current relative velocity is set to 4/s and the direction is fro to n. The dotted line is the prediction result fro our relative velocity odel in [1]. We also experiented with two different velocity cobinations for the given relative velocity. The solid lines are the siulation results for two different velocity cobinations and the corresponding dashed lines are the predictions results. Because it is very likely that the current relative velocity keeps unchanged for a period of tie, both odels produce the sae continuous link availability when t is sall. However, when t increases, our algorith works ore accurately than the relative velocity odel fro [1] if the velocities of both nodes are known. 1 notably the pervasive availability of 8.11 networks. The bandwidth provided by different fors of the 8.11 standard is sufficient for audio streaing, especially since odern audio copression algoriths provide excellent aural quality at a oderate data rate. In addition, new generation handheld devices such as the HP5555 PDA now provide high coputing capabilities along with longer battery life, which together ake our application feasible. The original MStrea protocol requires a central server to distribute ultiedia content initially. Here we extend it to pure obile peer-to-peer (pp) streaing scenarios. The interaction between two obile peers unfolds in several phases as follows. The application periodically polls the GPS reading fro an attached GPS unit as shown in Fig. 7. It utilizes such inforation to calculate its own speed distribution. If the GPS is unavailable, fixed speed distribution and position inforation is used. To request streaing service, a obile peer broadcasts the streaing request to nearby neighbors. After receiving the request, neighbors that store the requested ultiedia content reply to the peer with their velocity and location inforation. The peer then contacts the neighbor that has the highest continuous link availability for the reaining streaing process. VI. APPLY LINK AVAILABILITY MODEL TO MOBILE STREAMING Mobile devices have recently becoe a popular platfor for streaing applications. Most streaing applications require continuous link availability for a sooth streaing experience, and hence they can benefit greatly fro our link availability odel to reduce the nuber of link breaks and to achieve a soother streaing perforance. One benefit that our link availability algorith can provide is to help obile users select the best choice aong a nuber of sources or destinations. To deonstrate the benefits of our link availability odel in aintaining sooth streaing experiences, we integrated our link availability algorith into the MStrea syste. MStrea [13, 1] is designed to provide a location sensitive audio streaing service to ultiple obile devices. It runs on Microsoft Windows Mobile 3 and utilizes 8.11 wireless networks. The viability of such an approach has been enabled by the proliferation of affordable wireless networking, ost Fig. 8. The MStrea application running on an HP ipaq h5555 PDA with a Pharos GPS unit support. A Support MStrea with Known Streaing Tie When wireless bandwidth is stable, it is possible for streaing applications to predict the transission tie. Therefore, we can use the reaining streaing tie with our link availability odel to select the best source aong a nuber of neighboring nodes. For exaple, if an audio file requires 1 seconds to strea and the client has already recorded 3% of the content, then our algorith chooses an available neighbor that has the highest continuous link availability for streaing the reaining 7% of the content.

8 TABLE I SIMULATION SETTINGS Paraeters Total nuber of clients 1 Value Siulation area [, 3] Counication range of all nodes Mean epoch length of each client Nuber of initial content holders 1 Tie to strea the ultiedia file Connection setup tie (buffering, negotiation, etc) 1 3 sec 15 sec 1 sec To deonstrate the usefulness of our link availability odel, we conducte independent siulations with the settings shown in Table 1. We assue that all nodes are location aware and are able to easure their current velocity through GPS or acoustic sensing. 1 clients are randoly dispersed into the siulation area. Ten of the store the ultiedia content that will be requested by the other nodes. The streaing tie is set to 15 seconds for the ultiedia content to be successfully streaed. We chose rando walk with reflection as the obility odel. When a user reaches the boundary of the siulation area, she changes her direction as if she was reflected by a irror. During every second, each user has a.5% probability of requesting the ultiedia content. After finishing streaing the content once, the client no longer requests it and becoes a potential source for further requests. We run the syste until all users in the area have streaed the content once. If the link breaks up during the streaing, a client waits until an alternative link is available and requests the reaining strea fro that link. Each client is given a rando axiu velocity fro 1/s to 4/s. For a coparison with our algorith, we incorporated the link availability odels fro [9] and []. For the link availability odel fro [9], we first estiate a tie period T p that the current link between the peer and its neighbor will last if both nodes keep their current velocity. Then we let the peer choose the neighbor that has the highest T p L(T p ) value. For the odel fro [], we assue that all nodes will ove with the current velocity before the link breaks up. Therefore, we let the obile peer choose the source that has the highest T p value. Fig. 9 shows the siulation results by using these three different link availability odels with various ean epoch length settings. To have a sooth streaing experience, the nuber of link breaks during streaing should be kept to a iniu. Fig. 9(a) shows the average nuber of link breaks a client suffers during the streaing process. Our link prediction odel exhibits the sallest nuber of link breakdowns. As described in Section II, using T p L(T p ) cannot reflect the actual link availability over tie. The result of using T p L(T p ) (see dashed line in Fig. 9) is even worse than that of assuing nodes do not change their velocities in the future. However, it keeps iproving when the ean epoch length increases. On the other hand, our link availability odel can predict continuous link availability over any short period of tie. Therefore, it is ore accurate in predicting the reliability of a link, especially when nodes tend to change their velocities frequently. According to our study in [1], saller epoch lengths result in higher future link availability. When the ean epoch length reduces fro 4 to 1 seconds, as indicated in Figs. 9(a, b), our link availability odel can reduce the nuber of link breaks by.31 and increase the percentage of clients that do not suffer any link breaks by 19%. These results deonstrate a significant advantage of our algorith over the other two link prediction odels. Assuing no velocity change Link availability odel of [9] Our enhanced link availability odel # of link breaks Mean epoch length (sec) % of nodes Mean epoch length (sec) Continuous duration (sec) Mean epoch length (sec) (a) Average nuber of link (b) Percentage of clients that (c) Average continuous streaing breaks during the streaing do not suffer any link breaks duration before a link breaks or streaing finishes Fig. 9. Siulation results of supporting obile pp streaing with different link availability odels.

9 Fig. 9(c) shows the average duration that a streaing process can last without any interruptions. When the streaing process starts or when it recovers fro a link break, it is often desirable to prolong the duration that it can continuously strea the audio until a link breakdown takes place. Copared with the other two scenarios, our algorith yields a longer continuous streaing duration. Therefore, even if there are link breaks, the streaing process is continuous ost of the tie. As shown in Fig. 9, the perforances of these three link availability odels are converging when the ean epoch length increases. As the ean epoch length increases, nodes tend to ove in a straight direction. All three odels should have a siilar perforance when assuing nodes do not change their velocities in the future. Therefore, our link prediction odel is particularly suited for environents in which velocity change is frequent. B Locating Stable Links with Unknown Streaing Tie In any situations, predicting the actual streaing tie can be very hard due to bandwidth variations. Therefore, it is ore desirable to select a edia source that has the longest continuous link duration. For exaple, consider the scenario where a tourist is wandering in a dark forest and several firefighters are searching to try to locate her. If there is no cell phone signal in the surrounding area, ad-hoc live audio streaing becoes the preferred counication ethod. It ay require several inutes for the tourist to explain her situation clearly to firefighters. Thus how to select the best nearby firefighter so that she can talk to hi as long as possible is very crucial. In such a situation, it is hard to deterine how long the streaing application is going to last. Therefore, we should choose the source that has the axiu tl() t dt value. However, coputing is very tie consuing since it involves the calculation of L(t) for all possible t values. Therefore, it is ipractical to use tl() t dt in evaluating link reliabilities in real tie. To reduce the coputation tie, we can select a certain t and copare different links continuous availability during [t, t+t ]. To evaluate the correctness of such an approxiation, we conducted 3 independent siulations by randoly placing oving edia sources around a obile requester. Each node starts with a rando speed between 1 to 4/s and a rando direction between [, π]. When the siulation begins, the requester chooses a neighboring source that has the highest continuous link availability during [t +t]. Then we calculate the actual continuous link duration between the requester and the source. We experiented with three different t values and copared our results with those of the other two link prediction odels in the previous section. Fig. 1 shows the average continuous link duration between the source and the requester with different ean epoch length settings. As shown in Fig.1, our link availability odel achieves siilar link durations for different t values. Therefore, we can randoly choose a reasonable t value and it will not largely tl() t dt affect the perforance of our algorith. Unlike what is shown in Fig. 9, the perforance of the link availability odel fro [9] shown in Fig. 1 works better than that of assuing there will be no future velocity change. Therefore, it is ore suited for predicting the reliability of a link. Since all three link prediction odels try to find the link that is possibly the ost reliable, it is probable that they do not locate the link that is actually the longest lasting. Therefore, as shown in Fig. 1, the axiu possible link duration in the siulation is uch higher than that of using our enhanced odel. Our link prediction odel outperfors the other two odels by 16-8% when the ean epoch length decreases fro 6 to 1 seconds. This deonstrates a significant advantage when velocity change is frequent. Continuous link duration (sec) Our enhanced odel,t=5 Our enhanced odel,t=1 Our enhanced odel,t=15 Maxiu possible duration Assuing no velocity change Link availability odel of [9] Mean epoch length (sec) Fig.1. Average continuous link duration between the requester and the source with different link prediction odels. VII. CONCLUSIONS In this paper, we introduce a new algorith for predicting future link availability by utilizing the current velocity inforation of obile hosts. This algorith can help to iprove the streaing experience and select better sources in a wireless pp streaing environent. Copared to our previous techniques, this work provides a ore accurate prediction if velocity inforation of obile hosts is known. With an average error argin of less than %, our algorith is excellently suitable and advantageous for any applications. To study the usefulness of our link prediction odel, we integrated our algorith with the MStrea obile pp streaing architecture. Siulation results illustrate that our algorith can iprove the streaing experience by reducing the nuber of link breaks, increasing the probability of not suffering fro any link breaks and providing a longer continuous streaing duration. We plan to extend our work in ultiple directions. First, we intend to study other obility odels for their link availability estiation. Second, we will integrate our link availability odel into existing routing protocols, such as DSR and

10 AODV. We believe these protocols will benefit significantly fro our algorith. For obile ad-hoc networks, constraints such as battery power and available bandwidth are not addressed in this paper. We will extend our current research to further analyze these issues. ACKNOWLEDGMENT This research has been funded in part by NSF grants EEC (IMSC ERC), MRI-31377, and CMS-19463, and unrestricted cash/equipent gifts fro the Lord Foundation, Hewlett-Packard, Intel, Sun Microsystes and Raptor Networks Technology. REFERENCES [1] E. Bach, S. S. Bygdås and M. Flydal-Blichfeldt, et al. Bubbles: Navigating Multiedia Content in Mobile Ad-hoc Networks, Proc. nd International Conference on Mobile and Ubiquitous Multiedia, Norrköping, Sweden, Deceber 3. [] F. Bai, N. Sadagopan, A. Hely. IMPORTANT: A fraework to systeatically analyze the Ipact of Mobility on Perforance of RouTing protocols for Adhoc NeTworks, Proc. IEEE INFOCOM 3, San Francisco, CA, March 3. [3] F. Baccelli and P. Br eaud. 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