SILENCE BASED ADAPTATION ON MULTIHOP WIRELESS NETWORKS PALE SPANOS And FOTIOS KONSTANTINIDIS

OUTLINES Importance of adaptivity in networks Importance of silence detection and “removal” Our Simulation Results Silence detection model used Future Work based on that

Importance of Adaptivity in Networks The nature of Ad-hoc,multihop wireless multimedia networks is that which demands adaptive mechanisms at all design layers in order to gain a desired responsiveness due to unexpected changes in grades of service This nature consisted of the media high probability,burstiness and persistence of errors. So,we have to adapt the operation of the applications to changing QoS The normal Adaptive mechanism which is used is based on a periodical end-to-end feedback.

Audio Adaptation mechanism The conventional Adaptation mechanisms for audio applications are based on the QoS Notification Programming Model in which senders use QoS feedback to dynamically select audio encoding most appropriate to network conditions. This encoding is based on the principle of media scaling. These mechanisms are sufficient to improve the audio or video quality when lost packets are mostly caused by congestion.

Importance of silence based adaptation schemes Bandwidth requirements of video are signifficant. Reduce the end-to-end delay seen by the users. Imagine that the perceived audio quality drops sharply as packet loss reaches 20%,even if retransmission techniques are used to replace lost packets.

Simulation GLOMOSIM Platform Multihop Network number of nodes: by 80 unit 2 Mbps radio 40 units range(reasonable probability of the generated topology being disjoint and interference between connections) Mobility: random waypoint Radio Type:No Capture Propagation Function:Free Space

Effective BW increasing Increase effective BW when reducing the rate at the server side.In mobility these are more obvious As we can see from the figures: 1)In (mob) 102Kbps more effective BW(340%) Audio adapt 2)In CSMA (mob)15.5Kbps more effective BW(155%)Audio adapt

Effective BW increasing As concerns the silence adaptation,the effective BW was improved further: 1)In (mob) 169Kbps more effective BW(530%) 2)In CSMA(mob.)18Kbps more effective BW(180%)

Server consumed BW Drop In Kbps drop for only Audio Adaptation(26%) plus Silence 76.8 Kbps (30%) In CSMA Kbps drop for Audio only(47%) plus Silence Kbps drop (62%)

BRANDY MODEL Silence in speech can be classified into 3 categories: 1)Listening pauses 2)Short speaking pauses(words,syllables) 3)Long speaking pauses(phrases,sentences) We care for the 3rd category.

Characteristics of BRANDY model Is based on state per state probabilities. According to these,the overall length of a talkspurt is characterized as f(t)=λe -λt where λ=6.322 and t is expressed in seconds. The beauty of this model is that it has an expression also for the length of silence f(t)=G 1 ζe -ζt +G 2 γe -γt where G 1 =0.8745,G 2 =0.1469,ζ=27.62,γ=1.377

Statechart overview

Adaptation Approach Adaptation Scheme Silence

Advantages This approach achieved signifficant drop of the loss rate And drop of the server consumed BW It seemed to work quite well with the AudioTool of the GLOMOSIM No client’s involvement in the adaptation(possible lower delay) Softer traffic

Future work QoS parameter(notification packet when silence--2 packets when end) More measurements in many different conditions to ensure compatibility with the Notification based schemes. Timeout threshold issues(communicate with the other adaptation scheme)