IEEE Trans Image Process - Cooperation stimulation strategies for peer-to-peer wireless live video-sharing social networks.

Tópicos

{ health(1844) social(1437) communiti(874) }
{ motion(1329) object(1292) video(1091) }
{ monitor(1329) mobil(1314) devic(1160) }
{ data(3963) clinic(1234) research(1004) }
{ method(2212) result(1239) propos(1039) }
{ assess(1506) score(1403) qualiti(1306) }
{ framework(1458) process(801) describ(734) }
{ model(3480) simul(1196) paramet(876) }
{ drug(1928) target(777) effect(648) }
{ network(2748) neural(1063) input(814) }
{ risk(3053) factor(974) diseas(938) }
{ model(2341) predict(2261) use(1141) }
{ perform(1367) use(1326) method(1137) }
{ state(1844) use(1261) util(961) }
{ activ(1138) subject(705) human(624) }
{ inform(2794) health(2639) internet(1427) }
{ bind(1733) structur(1185) ligand(1036) }
{ featur(3375) classif(2383) classifi(1994) }
{ method(1557) propos(1049) approach(1037) }
{ control(1307) perform(991) simul(935) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ howev(809) still(633) remain(590) }
{ system(1050) medic(1026) inform(1018) }
{ model(2656) set(1616) predict(1553) }
{ cost(1906) reduc(1198) effect(832) }
{ first(2504) two(1366) second(1323) }
{ patient(1821) servic(1111) care(1106) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
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{ measur(2081) correl(1212) valu(896) }
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{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
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{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
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{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ model(2220) cell(1177) simul(1124) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ studi(1410) differ(1259) use(1210) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
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{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Human behavior analysis in video sharing social networks is an emerging research area, which analyzes the behavior of users who share multimedia content and investigates the impact of human dynamics on video sharing systems. Users watching live streaming in the same wireless network share the same limited bandwidth of backbone connection to the Internet, thus, they might want to cooperate with each other to obtain better video quality. These users form a wireless live-streaming social network. Every user wishes to watch video with high quality while paying as little as possible cost to help others. This paper focuses on providing incentives for user cooperation. We propose a game-theoretic framework to model user behavior and to analyze the optimal strategies for user cooperation simulation in wireless live streaming. We first analyze the Pareto optimality and the time-sensitive bargaining equilibrium of the two-person game. We then extend the solution to the multiuser scenario. We also consider potential selfish users' cheating behavior and malicious users' attacking behavior and analyze the performance of the proposed strategies with the existence of cheating users and malicious attackers. Both our analytical and simulation results show that the proposed strategies can effectively stimulate user cooperation, achieve cheat free and attack resistance, and help provide reliable services for wireless live streaming applications.

Resumo Limpo

human behavior analysi video share social network emerg research area analyz behavior user share multimedia content investig impact human dynam video share system user watch live stream wireless network share limit bandwidth backbon connect internet thus might want cooper obtain better video qualiti user form wireless livestream social network everi user wish watch video high qualiti pay littl possibl cost help other paper focus provid incent user cooper propos gametheoret framework model user behavior analyz optim strategi user cooper simul wireless live stream first analyz pareto optim timesensit bargain equilibrium twoperson game extend solut multius scenario also consid potenti selfish user cheat behavior malici user attack behavior analyz perform propos strategi exist cheat user malici attack analyt simul result show propos strategi can effect stimul user cooper achiev cheat free attack resist help provid reliabl servic wireless live stream applic

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