02645nas a2200325   4500000000100000008004100001260003800042653002900080653002700109653002500136653003200161653002200193100001600215700001800231700001700249700001700266700001900283700002200302700002200324700001500346700001700361700002200378700002000400245010100420856005100521300001200572490000900584520170400593020002202297       2014                            d  bSpringer International Publishing10aHistory-Based Prediction10aMobile Ad Hoc Networks10aMobile Collaboration10aNetwork Topology Prediction10aRouting Protocols1 aPere Millan1 aCarlos Molina1 aRoc Meseguer1 aSergio Ochoa1 aRodrigo Santos1 aGiancarlo Fortino1 aGiuseppe Di Fatta1 aWenfeng Li1 aSergio Ochoa1 aAlfredo Cuzzocrea1 aMukaddim Pathan00aUsing a History-Based Approach to Predict Topology Control Information in Mobile Ad Hoc Networks  uhttp://dx.doi.org/10.1007/978-3-319-11692-1_21  a237-2490 v87293 aSeveral social computing participation strategies, such as crowdsensing and crowdsourcing, use mobile ad hoc or opportunistic networks to support the users activities. The unreliability and dynamism of these communication links make routing protocols a key component to achieve efficient and reliable data communication in physical environments. Often these routing capabilities come at expenses of flooding the network with a huge amount of topology control information (TCI), which can overload the communication links and dramatically increase the energy consumption of the participating devices. In previous works the authors have shown that predicting the network topology in these work scenarios helps reduce the number of control packets delivered through the network. This saves energy and increases the available bandwidth. This paper presents a study that extends the authors’ previous works, by identifying the impact of predicting the TCI generated by routing protocols in these networks. The prediction process is done following a history-based approach that uses information of the nodes past behavior. The paper also determines the predictability limits of this strategy, assuming that a TCI message can be correctly predicted if it appeared at least once in the past. The results show that the upper-bound limit of the history-based prediction approach is high, and that realistic prediction mechanisms can achieve significant ratios of accuracy. Mobile collaborative applications and routing protocols using mobile ad hoc or opportunistic networks can take advantage of this prediction approach to reduce network traffic, and consequently, the energy consumption of their devices.  a978-3-319-11691-4