Opportunistic communications present a promising solution for disaster network recovery in emergency situations such as hurricanes, earthquakes, and floods, where infrastructure might be destroyed. Some recent works in the literature have proposed opportunistic-based disaster recovery solutions, but they have omitted the consideration of mobile devices that come with different network technologies and various initial energy levels. This work presents COPE, an energy-aware Cooperative OPportunistic alErt diffusion scheme for trapped survivors to use during disaster scenarios to report their position and ease their rescue operation. It aims to maintain mobile devices functional for as long as possible for maximum network coverage until reaching proximate rescuers. COPE deals with mobile devices that come with an assortment of networks and aims to perform systematic network interface selection. Furthermore, it considers mobile devices with various energy levels and allows low-energy nodes to hold their charge for longer time with the support of high-energy nodes. A proof-of-concept implementation has been performed to study the doability and efficiency of COPE, and to highlight the lessons learned.
The rapid increase of portable devices providing a multitude of mobile applications have led to excessive cellular traffic demands and consequently to the overload of cellular networks. Recently, migrating this traffic by opportunistic vehicular networks has attracted a great interest and appeared as a promising solution. Indeed, only a limited set of vehicles (seeds) is selected to download objects from an Internet-content server through the cellular network and then propagate the content gradually by opportunistic communications (i.e. vehicleto-vehicle V2V). This paper proposes SIEVE, an innovative seed selection scheme, that exploits two key criteria: users' interests and near-future contacts prediction. Based on these criteria, SIEVE allows to select the seeds in order to maximally satisfy the users' interests and, hence, achieve a maximum content utility (i.e. quantitative metric that determines how satisfied are the users). Simulations results show that SIEVE can improve the content utility when compared to other algorithms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.