Abstract
Opportunistic Networks (OppNets) is gaining popularity day-by-day due to their various applications in the real-life world. The two major reasons for its popularity are its suitability to be established without any requirement of additional infrastructure and the ability to tolerate long delays during data communication. Opportunistic Network is also considered as a descendant of Mobile Ad hoc Networks (Manets) and Wireless Sensor Networks (WSNs), therefore, it inherits most of the traits from both mentioned networking techniques. Apart from its popularity, Opportunistic Networks are also starting to face challenges nowadays to comply with the emerging issues of the large size of data to be communicated and blind forwarding of data among participating nodes in the network. These issues lower the overall performance of the network. Keeping this thing in mind, ML-Fresh-a novel framework has been proposed in this paper which focuses to overcome the issue of blind forwarding of data by maintaining an optimum path between any pair of participating nodes available in the OppNet using machine learning techniques viz. pattern prediction, decision tree prediction, adamic-adar method for complex networks. Apart from this, ML-Fresh also uses the history of successful encounters between a pair of communicating nodes for route prediction in the background. Simulation results prove that the ML-Fresh outperforms the existing framework of Opportunistic Networks on the grounds of standard Quality-of-Service (QoS) parameters.
Highlights
Owing to the hard efforts put by network science researchers, man today can communicate even beyond the planetary boundaries
Opportunistic Network is considered as a descendant of Mobile Ad hoc Networks (Manets) and Wireless Sensor Networks (WSNs), it inherits most of the traits from both mentioned networking techniques
These issues lower the overall performance of the network. Keeping this thing in mind, ML-Fresh-a novel framework has been proposed in this paper which focuses to overcome the issue of blind forwarding of data by maintaining an optimum path between any pair of participating nodes available in the Opportunistic Networks (OppNets) using machine learning techniques viz. pattern prediction, decision tree prediction, adamic-adar method for complex networks
Summary
Owing to the hard efforts put by network science researchers, man today can communicate even beyond the planetary boundaries. Due to the significant growth in the networking domain, data communication has been possible in such areas where it once seemed impossible. Opportunistic Networks are the result of such momentous growth in a couple of years in the field of wireless networking [1]. Opportunistic Networks inherited their basic traits from Mobile ad hoc Networks (MANETs) [2] and Wireless Sensor Networks (WSNs) [3,4]. Yuan et al [5] suggested that.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
