Anchor-Less Producer Mobility Management in Named Data Networking for Real-Time Multimedia

Information-centric networking (ICN) is one of the promising solutions that cater to the challenges of IP-based networking. ICN shifts the IP-based access model to a data-centric model. Named Data Networking (NDN) is a flexible ICN architecture, which is based on content distribution considering data as the core entity rather than IP-based hosts. User-generated mobile contents for real-time multimedia communication such as Internet telephony are very common these days and are increasing both in quality and quantity. In NDN, producer mobility is one of the challenging problems to support uninterrupted real-time multimedia communication and needs to be resolved for the adoption of NDN as future Internet architecture. We assert that mobile node’s future location prediction can aid in designing efficient anchor-less mobility management techniques. In this article, we show how location prediction techniques can be used to provide an anchor-less mobility management solution in order to ensure seamless handover of the producer during real-time multimedia communication. The results indicate that with a low level of location prediction accuracy, our proposed methodology still profoundly reduces the total handover latency and round trip time without creating network overhead

Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN

Vehicular Named Data Networking (VNDN) is one of the potential and future networking architectures that allow Connected and Autonomous Vehicles (CAV) to exchange data by simply disseminating the content over the network. VNDN only supports a pull-based data forwarding model, where the content information is forwarded upon request. However, in critical situations, it is essential to design a push-based data forwarding model in order to broadcast the critical data packets without any requests. One of the challenges of push-based data forwarding in VNDN is the broadcasting effect, which occurs when every vehicle broadcasts critical information over the network. For instance, in emergency situations such as accidents, road hazards, and bad weather conditions, the producer generates a critical data packet and broadcasts it to all the nearby vehicles. Subsequently, all vehicles broadcast the same critical data packet to each other, which leads to a broadcast storm on the network. Therefore, this paper proposes a Fuzzy Logic-based Push Data Forwarding (FLPDF) scheme to mitigate the broadcast storm effect. The novelty of this paper is the suggestion and application of a fuzzy logic approach to mitigate the critical data broadcast storm effect in VNDN. In the proposed scheme, vehicles are grouped into clusters using the K-means clustering algorithm, and then Cluster Heads (CHs) are selected using a fuzzy logic approach. A CH is uniquely responsible for broadcasting the critical data packets to all other vehicles in a cluster. A Gateway (GW) has the role of forwarding the critical data packets to the nearest clusters via their GWs. The simulation results show that the proposed scheme outperforms the naive method in terms of transmitted data packets and efficiency. The proposed scheme generates five times fewer data packets and achieves six times higher efficiency than the naive scheme

NDN Construction for Big Science: Lessons Learned from Establishing a Testbed

NDN is one instance of ICN, which is a cleanslate approach that promises to reduce inefficiencies in the current Internet. NDN provides intelligent data retrieval using the principles of name-based symmetrical forwarding of Interest/ Data packets and in-network caching. The continually increasing demand for the rapid dissemination of large-scale scientific data is driving the use of NDN in big science experiments. In this article, we establish the first intercontinental NDN testbed to offer complete insight into NDN construction for big science. In the testbed, an NDN-based application that targets climate science as an example big-science application is designed and implemented with differentiated features compared to previous works on NDNbased application design for big science. We first attempt to systematically address detailed analysis of why or how NDN benefits fit in big science and issues that must be resolved to improve each advantage, mostly based on lessons learned from establishing the NDN testbed for climate science. We extensively justify the needs of using NDN for large-scale scientific data in the intercontinental network, through experimental performance comparisons between classical deliveries and NDNbased climate data delivery, and detailed analysis of why or how NDN benefits fit in big science