Real-time Multicasting Research Articles

Next Generation 5G Mobile Health Network for User Interfacing in Radiology Workflows

5G is the next-generation mobile cellular network that offers significant advantages over other wireless network technologies, such as data throughput, latency, real-time multicasting, ad hoc peer-to-peer, and data encryption, to name a few. A critical sector for potentially disruptive 5G solutions is healthcare and its current IoMT infrastructure. This paper analyses the current pain points associated with the general radiology workflow for X-ray imaging in terms of user interfaces and network technologies. By understanding this workflow and the modern 5G technology standards, we have designed a system-level architecture for a private 5G network within a hospital that includes additional services over the core and a different architecture for telecommunication networks. In addition, we have designed a lower-level architecture for a 5G collimator interfacing use case, with a network protocol stack designed for use case realization. With these 5G architectures implemented, a new, novel radiology workflow, was visualized, with clear benefits arising from the 5G innovations highlighted. We also highlighted the resulting limitations of the current design and outlined the next development steps and hypothesized future visions.

Multicast Protocol for Real-Time Data Dissemination in Wireless Sensor Networks

Real-time data dissemination to a multicast group is data delivery to each member in the multicast group within the desired time deadline. The hardest aspect of this mission is to enforce the real-time constraint in the communication between a source and the furthest member since an end-to-end delay is proportional to a physical distance in wireless sensor networks. We call it the critical distance. The critical distance should be most important constraint for real-time multicasting. That is, the delivery distance from a source to each member should not be longer than the critical distance even by any reason. However, since the traditional multicast protocols lay the strong emphasis only on the overall communication cost rather than delivery distance to each member, they may violate the real-time constraint related to the critical distance. In this letter, we propose a novel multicast protocol for real-time data dissemination.

A Multi-objective QoS Optimization with Fuzzy Based Parameter Setting for Real-Time Multicasting

We propose a multi-objective Pareto-optimal technique using Genetic Algorithm (GA) for group communication, which determines a min-cost multicast tree satisfying end-to-end delay, jitter, packet loss rate and blocking probability constraints. The model incorporates a fuzzy-based selection technique for initialization of QoS parameter values at each instance of multicasting. The simulation results show that the proposed algorithm satisfies on-demand QoS requirements (like high availability, good load balancing and fault-tolerance) made by the hosts in varying topology and bursty data traffic in multimedia communication networks.

Optimal Design of Delay-Bounded WDM Networks using a Genetic Algorithm

We address the problem of real-time delay-bounded multicasting in wavelength-division multiplexed networks to avoid problems of synchronization between video and audio frames. We describe a genetic algorithm based technique to synthesize WDM network topologies that can, with a very high degree of confidence, assure that the multicast traffic is delivered in user-specified limits on time. Unlike existing approaches to WDM network design, we first find a virtual topology that can meet the delay constraints. An embedding of virtual links into physical links is then carried out, followed by an assignment of wavelengths to virtual links.The problem of finding the virtual topology is difficult because of a large number of parameters. A number of heuristic approaches have been proposed to solve such optimization problems. In our approach we aim to explore the suitability of Genetic Algorithms to solve the WDM network design problem. A genetic algorithm can explore a far greater range of potential solutions to a problem than do conventional approaches. The advantage of a genetic algorithm, compared with other algorithms which use a single initial guess e.g. gradient, descent is to use more information of estimation region, and to decrease the probability of falling into local minimum. We describe quantitative and qualitative results obtained by using our software tool on several benchmark examples.