Performance Evaluation of Mobility Anchor Point with Guard Load Reservation in Hierarchical Mobile IPv6

Hierarchical mobile IPv6 （HMIPv6） introduces a mobility anchor point to reduce the signaling overhead and handoff latency. In this paper, we apply the matrix-analytical approach to explore the performance measures of the ongoing mobile nodes （MNs） drop and new MNs block probabilities of mobility anchor point with a guard bandwidth reservation scheme. We apply the Markovian arrival process （MAP） to model ongoing MNs and new MNs. Five related performance measures are derived, including the long-term new MN block and ongoing MN drop probabilities, and the three short-term measures of average length of a block period and a non-block period, as well as the conditional ongoing MN drop probability during a block period. These performance measures greatly assist the guard bandwidth reservation mechanism in determining a proper threshold guard bandwidth. The results presented in this paper can provide guidelines for designing adaptive algorithms to adjust the threshold in the guard bandwidth reservation scheme.

New multicast scheme based on Dynamic mobility prediction in mobile IPv6 environment

A new mobile multicast scheme called mobility prediction based mobile multicast(MPBMM) was proposed. In MPBMM, when a mobile node (MN) roams among subnets during a multicast session, MN predicts the next subnet, to which MN will attach, by the information of its position and mobility speed, consequently speeds up the handoff procedure. Simulation results show that the proposed scheme can minimize the loss of multicast packets, reduce the delay of subnet handoff, decrease the frequency of multicast tree reconfiguration, and optimize the delivery path of multicast packets. When MN moves among subnets at different speeds (from 5 to 25 ms), the maximum loss ratio of multicast packets is less than0.2%, the maximum inter-arrival time of multicast packets is 117 ms, so the proposed scheme can meet the QoS requirements of real-time services. In addition, MPBMM can support the mobility of multicast source.

Integrating Mobile Ad Hoc Network to the Internet

A novel scheme is presented to integrate mobile ad hoc networks （MANETs） with the lnternet and support mobility across wireless local area networks （WLANs） and MANETs. The mobile nodes, connected as a MANET, employ the optimize d link state routing （OLSR） protocol for routing within the MANET. Mobility management across WLANs and MANETs is achieved through the hierarchical mobile IPv6 （HMIPv6） protocol. The performance is evaluated on a HMIPv6 based test-bed composed of WLANs and MANETs. The efficiency gain obtained from using HMIPv6 in such a hybrid network is investigated. The investigation result shows that the use of HMIPv6 can achieve up to 27% gain on reducing the handoff latency when a mobile roams within a domain. Concerning the reduction of the signaling load on the lnternet, the use of HMIPv6 can achieve at least a 54% gain and converges to 69%.

QoS in FANET Business and Swarm Data

This article shows the quality of services in a wireless swarm of drones that form an ad hoc network between them Fly Ad Hoc Networks(FANET).Each drone has the ability to send and receive information(like a router);and can behave as a hierarchical node whit the intregration of three protocols:Multiprotocol Label Switch(MPLS),Fast Hierarchical AD Hoc Mobile(FHAM)and Internet Protocol version 6(IPv6),in conclusion MPLS+FHAM+IPv6.The metrics analyzed in the FANET are:delay,jitter,throughput,lost and sent packets/received.Testing process was carried out with swarms composed of 10,20,30 and 40 units;In this work,the stage with 40 droneswas analyzed showing registration processes,and sentmessages sequences between different drones that were part of the same swarm.A special analysis about the traffic between drones(end-to-end)was carried out,as well as the possible security flaws in each drone and the current status and future trends in real services.Regarding future trends,in a real environment,we took as a starting point,metrics results obtained in the simulation(positive according to the obtained results).These results gave us a clear vision of how the network will behave in a real environment with the aim to carry out the experiment on a physical level in the near future.This work also shows the experience quality from the service quality metrics obtained through a mathematical model.This quality of experience model will allow us to use it objectively in the agricultural sector,which is a great interest area and is where we are working with drones.Finally in this article we show our advances for a business model applied to the aforementioned agricultural sector,as well as the data analysis and services available to the end customer.These services available to the end customer have been classified into a basic,medium,advanced and plus level.