The Sensor Proxy Mobile IPv6 (SPMIPv6) has been designed for IP-based wireless sensor networks mobility to potentially save energy consumption by relieving the sensor nodes from participating in the handoff process. However, SPMIPv6 is dependent on a single and central Local Mobility Anchor (LMA), and thus, it inherited most of the problems observed in the Proxy Mobile IPv6 (PMIPv6) protocol, including long handoff latency, non-optimized communication path, and bottleneck issues. In addition, SPMIPv6 extends the single point of failure to include both the authentication and network information. This study presents an enhanced architecture for SPMIPv6 called Clustered SPMIPv6 (CSPMIPv6) to overcome the problems above. In the proposed architecture, the Mobility Access Gateways (MAGs) are grouped into clusters, each with a distinguished cluster Head MAG (HMAG). The HMAG is mainly designed to reduce the load on LMA by performing intra-cluster handoff signaling and providing an optimized path for data communications. The proposed architecture is evaluated analytically, and the numerical results show that the proposed CSPMIPv6 outperforms both SPMIPv6 and PMIPv6 protocols in terms of LMA load, local handoff delay, and transmission cost performance metrics.
Network based mobility management has attracted significant research interest due to its salient feature of relieving mobile nodes from participating in the mobility process. This feature of relying the mobility functions on the network entities would indeed eases the deployment of mobility solutions. Proxy Mobile IPv6 (PMIPv6) is considered as a promising network-based mobility management protocol in the next-generation mobile network. However, since the emergence of basic specification of the PMIPv6 protocol, it is still being developed in different directions to enhance its performance in order to ensure the best service for mobile users. This paper presents the PMIPv6 basic specifications and surveys the different extensions that have been considered by both the standardization bodies and researchers to enhance the basic PMIPv6 protocol with interesting features needed to offer a richer mobility experience, namely, clustering, fast handoff, route optimization, network mobility support, and load sharing. The research works conducted for these extensions are analyzed to specify the main issues that should be considered during the design of such extensions. Also, an integrated solution is proposed to show the possibility of combining more than one enhancement feature into a single integrated scheme.
The rapid and enormous growth of the Internet of Things, as well as its widespread adoption, has resulted in the production of massive quantities of data that must be processed and sent to the cloud, but the delay in processing the data and the time it takes to send it to the cloud has resulted in the emergence of fog, a new generation of cloud in which the fog serves as an extension of cloud services at the edge of the network, reducing latency and traffic. The distribution of computational resources to minimize makespan and running costs is one of the disadvantages of fog computing. This paper provides a new approach for improving the task scheduling problem in a Cloud-Fog environment in terms of execution time(makespan) and operating costs for Bag-of-Tasks applications. A task scheduling evolutionary algorithm has been proposed. A single custom representation of the problem and a uniform intersection are built for the proposed algorithm. Furthermore, the individual initialization and perturbation operators (crossover and mutation) were created to resolve the inapplicability of any solution found or reached by the proposed evolutionary algorithm. The proposed ETS (Evolutionary Task Scheduling algorithm) algorithm was evaluated on 11 datasets of varying size in a number of tasks. The ETS outperformed the Bee Life (BLA), Modified Particle Swarm (MPSO), and RR algorithms in terms of Makespan and operating costs, according to the results of the experiments.
Proxy Mobile IPv6 (PMIPv6) was standardized to reduce the long handoff latency, packet loss and signaling overhead of MIPv6 protocol and to exempt the mobile node from any involvement in the handoff process. However, the basic PMIPv6 does not provide any buffering scheme for packets during MNs handoff. In addition, all the binding update messages are processed by a Local Mobility Anchor (LMA) which leads to increase the handoff latency. Previous works enhanced PMIPv6 performance by applying fast handoff mechanisms to reduce the packet loss during handoffs; however, the LMA is still involved during the location update operations. In this paper, we present a new fast handoff scheme based on a cluster-based architecture for the PMIPv6 named Fast handoff Clustered PMIPv6 (CFPMIPv6); it reduces both the handoff signaling and packet loss ratio. In the proposed scheme, the Mobility Access Gateways (MAGs) are grouped into clusters with a one distinguished Head MAG (HMAG) for each cluster. The main role of the HMAG is to carry out the intra-cluster handoff operations and provide fast and seamless handoff services. The proposed CFPMIPv6 is evaluated analytically and compared with the previous work including the basic PMIPv6, Fast PMIPv6 based on Multicast MAGs group (MFPMIPv6), and the Fast Handoff using Head MAG schemes (HFPMIPv6). The obtained numerical results show that the proposed CFPMIPv6 outperforms all the basic PMIPv6, MFPMIP6, and HFPMIPv6 schemes in terms of the handoff signaling cost.
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.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.