Recently, the mobility management of urban vehicular networks has become great challenges for researchers due to its unique mobility requirements imposed by mobile users when accessing different services in a random fashion. To provide a ubiquitous Internet and seamless connectivity, the Internet Engineering Task Force (IETF) has proposed a Proxy Mobile IPv6 (PMIPv6) protocol. This is meant to address the signaling of the mobility transparent to the Mobile Node (MN) and also guarantee session continuity while the MN is in motion. However, performing a handoff by tens of thousands of MNs may harm the performance of the system significantly due to the high signaling overhead and the insufficient utilization of so-called Binding Cash Entry (BCE) at the Local Mobility Anchor (LMA). To address these issues, we propose an efficient scheme within the PMIPv6 protocol, named AE-PMIPv6 scheme, to effectively utilize the BCE at the LMA. This is primarily achieved by merging the BCEs of the MNs, thus, reducing the signaling overhead. Better utilization of the BCEs has been attained by employing virtual addresses and addressing pool mechanisms for the purpose of binding information of the MNs that are moving together towards the same network at a specific time, during their handoff process. Results obtained from our simulation demonstrates the superiority of AE-PMIPv6 scheme over E-PMIPv6 scheme. The AE-PMIPv6 succeeds in minimizing the signaling overhead, reduces the handover time and at the same time efficiently utilize the buffer resources.
Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on "Diabetic Retinopathy - Segmentation and Grading" was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal sub-challenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.