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SUMMARYCurrent IP-level mobility management protocols are unable to satisfy the stringent handover latency requirements for supporting real-time multimedia services between different domains in a high-speed vehicular environment. This paper proposes an inter-domain handover scheme for Proxy Mobile IPv6 (PMIPv6) with a new network entity, called intermediate-mobile access gateway (iMAG). The iMAG is located between the home and the foreign domains and is connected to both domains. The proposed scheme is a proactive handover approach that performs the inter-domain L3 handover in advance before the inter-domain L2 handover while the Mobile Node (MN) is still connected to the iMAG in the home domain. Therefore, the proposed scheme enables the inter-domain handover latency to reduce as low as the intra-domain handover latency. Numerical analysis and simulation results demonstrate that the proposed scheme is able to support seamless service continuity in inter-domain handovers.

The throughput of a communication system depends on the offered traffic load and the available capacity to support that load. When an unmanned aerial verhicle (UAV) is responsible for providing the communication service to users within its transmission range, the position of the UAV determines how much capacity each user gets. The closer the UAV to a user, the greater the capacity that the user gets. For a given set of user traffic demands and user locations, it is possible to maximize the total throughput by optimally positioning the UAV. This paper presents two methods, a heuristic method and an approximation algorithm, for determining the UAV position that maximizes the overall system throughput. This paper also considers a related problem of keeping all users within the transmission range while determining the UAV position that maximizes throughput. The proposed schemes were evaluated with extensive simulations using MATLAB and the ns-2 simulator. The results show that significant throughput enhancement is possible by optimally positioning the UAV when user positions are unevenly distributed and/or data rate demands are widely spread.

The outbreak of Coronavirus Disease 2019 (Covid-19) had an enormous impact on humanity. Till May 2021, almost 172 million people have been affected globally due to the contagious spread of Covid-19. Although the distribution of vaccines has been started, the worldwide mass distribution is yet to happen. According to the World Health Organization, wearing a facemask can reduce the contagious spread of Covid-19 significantly. The governments of different countries have recommended implementing the "no mask, no service" method to impede the spread of Covid-19. However, even the improper wearing of a facemask can obstruct the goal and lead to the spread of the virus. Therefore, to ensure public safety, a system for monitoring facemasks on faces, commonly known as a facemask detection algorithm, is essential for overcoming this crisis. The facemask detection algorithms are part of the object detection algorithms which are used to detect objects in an image. Among the various object detection algorithms, deep learning showed tremendous performance in facemask detection for its excellent feature extraction capability than the traditional machine learning algorithms. However, there remains a lot of scope for future research to build an efficient facemask detection system. Therefore, this study aims to draw attention to the researchers by providing a narrative and meta-analytic review on all the published works related to facemask detection in the context of Covid-19. Because facemask detection algorithms are run mainly by adopting object detection algorithms, this paper also explores the progress of object detection algorithms over the last few decades. A comprehensive analysis of different datasets used in facemask detection techniques by many studies has been explored. The performance comparison among these algorithms is discussed in narrative and meta-analytic approaches. Finally, this study concludes with a discussion of some of the major challenges and future scope in the related field.INDEX TERMS Covid-19, convolutional neural network, deep neural network, facemask detection, Covid-19 health, object detection, machine learning.