With the recent advances in Unmanned Aerial Vehicles (UAVs), the development of energy-efficient networking technology for mission-oriented multiple cooperative UAVs has become crucial. Routing in Flying Ad-Hoc Networks (FANETs) with UAVs is a challenging issue because of the high speed and sudden changes in direction of UAVs. Traditional routing protocols in FANETs periodically send hello messages for the establishment and maintenance of the routes. However, sending hello messages periodically after a fixed interval increases bandwidth wastage when the hello interval is excessively short, or causes long delays in neighbour discovery when the hello interval is overly long. Moreover, several disconnected UAV groups have been observed in which the group members are connected among themselves but detached from the main network. By exchanging excessive hello messages inside the group, the UAVs maintain an unnecessary neighbourhood, causing wastage of energy. However, FANETs have certain advantages, such as knowledge about mission-related information. To solve the problem of unnecessary energy drain, we propose a novel adaptive hello interval scheme—Energy Efficient Hello (EE-Hello)—based on available mission-related information, such as the volume of the allowed airspace, number of UAVs, UAV transmission range, and UAV speed. We present a method to decide the distance that a UAV needs to travel before sending a hello message. We also specify a technique to determine the number of UAVs necessary to achieve specific network requirements, such as packet delivery ratio or throughput, with the expenditure of minimum energy. We show that the proposed EE-Hello can save about 25% of the energy currently used, by suppressing unnecessary hello messages without degrading the overall network throughput.

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 (WHO), 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.

In late 2016, Google proposed the Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control algorithm to achieve high bandwidth and low latency for Internet traffic. Unlike loss-based congestion control algorithms, BBR works without filling the bottleneck buffer. Consequently, BBR can reduce packet loss and minimize end-to-end packet delay, which has attracted the attention of many researchers in recent years. However, some studies have reported the creation of persistent queues that cause unintended problems, resulting in a serious fairness issue between TCP BBR flows with different round-trip times (RTTs). Although existing congestion control algorithms also exhibit fairness issue between different RTT flows, BBR has a more serious problem in that the imbalance is considerable even with small RTT difference between the two flows, and the long RTT flow uses most of the bandwidth. The preponderance of long RTT flows is a serious problem because a particular user may cause imbalance by maliciously increasing the delay. Therefore, we propose a Delay-Aware BBR (DA-BBR) congestion control algorithm to mitigate the RTT fairness issue between BBR flows. In a network emulation experiment using the Mininet, the DA-BBR increased the fairness index by 1.6 times that of the original BBR, and the retransmission was greatly reduced. DA-BBR flow with short RTT demonstrated fair throughput even in competition with DA-BBR flows where RTT is five times higher.

Social media platforms have many users who share their thoughts and use these platforms to organize various events collectively. However, different upsetting incidents have occurred in recent years by taking advantage of social media, raising significant concerns. Therefore, considerable research has been carried out to detect any disturbing event and take appropriate measures. This review paper presents a thorough survey to acquire in-depth knowledge about the current research in this field and provide a guideline for future research. We systematically review 67 articles on event detection by sensing social media data from the last decade. We summarize their event detection techniques, tools, technologies, datasets, performance metrics, etc. The reviewed papers mainly address the detection of events, such as natural disasters, traffic, sports, real-time events, and some others. As these detected events can quickly provide an overview of the overall condition of the society, they can significantly help in scrutinizing events disrupting social security. We found that compatibility with different languages, spelling, and dialects is one of the vital challenges the event detection algorithms face. On the other hand, the event detection algorithms need to be robust to process different media, such as texts, images, videos, and locations. We outline that the event detection techniques compatible with heterogeneous data, language, and the platform are still missing. Moreover, the event and its location with a 24 × 7 real-time detection system will bolster the overall event detection performance.

Google proposed a bottleneck bandwidth round-trip propagation time (BBR) for TCP to replace the loss-based congestion control algorithms, such as Reno and CUBIC. Unlike the loss-based algorithms, BBR models a network path from source to destination and dynamically controls the sending rate using control parameters, such as pacing rate, congestion window, and quantum to achieve high throughput and low latency. However, many studies have reported performance issues in BBR operation, such as excessive packet loss in shallow buffers, the unfairness among different RTT flows, the unfairness with loss-based algorithms, and so on. Google is developing a new version of BBR, BBRv2, to resolve these performance issues. In this study, we evaluate and compare two versions of BBR on a Mininet emulator and a physical testbed, focusing on whether the BBRv2 alpha can alleviate the performance issues of BBRv1 and whether other novel issues will arise in BBRv2. The experiment results show that BBRv2 improves the unfairness and aggressiveness in small buffer less than 1 BDP. Moreover, multiple BBRv2 flows not only show better fairness in bandwidth sharing, but also reduce the amount of packet retransmissions. However, we observed that the challenging issues such as RTT unfairness, coexistence with loss-based algorithms, and synchronization between BBRv2 flows still exist. This study explores BBRv2’s current behavior in various network scenarios and compares the performance of BBRv2 with the BBRv1 congestion control algorithm.

This paper presents an Internet-of-Things-based platform for emergency vehicle priority and self-organised traffic control (EVP-STC) management at intersections. With the increasing number of automobiles, traffic jams in urban areas are becoming a critical issue. Traffic jams, especially those at intersections, not only increase delays for drivers but also increase fuel consumption and air pollution. We propose a novel platform and protocol called EVP-STC that contains three main systems. The first system, called the intersection controller, is installed at traffic lights and collects emergency vehicle position information and vehicle density data at each road segment approaching an intersection. The intersection controller then adjusts the timings of traffic lights based on detected real-time traffic. The second system is installed at each road segment and contains force resistive sensors to detect vehicles. It transmits the detected information to the intersection controller via ZigBee. The third system is installed in emergency vehicles and provides GPS coordinates to the intersection controller to avoid any waiting time for emergency vehicles at intersections. Simulation results demonstrate the effectiveness of the proposed platform, which minimises total delays, lane opening times, and waiting times for emergency vehicles.