Hajj and Umrah are Muslims’ religious pilgrimages to Makkah, Saudi Arabia. Hajj is considered the largest annual gathering in the world. In Islam, each and every able-bodied adult is obligated to go for Hajj at least once in his/her lifetime. Hajj takes place between the 8^th and the 13^th day of Dhul Hijjah, which is the last month of the Islamic year while Umrah, the minor pilgrimage, could be done any time of the year. In Hajj, pilgrims, also known as Hajjis, travel around 15 km and pilgrims have the choice to do it by foot, bus or by train. The Hajj journey starts from Mina, which is considered the largest tent city in the world because it hosts more than 10,000 fire resistant tents, and includes various religious rituals. Around 3 million Muslims from more than 183 countries make Hajj every year and the Kingdom of Saudi Arabia is working on increasing this number. Since 1989, researchers in various ICT domains have tried leveraging their expertise in solving challenges and issues related to Hajj and Umrah. Although several papers have surveyed efforts using technology in Hajj and Umrah research, none of them was comprehensive. In this paper, I classified research efforts that used information and communication technologies for solving Hajj and Umrah challenges over the past 33 years based on their applications in 10 categories. I also identified more than 30 technologies used by researchers all over the world to address Hajj and Umrah issues and group research efforts based on these technologies for ease of access. Furthermore, open challenges were discussed and new technologies that could be used to address these challenges were proposed.

Recent advancements of Industrial IoT (IIoT) have revolutionized modern urbanization and smart cities. While IIoT data contains rich events and objects of interest, processing a massive amount of IIoT data and making predictions in real-time is challenging. Recent advancements in AI allow processing such a massive amount of IIoT data and generating insights for further decision-making processes. In this paper, we propose several key aspects of AI-enabled IIoT data for smart city monitoring. Firstly, we have combined a human-intelligence-enabled crowdsourcing application with that of an AI-enabled IIoT framework to capture events and objects from IIoT data in real-time. Secondly, we have combined multiple AI algorithms that can run on distributed edge and cloud nodes to automatically categorize the captured events and objects and generate analytics, reports, and alerts from the IIoT data in real-time. The results can be utilized in two scenarios. In the first scenario, the smart city authority can authenticate the AI-processed events and assign these events to the appropriate authority for managing the events. In the second scenario, the AI algorithms are allowed to interact with humans or IIoT for further processes. Finally, we will present the implementation details of the scenarios mentioned above and the test results. The test results show that the framework has the potential to be deployed within a smart city.

Buffer sizing is an important network configuration parameter that impacts the quality of service characteristics of data traffic. With falling memory costs and the fallacy that "more is better," network devices are being overprovisioned with large buffers. This may increase queueing delays experienced by a packet and subsequently impact stability of core protocols such as TCP. The problem has been studied extensively for wired networks. However, there is little work addressing the unique challenges of wireless environments such as time-varying channel capacity, variable packet inter-service time, and packet aggregation, among others. In this article we discuss these challenges, classify the current state-of-the-art solutions, discuss their limitations, and provide directions for future research in the area.

In this paper, we analyze the impact of large, persistently-full buffers (`bufferbloat') on various network dynamics in IEEE 802.11n wireless networks. Bufferbloat has mostly been studied in the context of wired networks. We study the impact of bufferbloat on a variety of wireless network topologies, including wireless LAN (WLAN) and multi-hop wireless networks. Our results show that a single FTP transfer between two Linux wireless hosts can saturate the buffers in the network stack, leading to RTT delays exceeding 4.5 s in multi-hop configurations. We show that well-designed Aggregate MAC Protocol Data Unit (A-MPDU) MAC-layer frame aggregation can reduce RTT delays while simultaneously increasing network throughput. However, additional measures may still be required to meet the constraints of real-time flows (such as VoIP). Our experiments show that large buffers can deteriorate the fairness in rate allocation in parking lot based multi-hop networks.

Choosing the right buffer size in Wi-Fi networks is challenging due to the dynamic nature of the wireless environment. Over buffering or 'bufferbloat' may produce unacceptable end-to-end delays, while static small buffers may limit the performance gains that can be achieved with various 802.11n enhancements, such as frame aggregation. We propose WQM, a queue management scheme customized for wireless networks. WQM adapts the buffer size based on measured link characteristics and network load. Furthermore, it accounts for aggregate length when deciding about the optimal buffer size. We implement WQM on Linux and evaluate it on a wireless testbed. WQM reduces the end-to-end delay by up to 8x compared to Linux default buffer size, and 2x compared to CoDel, the state-of-the-art bufferbloat solution, while achieving comparable network goodput. Further, WQM improves fairness as it limits the ability of a single flow to saturate the buffer