Managing large-scale gatherings, such as global festivals, sporting events, and religious congregations, presents substantial challenges in ensuring crowd safety and control. Innovative frameworks are essential to address these complexities effectively. The Integrated Intelligent Crowd Control and Management (IICCM) framework combines cutting-edge technologies, including Computer Vision (CV), Artificial Intelligence (AI), and the Internet of Things (IoT), to enhance participant safety and optimize crowd management. CV enables precise real time identification and tracking, AI analyzes crowd behavior to anticipate risks, and IoT gathers environmental data to improve crowd flow, alleviate congestion, and provide timely assistance. Additionally, the framework facilitates emergency evacuation planning by modeling crowd dynamics and identifying safe, efficient escape routes. Although suitable for diverse events, the Hajj pilgrimage—a uniquely large and dynamic annual gathering—provides a rigorous test case for the IICCM framework. Managing millions of participants from varied cultural and linguistic backgrounds highlights the system’s adaptability and robustness. By effectively addressing Hajj specific challenges, the IICCM framework demonstrates its scalability and applicability to other large-scale events. This research offers valuable insights for decision-makers seeking to implement advanced crowd management technologies.

In this paper, we proposed two methods to solve Minimum Connected Dominating Set (MCDS) problem. It is known that efficient methods for computing the MCDS are essential for solving many practical problems, such as finding a minimum size backbone in ad hoc networks. We propose two methods to solve the MCDS problem. In the first method, we propose a new hybrid method based on genetic Algorithm combine with local search strategy for the MCDS problem called Memetic Algorithm for the MCDS problem (MA-MCDS). MAMCDS uses local search and intensification schemes beside the GA search methodology in order to achieve faster performance. The scorned method is called Simulated Annealing for the MCDS problem (SA-MCDS). SA-MCDS is invoked to enhance the stochastic local search (SLS) method with the ability of escaping from local solutions. In addition, proposed methods invoke a new objective function to effectively measure the solution qualities. The proposed algorithms is tested on a number of publically available benchmark test graph sets and numerical results indicate that they are working well in terms of solution qualities and computational costs.

This paper presents a Markov chain analysis for studying the performance of the IEEE 802.11 Distributed Coordination Function. The model supports an arbitrary back-off strategy. We found that the most important parameter affecting the performance of binary exponential back-off is the initial back-off window size. Our experimental results show that the probability of collision can be reduced when the initial back-off window size equals the number of stations. Thus, the throughput of the system increases and, at the same time, the delay to transmit the frame is reduced.

Number Theoretic Transform (NTT)-based polynomial multiplication is a computationally intensive operation in lattice-based post-quantum cryptography (PQC) schemes such as CRYSTALS-Dilithium. Existing hardware accelerators optimize area and timing performance, without focusing on evaluating trade-offs among hardware utilization, execution latency, operating frequency, and power consumption. This article investigates such trade-offs through two lightweight field-programmable gate array (FPGA) implementations of an iterative NTT-based polynomial multiplication accelerator, namely non-pipelined and 4-stage pipelined architectures. Both implementations employ a single butterfly unit based on Cooley–Tukey and Gentleman–Sande configurations to compute the forward NTT (FNTT), inverse NTT (INTT), and coefficient-wise multiplication (CWM). The 4-stage pipelined architecture employs pipeline registers in the modular multiplication and Barrett reduction datapaths to maximize the operating frequency. Both architectures are implemented on an Artix-7 FPGA and evaluated across operating frequencies ranging from 10 MHz to 280 MHz. The results show that the non-pipelined architecture provides reduced hardware overhead and lower power consumption, whereas the pipelined architecture improves timing scalability and successfully operates at 280 MHz. At the maximum operating frequency, the pipelined implementation utilizes 1115 slices and achieves execution times of 4.58 μs, $0.93\mathsf{\mu }$s, and $4.58\mathsf{\mu }$s for FNTT, CWM, and INTT computations, respectively, with an average power consumption of 133 mW. The Area–Time Product (ATP) and Energy–Delay Product (EDP) evaluations demonstrate that the pipelined architecture achieves improved overall efficiency within the proposed lightweight single-butterfly-based polynomial multiplication architecture at higher operating frequencies, obtaining an ATP of $7.74\times {10}^3$ Slice-$\mathsf{\mu }$s and EDP of 1501.52 nJ-$\mathsf{\mu }$s.

We present a new analytical model of MAC layer for wireless ad hoc networks that takes into account frame retry limits for four-way handshaking mechanism. This model offers flexibility to address key design issues such as the effects of traffic parameters and possible improvements for wireless ad hoc networks. It effectively captures the important network performance characteristics such as throughput, channel utilization, delay and average energy. Under this analytical framework, we evaluate the effect of RTS (request-to-send) state on unsuccessful transmission probability and its effect on the performance specially when the hidden terminal problem is dominant, the traffic is heavy, or the data frame length is very large. By using our proposed model, we show that the probability of collision can be reduced when using RTS/CTS mechanism. Thus, the throughput increases and, at the same time, the delay and the average energy to transmit the frame is reduced.