Mahmoud Elsharief

Vehicular sidelink communications are essential to autonomous driving, yet broadcast reliability under distributed operation remains constrained by collisions and inefficient resource use. The third-generation partnership project (3GPP) has defined a new radio (NR) for vehicle-to-everything (V2X) Mode 2 for autonomous resource reservation and selection. An important challenge in the design of NR-V2X systems is the efficient allocation of resources. Resource allocation facilitates reliable communication between user equipment (UEs) and other network elements. A cooperative approach among UEs is required to ensure reliable resource allocation and communication in such networks. This paper presents cooperative resource status exchange (CRSE), a cooperative scheme that augments autonomous sidelink broadcasts by disseminating a compact resource status map in each packet. It significantly maximizes the packet reception ratio (PRR) and resource utilization. In addition, CRSE integrates game theory to provide a sophisticated method for analyzing and optimizing resource selection strategies among UEs. The analytical model and simulation results show that CRSE performs better than new radio NR-V2X Mode 2, UE-scheduling, and short-term sensing-based resource selection (STS-RS) in terms of PRR and successful transmission rate. The results show that CRSE improves the PRR by approximately 24.6%, 15.2%, and 12.3% over NR-V2X, UE-scheduling, and STS-RS, respectively. Furthermore, the results demonstrate the possibility of realizing the maximum value of PRR and a successful transmission rate.

The third-generation partnership project (3GPP) has introduced a new radio (NR) for vehicle-to-everything (V2X). It assigns Mode 2 to allow user equipment (UEs) to reserve and select radio resources autonomously. This paper comprehensively analyzes the NR-V2X Mode 2 performance, focusing on the packet reception ratio (PRR). PRR is a critical metric in evaluating the efficacy of vehicular networks. We propose an analytical model that correlates PRR with the distance between the transmitter and receiver. This model helps us understand NR-V2X Mode 2 communication in autonomous operation. We also conduct simulations to validate our model. These simulations reflect various vehicular traffic scenarios. Our findings reveal strategies to optimize resource allocation in NR-V2X Mode 2 networks. Specifically, altering specific parameters can notably enhance network performance. This research is valuable for both academic and industry circles. It provides insights into improving communications in autonomous vehicles. Moreover, it offers practical guidance for industry experts aiming to advance V2X communication in the era of autonomous driving.

In the Industrial Internet of Things (IIoT), energy efficiency is a paramount concern as it directly affects operational longevity. Traditional approaches, like flooding for time synchronization, often result in redundant message transmissions, thereby wasting energy. This article introduces an intelligent neighbor-knowledge synchronization (INKS) method to mitigate this problem. The INKS algorithm leverages each node's understanding of its neighboring nodes to optimize the synchronization process, thereby reducing the total number of synchronization messages and conserving energy. The INKS is implemented and evaluated using real wireless sensor networks with varying configurations. The experimental results demonstrate their superior performance to existing techniques, such as rapid flooding multiple one-way broadcast time synchronization (RMTS). Additionally, the performance of INKS is evaluated through simulations conducted on large-scale networks. For the network topology of the four-way grid, the findings reveal that INKS reduces the number of transmitted messages by approximately 72% compared to RMTS. Moreover, INKS matches the efficiency of scheduling-based low-energy synchronization for IIoT.

The Internet of Things (IoT) is a network of electrical devices that are connected to the Internet wirelessly. This group of devices generates a large amount of data with information about users, which makes the whole system sensitive and prone to malicious attacks eventually. The rapidly growing IoT-connected devices under a centralized ML system could threaten data privacy. The popular centralized machine learning (ML)-assisted approaches are difficult to apply due to their requirement of enormous amounts of data in a central entity. Owing to the growing distribution of data over numerous networks of connected devices, decentralized ML solutions are needed. In this paper, we propose a Federated Learning (FL) method for detecting unwanted intrusions to guarantee the protection of IoT networks. This method ensures privacy and security by federated training of local IoT device data. Local IoT clients share only parameter updates with a central global server, which aggregates them and distributes an improved detection algorithm. After each round of FL training, each of the IoT clients receives an updated model from the global server and trains their local dataset, where IoT devices can keep their own privacy intact while optimizing the overall model. To evaluate the efficiency of the proposed method, we conducted exhaustive experiments on a new dataset named Edge-IIoTset. The performance evaluation demonstrates the reliability and effectiveness of the proposed intrusion detection model by achieving an accuracy (92.49%) close to that offered by the conventional centralized ML models’ accuracy (93.92%) using the FL method.

The growth in the Industrial Internet of Things (IIoT) drives the development of large-scale wireless sensor networks (WSN), such as smart cities. Time synchronization is essential and primary for enabling collaborative operations among sensor nodes. Generally, all sensor nodes in the network synchronize through exchanging messages, increasing energy consumption. This study introduces scheduling-based low-energy synchronization (SLES) for IIoT. SLES aims to reduce the number of transmitted messages, which in turn reduces the overall energy consumption. In addition, SLES establishes a schedule of distinct synchronization messaging times for each reference node to alleviate the collision problem among the sensor nodes. SLES is constructed and evaluated using real WSNs with different configurations. The experimental results showed that SLES achieves significantly less message traffic than the previous algorithms: FADS – fast scheduling and accurate drift compensation for time synchronization, EERS – energy-efficient reference node selection, and R-sync – robust time synchronization. In this study, we used a simulation to evaluate the performance of SLES in large-scale networks. For the 4-way grid network, the experiential results show that SLES reduced the number of transmitted messages by approximately 22% compared to EERS and FADS. Moreover, SLES outperformed the R-sync algorithm by a factor of 2.5.

Time synchronization is one of the most fundamental problems on the internet of things (IoT). The IoT requires low power and an efficient synchronization protocol to minimize power consumption and conserve battery power. This paper introduces an efficient method for time synchronization in the IoT called low-power multi-hop synchronization (LPSRS). It employs a reference node scheduling mechanism to avoid packet collisions and minimize the communication overhead, which has a big impact on power consumption. The performance of LPSRS has been evaluated and compared to previous synchronization methods, HRTS and R-Sync, via real hardware networks and simulations. The results show that LPSRS achieves a better performance in terms of power consumption (transmitted messages). In particular, for a large network of 450 nodes, LPSRS reduced the total number of transmitted messages by 53% and 49% compared to HRTS and R-Sync, respectively.

Time synchronization is an essential issue in industrial wireless sensor networks (IWSNs). It assists perfect coordinated communications among the sensor nodes to preserve battery power. Generally, time synchronization in IWSNs has two major aspects of energy consumption and accuracy. In the literature, the energy consumption has not received much attention in contrast to the accuracy. In this paper, focusing on the energy consumption aspect, we introduce an energy-efficient reference node selection (EERS) algorithm for time synchronization in IWSNs. It selects and schedules a minimal sequence of connected reference nodes that are responsible for spreading timing messages. EERS achieves energy consumption synchronization by reducing the number of transmitted messages among the sensor nodes. To evaluate the performance of EERS, we conducted extensive experiments with Arduino Nano RF sensors and revealed that EERS achieves considerably fewer messages than previous techniques, robust time synchronization (R-Sync), fast scheduling and accurate drift compensation for time synchronization (FADS), and low power scheduling for time synchronization protocols (LPSS). In addition, simulation results for a large sensor network of 450 nodes demonstrate that EERS reduces the whole number of transmitted messages by 52%, 30%, and 13% compared to R-Sync, FADS, and LPSS, respectively.

The deployment of vehicular networks is considered crucial for traffic safety of future vehicles. Thus, researchers are making extensive efforts to improve the performance of the IEEE802.11p standard. Many researchers have proposed various MAC protocols to mitigate the chronicle problems of the IEEE802.11p - for example, the unreliable transmission of safety-related messages. However, most of the previous evaluations of the reliability problem have been done either via mathematical analysis or simulations. In this paper, we conducted actual experiments and analyzed the performance of two MAC protocols: IEEE802.11p and HCMAC, a hybrid MAC protocol recently reported. Using commercial V2X devices, we measured the performance in terms of received signal strength indicator (RSSI), packet delivery ratio (PDR), and packet inter-reception time (PIR). We tested the connectivity performance under various mobility scenarios. In addition, this paper investigates the impact of collisions on the overall performance. For a range of collision levels, an extensive set of experiments demonstrate that HCMAC outperforms the MAC of IEEE802.11p in terms of PDR and PIR up to 88% and 47%, respectively.