Indoor positioning has become a very promising research topic due to the growing demand for accurate node location information for indoor environments. Nonetheless, current positioning algorithms typically present the issue of inaccurate positioning due to communication noise and interferences. In addition, most of the indoor positioning techniques require additional hardware equipment and complex algorithms to achieve high positioning accuracy. This leads to higher energy consumption and communication cost. Therefore, this paper proposes an enhanced indoor positioning technique based on a novel received signal strength indication (RSSI) distance prediction and correction model to improve the positioning accuracy of target nodes in indoor environments, with contributions including a new distance correction formula based on RSSI log-distance model, a correction factor (Beta) with a correction exponent (Sigma) for each distance between unknown node and beacon (anchor nodes) which are driven from the correction formula, and by utilizing the previous factors in the unknown node, enhanced centroid positioning algorithm is applied to calculate the final node positioning coordinates. Moreover, in this study, we used Bluetooth Low Energy (BLE) beacons to meet the principle of low energy consumption. The experimental results of the proposed enhanced centroid positioning algorithm have a significantly lower average localization error (ALE) than the currently existing algorithms. Also, the proposed technique achieves higher positioning stability than conventional methods. The proposed technique was experimentally tested for different received RSSI samples’ number to verify its feasibility in real-time. The proposed technique’s positioning accuracy is promoted by 80.97% and 67.51% at the office room and the corridor, respectively, compared with the conventional RSSI trilateration positioning technique. The proposed technique also improves localization stability by 1.64 and 2.3-fold at the office room and the corridor, respectively, compared to the traditional RSSI localization method. Finally, the proposed correction model is totally possible in real-time when the RSSI sample number is 50 or more.

The huge bandwidth and immunity to electromagnetic interference make visible light communication (VLC) systems the preferred technique for many applications. Unfortunately, the superposition of multiple subcarriers in VLC orthogonal frequency division multiplexing systems leads to a high peak-to-average power ratio (PAPR). So, in this study, we aim to reduce PAPR in VLC systems and improve the system performance by proposing non-distorting PAPR reduction techniques like precoding techniques as it doesn’t affect the system data rate because they do not require any obligatory transmission of side information. Moreover, it has a very good ability to reduce the PAPR without affecting the system BER performance. So, different precoding reduction techniques are proposed like discrete cosine transform (DCT), discrete sine transform (DST), discrete Hatley transform (DHT), and Vandermonde like matrix (VLM), to address the high PAPR and light-emitting diode-restricted linear range problems in VLC systems. The proposed technique using DHT, DCT, DST, and VLM provided a significant advantage in reducing the PAPR by 1.35 dB, 1.46 dB, 2.12 dB, and 2.17 dB, respectively. So the proposed technique is based on using the VLM precoding technique to achieve maximum reachable PAPR reduction value. Also, a comparison of the presented work and related literature reviews for PAPR reduction techniques are held to ensure the validity and effectiveness of the proposed scheme.

Dense communication networks have been investigated recently to enable communications of low power wireless devices such as Internet-of-things (IoT) applications in the fifth cellular generation (5G). The wireless sensor network (WSN) is one of the principal technologies of IoT as it plays a critical role in numerous industries such as agriculture, healthcare, and environmental applications. Despite the advantages of the WSN, it is yet difficult to be deployed due to the scarcity of the radio spectrum with the increasing popularity of wireless applications. Therefore, merging of two technologies WSN and cognitive radio network (CRN), as cognitive radio wireless sensor network (CR-WSN), became essential for IoT applications. Another major challenge in such systems is the power constrain and delay sensitivity in such numerous wireless devices. Radio-frequency energy harvesting (EH) capability is supposed to merge with such systems in order to efficiently power and enhance the overall system energy. Thus, this paper discusses a CR-WSN model based on EH in a non-ordinary M/M/1 Markovian battery model with the proposal of a frame structure of the wireless node's charging and sensing time. The contribution of the power obtained from harvesting is derived with the proposal of a realistic power required/harvested model in RF EH CRN. Moreover, the power efficiency of the CR-WSN model is calculated and the derivation of the transmission delay is introduced in the same model. Furthermore, combined optimization of the required power, probability of packet loss and the transmission delay is proposed to validate the overall system performance with the recommended system operational parameters.

Smart cities are now embracing the new frontier of urban living, with advanced technology being used to enhance the quality of life for residents. Many of these cities have developed transportation systems that improve efficiency and sustainability, as well as quality. Integrating cutting-edge transportation technology and data-driven solutions improves safety, reduces environmental impact, optimizes traffic flow during peak hours, and reduces congestion. Intelligent transportation systems consist of many systems, one of which is traffic sign detection. This type of system utilizes many advanced techniques and technologies, such as machine learning and computer vision techniques. A variety of traffic signs, such as yield signs, stop signs, speed limits, and pedestrian crossings, are among those that the traffic sign detection system is trained to recognize and interpret. Ensuring accurate and robust traffic sign recognition is paramount for the safe deployment of self-driving cars in diverse and challenging environments like the Arab world. However, existing methods often face many challenges, such as variability in the appearance of signs, real-time processing, occlusions that can block signs, low-quality images, and others. This paper introduces an advanced Lightweight and Efficient Convolutional Neural Network (LE-CNN) architecture specifically designed for accurate and real-time Arabic traffic sign classification. The proposed LE-CNN architecture leverages the efficacy of depth-wise separable convolutions and channel pruning to achieve significant performance improvements in both speed and accuracy compared to existing models. An extensive evaluation of the LE-CNN on the Arabic traffic sign dataset that was carried out demonstrates an impressive accuracy of 96.5% while maintaining superior performance with a remarkably low inference time of 1.65 s, crucial for real-time applications in self-driving cars. It achieves high accuracy with low false positive and false negative rates, demonstrating its potential for real-world applications like autonomous driving and advanced driver-assistance systems.

Mobile Ad Hoc Networks (MANETs) are decentralized wireless networks characterized by dynamic topologies and the absence of fixed infrastructure. These unique features make MANETs critical for applications such as disaster recovery, military operations, and IoT systems. However, they also pose significant challenges for efficient and effective routing. This study evaluates the performance of eight MANET routing protocols: Optimized Link State Routing (OLSR), Destination-Sequenced Distance Vector (DSDV), Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Ad Hoc On-Demand Multipath Distance Vector (AOMDV), Temporally Ordered Routing Algorithm (TORA), Zone Routing Protocol (ZRP), and Geographic Routing Protocol (GRP). Using a custom simulation environment in OMNeT++ 6.0.1 with INET-4.5.0, the protocols were tested under four scenarios with varying node densities (20, 80, 200, and 500 nodes). The simulations utilized the Random Waypoint Mobility model to mimic dynamic node movement and evaluated key performance metrics, including network load, throughput, delay, energy consumption, jitter, packet loss rate, and packet delivery ratio. The results reveal that proactive protocols like OLSR are ideal for stable, low-density environments, while reactive protocols such as AOMDV and TORA excel in dynamic, high-mobility scenarios. Hybrid protocols, particularly GRP, demonstrate a balanced approach; achieving superior overall performance with up to 30% lower energy consumption and higher packet delivery ratios compared to reactive protocols. These findings provide practical insights into the optimal selection and deployment of MANET routing protocols for diverse applications, emphasizing the potential of hybrid protocols for modern networks like IoT and emergency response systems.

Visible Light Communications (VLC) are receiving increased attention in the wireless communications research community. VLC is secured, power efficient, and operates in the visible light range, thus RF communication bandwidth limitation is overcome. In this article, the authors enhance the data rate, system complexity, power efficiency, and spectrum efficiency in VLC systems. An innovative unipolar transceiver system is proposed, mathematically analyzed, and compared with other existing techniques and it demonstrates to have a very high data rate ratio (43.75%) with a good system bit energy to noise ratio (E_b/N_o) compared to other existing techniques. Development for the traditional asymmetrically and symmetrically clipping optical (ASCO-OFDM) system is also proposed, which involves combining a modified receiver with the ASCO-OFDM system traditional transmitter. The proposed receiver reduces the system complexity by O (N log₂ N) with better E_b/N_o than the conventional ASCO-OFDM. Detailed analysis, simulation results, and comparison of the proposed systems with the existing systems are presented beside a brief assessment of existing techniques.