The development of autonomous vehicles in compliance with safety requirements requires them being equipped with various types of sensors. This integration presents significant potential for enhancing communication performance within Vehicular Ad-hoc Networks (VANETs). A critical communication requirement in VANETs is achieving low delay to enable realtime communication and reducing routing overhead to increase the generation rate of safety-related packets, thereby enhancing vehicular safety. This paper aims to address this issue by leveraging sensor data through proposing an optimized packet routing method within the network. To establish a common structure among different vehicles, roadways are divided into smaller segments with adaptive lengths, and sensor information from each occupied segment is disseminated within a shared framework inspired by 5G sidelink using a Time Division Integrated Sensing and Communication (TD-ISAC) frame structure. The dimensions of this frame are optimized using a combined Lagrangian Relaxation and Branch and Bound approach. Additionally, vehicle positions are estimated in real-time based on sensor data through the integration of a Kalman filter and a Transformer model, resulting in the formation of radial awareness in the network topology. Finally, the relay station selection process is stabilized by incorporating reserve relay stations. The proposed model is evaluated using SUMO and compared to the Zone Routing Protocol (ZRP) and Ad Hoc On-Demand Distance Vector (AODV) algorithms, demonstrating up to a 60% improvement in delay and a 5% reduction in routing overhead under multi-hop scenarios.

Indoor localization within buildings is paramount due to its diverse applications, including the Internet of Things (IoT), healthcare, and personnel monitoring. To improve the performance of indoor localization, this study proposes an algorithm combining Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) used within a hybrid model that uses both cellular technology, i.e, base station (BS) and Wi-Fi access point (AP). Within a realistic environment, we evaluate the performance of our approach across four scenarios: (1) (2 APs + 2 BSs), (2) (1 AP + 2 BSs), (3) (2 APs), and (4) (2 BSs). Additionally, we consider both stationary and seated user positions. Our results demonstrate significant improvements over existing methods. Specifically, in the 2 APs+2 BSs scenario, we achieve an average Euclidean distance error of 1.04 meters, with a maximum error of 2.51 meters, and the Root Mean Square Error (RMSE) of 1.15 meters. We also obtain the corresponding Cumulative Distribution Function (CDF) for the error which indicates that 90% of the time, the error is less than 1.69 meters. Our hybrid model achieves over 99% accuracy in classifying different building floors and over 88% accuracy in identifying user states (e.g., standing or sitting). We validate our approach using smartphone-based indoor localization in a twostory residential building with robust construction materials and double-layered walls.

In numerous mission-critical applications anticipated by future sixth-generation (6G), such as autonomous vehicles, achieving high accuracy in image recognition is essential. Concurrently, minimizing the data traffic, or equivalently, effective data compression due to constraints posed by transmission delay are crucial. To overcome these challenges, this paper proposes a task-oriented semantic communication system dedicated to image data, designed to extract and transmit the information required by the receiver. The goal of the system is to recognize the semantic boundaries of the images. For this For this relevant, a novel semantic encoder, based on compressed sensing (CS) is developed at the transmitter to extract semantic information. Additionally, a novel semantic decoder is proposed in the receiver, utilizing sparse reconstruction techniques to reconstruct semantic information. In contrast to prior studies that focused on conveying a broad spectrum of semantic information related to images along with all extracted features, this approach concentrates solely on isolating the semantic features relevant to the specific target edges. This method generates a sparse feature map, allowing for a reduction in compression rates by a new compressed sensing techniques implementing new sensing matrix based on polar code (SMPC)compressed sensing techniques. Furthermore, the study examines two distinct typical scenarios: noiseless measurements and noisy measurements. Our simulations show that with a lower compression rate, the classification accuracy, and exact recovery probability of 100 % can be attained in both scenarios.

Indoor localization within buildings is paramount due to its diverse applications, including the Internet of Things (IoT), healthcare, and personnel monitoring. To improve the performance of indoor localization, this study proposes an algorithm combining Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) used within a hybrid model that uses both cellular technology, i.e, base station (BS) and Wi-Fi access point (AP). Within a realistic environment, we evaluate the performance of our approach across four scenarios: (1) (2 APs + 2 BSs), (2) (1 AP + 2 BSs), (3) (2 APs), and (4) (2 BSs). Additionally, we consider both stationary and seated user positions. Our results demonstrate significant improvements over existing methods. Specifically, in the 2 APs + 2 BSs scenario, we achieve an average Euclidean distance error of 1.04 meters, with a maximum error of 2.51 meters, and the Root Mean Square Error (RMSE) of 1.15 meters. We also obtain the corresponding Cumulative Distribution Function (CDF) for the error which indicates that 90% of the time, the error is less than 1.69 meters. Our hybrid model achieves over 99% accuracy in classifying different building floors and over 88% accuracy in identifying user states (e.g., standing or sitting). We validate our approach using smartphone-based indoor localization in a two story residential building with robust construction materials and double-layered walls.

In this paper, we develop a hierarchical aerial computing framework composed of high altitude platform (HAP) and unmanned aerial vehicles (UAVs) to compute the fully offloaded tasks of terrestrial mobile users which are connected through an uplink non-orthogonal multiple access (UL-NOMA). To better assess the freshness of information in computationintensive applications the criterion of age of information (AoI) is considered. In particular, the problem is formulated to minimize the average AoI of users with elastic tasks, by adjusting UAVs’ trajectory and resource allocation on both UAVs and HAP, which is restricted by the channel state information (CSI) uncertainty and multiple resource constraints of UAVs and HAP. In order to solve this non-convex optimization problem, two methods of multi-agent deep deterministic policy gradient (MADDPG) and federated reinforcement learning (FRL) are proposed to design the UAVs’ trajectory, and obtain channel, power, and CPU allocations. It is shown that task scheduling significantly reduces the average AoI. This improvement is more pronounced for larger task sizes. On one hand, it is shown that power allocation has a marginal effect on the average AoI compared to using full transmission power for all users. Compared with traditional transmission schemes, the simulation results show our scheduling scheme results in a substantial improvement in average AoI.

This paper presents an analysis for the Age of Information (AoI) in a wireless sensor network consisting of multiple VLC/RF IoT sensors. In this network, we use an energy harvesting method, and all sensors utilize the stored energy in their batteries and transmit update packets with a specific power. We present two main optimization problems based on PD-NOMA scheme for average AoI with the sensor’s transmit power constraint. By optimizing these problems, the average AoI for each sensor and the total average AoI in the proposed system are reduced by 10% and 15%, respectively.

Abstract: In this paper, we propose a novel resource management scheme that jointly allocates the transmit power and computational resources in a centralized radio access network architecture. The network comprises a set of computing nodes to which the requested tasks of different users are offloaded. The optimization problem minimizes the energy consumption of task offloading while takes the end-to-end-latency, i.e., the transmission, execution, and propagation latencies of each task, into account. We aim to allocate the transmit power and computational resources such that the maximum acceptable latency of each task is satisfied. Since the optimization problem is non-convex, we divide it into two sub-problems, one for transmit power allocation and another for task placement and computational resource allocation. Transmit power is allocated via the convex-concave procedure. In addition, a heuristic algorithm is proposed to jointly manage computational resources and task placement. We also propose a feasibility analysis that finds a feasible subset of tasks. Furthermore, a disjoint method that separately allocates the transmit power and the computational resources is proposed as the baseline of comparison. A lower bound on the optimal solution of the optimization problem is also derived based on exhaustive search over task placement decisions and utilizing Karush–Kuhn–Tucker conditions. Simulation results show that the joint method outperforms the disjoint method in terms of acceptance ratio. Simulations also show that the optimality gap of the joint method is less than 5%.