Graph Neural Networks (GNNs) have emerged as powerful tools for modeling complex, interconnected data, making them particularly well suited for a wide range of Intelligent Transportation System (ITS) applications. This survey presents the first comprehensive review dedicated specifically to the use of GNNs within Vehicular Social Networks (VSNs). By leveraging both Euclidean and non-Euclidean transportation-related data, including traffic patterns, road users, and weather conditions, GNNs offer promising solutions for analyzing and enhancing VSN applications. The survey systematically categorizes and analyzes existing studies according to major VSN-related tasks, including traffic flow and trajectory prediction, traffic forecasting, signal control, driving assistance, routing problem, and connectivity management. It further provides quantitative insights and synthesizes key takeaways derived from the literature review. Additionally, the survey examines the available datasets and outlines open research directions needed to advance GNN-based VSN applications. The findings indicate that, although GNNs demonstrate strong potential for improving the accuracy, robustness, and real-time performances of on task-specific or sub-VSN graphs, there remains a notable absence of studies that model a complete, standalone VSN encompassing all functional components. With the increasing availability of data and continued progress in graph learning, GNNs are expected to play a central role in enabling future large-scale and fully integrated VSN applications.

With the growing adoption of Artificial Intelligence (AI) in sensitive sectors such as healthcare and finance, protecting user privacy during data processing has become paramount. One promising approach is Fully Homomorphic Encryption (FHE), which offers a viable solution by allowing computations to be performed directly on encrypted data, thus safeguarding sensitive information. In this study, we investigate the practical application of the Cheon Kim Kim Song (CKKS) FHE scheme to perform inference with various machine learning models for heart disease detection. We evaluated five models: Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and a simple Neural Network, across multiple heart disease datasets. Our analysis compares their performance on both standard (plain-text) and encrypted data, using metrics including accuracy, precision, recall, and F1-score. Results demonstrate that encrypted models deliver predictive accuracy comparable to their standard counterparts, confirming the viability of privacypreserving inference with FHE despite the expected increase in computational time. Furthermore, our findings highlight up to $100 \%$ consistency between the predictions made on encrypted and plain-text inputs.

Ensuring accurate traffic perception and road safety in complex urban environments remains a significant challenge. Advanced traffic monitoring increasingly relies on deep learning, which requires large data volumes. However, existing datasets are often limited to CCTV video footage or focus on dynamic scenarios captured by sensors mounted on ego vehicles. This narrow perspective reduces the effectiveness of comprehensive traffic monitoring, particularly for LiDAR sensors, which typically capture only the vehicle’s viewpoint and miss critical areas such as intersections and pedestrian crossings. To address these limitations, we propose a holistic strategy for rapid data collection in urban settings using simulated 3D intersections. Our approach introduces a point cloud collection framework using static LiDAR sensors to provide a global view of the entire traffic scene. By incorporating randomized traffic patterns observed from multiple angles, this method generates a diverse, comprehensive dataset for object detection and instance segmentation, showcasing its advantages for benchmarking smart mobility applications.

The automotive industry has seen rapid advancements with the integration of artificial intelligence (AI), particularly in autonomous driving. While significant progress has been made, navigating complex traffic scenarios, such as intersections, remains challenging due to their dynamic nature. Traditional rule-based systems often struggle to adapt, prompting the need for more advanced solutions. This study aims to enhance autonomous driving at intersections by integrating bird’s eye view (BEV) fusion and reinforcement learning (RL). Using the CARLA simulator, we fine-tune the UNetXST model to fuse multiple camera perspectives into a BEV representation, providing a holistic view of the vehicle’s surroundings. This comprehensive view serves as input for the RL agent, which is trained using the proximal policy optimization (PPO) algorithm to learn optimized driving strategies, avoid collisions, and ensure efficient navigation. Our results show that the proposed framework outperforms baseline techniques.

Video surveillance in remote areas poses significant challenges due to limited network coverage and the high data requirements of video transmission. In such environments, satellite communication offers a viable solution to provide coverage under limited bandwidth conditions. At the same time, emerging semantic communication technology addresses the issue of data volume by transmitting only the most relevant information. This work proposes a semantic communication framework for video surveillance data transmission in remote areas. The framework transmits compact semantic representations from cameras over LEO satellites using Narrowband communication technology. More importantly, we formulate and solve a resource allocation problem that maximizes semantic information transmission under bandwidth constraints by optimally selecting semantic representations and satellite resource units. Our approach demonstrates the feasibility of semantic video surveillance using limited-bandwidth IoT standards such as LTE eMTC and NB-IoT over LEO satellites.

This paper presents a novel decentralized navigation framework for urban environments, integrating an Unmanned Aerial Vehicle (UAV) with an Unmanned Ground Vehicle (UGV). The system is designed such that the UAV aims to reach a predefined destination to perform critical aerial tasks while the UGV maintains proximity to support dynamic charging and continuous operation. The navigation strategy employs a hybrid path planning approach, combining a Probabilistic Roadmap (PRM) with an$A^{\ast}$search algorithm, further optimized by path pruning for the UAV. In contrast, the UGV utilizes the Dynamic Window Approach (DWA) for adaptive navigation. Both unmanned vehicles operate while maintaining a safe distance to ensure uninterrupted communication and prevent connectivity loss. Validated through extensive simulations in ROS2 and Gazebo, this framework enhances mission efficiency and enables real-time coordination for effective decentralized navigation.

Collaborative perception is essential in autonomous driving, enabling connected autonomous vehicles (CAVs) to share sensor data and improve awareness of their surroundings. This is especially critical for detecting occluded objects at intersections, where limited visibility can compromise safety and hinder real-time decision making. However, early fusion of sensor data across multiple CAVs presents significant challenges in data management, as the sheer volume of 3D point cloud information demands substantial communication bandwidth. To address these challenges, this article proposes an optimized collaborative perception framework specifically designed for CAVs at intersections. Our approach begins with each CAV generating a Bird's Eye View (BEV) map from LiDAR data, which is then divided into sectors. The quality and size of the data for each sector are assessed and sent to a roadside unit (RSU) that acts as a data center. The RSU selectively coordinates the sharing of high-quality sectors only, reducing redundant data transmission by avoiding empty or low-density regions. Through this targeted data sharing, our framework minimizes communication loads and computational demands while preserving perception accuracy, thus supporting efficient and scalable collaborative perception in complex intersection environments.

Unmanned aerial vehicles (UAVs) can enhance wireless access by dynamically positioning themselves closer to users while maintaining a backhaul connection to cellular base stations. In scenarios where users are geographically dispersed, multiple UAVs can be orchestrated to establish multi-hop integrated access and backhaul (IAB) connections. Traditionally, finding optimal UAV placement has required computationally demanding methods, such as combinatorial optimization or reinforcement learning, which are often impractical for real-time applications due to their complexity and training requirements. Additionally, UAV operators may lack the capability to solve complex optimization problems during live operations. This paper presents a novel framework that leverages large language models (LLMs) for optimizing the placement of multiple UAVs through iterative structured prompting. The proposed method achieves near-optimal solutions in significantly fewer iterations compared to traditional methods, making it suitable for real-time deployment without extensive mathematical modeling. Simulation result demonstrate that the proposed method achieves scores over 82% of the theoretical optimal solution while reducing computational time from hours to minutes compared to the baseline deep Q network approach, ensuring robust network connectivity and service quality. The LLM-driven framework simplifies problem-solving for UAV network operators, paving the way for its application in more complex real-world scenarios.

The advent of multi-unmanned aerial vehicle (multi-UAV) networks in mobile edge computing (MEC) introduces dynamic computational topologies where UAVs, acting as mobile edge servers, are tasked with processing data from ground-based user equipment (UE). This paper addresses the dual challenges of optimizing both UAV deployment and task offloading within such networks to minimize communication latency and efficiently utilize UAV resources, which are limited by battery life and processing capabilities. We propose a bi-level optimization framework that simultaneously tackles the placement of UAVs and the distribution of computational tasks among them. At the higher level, UAV deployment is optimized to ensure minimal distance to the UEs, thereby reducing latency and energy consumption during data transmission. At the lower level, task offloading is optimized to balance the computational load across the UAV network, considering each UAV's capacity and battery constraints. We demonstrate through extensive simulations the significant improvements in system efficiency, latency, and resilience. This approach not only enhances the performance of UAV-assisted MEC networks but also provides scalable solutions adaptable to various operational scenarios.

Multi-access edge computing (MEC) has emerged as a proven solution for reducing communication latency and enhancing user experience in delay-sensitive applications by offloading computation-intensive tasks to edge servers. In future networks, unmanned aerial vehicles (UAVs), with their flexible deployment and reliable communication capabilities, have the potential to be deployed as aerial MEC servers in areas lacking cellular infrastructure. However, the joint optimization of UAV placement and task offloading poses significant challenges due to the interdependence between communication latency, computational demands, and the resource limitations of UAVs. In this paper, we propose a novel joint optimization framework utilizing proximal policy optimization (PPO) to simultaneously address UAV placement and computation offloading in UAVenabled MEC networks. The framework dynamically adapts to changing network conditions, minimizing end-to-end latency while balancing computational loads and energy consumption. Extensive simulations demonstrate that the proposed PPO-based approach achieves superior performance compared to conventional optimization methods, with significant improvements in system latency, resource utilization, and network resilience. This work contributes scalable, adaptive solutions for UAV-assisted MEC networks in dynamic environments, enabling robust support for mission-critical and latency-sensitive applications.

This paper presents a novel sequence-to-sequence LSTM-based routing algorithm, designed for delay-sensitive remote IoT communication via both terrestrial and Low Earth Orbit (LEO) satellite relays when no inter-satellite links are available. We introduce a matrix-based approach to compute the communication delay between pairs of IoT nodes and satellites while considering their orbital passes and temporary visibility. Then, we develop an encoder-decoder architecture based on LSTM networks augmented with a beam-search strategy that enables the efficient prediction of optimal routing paths. Through experimental evaluations, incorporating a K − 2 beam search optimization, the algorithm demonstrates superior performance in generating near-optimal and scalable routes. Comparative analyses indicate that this approach outperforms traditional routing methods, offering lower data delays with reduced computational overhead.

Lane change in dense traffic typically requires the recognition of an appropriate opportunity for maneuvers, which remains a challenging problem in self-driving. In this work, we propose a chance-aware lane-change strategy with high-level model predictive control (MPC) through curriculum reinforcement learning (CRL). In our proposed framework, full-state references and regulatory factors concerning the relative importance of each cost term in the embodied MPC are generated by a neural policy. Furthermore, effective curricula are designed and integrated into an episodic reinforcement learning (RL) framework with policy transfer and enhancement, to improve the convergence speed and ensure a high-quality policy. The proposed framework is deployed and evaluated in numerical simulations of dense and dynamic traffic. It is noteworthy that, given a narrow chance, the proposed approach generates high-quality lane-change maneuvers such that the vehicle merges into the traffic flow with a high success rate of 96%. Finally, our framework is validated in the high-fidelity simulator under dense traffic, demonstrating satisfactory practicality and generalizability.

The rapid evolution of autonomous vehicular technologies demands advanced solutions for reliable navigation in diverse and often unfamiliar environments. This paper introduces a novel CNN-based federated learning approach for vehicular systems, designed to enhance their self-driving adaptability and decision-making capabilities in novel environments through collaborative training. Our method leverages the decentralized nature of FL to enable vehicles to learn collectively from shared experiences while maintaining the privacy of individual data. We utilize the CARLA simulation environment to generate a wide range of driving scenarios, including multiple vehicles operating under varied weather conditions. This diverse dataset serves as a testbed for our FL framework, allowing us to evaluate its effectiveness in accurately predicting steering wheel angles while simultaneously adapting to different environmental challenges. Our results demonstrate that vehicles trained using our FL framework exhibit enhanced performance in predictive analytics, showing greater resilience to environmental changes and improved decision-making in real-time scenarios with a much lower computational complexity.

LiDAR sensor data is essential for autonomous vehicle navigation, traffic flow monitoring, obstacle detection, and passenger safety. However, the reliability of LiDAR data can be compromised by anomalies caused by sensor malfunctions, environmental conditions, or unexpected road events. To address this, detecting anomalies in spatial-temporal (ST) LiDAR data is critical for ensuring safety. This paper proposes a novel low-complexity unsupervised framework named CNN-BiLSTM VAE for anomaly detection (AD) in non-image LiDAR data. The framework combines variational auto-encoder (VAE) reconstruction, CNN for spatial learning, and bidirectional LSTM for time-series learning in a mirror-to-mirror (M2M) architecture. Experimental results show that this method effectively detects anomalies in multidimensional ST LiDAR data, thereby maintaining robustness under various environmental conditions.

Data augmentation is an effective method to improve model robustness and generalization. Conventional data augmentation pipelines are commonly used as preprocessing modules for neural networks with predefined heuristics and restricted differentiability. Some recent works indicated that the differentiable data augmentation (DDA) could effectively contribute to the training of neural networks and the augmentation policy searching strategies. Some recent works indicated that the differentiable data augmentation (DDA) could effectively contribute to the training of neural networks and the searching of augmentation policy strategies. This survey provides a comprehensive and structured overview of the advances in DDA. Specifically, we focus on fundamental elements including differentiable operations, operation relaxations, and gradient estimations, then categorize existing DDA works accordingly, and investigate the utilization of DDA in selected of practical applications, specifically neural augmentation networks and differentiable augmentation search. Finally, we discuss current challenges of DDA and future research directions.

An unmanned aerial vehicle (UAV) integrated with the remote sensing technology of light detection and ranging (LiDAR) can provide accurate and real-time road traffic information. In this paper, we propose to equip UAVs with LiDAR sensors for Intelligent Transportation Systems (ITS) applications. The goal is to find the optimal 3D placement of multiple UAV-LiDAR (ULiDs) for a given road segmentation. We formulate an optimization problem to find the optimal placement such that the road coverage efficiency is maximized. The optimization problem is constrained by notable ULiD specifications such as field-of-view (FoV), point-cloud density, geographic information system (GIS) location, and road segment coverage priorities. We propose to use a computational intelligent algorithm based on particle swarm optimization to solve the problem. Finally, we illustrate the benefits of using our proposed algorithm over other baselines.

The proliferation of Light Detection and Ranging (LiDAR) technology in the automotive industry has quickly promoted its use in many emerging areas in smart cities and internet-of-things. Compared to other sensors, like cameras and radars, LiDAR provides up to 64 scanning channels, vertical and horizontal field of view, high precision, high detection range, and great performance under poor weather conditions. In this paper, we propose a novel aerial traffic monitoring solution based on Light Detection and Ranging (LiDAR) technology. By equipping unmanned aerial vehicles (UAVs) with a LiDAR sensor, we generate 3D point cloud data that can be used for object detection and tracking. Due to the unavailability of LiDAR data from the sky, we propose to use a 3D simulator. Then, we implement PointVoxel-RCNN (PV-RCNN) to perform road user detection (e.g., vehicles and pedestrians). Subsequently, we implement an Unscented Kalman filter, which takes a 3D detected object as input and uses its information to predict the state of the 3D box before the next LiDAR scan gets loaded. Finally, we update the measurement by using the new observation of the point cloud and correct the previous prediction's belief. The simulation results illustrate the performance gain (around 8 %) achieved by our solution compared to other 3D point cloud solutions.

This study proposes a new algorithm for parameter estimation of the electric circuit model of Lithium (Li)-ion battery. The first-order battery-electric circuit model is considered in this work that resembles battery charging and discharging behaviors. The battery circuit element values have been modeled as polynomial equations with unknown coefficients. An accurate estimation of the battery circuit element values is profound to accurately find the battery State of Charge (SoC), an immeasurable quantity required in battery management systems (BMS). The ElectroStatic discharge algorithm (ESDA) is used in this study to estimate the unknown polynomial coefficients and, in turn, the values of the battery circuit elements. The accuracy of the proposed ESDA in estimating the battery circuit element values is compared to the recently proposed Artificial Hummingbird Optimization Technique (AHOT), Chameleon Swarm Algorithm (CSA), and Tuna Swarm Optimization (TSO). The results demonstrate the superiority of the proposed algorithm for charging and discharging in battery parameters estimation over the other algorithms with an accuracy gain of at least 10%.

Photovoltaic (PV) arrays, when subjected to Partial Shading (PS), exhibit several power losses due to diminished current across the array. Therefore, bypass diodes are connected across array modules to avoid the PS effect. Although they reduce the PS effect, the bypass diodes make the Power versus Voltage (P- V) relation of a PV non-convex. This paper investigates the Maximum Power Point Tracking (MPPT) problem under PS conditions to track the PV array's Maximum Power Point (MPP). Because previously proposed algorithms for this problem either failed to track the MPP or were computationally expensive, we propose a modified version of the Bat metaheuristic algorithm with dynamically narrowing search space (DNSS) exploration to avoid exploring low-power regions. The results show around 35 % gain in terms of rapidity and efficiency of the proposed metaheuristic approach in mitigating power losses compared to other existing algorithms.

Photovoltaic (PV) forecasting plays a major role in residential and industrial PV installation as well as penetration with the grid. An inaccurate PV power forecasting may result in increased monetary and energy losses. This study proposes a metaheuristic-based strategy for accurate PV power forecasting using a heuristic-based data-driven PV model. The proposed algorithm integrates a dense explorative strategy with the existing PV equation knowledge by a multilayer perceptron (MLP) network with Sigmoid activation functions to predict the best coefficients for the inputs of the data-driven PV model. The proposed method is compared to a recently proposed metaheuristic algorithm, the artificial hummingbird optimizer algorithm (AHOA). The comparison is performed for inside distribution (ID) and out-of-distribution (OOD) irradiance datasets and with varying temperatures. The results prove that the proposed NN-based algorithm achieves higher accuracy in PV power parameter prediction and hence forecasting.

In this paper, we investigate an innovative car-following framework where a self-driving vehicle, identified as the follower, autonomously follows another leading vehicle. We propose to design the car-following strategy based only on the environmental LiDAR data captured by the follower and the GNSS input. The proposed framework is composed of several modules, including the detection module using the PointNet++ neural network, the continuous calculation of the leader's driving trajectory, and the trajectory following control module using the BP-PID method. Comparison experiments and analysis have been performed on the Carla simulator. Results show that our proposed framework can work effectively and efficiently in the defined car-following tasks, and its success rate exceeds that of the Yolo-v5-based method by more than 13% under night conditions or rainy weather settings.

Driver behavior profiling is one of the main issues in the insurance industries and fleet management, thus being able to classify the driver behavior with low-cost mobile applications remains in the spotlight of autonomous driving. However, using mobile sensors may face the challenge of security, privacy, and trust issues. To overcome those challenges, we propose to collect data sensors using Carla Simulator available in smartphones (Accelerometer, Gyroscope, GPS) in order to classify the driver behavior using speed, acceleration, direction, the 3-axis rotation angles (Yaw, Pitch, Roll) taking into account the speed limit of the current road and weather conditions to better identify the risky behavior. Secondly, after fusing inter-axial data from multiple sensors into a single file, we explore different machine learning algorithms for time series classification to evaluate which algorithm results in the highest performance.

Collaborative mobile crowdsourcing (CMCS) allows entities, e.g., local authorities or individuals, to hire a team of workers from the crowd of connected people, to execute complex tasks. In this article, we investigate two different CMCS recruitment strategies allowing task requesters to form teams of socially connected and skilled workers: 1) a platform-based strategy where the platform exploits its own knowledge about the workers to form a team and 2) a leader-based strategy where the platform designates a group leader that recruits its own suitable team given its own knowledge about its social network (SN) neighbors. We first formulate the recruitment as an integer linear program (ILP) that optimally forms teams according to four fuzzy-logic-based criteria: 1) level of expertise; 2) social relationship strength; 3) recruitment cost; and 4) recruiter’s confidence level. To cope with NP-hardness, we design a novel low-complexity CMCS recruitment approach relying on graph neural networks (GNNs), specifically graph embedding and clustering techniques, to shrink the workers’ search space and afterwards, exploiting a metaheuristic genetic algorithm to select appropriate workers. Simulation results applied on a real-world data set illustrate the performance of both proposed CMCS recruitment approaches. It is shown that our proposed low-complexity GNN-based recruitment algorithm achieves close performances to those of the baseline ILP with significant computational time saving and ability to operate on large-scale mobile crowdsourcing platforms. It is also shown that compared to the leader-based strategy, the platform-based strategy recruits a more skilled team but with lower SN relationships and higher cost.

The advent in Wireless Body Area Networks (WBANs) provides promising services for remote diagnostic and monitoring. The security requirements in this context are challenging and evolving. The WBAN should support these security requirements to continuously provide efficient services to patients. Trust management is one of the interesting security mechanisms that can be enforced to enhance the WBAN security. In this paper, we propose a new cluster-based approach to manage trust in WBAN. The clusters are defined based on the direct and indirect exchanges between nodes. Our approach is hybrid: it calculates the direct and the indirect trust based on the priority of forwarded messages, the feedbacks of different exchanges, and the credibility coefficient. The simulation results show the efficiency of our model against three different attacks: the on-off attack, the collusion attack, and the newcomer attack.

Successful portfolio management requires, in addition to advanced optimization strategies, effective recruitment of specialized financial advisors. Hiring the wrong ones can be detrimental to investors' financial goals. In this article, we propose an automated crowdsourcing system to organize cooperation between financial advisors and investors. Without interfering with their private portfolio optimization techniques, we design a recruitment framework that matches financial advisors to investors based on their profiles and features, as well as the previous activities of their peers. Using the database of the crowdsourcing platform, we employ an unsupervised technique to regroup advisors with a high degree of similarities into clusters and, hence, shrink the search space. Afterward, we train a machine learning regression model to predict the matching score that can be achieved if an investor hires a particular advisor. These scores are converted to weights of bipartite graphs to which we apply a double-phased many-to-many maximum weight matching algorithm to determine a suitable investor-advisor combination. In the simulations, we investigate the performance of the proposed recruitment approach and show that, compared with other traditional approaches, higher returns can be reached for both investors and financial advisors.

In this paper, we propose an autonomous UAV path planning framework using deep reinforcement learning approach. The objective is to employ a self-trained UAV as a flying mobile unit to reach spatially distributed moving or static targets in a given three dimensional urban area. In this approach, a Deep Deterministic Policy Gradient (DDPG) with continuous action space is designed to train the UAV to navigate through or over the obstacles to reach its assigned target. A customized reward function is developed to minimize the distance separating the UAV and its destination while penalizing collisions. Numerical simulations investigate the behavior of the UAV in learning the environment and autonomously determining trajectories for different selected scenarios.

In this paper, we propose to design an automated service discovery process to allow mobile crowdsourcing task requesters select a small set of devices out of a large-scale Internet-of-things (IoT) network to execute their tasks. To this end, we proceed by dividing the large-scale IoT network into several virtual communities whose members share strong social IoT relations. Two community detection algorithms, namely Louvain and order statistics local method (OSLOM) algorithms, are investigated and applied to a real-world IoT dataset to form non-overlapping and overlapping IoT devices groups. Afterwards, a natural language process (NLP)-based approach is executed to handle crowdsourcing textual requests and accordingly find the list of IoT devices capable of effectively accomplishing the tasks. This is performed by matching the NLP outputs, e.g., type of application, location, required trustworthiness level, with the different detected communities. The proposed approach effectively helps in automating and reducing the service discovery procedure and recruitment process for mobile crowdsourcing applications.

Recently, elevated LiDAR (ELiD) has been proposed as an alternative to local LiDAR sensors in autonomous vehicles (AV) because of the ability to reduce costs and computational requirements of AVs, reduce the number of overlapping sensors mapping an area, and to allow for a multiplicity of LiDAR sensing applications with the same shared LiDAR map data. Since ELiDs have been removed from the vehicle, their data must be processed externally in the cloud or on the edge, necessitating an optimized backhaul system that allocates data efficiently to compute servers. In this paper, we address this need for an optimized backhaul system by formulating a mixed-integer programming problem that minimizes the average latency of the uplink and downlink hop-by-hop transmission plus computation time for each ELiD while considering different bandwidth allocation schemes. We show that our model is capable of allocating resources for differing topologies, and we perform a sensitivity analysis that demonstrates the robustness of our problem formulation under different circumstances.

The widespread use of advanced mobile devices has led to the emergence of a new class of mobile crowdsourcing called spatial mobile crowdsourcing (SMCS). The main feature of SMCS is the presence of spatial tasks that require workers to be physically present at a particular location for task fulfillment. These tasks usually take advantage of the built-in sensors in mobile devices by requesting environment sensing services. Because cameras are becoming the most common way for visual logging techniques and sensing in our daily lives, we propose, in this article, a photo-based SMCS framework for event reporting. The proposed framework allows event report requesters to solicit photos of ongoing events and keep track of any updates. We propose a full architecture in which we solve the SMCS recruitment problem using different fairness strategies in the presence of multiple events and reporters. Then, once submissions are received and before forwarding final responses to event requesters, we proceed with a data processing phase for data quality monitoring. In short, our event reporting platform helps requesters recruit ideal reporters, select highly relevant data from an evolving picture stream, and receive accurate responses. This solution mainly incorporates: 1) a strategic and generic recruitment algorithm for recruiting and scheduling suitable reporters to events; 2) a deep learning model that eliminates false submissions and ensures photo's credibility; and 3) an A-tree shape data structure model for clustering streaming pictures to reduce information redundancy and provide maximum event coverage. Experiment results investigate the performances of the proposed recruitment approach and show that our algorithm outperforms two other benchmarking approaches. Also, we conduct simulations to evaluate the strategies of the proposed recruitment algorithm, given different fairness levels among events. Data quality simulation results show effectiveness in reducing false submissions and delivering high-quality responses. Finally, framework implementation for real-world applications is provided.

This paper proposes an energy management framework for cellular heterogeneous networks (HetNets) supported by dynamic solar powered drones. A HetNet composed of a macrocell base station (BS), micro cell BSs, and drone small cell BSs are deployed to serve the networks' subscribers. The drones can land at pre-planned locations defined by the mobile operator and at the macrocell BS site where they can charge their batteries. The objective of the framework is to jointly determine the optimal trips of the drones and the MBSs that can be safely turned off in order to minimize the total energy consumption of the network. This is done while considering the cells' capacities and the minimum receiving power guaranteeing successful communications. To do so, an integer linear programming problem is formulated and optimally solved for three cases based on the knowledge level about future renewable energy statistics of the drones. A low complex relaxed solution is also developed. Its performances are shown to be close to those of the optimal solutions. However, the gap increases as the network becomes more congested. Numerical results investigate the performance of the proposed drone-based approach and show notable improvements in terms of energy saving and network capacity.

Future mobile traffic growth will require 5G cellular networks to densify the deployment of small cell base stations (BS). As it is not feasible to form a backhaul (BH) by wiring all BSs to the core network, directional mmWave links can be an attractive solution to form BH links, due to their large available capacity. When small cells are powered on/off or traffic demands change, the BH may require reconfiguration, leading to topology and traffic routing changes. Ideally, such reconfiguration should be seamless and should not impact existing traffic. However, when using highly directional BH antennas which can be dynamically rotated to form new links, this can become time- consuming, requiring the coordination of BH interface movements, link establishment and traffic routing. In this paper, we propose greedy-based heuristic algorithms to solve the BH reconfiguration problem in real-time. We numerically compare the proposed algorithms with the optimal solution obtained by solving a mixed integer linear program (MILP) for smaller instances, and with a sub- optimal reduced MILP for larger instances. The obtained results indicate that the greedy-based algorithms achieve good quality solutions with significantly decreased execution time.

This contribution presents a solution to improve the performances of micro unmanned aerial vehicles (UAVs) by increasing their missions coverage in terms of distance and time. This is achieved by letting the UAV ride existing land public transport such as the city bus throughout the route to its mission location. Indeed, due to their limited battery capacity, micro-UAVs flying time is restrained, which affects their mission and coverage performances. In this paper, we propose to leverage the use of public transport infrastructure, such as city buses, to carry the UAVs whenever it is possible in order to minimize their flight energy consumption. For this purpose, a generic scheduling framework to efficiently cover spatially and temporally distributed events in a geographical area of interest over a long period of time is proposed. By considering the public transport schedule table, a mixed integer linear programming problem (MILP) aiming at minimizing the total energy consumption of the UAVs is formulated while accomplishing all the pre-scheduled missions. The proposed proactive UAV scheduling framework optimizes the UAV trips according to the mission occurrence and the schedule table of the buses. The obtained results demonstrate the effectiveness of the collaboration between the UAVs and the land transport to improve the overall UAV missions' performances in terms of distance coverage.

The use of micro unmanned aerial vehicles (UAVs) have gained a lot of interests especially for smart city applications due to their three- dimensional (3D) mobility and flexibility. Path planning is one of the most important UAV problems that needs to be addressed. In this paper, we focus on determining the optimal routes for a fleet of UAVs in urban cities that minimize the total arrival time of all UAV swarms while respecting their energy consumption constraints and avoiding the risk of collision. Through a mixed integer linear program, we develop a safe navigation framework for realistic 3D maps where charging stations are made available to recharge UAV batteries on their ways to destination. Our results investigate different scenarios for selected system parameters and illustrate different collision avoidance techniques followed by the UAVs.

Future mobile data traffic predictions expect a significant increase in user data traffic, requiring new forms of mobile network infrastructures. Fifth generation (5G) communication standards propose the densification of small cell access base stations (BSs) in order to provide multigigabit and low latency connectivity. This densification requires a high capacity backhaul network. Using optical links to connect all the small cells is economically not feasible for large scale radio access networks where multiple BSs are deployed. A wireless backhaul formed by a mesh of millimeter-wave (mmWave) links is an attractive mobile backhaul solution, as flexible wireless (multihop) paths can be formed to interconnect all the access BSs. Moreover, a wireless backhaul allows the dynamic reconfiguration of the backhaul topology to match varying traffic demands or adaptively power on/off small cells for green backhaul operation. However, conducting and precisely controlling reconfiguration experiments over real mmWave multihop networks is a challenging task. In this paper, we develop a Software-Defined Networking (SDN) based approach to enable such a dynamic backhaul reconfiguration and use real-world mmWave equipment to setup a SDN-enabled mmWave testbed to conduct various reconfiguration experiments. In our approach, the SDN control plane is not only responsible for configuring the forwarding plane but also for the link configuration, antenna alignment, and adaptive mesh node power on/off operations. We implement the SDN-based reconfiguration operations in a testbed with four nodes, each equipped with multiple mmWave interfaces that can be mechanically steered to connect to different neighbors. We evaluate the impact of various reconfiguration operations on existing user traffic using a set of extensive testbed measurements. Moreover, we measure the impact of the channel assignment on existing traffic, showing that a setup with an optimal channel assignment between the mesh links can result in a 44% throughput increase, when compared to a suboptimal configuration.

Unmanned aerial vehicles (UAVs) have gained a lot of popularity in diverse wireless communication fields. They can act as high- altitude flying relays to support communications between ground nodes due to their ability to provide line-of- sight links. With the flourishing Internet of Things, several types of new applications are emerging. In this paper, we focus on bandwidth hungry and delay-tolerant applications where multiple pairs of transceivers require the support of UAVs to complete their transmissions. To do so, the UAVs have the possibility to employ two different bands namely the typical microwave and the high-rate millimeter wave bands. In this paper, we develop a generic framework to assign UAVs to supported transceivers and optimize their trajectories such that a weighted function of the total service time is minimized. Taking into account both the communication time needed to relay the message and the flying time of the UAVs, a mixed non-linear programming problem aiming at finding the stops at which the UAVs hover to forward the data to the receivers is formulated. An iterative approach is then developed to solve the problem. First, a mixed linear programming problem is optimally solved to determine the path of each available UAV. Then, a hierarchical iterative search is executed to enhance the UAV stops' locations and reduce the service time. The behavior of the UAVs and the benefits of the proposed framework are showcased for selected scenarios.

Most of the drone-based applications require a time-limited access to the spectrum to complete data transmission due to limited battery capacity of these flying units. This paper proposes an efficient spectrum and energy management solution by integrating the overlay cognitive radio technology. Therefore, we aim to use the drone as a secondary node and target to determine an optimized three-dimensional location and a resource control solution by which it can complete its data transfer and in parallel support the primary communication. To this end, a non-convex optimization problem is developed. The obtained solution minimizes the total energy consumption of the drone and, at the same time, maintains the required data rate level of the spectrum's owner. A resource allocation procedure and swarm intelligence-based positioning algorithm are jointly designed for this purpose. Numerical results show the efficiency of the proposed approach in terms of energy consumption savings and additional transmission opportunities as compared to other schemes.

With the emergence of millimeter-Wave (mmWave) communication technology, the capacity of mobile backhaul networks can be significantly increased. On the other hand, Mobile Edge Computing (MEC) provides an appropriate infrastructure to offload latency-sensitive tasks. However, the amount of resources in MEC servers is typically limited. Therefore, it is important to intelligently manage the MEC task offloading by optimizing the backhaul bandwidth and edge server resource allocation in order to decrease the overall latency of the offloaded tasks. This paper investigates the task allocation problem in MEC environment, where the mmWave technology is used in the backhaul network. We formulate a Mixed Integer NonLinear Programming (MINLP) problem with the goal to minimize the total task serving time. Its objective is to determine an optimized network topology, identify which server is used to process a given offloaded task, find the path of each user task, and determine the allocated bandwidth to each task on mmWave backhaul links. Because the problem is difficult to solve, we develop a two-step approach. First, a Mixed Integer Linear Program (MILP) determining the network topology and the routing paths is optimally solved. Then, the fractions of bandwidth allocated to each user task are optimized by solving a quasi-convex problem. Numerical results illustrate the obtained topology and routing paths for selected scenarios and show that optimizing the bandwidth allocation significantly improves the total serving time, particularly for bandwidth-intensive tasks.

Future 5G mobile networks will require increased backhaul (BH) capacity to connect a massive amount of high capacity small cells (SCs) to the network. Because having an optical connection to each SC might be infeasible, mmWave-based (e.g. 60 GHz) BH links are an interesting alternative due to their large available bandwidth. To cope with the increased path loss, mmWave links require directional antennas that should be able to direct their beams to different neighbors, to dynamically change the BH topology, in case new nodes are powered on/off or the traffic demand has changed. Such BH adaptation needs to be orchestrated to minimize the impact on existing traffic. This paper develops a Software-defined networking-based framework that guides the optimal reconfiguration of mesh BH networks composed by mmWave links, where antennas need to be mechanically aligned. By modelling the problem as a Mixed Integer Linear Program (MILP), its solution returns the optimal ordering of events necessary to transition between two BH network configurations. The model creates backup paths whenever it is possible, while minimizing the packet loss of ongoing flows. A numerical evaluation with different topologies and traffic demands shows that increasing the number of BH interfaces per SC from 2 to 4 can decrease the total loss by more than 50%. Moreover, when increasing the total reconfiguration time, additional backup paths can be created, consequently reducing the reconfiguration impact on existing traffic.

In this paper, a generic scheduling framework to manage a fleet of micro unmanned aerial vehicles (UAVs) is proposed. The objective is to employ multiple UAVs in sequential and parallel ways to cover spatially and temporally distributed events in a geographical area of interest over a long period of time. The proactive scheduling framework considers several constraints and challenges including the limited battery capacities and technical specifications of the UAVs in addition to the necessity to regularly send back the UAVs to a docking station. A mixed integer linear programming (MILP) problem aiming at minimizing the total energy consumption is formulated after a series of linearization steps. Optimal UAV scheduling solutions are then obtained using off-the-shelf software. The proposed UAV scheduling framework is formulated in a generic manner and can be applied in multiple domains comprising short and/or long-term UAV missions while ensuring uninterrupted service. The obtained results can be used as convenient benchmarks for future heuristic UAV scheduling approaches.

This paper proposes an energy-efficient solution to employ a drone to gather data from spatially distributed wireless sensors. Specifically, we minimize the energy consumption of the drone while accomplishing a tour to collect the needed data. This tour accounts for both the energies due to data transfer and to the motion of the drone. The objective is to determine the positions where the drone stops to collect data from each cluster of sensors as well as the path that the drone needs to follow to complete its data gathering tour. The problem is decomposed into two sub-problems. The first sub-problem targets to determine the sub-groups of sensors and stop positions to collect data from each sub- group using a clustering approach. In the second sub- problem, we employ the travel salesman solution to find the path among these stops Selected numerical results show that the achieved energy is considerably reduced compared to the one of traditional approaches. Moreover, we study the behavior of the drone versus various system parameters.

Micro unmanned aerial vehicles (MUAVs) have attracted much interest as flexible communication means for multiple applications due to their versatility. Most of the MUAV-based applications require a time-limited access to the spectrum to complete data transmission due to limited battery capacity of the flying units. These characteristics are the origin of two main challenges faced by MUAV-based communication: 1) efficient-energy management, and 2) opportunistic spectrum access. This paper proposes an energy-efficient solution, considering the hover and communication energy, to address these issues by integrating cognitive radio (CR) technology with MUAVs. A non-convex optimization problem exploiting the mobility of MUAVs is developed for the underlay CR technique. The objective is to determine an optimized three-dimension (3D) location, for a secondary MUAV, at which it can complete its data transfer with minimum energy consumption and without harming the data rate requirement of the primary spectrum owner. Two algorithms are proposed to solve these optimization problems: a meta-heuristic particle swarm optimization algorithm (PSO) and a deterministic algorithm based on Weber formulation. Selected numerical results show the behavior of the MUAV versus various system parameters and that the proposed solutions achieve very close results in spite of the different conceptional constructions.

We analyze the achievable rates of a cognitive radio MIMO system assisted by an unmanned aerial vehicle (UAV) relay. The primary user (PU) and the secondary user (SU) aim to communicate to the closest primary base station (BS) via a multi-access channel through the same UAV relay. The SU message is then forwarded from the primary BS to the secondary network with a certain incentive reward as a part of the cooperation protocol between both networks. We propose a special linear precoding scheme to enable the SU to exploit the PU free eigenmodes. We, also, present the expression of the power maximizing both primary and secondary rates under power budget, relay power, and interference constraints. In the numerical results, we evaluate the PU and SU rates of proposed scheme with respect to various problem parameters. We also highlight the effect of the UAV altitude on the SU and PU rates. Finally, we show that the relay matrix variation affects both rates that reach their peaks at different values of the matrix.

This paper investigates the problem of data routing in flying ad hoc networks (FANETs) composed of multiple flying nodes, i.e., unmanned aerial vehicles (UAVs), supported by communication platforms. The objective is to exploit the mobility of UAVs in order to establish routing paths and transfer a message between two ground nodes at minimum transmission time. Assuming that the UAVs are already deployed to execute a given primary task, the cooperation of the UAVs in the data transfer process, considered as a secondary task, becomes subject to three conditions. First, the energy consumed by each UAV has to respect the allocated budget for the data routing process. Second, the UAVs cannot move out of the boundaries of a tolerated and well-defined region in order to maintain the operation of the primary task. Finally, the UAVs need to reduce their traveled distances in order to reduce the delay of the transfer. A mixed non-linear integer programming problem determining the routing path and the new locations of the UAVs participating in the data transfer process is formulated. Due to its nonconvexity, we proceed with a deterministic exploratory strategy inspired from the Hooke-Jeeves algorithm to meet the problem goals. Selected numerical results investigate the performance of the proposed solution for different scenarios and compare some of them to those of a metaheuristic approach based on swarm intelligence.

In this paper, we propose a new cellular network operation scheme fulfilling the 5G requirements related to spectrum management and green communications. We focus on cognitive radio cellular networks in which both the primary network (PN) and the secondary network (SN) are maximizing their operational profits. The PN and the SN are required to respect a CO2emissions threshold by switching off one or more lightly loaded base stations (BSs). In addition, the PN accepts to cooperate with the SN by leasing its spectrum in the cells where the PN is turned off. In return, the corresponding SN BSs host the PN users and impose extra roaming fees to the PN. We propose a low-complexity algorithm that maximizes the profit per CO2emissions metric while switching on/off the BSs. In the simulations, we show that our proposed algorithm achieves performances close to the exhaustive search method. In addition, we find that the roaming price is a key parameter that affects both PN and SN profits1.

Unmanned Aerial Vehicles (UAV) have attracted a lot of attention in a variety of fields especially in intelligent transportation systems (ITS). They constitute an innovative mean to support existing technologies to control road traffic and monitor incidents. Due to their energy-limited capacity, UAVs are employed for temporary missions and, during idle periods, they are placed in stations where they can replenish their batteries. In this paper, the problem of UAV docking station placement for ITS is investigated. This constitutes the first step in managing UAV-assisted ITS. The objective is to determine the best locations for a given number of docking stations that the operator aims to install in a large geographical area. Based on average road network statistics, two essential conditions are imposed in making the placement decision: i) the UAV has to reach the incident location in a reasonable time, ii) there is no risk of UAV's battery failure during the mission. Two algorithms, namely a penalized weighted k-means algorithm and the particle swarm optimization algorithm, are proposed. Results show that both algorithms achieve close coverage efficiency in spite of their different conceptual constructions.

We study the deployment of unmanned aerial vehicles (UAV) based cognitive system in an area covered by the primary network (PN). An UAV shares the spectrum of the PN and aims to maximize its energy efficiency (EE) by optimizing the transmit power. We focus on the case where the UAV simultaneously communicates with the ground receiver (G), under interference limitation, and with another relaying UAV (A), with a minimal required rate. We analytically develop the power allocation framework that maximizes the EE subject to power budget, interference, and minimal rate constraints. In the numerical results, we show that the minimal rate may cause a transmission outage at low power budget values. We also highlighted the existence of optimal altitudes given the UAV location with respect to the different other terminals.

This paper proposes an energy management framework for cellular heterogeneous networks (HetNets) supported by dynamic drone small cells. A 3-tier HetNet is considered where macrocell, on#x002F;off switching micro cells, and solar-powered drone small cells are deployed to serve the networks' subscribers. In addition to energy harvesting, the drones can power their batteries via a charging station located at the macrocell site. Pre-planned locations are identified by the mobile operator for possible drones' placement. The objective of the framework is to optimally determine the positioning of the drones in addition to the micro cells status that can be turned off in order to minimize the daily energy consumption of the network. The framework takes also into account the cells' capacity and quality of service (QoS) metric defined by the minimum received power. An integer linear programming problem is formulated to optimally determine the network status during a time blocked period. The performance of this online scheme shows important advantages in terms of energy efficiency and connectivity compared to the traditional case without drones specially when the network is congested.

This paper proposes an optimized transmission scheme for Energy Harvesting (EH)-based two-way multiple-relay systems. All relays are considered as EH nodes that harvest energy from renewable and radio frequency (RF) sources, then use it to forward the information to the destinations. The power-splitting (PS) protocol, by which the EH node splits the input RF signal into two components for information transmission and energy harvesting, is adopted in the relay side. The objective is to optimize the PS ratios and the relays' transmit power levels in order to maximize the total sum-rate utility over multiple coherent time slots. An optimization approach based on geometric programming is proposed to solve the problem. Numerical results illustrate the behavior of the EH-based two-way multiple-relay system with respect to various parameters and compare the performance of the proposed approach with that of the dual problem-based approach.

Cellular operators are increasingly turning toward renewable energy (RE) as an alternative to using traditional electricity in order to reduce operational expenditure and carbon footprint. Due to the randomness in both RE generation and mobile traffic at each base station (BS), a surplus or shortfall of energy may occur at any given time. To increase energy self-reliance and minimize the network’s energy cost, the operator needs to efficiently exploit the RE generated across all BSs. In this paper, a hybrid energy sharing framework for cellular network is proposed, where a combination of physical power lines and energy trading with other BSs using smart grid is used. Algorithms for physical power lines deployment between BSs, based on average and complete statistics of the net RE available, are developed. Afterward, an energy management framework is formulated to optimally determine the quantities of electricity and RE to be procured and exchanged among BSs, respectively, while considering battery capacities and real-time energy pricing. Three cases are investigated, where RE generation is unknown, perfectly known, and partially known ahead of time. Results investigate the time varying energy management of BSs and demonstrate considerable reduction in average energy cost thanks to the hybrid energy sharing scheme.

Cellular base stations (BSs) are increasingly becoming equipped with renewable energy generators to reduce operational expenditures and carbon footprint of wireless communications. Moreover, advancements in the traditional electricity grid allow two-way power flow and metering that enable the integration of distributed renewable energy generators at BS sites into a microgrid. In this paper, we develop an optimized energy management framework for microgrid-connected cellular BSs that are equipped with renewable energy generators and finite battery storage to minimize energy cost. The BSs share excess renewable energy with others to reduce the dependency on the conventional electricity grid. Three cases are investigated where the renewable energy generation is unknown, perfectly known, and partially known ahead of time. For the partially known case where only the statistics of renewable energy generation are available, stochastic programming is used to achieve a conservative solution. Results show the time varying energy management behaviour of the BSs and the effect of energy sharing between them.

Renewable energy (RE) is an emerging solution for reducing carbon dioxide (CO2) emissions from cellular networks. One of the challenges of using RE sources is to handle its inherent uncertainty. In this paper, a RE powered cellular network is investigated. For a one-day operation cycle, the cellular network aims to reduce energy procurement costs from the smart grid by optimizing the amounts of energy procured from their locally deployed RE sources as well as from the smart grid. In addition to that, it aims to determine the extra amount of energy to be sold to the electrical grid at each time period. Chance constrained optimization is first proposed to deal with the randomness in the RE generation. Then, to make the optimization problem tractable, two well- know convex approximation methods, namely; Chernoff and Chebyshev based-approaches, are analyzed in details. Numerical results investigate the optimized energy procurement for various daily scenarios and compare between the performances of the employed convex approximation approaches.

In this paper, a multiple relay selection scheme for Energy Harvesting (EH)-based two-way relaying is investigated. All the relays are considered as EH nodes that harvest energy from renewable and radio frequency sources, then use it to forward the information to the sources. The time-switching protocol (TS), in which the receiver switches between transmitted information and harvested energy, is adopted in the relay side. The goal is to find the optimal TS ratios associated with the selected relays that maximize a rate-based utility function over multiple coherent time slots. Two metrics reflecting the degrees of fairness in the optimization are investigated. A joint-optimization solution based on binary particle swarm optimization is proposed to solve the problem. Numerical results illustrate the behavior of the TWR network according to the considered utility functions, the generated amount renewable energy, in addition to other system parameters.

In this paper, we investigate the Energy Harvesting (EH)-based two-way relaying system using Amplify-and-Forward (AF) and Decode-and-Forward (DF) strategies. The relay is considered as an EH node that harvests the received Radio Frequency (RF) signal and uses this harvested energy to forward the information. Two relaying protocols based on Time Switching (TS) and Power Splitting (PS) receiver architectures are proposed to enable EH and information processing at the relay. Analytical throughput expressions are derived and optimized for both protocols. The goal is to find the optimal TS and PS ratios that maximize the total throughput Numerical results illustrate the performance of TS and PS protocols for different strategies, and show that at high signal-to-noise ratio, PS is superior to TS, and AF is superior to DF in terms of achievable sum-rate.

In this paper, the production decisions across multiple energy suppliers in smart grid, powering cellular networks are investigated. The suppliers are characterized by different offered prices and pollutant emissions levels. The challenge is to decide the amount of energy provided by each supplier to each of the operators such that their profitability is maximized while respecting the maximum tolerated level of CO2 emissions. The cellular operators are characterized by their offered quality of service (QoS) to the subscribers and the number of users that determines their energy requirements. Stochastic geometry is used to determine the average power needed to achieve the target probability of coverage for each operator. The total average power requirements of all networks are fed to an optimization framework to find the optimal amount of energy to be provided from each supplier to the operators. The generalized alpha-fair utility function is used to avoid production bias among the suppliers based on profitability of generation. Results illustrate the production behavior of the energy suppliers versus QoS level, cost of energy, capacity of generation, and level of fairness.

In this paper, we study a multiple-antenna two-way relaying (TWR) cognitive radio (CR) system. A space alignment (SA) technique is adopted by the secondary users (SUs) to avoid interference with the primary users (PUs). We derive the optimal power allocation that maximizes the TWR achievable SU sum-rate while respecting the total power budget and the relay power constraints. We also analyze the case in which the relay is able to optimize its gain matrix structure to enhance the SU sum-rate. In the numerical results, we quantify the sum-rate gain of using the SA in the TWR CR and we show that the SU sum-rate is very limited when the relay power is low or the PU power and its resulting interference are high. In addition, we optimize the relay gain using an iterative algorithm and compare between different relay matrix structures.

This paper investigates the interactions between multiple heterogeneous cellular networks and energy retailers existing in the smart grid. The energy procurement decision of cellular networks is optimized while considering dynamic energy pricing and pollution levels of energy sources. The objective of the optimization problem is to maximize a utility metric based on mobile operators' profits while limiting the amount of CO2 emitted by all networks. In this study, we derive closed-form expressions of the optimal energy to procure from each retailer in order to power the marcocell and small cell base stations. The derived solutions depend on the retailer pricing function and the optimized metrics. Three metrics reflecting the degree of fairness among mobile operators are considered. Selected simulations investigate the behavior of different actors in the energy procurement decision. Results show a significant CO2 emissions reduction can be achieved thanks to the optimized demand side management from smart grid.

In this paper, we investigate the performance of a green mobile operator collaborating with other traditional mobile operators. Its goal is to minimize its CO2emissions, maximize its profit or achieve or tradeoff between both objectives by offloading its users to neighbor networks and exploiting renewable energies. On the other hand, traditional mobile operators aim to maximize their profits by attracting the maximum number of roamed users. The problem is modeled as a two-level Stackelberg game and its equilibrium is derived. A green mobile operator level that determines how many users per each base station to offload to each neighbor network, and a non-green mobile operator level where operators focus on finding the optimal roaming price. Our simulation results show a significant saving in terms of CO2emissions compared to the non-cooperation case and that roaming decision depends essentially on the availability of renewable energy in base station sites.

We study a multiple-antenna two-way relaying (TWR) spectrum sharing system. A space alignment (SA) technique is adopted by the secondary users (SU's) to avoid interference with the primary users (PU's). We derive the optimal power allocation that maximizes the TWR achievable sum-rate of the SU while respecting the total power budget and the relay power constraints. In the numerical results, we quantify the sum-rate gain of using the SA in the TWR CR and we show that the SU sum-rate is very limited when the relay power is low or the PU's power and its resulting interference is high.

In this paper, we propose a new cognitive cellular network architecture based on the coexistence of primary and secondary networks, (PN) and (SN), respectively. The PN aims to minimize its energy consumption by switching off the maximum number of its BSs and offloading its users to the SN's infrastructure to maintain its QoS. In return, the PN pays a roaming price and permits the SN to share or lease the spectrum at a certain price. We propose a low-complexity algorithm allowing the PN to minimize its energy consumption by selecting a suboptimal combination of active base stations. Our algorithm also optimizes the resource allocation of the SN to maximize its total sum-rate while respecting the minimal profit constraints for both networks. In the numerical results, we show that our proposed algorithm achieves close performances to the optimal exhaustive search algorithm. In addition, we investigate the impact of various system parameters in the collaboration decision.

In this paper, the problem of radio and power resource management in long term evolution heterogeneous networks (LTE HetNets) is investigated. The goal is to minimize the total power consumption of the network while satisfying the user quality of service determined by each target data rate. We study the model where one macrocell base station is placed in the cell center, and multiple small cell base stations and femtocell access points are distributed around it. The dual decomposition technique is adopted to jointly optimize the power and carrier allocation in the downlink direction in addition to the selection of turned off small cell base stations. Our numerical results investigate the performance of the proposed scheme versus different system parameters and show an important saving in terms of total power consumption.

We investigate the spectral efficiency gain of an uplink cognitive radio (CR) multi-input-multi-output system in which the secondary user (SU) is allowed to share the spectrum with the primary user (PU) using a specific precoding scheme to communicate with a common receiver. The proposed scheme exploits, at the same time, the free eigenmodes of the primary channel after a space alignment procedure and the interference threshold tolerated by the PU. At the common receiver, we adopt a successive interference cancellation (SIC) technique to eliminate the effect of the detected primary signal transmitted through the exploited eigenmodes. Furthermore, we analyze the SIC operation inaccuracy as well as the CSI estimation imperfection on the PU and SU throughputs. Numerical results show that our proposed scheme enhances considerably the cognitive achievable rate. For instance, in case of a perfect detection of the PU signal, the CR rate remains non-zero for high signal to noise ratio, which is usually impossible when we only employ a space alignment technique. We show that a modified water-filling power allocation policy at the PU can increase the secondary rate with a marginal degradation of the primary rate. Finally, we investigate the behavior of the PU and SU rates through the study of the rate achievable region.

Abstract In this paper, the interplay between cooperative device‐to‐device (D2D) communications and green cellular communications in the long term evolution (LTE) and LTE‐advanced (LTE‐A) cellular systems is investigated. An efficient approach for grouping mobile terminals (MTs) into cooperative clusters is described. In each cluster, MTs cooperate via D2D communications to share content of common interest. In addition, an energy‐efficient approach for putting base stations in sleep mode in an LTE‐A heterogeneous network is presented. Finally, both methods are combined in order to ensure green communications for both the users' MTs and the operator's base stations. The presented techniques are investigated in the framework of orthogonal frequency division multiple access‐based state‐of‐the‐art LTE cellular networks, while taking resource allocation and intercell interference into account. Results show that the proposed approach leads to energy savings for both the operator and the MTs, while leading to enhanced quality of service for mobile users. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, the problem of both bandwidth and power allocation for two-way multiple relay systems in overlay cognitive radio (CR) setup is investigated. In the CR overlay mode, primary users (PUs) cooperate with cognitive users (CUs) for mutual benefits. In our framework, we propose that the CUs are allowed to allocate a part of the PUs spectrum to perform their cognitive transmission. In return, acting as an amplify-and-forward two-way relays, they are used to support PUs to achieve their target data rates over the remaining bandwidth. More specifically, CUs acts as relays for the PUs and gain some spectrum as long as they respect a specific power budget and primary quality-of-service constraints. In this context, we first derive closed-form expressions for optimal transmit power allocated to PUs and CUs in order to maximize the cognitive objective. Then, we employ a strong optimization tool based on particle swarm optimization algorithm to find the optimal relay amplification gains and optimal cognitive released bandwidths as well. Our numerical results illustrate the performance of our proposed algorithm for different utility metrics and analyze the impact of some system parameters on the achieved performance.

In this paper, the problem of power allocation for a multiple-input multiple-output two-way system is investigated in underlay Cognitive Radio (CR) set-up. In the CR underlay mode, secondary users are allowed to exploit the spectrum allocated to primary users in an opportunistic manner by respecting a tolerated temperature limit. The secondary networks employ an amplify-and-forward two-way relaying technique in order to maximize the sum rate under power budget and interference constraints. In this context, we formulate an optimization problem that is solved in two steps. First, we derive a closed-form expression of the optimal power allocated to terminals. Then, we employ a strong optimization tool based on particle swarm optimization algorithm to find the power allocated to secondary relays. Simulation results demonstrate the efficiency of the proposed solution and analyze the impact of some system parameters on the achieved performance.

This paper investigates the collaboration between multiple mobile operators to optimize the energy efficiency of cellular networks. Our framework studies the case of LTE-Advanced networks deployed in the same area and owning renewable energy generators. The objective is to reduce the CO2emissions of cellular networks via collaborative techniques and using base station sleeping strategy while respecting the network quality of service. Low complexity and practical algorithm is employed to achieve green goals during low traffic periods. Cooperation decision criteria are also established basing on derived roaming prices and profit gains of competitive mobile operators. Our numerical results show a significant save in terms of CO2compared to the non-collaboration case and that cooperative mobile operator exploiting renewables are more awarded than traditional operators.

Base station deployment in cellular networks is one of the most fundamental problems in network design. This paper proposes a novel method for the cell planning problem for the fourth generation 4G-LTE cellular networks using meta heuristic algorithms. In this approach, we aim to satisfy both coverage and cell capacity constraints simultaneously by formulating a practical optimization problem. We start by performing a typical coverage and capacity dimensioning to identify the initial required number of base stations. Afterwards, we implement a Particle Swarm Optimization algorithm or a recently- proposed Grey Wolf Optimizer to find the optimal base station locations that satisfy both problem constraints in the area of interest which can be divided into several subareas with different user densities. Subsequently, an iterative approach is executed to eliminate eventual redundant base stations. We have also performed Monte Carlo simulations to study the performance of the proposed scheme and computed the average number of users in outage. Results show that our proposed approach respects in all cases the desired network quality of services even for large-scale dimension problems.

This paper studies the energy efficient transmission and the power allocation problem for multiple two-way relay networks equipped with multi-input multi-output antennas where each relay employs an amplify-and-forward strategy. The goal is to minimize the total power consumption without degrading the quality of service of the terminals. In our analysis, we start by deriving closed-form expressions of the optimal powers allocated to terminals. We then employ a strong optimization tool based on the particle swarm optimization technique to find the optimal power allocated at each relay antenna. Our numerical results illustrate the performance of the proposed scheme and show that it achieves a sub-optimal solution very close to the optimal one.

High-speed railway system equipped with moving relay stations placed on the middle of the ceiling of each train wagon is investigated. The users inside the train are served in two hops via the 3GPP Long Term Evolution (LTE) technology. The objective of this work is to maximize the number of served users by respecting a specific quality-of-service constraint while minimizing the total power consumption of the eNodeB and the moving relays. We propose an efficient algorithm based on the Hungarian method to find the optimal resource allocation over the LTE resource blocks in order to serve the maximum number of users with the minimum power consumption. Moreover, we derive a closed-form expression for the power allocation problem. Our simulation results illustrate the performance of the proposed scheme and compare it with various previously developed algorithms as well as with the direct transmission scenario.

We study the achievable rate of a multiple antenna relay-assisted cognitive radio system where a secondary user (SU) aims to communicate instantaneously with the primary user (PU). A special linear precoding scheme is proposed to enable the SU to take advantage of the primary eigenmodes. The used eigenmodes are subject to an interference constraint fixed beforehand by the primary transmitter. Due to the absence of a direct link, both users exploit an amplify-and-forward relay to accomplish their transmissions to a common receiver. After decoding the PU signal, the receiver employs a successive interference cancellation (SIC) to estimate the secondary message. We derive the optimal power allocation that maximizes the achievable rate of the SU respecting interference, peak and relay power constraints. Furthermore, we analyze the SIC detection accuracy on the PU throughput. Numerical results highlight the cognitive rate gain achieved by our proposed scheme without harming the primary rate. In addition, we show that the relay has an important role in increasing or decreasing PU and SU rates especially when varying its power and/or its amplifying gain.

In this paper, we investigate the spectral efficiency gain of an uplink Cognitive Radio (CR) Multi-Input Multi-Output (MIMO) system in which the Secondary/unlicensed User (SU) is allowed to share the spectrum with the Primary/licensed User (PU) using a specific precoding scheme to communicate with a common receiver. The proposed scheme exploits at the same time the free eigenmodes of the primary channel after a space alignment procedure and the interference threshold tolerated by the PU. In our work, we study the maximum achievable rate of the CR node after deriving an optimal power allocation with respect to an outage interference and an average power constraints. We, then, study a protection protocol that considers a fixed interference threshold. Applied to Rayleigh fading channels, we show, through numerical results, that our proposed scheme enhances considerably the cognitive achievable rate. For instance, in case of a perfect detection of the PU signal, after applying Successive Interference Cancellation (SIC), the CR rate remains non-zero for high Signal to Noise Ratio (SNR) which is usually impossible when we only use space alignment technique. In addition, we show that the rate gain is proportional to the allowed interference threshold by providing a fixed rate even in the high SNR range.

In this paper, the joint operation of cooperative device-to-device (D2D) communications and green cellular communications is investigated. An efficient approach for grouping mobile terminals (MTs) into cooperative clusters is described. In each cluster, MTs cooperate via D2D communications to share content of common interest. Furthermore, an energy-efficient technique for putting BSs in sleep mode in an LTE cellular network is presented. Finally, both methods are combined in order to ensure green communications for both the users' MTs and the operator's BSs. The studied methods are investigated in the framework of OFDMA-based state-of-the-art LTE cellular networks, while taking into account intercell interference and resource allocation.

In this paper, we investigate a multiple relay selection scheme for two-way relaying cognitive radio networks where primary users and secondary users operate on the same frequency band. More specifically, cooperative relays using Amplify-and- Forward (AF) protocol are optimally selected to maximize the sum rate of the secondary users without degrading the Quality of Service (QoS) of the primary users by respecting a tolerated interference threshold. A strong optimization tool based on genetic algorithm is employed to solve our formulated optimization problem where discrete relay power levels are considered. Our simulation results show that the practical heuristic approach achieves almost the same performance of the optimal multiple relay selection scheme either with discrete or continuous power distributions.

In this study, we implement a green heuristic algorithm involving the base station sleeping strategy that aims to ensure energy saving for the radio access network of the 4GLTE (Fourth Generation Long Term Evolution) mobile networks. We propose an energy procurement model that takes into consideration the existence of multiple energy providers in the smart grid power system (e.g. fossil fuel and renewable energy sources, etc.) in addition to deployed photovoltaic panels in base station sites. Moreover, the analysis is based on the dynamic time variation of daily traffic and aims to maintain the network quality of service. Our simulation results show an important contribution in the reduction of CO2emissions that can be reached by optimal power allocation over the active base stations.