Unmanned aerial vehicles (UAVs) have emerged as a promising solution for establishing wireless communications in regions lacking terrestrial network infrastructure, such as remote or emergency areas. Deploying UAV networks effectively in these scenarios poses significant challenges due to the unknown and potentially complex locations of users. In scenarios where users are dispersed in intricate spatial patterns, achieving high coverage and resilient network connectivity among the UAV networks is challenging. The irregular and arbitrary distribution of users can lead to gaps in coverage, as traditional UAV placement optimization approaches are often unable to adapt to such dynamic environments. This complexity necessitates advanced strategies to ensure reliable and continuous network service to users. In this paper, we propose a distributed approach that leverages flocking dynamics and distributed consensus algorithms for dynamic UAV positioning. By enabling a multi-modal UAV operation policy, we develop a framework which enables the network to dynamically respond to complex user locations and establish backhaul connectivity between dispersed user clusters. Simulation results demonstrate that our approach successfully establishes a robust and adaptable UAV network capable of providing seamless coverage for complex user configurations and also ensuring comprehensive inter-cluster connectivity among dispersed user clusters. Additionally, the network exhibits strong resilience against random failures, swiftly recovering from disruptions to ensure stable and reliable communication even when UAVs are compromised.

Optimizing resource orchestration in network slicing is essential for the performance of diverse applications in 5G edge-cloud networks. This paper introduces a novel approach utilizing multi-agent reinforcement learning (MARL) with a dueling double deep Q-network (D3QN) to efficiently manage dynamic resource provisioning to the different traffic flows. We model a network slicing environment with applications generating stochastic resource demands, simulating real-world virtual network patterns over physical infrastructure. Our MARL-based scheme adapts to the varying needs of traffic flows, balancing compute and memory resource allocation under limited information. Comparative analysis demonstrates the superiority of our approach over traditional static methods, particularly for ultra-reliable low-latency communication (URLLC) traffic flows, by minimizing latency and enhancing resource efficiency. The effectiveness of the proposed framework is validated through extensive simulations, which demonstrate up to 45% higher average utility for URLLC traffic flows and 18% improvement in overall resource efficiency compared with baseline strategies. These results confirm that the framework can simultaneously ensure stringent service requirements and enhance system-wide performance in Next-Generation networks.

Backdoor attacks create significant security threats to language models by embedding hidden triggers that manipulate model behavior during inference, presenting critical risks for AI systems deployed in healthcare and other sensitive domains. While existing defenses effectively counter obvious threats such as out-of-context trigger words and safety alignment violations, they fail against sophisticated attacks using contextually-appropriate triggers that blend seamlessly into natural language. This paper introduces three novel contextually-aware attack scenarios that exploit domain-specific knowledge and semantic plausibility: the ViralApp attack targeting social media addiction classification, the Fever attack manipulating medical diagnosis toward hypertension, and the Referral attack steering clinical recommendations. These attacks represent realistic threats where malicious actors exploit domain-specific vocabulary while maintaining semantic coherence, demonstrating how adversaries can weaponize contextual appropriateness to evade conventional detection methods. To counter both traditional and these sophisticated attacks, we present \textbf{SCOUT (Saliency-based Classification Of Untrusted Tokens)}, a novel defense framework that identifies backdoor triggers through token-level saliency analysis rather than traditional context-based detection methods. SCOUT constructs a saliency map by measuring how the removal of individual tokens affects the model's output logits for the target label, enabling detection of both conspicuous and subtle manipulation attempts. We evaluate SCOUT on established benchmark datasets (SST-2, IMDB, AG News) against conventional attacks (BadNet, AddSent, SynBkd, StyleBkd) and our novel attacks, demonstrating that SCOUT successfully detects these sophisticated threats while preserving accuracy on clean inputs.

Quantum Neural Networks (QNNs) offer promising capabilities for complex data tasks, but are often constrained by limited qubit resources and high entanglement, which can hinder scalability and efficiency. In this paper, we introduce Adaptive Threshold Pruning (ATP), an encoding method that reduces entanglement and optimizes data complexity for efficient computations in QNNs. ATP dynamically prunes non-essential features in the data based on adaptive thresholds, effectively reducing quantum circuit requirements while preserving high performance. Extensive experiments across multiple datasets demonstrate that ATP reduces entanglement entropy and improves adversarial robustness when combined with adversarial training methods like FGSM. Our results highlight ATP’s ability to balance computational efficiency and model resilience, achieving significant performance improvements with fewer resources, which will help make QNNs more feasible in practical, resource-constrained settings.

Achieving ultra-reliable low-latency communication (URLLC) in next-generation radio access networks (RAN) is crucial for mission-critical applications, such as industrial robotics and remote healthcare. However, high traffic loads in RAN environments may lead to resource contention and overload, jeopardizing latency and reliability requirements. To address this, Open RAN (O-RAN) architecture provides a flexible, softwaredefined framework that can manage admission control and resource allocation strategies. This paper introduces a joint admission control and resource provisioning framework tailored for O-RAN environments, using a constrained reinforcement learning model to dynamically balance user admissions and allocate resources to those admitted. By selectively granting service requests and efficiently managing resources, our approach mitigates latency violations and improves energy efficiency under high traffic conditions. Simulation results indicate substantial performance gains over traditional methods, demonstrating the potential of reinforcement learning to optimize URLLC performance in resource-constrained NextG networks.

User equipments (UEs) sharing a RAN slice can generate bursty or unpredictable traffic that degrades the quality of service (QoS) for other slice members. We present triage slicing, a control mechanism that detects disruptive UEs using an online long short term memory (LSTM) predictor and migrates them to a monitored slice with constrained, risk proportional resources. Implemented on the Open AI Cellular (OAIC) testbed, triage slicing rapidly contains disruptive UEs, preserves benign QoS, and incurs minimal control overhead.

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.

Electric vehicle (EV) charging systems are increasingly vulnerable to both cyber and physical attacks, posing significant risks to grid stability and operational security. Detecting such attacks remains a major challenge due to the complex nature of charging infrastructure and the scarcity of labeled attack data. Traditional machine learning (ML) models have demonstrated promise in intrusion and anomaly detection, however, their effectiveness is often limited by the lack of diverse real-world attack datasets, making them less reliable in detecting stealthy or emerging threats. To address these limitations, we leverage pretrained large language models (LLMs) enhanced with retrieval-augmented generation (RAG) for real-time anomaly detection in EV charging networks. The proposed system integrates domain-specific knowledge with live charging session data, enabling accurate classification of malicious activities such as billing fraud, energy theft, and communication tampering. Experimental results demonstrate that LLM-based detection improves classification accuracy while reducing false positives compared to traditional ML approaches. The developed methodology is adaptable across various cybersecurity applications, making it applicable to a wide range of attack scenarios beyond EV infrastructure. By combining AI-driven anomaly detection with real-time contextual analysis, this approach enhances the resilience of EV charging networks against evolving threats, ensuring secure and reliable operations.

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.

The escalating threat of phishing emails has become increasingly sophisticated with the rise of Large Language Models (LLMs). As attackers exploit LLMs to craft more convincing and evasive phishing emails, it is crucial to assess the resilience of current phishing defenses. In this study we conduct a comprehensive evaluation of traditional phishing detectors, such as Gmail Spam Filter, Apache SpamAssassin, and Proofpoint, as well as machine learning models like SVM, Logistic Regression, and Naive Bayes, in identifying both traditional and LLM-rephrased phishing emails. We also explore the emerging role of LLMs as phishing detection tools, a method already adopted by companies like NTT Security Holdings and JPMorgan Chase. Our results reveal notable declines in detection accuracy for rephrased emails across all detectors, highlighting critical weaknesses in current phishing defenses. As the threat landscape evolves, our findings underscore the need for stronger security controls and regulatory oversight on LLM-generated content to prevent its misuse in creating advanced phishing attacks. This study contributes to the development of more effective Cyber Threat Intelligence (CTI) by leveraging LLMs to generate diverse phishing variants that can be used for data augmentation, harnessing the power of LLMs to enhance phishing detection, and paving the way for more robust and adaptable threat detection systems.

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.

The advent of fifth generation (5G) networks has opened new avenues for enhancing connectivity, particularly in challenging environments like remote areas or disaster-struck regions. Unmanned aerial vehicles (UAVs) have been identified as a versatile tool in this context, particularly for improving network performance through the Integrated access and backhaul (IAB) feature of 5G. However, existing approaches to UAV-assisted network enhancement face limitations in dynamically adapting to varying user locations and network demands. This paper introduces a novel approach leveraging deep reinforcement learning (DRL) to optimize UAV placement in real-time, dynamically adjusting to changing network conditions and user requirements. Our method focuses on the intricate balance between fronthaul and backhaul links, a critical aspect often overlooked in current solutions. The unique contribution of this work lies in its ability to autonomously position UAVs in a way that not only ensures robust connectivity to ground users but also maintains seamless integration with central network infrastructure. Through various simulated scenarios, we demonstrate how our approach effectively addresses these challenges, enhancing coverage and network performance in critical areas. This research fills a significant gap in UAV-assisted 5G networks, providing a scalable and adaptive solution for future mobile networks.

The massive deployment of Internet of Things (IoT) devices, including sensors and actuators, is ushering in smart and connected communities of the future. The massive deployment of IoT devices, including sensors and actuators, is ushering in smart and connected communities of the future. The availability of real-time and high-quality sensor data is crucial for various IoT applications, particularly in healthcare, energy, transportation, etc. However, data collection may have to be outsourced to external service providers (SPs) due to cost considerations or lack of specialized equipment. Hence, the data market plays a critical role in such scenarios where SPs have different quality levels of available data, and IoT users have different application-specific data needs. The pairing between data available to the SP and users in the data market requires an effective mechanism design that considers the SPs’ profitability and the Quality-of-Service (QoS) needs of the users. We develop a generic framework to analyze and enable such interactions efficiently, leveraging tools from contract theory and mechanism design theory. It can enable and empower emerging data-sharing paradigms, such as Sensing-as-a-Service (SaaS). The contract design creates a pricing structure for on-demand sensing data for IoT users. By considering a continuum of user types, we capture a diverse range of application requirements and propose optimal pricing and allocation rules that ensure QoS provisioning and maximum profitability for the SP. Furthermore, we provide analytical solutions for fixed distributions of user types to analyze the developed approach. For comparison, we consider the benchmark case assuming complete information of the user types and obtain optimal contract solutions. Finally, a case study based on the example of a virtual reality application delivered using unmanned aerial vehicles (UAVs) is presented to demonstrate the efficacy of the proposed contract design framework.

Unmanned aerial vehicles (UAVs) are a convenient choice for carrying mobile base stations to rapidly setup communication services for ground users. Unlike terrestrial networks, UAVs do not have fiber optic back-haul connectivity except when they are tethered to the ground, which restricts their mobility. In the absence of back-haul, e.g., in remote areas, emergency situations, or in battlefields, there is a need to ensure connectivity among UAVs in addition to coverage of ground users for creating local area networks. This paper provides a distributed and dynamic approach for UAV formation-based control for coverage and connectivity of spatially dispersed users. We use flocking dynamics as a guide to constructing tailored formations of UAVs on the fly. Simulation results demonstrate that if sufficient aerial base stations are available, the proposed approach results in a strongly connected network of UAVs that is able to provide both a backhaul and fronthaul network. The approach can be further extended to create multi-tier extra-terrestrial networks to cater for large-scale applications.

Fog computing is becoming a vital component for Internet of things (IoT) applications, acting as its computational engine. Mission-critical IoT applications are highly sensitive to latency, which depends on the physical location of the cloud server. Fog nodes of varying response rates are available to the cloud service provider (CSP) and it is faced with a challenge of forwarding the sequentially received IoT data to one of the fog nodes for processing. Since the arrival times and nature of requests is random, it is important to optimally classify the requests in real-time and allocate available virtual machine instances (VMIs) at the fog nodes to provide a high QoE to the users and consequently generate higher revenues for the CSP. In this paper, we use a pricing policy based on the QoE of the applications as a result of the allocation and obtain an optimal dynamic allocation rule based on the statistical information of the computational requests. The developed solution is statistically optimal, dynamic, and implementable in real-time as opposed to other static matching schemes in the literature. The performance of the proposed framework has been evaluated using simulations and the results show significant improvement as compared with benchmark schemes.

The Internet of Things (IoT) relies heavily on wireless communication devices that are able to discover and interact with other wireless devices in their vicinity. The communication flexibility coupled with software vulnerabilities in devices, due to low cost and short time-to-market, exposes them to a high risk of malware infiltration. Malware may infect a large number of network devices using device-to-device (D2D) communication resulting in the formation of a botnet, i.e., a network of infected devices controlled by a common malware. A botmaster may exploit it to launch a network-wide attack sabotaging infrastructure and facilities, or for malicious purposes such as collecting ransom. In this paper, we propose an analytical model to study the D2D propagation of malware in wireless IoT networks. Leveraging tools from dynamic population processes and point process theory, we capture malware infiltration and coordination process over a network topology. The analysis of mean-field equilibrium in the population is used to construct and solve an optimization problem for the network defender to prevent botnet formation by patching devices while causing minimum overhead to network operation. The developed analytical model serves as a basis for assisting the planning, design, and defense of such networks from a defender’s standpoint.

Spectrum reservation is emerging as one of the potential solutions to cater for the communication needs of massive number of wireless Internet of Things (IoT) devices with reliability constraints particularly in mission-critical scenarios. In most mission-critical systems, the true utility of a reservation may not be completely known ahead of time as the unforseen events might not be completely predictable. In this paper, we present a dynamic contract approach where an advance payment is made at the time of reservation based on partial information about spectrum reservation utility. Once the complete information is obtained, a rebate on the payment is made if the reservation is released. In this paper, we present a contract theoretic approach to design an incentivized mechanism that coerces the applications to reveal their true application type resulting in greater profitability of the IoT network operator. The operator offers a menu of contracts with advanced payments and rebate to the IoT applications without having knowledge about the types of applications. The decision of the applications in selecting a contract leads to a revelation of their true type to the operator which allows it to generate higher profits than a traditional spectrum auction mechanism. Under some assumptions on distribution of the utility of the applications, closed form solutions for the optimal dynamic spectrum reservation contract are provided and the sensitivity against system parameters is analyzed.

The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas, such as smart homes, smart cities, health care, and transportation. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to battlefield specific challenges, such as the absence of communication infrastructure, heterogeneity of devices, and susceptibility to cyber-physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and information dissemination in the presence of adversaries. This paper aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to quantify the information dissemination among heterogeneous network devices. Consequently, a tractable optimization problem is formulated that can assist commanders in cost effectively planning the network and reconfiguring it according to the changing mission requirements.

Enabling the Internet of things in remote areas without traditional communication infrastructure requires a multi-layer network architecture. The devices in the overlay network are required to provide coverage to the underlay devices as well as to remain connected to other overlay devices. The coordination, planning, and design of such two-layer heterogeneous networks is an important problem to address. Moreover, the mobility of the nodes and their vulnerability to adversaries pose new challenges to the connectivity. For instance, the connectivity of devices can be affected by changes in the network, e.g., the mobility of the underlay devices or the unavailability of overlay devices due to failure or adversarial attacks. To this end, this work proposes a feedback based adaptive, self-configurable, and resilient framework for the overlay network that cognitively adapts to the changes in the network to provide reliable connectivity between spatially dispersed smart devices. Our results show that if sufficient overlay devices are available, the framework leads to a connected configuration that ensures a high coverage of the mobile underlay network. Moreover, the framework can actively reconfigure itself in the event of varying levels of device failure.

Mission critical data dissemination in massive Internet of things (IoT) networks imposes constraints on the message transfer delay between devices. Due to low power and communication range of IoT devices, data is foreseen to be relayed over multiple device-to-device (D2D) links before reaching the destination. The coexistence of a massive number of IoT devices poses a challenge in maximizing the successful transmission capacity of the overall network alongside reducing the multi-hop transmission delay in order to support mission critical applications. There is a delicate interplay between the carrier sensing threshold of the contention based medium access protocol and the choice of packet forwarding strategy selected at each hop by the devices. The fundamental problem in optimizing the performance of such networks is to balance the tradeoff between conflicting performance objectives such as the spatial frequency reuse, transmission quality, and packet progress towards the destination. In this paper, we use a stochastic geometry approach to quantify the performance of multi-hop massive IoT networks in terms of the spatial frequency reuse and the transmission quality under different packet forwarding schemes. We also develop a comprehensive performance metric that can be used to optimize the system to achieve the best performance. The results can be used to select the best forwarding scheme and tune the carrier sensing threshold to optimize the performance of the network according to the delay constraints and transmission quality requirements.

The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas such as smart homes, smart cities, health care, transportation, etc. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to the battlefield specific challenges such as the absence of communication infrastructure, and the susceptibility of devices to cyber and physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and the information dissemination in the presence of adversaries. This work aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to study the communication of mission-critical data among different types of network devices and consequently design the network in a cost effective manner.

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.

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.

Carrier sense multiple access (CSMA) protocol is standardized for vehicular communication to ensure a distributed and efficient communication between vehicles. However, several vehicular applications require efficient multi-hop information dissemination. This paper exploits stochastic geometry to develop a tractable and accurate modeling framework to characterize the multi-hop transmissions for vehicular networks in a multilane highway setup. In particular, we study the tradeoffs between per-hop packet forward progress, per-hop transmission success probability, and spatial frequency reuse (SFR) efficiency imposed by different packet forwarding schemes, namely, most forward with fixed radius (MFR), the nearest with forward progress (NFP), and the random with forward progress (RFP). We also define a new performance metric, denoted as the aggregate packet progress (APP), which is a dimensionless quantity that captures the aforementioned tradeoffs. To this end, the developed model reveals the interplay between the spectrum sensing threshold ($\boldsymbol{\rho}_{\boldsymbol{th}}$) of the CSMA protocol and the packet forwarding scheme. Our results show that, contrary to ALOHA networks, which always favor NFP, MFR may achieve the highest APP in CSMA networks if$\boldsymbol{\rho}_{\boldsymbol{th}}$is properly chosen.

This paper develops a modeling framework, based on stochastic geometry and queueing theory, for carrier sense multiple access (CSMA) coordinated inter-vehicle communication, with unsaturated buffers, in a multi-lane highway scenario. We show that the spectrum sensing threshold (ρth) of the CSMA protocol is a critical design parameter that imposes a tradeoff between the transmission success probability and the spatial frequency reuse. To this end, we show that there exists an optimal value for ρth, which maximizes the transmission capacity that depends on the highway setup and the traffic intensity.

In this paper, we present a game theoretic framework for spectrum allocation in distributed cognitive radio networks containing both unicast and broadcast traffic. Our proposed scheme aims to minimize broadcast latency for broadcast traffic and minimize interference and access contention for both types of traffic. We develop a utility function that ensures that both objectives are met yielding a higher network throughput. Our proposed spectrum allocation game is also formulated as a potential game and is guaranteed to converge to a Nash equilibrium if the sequential best response dynamics is followed. A proof of concept of the proposed algorithm has been implemented on the Orbit radio testbed and the results verify the convergence of the potential game. Our simulation and experimental results also reveal that the choice of utility function improves the average network throughput for a mixed traffic profile.