The paradigm of dynamic shared access aims to provide flexible spectrum usage. Recently, Federal Communications Commission (FCC) has proposed a new dynamic spectrum management framework for the sharing of a 3.5 GHz (3550–3700 MHz) federal band, called a citizen broadband radio service (CBRS) band, which is governed by spectrum access system (SAS). It is the responsibility of SAS to manage the set of CBRS-SAS users. The set of users are classified in three tiers: incumbent access (IA) users, primary access license (PAL) users and the general authorized access (GAA) users. In this article, dynamic channel assignment algorithm for PAL and GAA users is designed with the goal of maximizing the transmission rate and minimizing the total cost of GAA users accessing PAL reserved channels. We proposed a new mathematical model based on multi-objective optimization for the selection of PAL operators and idle PAL reserved channels allocation to GAA users considering the diversity of PAL reserved channels’ attributes and the diversification of GAA users’ business needs. The proposed model is estimated and validated on various performance metrics through extensive simulations and compared with existing algorithms such as Hungarian algorithm, auction algorithm and Gale–Shapley algorithm. The proposed model results indicate that overall transmission rate, net cost and data-rate per unit cost remain the same in comparison to the classical Hungarian method and auction algorithm. However, the improved model solves the resource allocation problem approximately up to four times faster with better load management, which validates the efficiency of our model.

With the growing demand for delay-sensitive voice, video, and data-based applications, the challenge of efficient spectrum utilization is becoming more critical. To address the challenges of spectrum scarcity, the Federal Communications Commission (FCC) has authorized the sharing of the federally held Citizen Broadband Radio Service (CBRS) band, also known as the 3.5 GHz spectrum band, with commercial use. The 3.5 GHz spectrum band controlled by the spectrum access system (SAS) is shared with three types of users i.e., Incumbent users, Primary access licensee (PAL) users, and general authorized access (GAA) users as opportunistic users. In this paper we proposed the SAS-based channel allocation (SAS-CA) algorithm to optimize spectrum utilization and system performance, considering the potential risks of adding opportunistic GAA users. The proposed algorithm ensures low outage probability for PAL users and provides an optimized channel allocation strategy for GAA users when PAL users are inactive in PAL reserved channels. Additionally, a security module integrated with SAS enhances spectrum utilization and reduces interference levels. The algorithm’s efficiency is validated through multiple test sets, showing rapid convergence within 100 iterations. The integration of the security module results in a median spectrum utilization increase of 16 points and a notable reduction in interference levels, enhancing execution efficiency by approximately 37% compared to traditional methods. These findings demonstrate the substantial benefits of advanced allocation algorithms and robust security mechanisms in ensuring equitable access, stabilizing system performance, and optimizing spectrum utilization within the CBRS band.

Vehicle-to-everything (V2X) communication systems are increasingly susceptible to cyber-physical threats that exploit trust assumptions, coordination latency, and semantic inconsistencies across agents. These vulnerabilities, particularly in dense or adversarial environments, undermine the reliability of cooperative perception, anomaly detection, and safety-critical maneuver execution. This paper presents CR-V2XR, a cross-layer, federated, and trust-aware coordination framework designed to enhance resilience in connected and autonomous vehicular networks. CR-V2XR integrates multi-modal anomaly detection with delay-sensitive trust estimation using features extracted from basic safety messages (BSMs), behavioral deviations, entropy shifts, and inter-vehicle trust validation. The architecture employs federated learning for distributed anomaly detection without centralized aggregation and uses a control layer that supports delay-aware trajectory selection. A multi-objective NSGA-III optimizer enables online trade-off adaptation across detection accuracy (DA), collision probability, and communication overhead. Simulations across eleven adversarial scenarios, including Sybil, wormhole, falsification, and replay attacks, demonstrate that CR-V2XR achieves 95% detection accuracy under worst-case attacks, reduces collision probability from 0.61 to 0.27 at 300 vehicles, maintains bounded delay, typically 45–60 ms under nominal load, and remains resilient under high-stress conditions with delays up to 180 ms and communication overhead (≤ 6.1 MB/s). Compared to centralized IDS and stateless baselines, CR-V2XR improves detection fidelity, scalability, and robustness under non-IID data and partial synchronization. These results establish CR-V2XR as a viable architecture for delay-constrained, trust-centric coordination in federated V2X environments subject to persistent adversarial threats.

The Internet of Medical Things (IoMT) enables real-time healthcare monitoring but faces challenges in energy efficiency, latency, security, and scalability. This paper presents a novel and comprehensive hierarchical data aggregation framework for the Internet of Medical Things (IoMT), optimized using a constrained multi-objective approach. Unlike existing methods that treat objectives in isolation, the model simultaneously minimizes energy consumption, latency, security risks, and authentication overhead under real-world constraints (500 mJ energy, 200 ms latency, 0.9 Sybil detection rate, 0.95 data integrity). Utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II), a diverse Pareto front of solutions is generated, allowing adaptability to different healthcare scenarios. The framework is further validated through dual-environment simulations (MATLAB and NS-3) under realistic attack conditions (Sybil, spoofing, and DoS), demonstrating superior performance and robustness compared to centralized and fog-only baselines.s For instance, the system achieves up to 43% energy savings and 84% latency reduction. These results illustrate the framework’s practical viability for scalable, secure, and efficient IoMT deployments.

Federated learning (FL) offers a promising paradigm for distributed model training in Internet of Medical Things (IoMT) systems, where patient data privacy and device heterogeneity are critical concerns. However, conventional FL remains vulnerable to gradient leakage, model poisoning, and adversarial inference, particularly in privacy-sensitive and resource-constrained medical environments. To address these challenges, we propose SAFE-MED, a secure and adversarially robust framework for privacy-preserving FL tailored for IoMT deployments. SAFE-MED integrates neural encryption, adversarial co-training, anomaly-aware gradient filtering, and trust-weighted aggregation into a unified learning pipeline. The encryption and decryption components are jointly optimized with a simulated adversary under a minimax objective, ensuring high reconstruction fidelity while suppressing inference risk. To enhance robustness, the system dynamically adjusts client influence based on behavioral trust metrics and detects malicious updates using entropy-based anomaly scores. Comprehensive experiments are conducted on three representative medical datasets: Cleveland Heart Disease (tabular), MIT-BIH Arrhythmia (ECG time series), and PhysioNet Respiratory Signals. SAFE-MED achieves near-baseline accuracy with less than 2% degradation, while reducing gradient leakage by up to 85% compared to vanilla FedAvg and over 66% compared to recent neural cryptographic FL baselines. The framework maintains over 90% model accuracy under 20% poisoning attacks and reduces communication cost by 42% relative to homomorphic encryption-based methods. SAFE-MED demonstrates strong scalability, reliable convergence, and practical runtime efficiency across heterogeneous network conditions. These findings validate its potential as a secure, efficient, and deployable FL solution for next-generation medical AI applications.

Wireless multimedia sensor networks (WMSNs) have stringent constraints and need to deliver data packets to the sink node within a predefined limited time. However, due to congestion, buffer overflow occurs and leads to the degradation of the quality-of-service (QoS) parameters of event information. Congestion in WMSNs results in exhausted node energy, degraded network performance, increased transmission delays, and high packet loss. Congestion occurs when the volume of data trying to pass through a network exceeds its capacity. First, the BOCC protocol uses two congestion indicators to detect congestion. One is the buffer occupancy and other is the buffer occupancy change rate. Second, a rate controller is proposed to protect high-priority I-frame packets during congestion. BOCC sends a congestion notification to the source node to reduce congestion in the network. The source node adjusts its data transmission rate after receiving the congestion notification message. In the proposed algorithm, the rate adjustment is made by discarding low-priority P-frame packets from the source nodes. Third, to further improve the performance of the BOCC protocol, the problem is formulated as a constrained optimization problem and solved using convex optimization and sequential quadratic programming (SQP) methods. Experimental results based on Raspberry Pi sensor nodes show that the BOCC protocol achieves up to 16% reduction in packet loss and up to 23% reduction in average end-to-end delay compared to state-of-the-art congestion control algorithms.