In intelligent Internet of Things (IoT) systems with split analytic functions, time-critical applications perform tasks that require instant data processing and immediate physical responses within milliseconds to prevent danger, injury, or system failure, and time-tolerant applications perform non-urgent background tasks that do not need an immediate response. Conflicts between applications occur when two or more applications send opposite or incompatible commands to the same physical devices at the same time. Cross-layer conflicts between time-critical and time-tolerant applications occur when resource demands overlap and the system reaches its limits. In contrast, conflicts between time-critical applications occur when multiple high-priority tasks compete for limited shared resources simultaneously. This research focuses on the security aspects of conflicting applications in intelligent IoT system architectures with split analytic functions, analyzes cross-layer conflicts between applications, and identifies security threats. The study introduces a threat model designed to analyze the operational and security risks that arise when applications from different vendors simultaneously apply conflicting configurations to a single IoT device. Through simulations, the research evaluates these security risks, analyzes alternative defense strategies, and provides a sensitivity analysis. Finally, it proposes a conflict-alleviation architectural framework tailored for time-critical applications; this framework proactively detects and resolves policy conflicts during the application onboarding stage before they can affect active IoT network operations.
Resource allocation plays a crucial role in determining the overall performance of 6G wireless networks; however, the growing number of users and the integration of heterogeneous infrastructures make this task increasingly challenging. Even with higher capacity and wider coverage, spectrum and energy resources remain limited. Furthermore, different network infrastructures operate under rapidly changing wireless conditions. Therefore, achieving the expected 6G network performance also depends on allocating these limited resources based on current network conditions. This requirement has led to the development of various optimization methods for wireless-resource allocation. However, the performance improvements reported in existing studies do not necessarily establish whether the corresponding methods can operate under 6G networks. Most evaluations emphasize the resulting allocation quality while providing limited evidence on whether the complete decision process remains efficient under changing network conditions. This paper therefore evaluates the operational evidence reported for centralized, distributed, and hybrid resource allocation against criteria derived from 6G requirements. The assessment of several state-of-the-art solutions shows that solution quality is consistently demonstrated; however, evidence concerning decision timeliness, signaling overhead, and resource-management energy remains limited. Based on these findings, the paper derives four operational design principles: validity-aware control, efficient coordination, feasibility-preserving adaptation, and net-benefit management. Accordingly, it identifies open challenges for practical 6G resource allocation.
In highly mobile 3D flying ad hoc networks (FANETs) for UAV swarms, conventional GPSR suffers from significant packet loss and transmission delays. GPSR implicitly assumes that links to nearby nodes remain stable, an assumption that often fails for fast-moving UAVs. Its greedy forwarding tends to select edge-of-range neighbors, and minor changes in UAV speed or heading can readily break these fragile wireless connections. Existing GPSR enhancements have two key limitations: trajectory-prediction-based schemes introduce heavy overhead for onboard UAV hardware and incur large prediction errors during sharp turns or obstacle avoidance, whereas static RSSI-threshold solutions cannot track time-varying channel fading. To address these limitations, this paper proposes LL-SQ-GPSR, a lightweight stateless geographic routing protocol that moves beyond pure distance-based forwarding. The dual-metric link stability evaluation framework combines Expected Link Duration (ELD) with Signal Decay Rate (SDR). ELD is derived analytically from relative motion vectors between neighboring nodes, whereas SDR is estimated from sliding-window RSSI sequences using low-complexity linear regression. The proposed framework selects next-hop relays that simultaneously approach the destination and maintain long kinematic link lifetimes and stable physical-layer signals. Monte Carlo simulations in highly mobile 3D mission scenarios indicate that, compared with baseline GPSR, LL-SQ-GPSR reduces path length by 24.1–76.5% and hop count by 20.0–76.2%. Even at a high flight speed of 30 m/s, the proposed protocol achieves a higher packet delivery ratio and lower routing-level end-to-end latency than existing protocols, including GPSR+ and GPSR. It maintains asymptotic complexity comparable to that of the original GPSR, with only bounded per-node overhead. These results demonstrate its effectiveness as a communication solution for resource-limited cooperative UAV swarms operating in highly dynamic 3D airspace.
Meeting the stringent latency requirements of safety-critical vehicular services is a major challenge for beyond fifth generation (B5G) systems, and rapidly varying channel state information (CSI) in device-to-device (D2D) enabled vehicle-to-everything (V2X) communication makes the problem harder. This paper addresses power allocation in a D2D-enabled vehicular network in which vehicle-to-vehicle (V2V) links reuse the spectrum of vehicle-to-infrastructure (V2I) links. The problem is formulated as a non-convex optimization problem that maximizes the V2I sum capacity subject to a V2V latency constraint. We use deep reinforcement learning to jointly select the V2I and V2V transmit powers from large-scale channel information, trained from measured outcomes rather than from the closed-form channel expressions used by the optimization-based solution, and a normalized preference weight that trades V2I capacity against V2V latency within a single network. The framework is implemented with a discrete-action deep Q-network (DQN) and with a continuous-action deep deterministic policy gradient (DDPG) agent. Under the nominal channel model, the DQN attains 85–95% and the DDPG agent 90–99% of the sum capacity of the model-based optimum, with a per-block computation time two to three orders of magnitude lower. When the reported large-scale channel gains contain errors of 2 dB, the model-based solution violates the V2V latency constraint on 46–56% of the links, whereas agents trained under such errors keep the violation rate below 0.5%. DDPG is preferable when the channel model is accurate, whereas the DQN trains more reliably under CSI errors. Queue-level simulations further show that, at the considered traffic loads, queueing rather than transmission failures dominates the delay tail.
This study compares 4G and 5G mobile network performance across Colombian municipalities using regulatory crowdsourced data. The dataset contains 471,800 indicator records from June 2023 through June 2026. Download speed, upload speed, latency, jitter, and packet loss were matched by municipality, operator, and reporting period. Because published values are rolling three-month means, the primary analysis uses March and June 2026, yielding 1461 complete paired observations across 634 municipalities. Geometric mean ratios with equal municipality weights and municipality-block bootstrap intervals summarize the comparisons. The 5G/4G download ratio is 1.94 (95% bootstrap interval: 1.80–2.09); the corresponding ratios are 1.19 for upload, 0.90 for latency, 0.78 for jitter, and 0.68 for packet loss. All five indicators strictly favor 5G in 38.5% of municipality-weighted observations; this share is 25.9% when each indicator must improve by at least 10%. The download ratio rises to 2.59 with equal observation weights and to 5.49 in the 69 municipalities with complete reporting for all three 5G operators at both periods. Thus, average differences favor 5G, but their magnitude varies with sample composition and weighting, and improvements are not uniform across indicators. The findings describe a first-half-2026 performance snapshot, not causal deployment effects or long-term evolution; second-quarter data remain preliminary.
