Search Results (20)

Bandwidth-limited aerial–ground sensing requires an explicit trade-off among payload size, decoded-map quality, and downstream route utility. This paper evaluates a strict RGB-D edge-perception interface in which the receiver accesses only a quantized C × 16 × 16 tensor and no encoder-side skip features. Under a fixed AeroScapes protocol, five-seed latent-8 training gives a test intersection over union (IoU) of 0.5617 ± 0.0585 and route utility of 0.0539 ± 0.0227; the strongest validation-selected checkpoint reaches an IoU of 0.6584 but is reported only as a checkpoint-specific result. Acontrolled five-seed width ablation identifies a Pareto set: latent-8 is the lowest-rate operating point at 16.448 kbit, latent-16 has the smallest IoU standard deviation, and latent-32 gives the highest mean IoU (0.5788) and route utility (0.0921), with no significant pairwise differences between widths. A paired threeseed stabilization test likewise finds no significant IoU improvement from depth dropout, a soft topology loss, or their combination (p ≥ 0.6060); the combined configuration raises mean IoU to 0.5700 but increases dispersion. Relative to a practical reference combining JPEG (quality 75) RGB and 8-bit PNG depth, latent-8 reduces the mean payload by a factor of 4.32. The digital-link study extends the additive-noise analysis to fading, intersymbol interference, timevariation, packet and burst errors, near–far interference, and corrupted range metadata. Conventional short codes, source-aware unequal protection, and a 3GPP NR LDPC implementation are evaluated with framing, automatic repeat request, mediumaccess overhead, and transmission delay. Cross-domain evaluation establishes an important limitation: zero-shot AeroScapes-to-UAVid transfer is weak, whereas sequence-disjoint tenseed UAVid training improves the eight-class mean IoU from 0.2417 ± 0.0221 for RGB-only to 0.2602 ± 0.0129 for strict RGB-D (p = 0.0161). Lightweight depth, visibility, missingdepth, weighted-topology, and coarse-to-local refinement experiments further delimit deployment. Together, these experiments provide an auditable cross-layer evaluation linking source representation, channel reliability, protocol cost, spatial error, and receivergrid connectivity without equating one favorable checkpoint with general superiority. Full article

New Radio Vehicle-to-Everything (NR-V2X) safety scheduling is difficult because burst episodes increase urgent arrivals, lower transmission success, and create retransmissions that compete for future slots. A scheduler that waits for the visible queue can react late, whereas always reserving extra safety physical resource blocks (PRBs) consumes the best-effort (BE) capacity after the stress has passed. This study proposes Memory-Based Predictive Allocation (MPA), a finite-action PRB allocation rule for safety and BE coexistence. MPA combines the deadline queue and retry state with a decayed transient-deficit memory, online success calibration, and a recoverability-aware BE cost guard. At each slot, it tests feasible safety PRB increments and chooses the action that first limits urgent safety loss, then reduces next-slot carryover, and finally avoids unnecessary PRB use. The model uses an NR-V2X resource pool interpretation and a calibrated signal-to-interference-plus-noise-ratio (SINR)-to-success mapping with hybrid automatic repeat request (HARQ)-like combining. Monte Carlo results show that MPA lowers safety misses relative to queue-reactive scheduling while preserving more BE throughput than a maximum safety reservation. In dense non-line-of-sight (NLOS) stress, MPA keeps the 95th-percentile (p95) delivered packet delay within the three-millisecond budget and preserves 0.892 normalized BE throughput, versus 0.534 under fixed maximum reservation. Full article

Degraded network conditions and intermittent disconnections can impair solar greenhouse Internet of Things (IoT) systems by delaying cloud-to-field control, generating burst traffic after reconnection, and disrupting alarm feedback loops. This paper proposes a resilient end–edge–cloud collaborative framework for maintaining control continuity and closed-loop alarm reliability under unstable edge–cloud communication. The framework evaluates network quality using round-trip time, packet loss rate, and consecutive no-response duration, and combines hysteresis-based state switching, control leases, edge takeover, differential backfill, and locally persistent alarm-state synchronization. During disconnection, the edge gateway uses the latest valid configuration to execute fallback local control; after reconnection, high-priority events are uploaded first through a hierarchically rate-limited recovery strategy. In the scripted simulation experiments, the proposed method reduced peak backfill throughput from 2.16 ± 0.06 MB/s to 0.69 ± 0.01 MB/s, shortened high-priority event completion time from 17.3 ± 2.7 s to 2.0 ± 0.7 s, and increased the acknowledgment success rate at 20% packet loss from 76.5 ± 2.2% to 98.4 ± 0.8%. It also reduced the maximum temperature deviation during disconnection from 7.20 °C to 3.50 °C. These results suggest that the proposed framework can improve control continuity and alarm-loop completeness under the specified simulation settings. A supplementary trace-driven recovery evaluation using public 5G testbed measurements showed a similar qualitative trend. Broader validation with field-deployed greenhouse IoT platforms or hardware-in-the-loop testbeds is still needed. Full article

To mitigate local congestion and address the adaptability limitations of traditional static routing under dynamic traffic, this paper proposes an end-to-end routing method based on a Graph Attention Network (GAT), termed Congestion-Aware Graph Attention Routing (CA-GAR). To alleviate the issue of local optima in traditional heuristic iterative optimization, we design a dynamic link cost optimization algorithm with multi-start parallel exploration. This algorithm employs a ”penalty–reselection–reward” closed-loop feedback mechanism, performing global searches from multiple random initial states to generate a high-quality, empirically near-optimal cost matrix as supervised labels. Building on this, CA-GAR leverages a multi-head attention mechanism to adaptively aggregate high-order topological features of nodes and edges, and incorporates a staged hierarchical hyperparameter optimization strategy to map real-time network states to link costs. Simulation results demonstrate that CA-GAR outperforms traditional static routing under light, medium, and heavy loads. Under high-load burst conditions, the method exhibits effective congestion avoidance capability, reducing end-to-end delay by approximately 50% and lowering the packet loss rate to as low as 2%. Compared with QLRA, CA-GAR shows promising performance in multi-path traffic splitting and possesses robust fast rerouting capabilities during node failures, thereby achieving intelligent traffic distribution and global load balancing. Full article

Packet losses cause a decline in the performance of packet networks, and this decline is related not only to the percentage of losses but also to the clustering of them together in series. We study how the correlation of packet sizes influences this clustering when the losses are caused by buffer overflows. Specifically, for a model of a buffer with correlated packet sizes, we derive the burst ratio parameter, an intuitive metric for the inclination of losses to cluster. In addition to the burst ratio, we obtain the sequential losses distribution in the first, k-th, and stationary overflow periods. The Markovian Service Process (MSP) used by the model empowers it to mimic arbitrary packet size distributions and arbitrary correlation strengths. Using numeric examples, the impact of packet size correlation, buffer size, and traffic intensity on the burst ratio is showcased and discussed. Full article

Background: In brain–computer interfaces, neural decoding plays a central role in translating neural signals into meaningful physical actions. These signals are transmitted to processors for decoding via wired or wireless channels; however, they are often subject to data loss, commonly referred to as “packet loss”. Despite their importance, the effects of different types and degrees of packet loss on neural decoding have not yet been comprehensively studied. Understanding these effects is critical for advancing neural signal processing. Methods: This study addresses this gap by constructing four distinct packet loss models that simulate the congestion, distribution, and burst loss scenarios. Using macaque superior arm movement decoding experiments, we analyzed the effects of the aforementioned packet loss types on decoding performance across six parameters (position, velocity, and acceleration in the x and y dimensions). The performance was assessed using the $R^2$ metric and statistical comparisons across different loss scenarios. Results: Our results indicate that sudden, consecutive packet loss significantly degraded decoding performance. For the same packet loss probability, burst loss led to the largest decrease in the $R^2$ value. Notably, when the packet loss rate reached 10%, the decoding performance for acceleration dropped to 73% of the original $R^2$ value. On the other hand, when the packet loss rate was within 2%, the neural signal decoding results across all packet loss models remained largely unaffected. However, as the packet loss rate increased, the impact became more pronounced. These findings highlight the varying degrees to which different packet loss models affect decoding outcomes. Conclusions: This study quantitatively evaluated the relationship between packet loss and neural decoding outcomes, highlighting the differential effects of loss patterns on decoding parameters, and it proposed some methods and devices to solve the problem of packet loss. These findings offer valuable insights for the development of resilient neural signal acquisition and processing systems capable of mitigating the impact of packet loss. Full article

With the rapid advancement of the Internet of Things (IoT), there is a global surge in network traffic. Software-Defined Networks (SDNs) provide a holistic network perspective, facilitating software-based traffic analysis, and are more suitable to handle dynamic loads than a traditional network. The standard SDN architecture control plane has been designed for a single controller or multiple distributed controllers; however, a logically centralized single controller faces severe bottleneck issues. Most proposed solutions in the literature are based on the static deployment of multiple controllers without the consideration of flow fluctuations and traffic bursts, which ultimately leads to a lack of load balancing among controllers in real time, resulting in increased network latency. Moreover, some methods addressing dynamic controller mapping in multi-controller SDNs consider load fluctuation and latency but face controller placement problems. Earlier, we proposed priority scheduling and congestion control algorithm (eSDN) and dynamic mapping of controllers for dynamic SDN (dSDN) to address this issue. However, the future growth of IoT is unpredictable and potentially exponential; to accommodate this futuristic trend, we need an intelligent solution to handle the complexity of growing heterogeneous devices and minimize network latency. Therefore, this paper continues our previous research and proposes temporal deep Q learning in the dSDN controller. A Temporal Deep Q learning Network (tDQN) serves as a self-learning reinforcement-based model. The agent in the tDQN learns to improve decision-making for switch-controller mapping through a reward–punish scheme, maximizing the goal of reducing network latency during the iterative learning process. Our approach—tDQN—effectively addresses dynamic flow mapping and latency optimization without increasing the number of optimally placed controllers. A multi-objective optimization problem for flow fluctuation is formulated to divert the traffic to the best-suited controller dynamically. Extensive simulation results with varied network scenarios and traffic show that the tDQN outperforms traditional networks, eSDNs, and dSDNs in terms of throughput, delay, jitter, packet delivery ratio, and packet loss. Full article

When working with the Wireless Access in Vehicular Environment (WAVE) protocol stack, the multi-channel operation mechanism of the IEEE 1609.4 protocol may impact the overall network performance, especially when using video streaming applications. In general, packets delivered from the application layer during a Control Channel (CCH) time slot have to wait for transmission until the next Service Channel (SCH) time slot arrives. The accumulation of packets at the beginning of the latter time slot may introduce additional delays and higher contention when all the network nodes try, at the same time, to obtain access to the shared channel in order to send the delayed packets as soon as possible. In this work, we have analyzed these performance issues and proposed a new method, which we call SkipCCH, that helps the MAC layer to overcome the high contention produced by the packet transmission bursts at the beginning of every SCH slot. This high contention implies an increase in the number of packet losses, which directly impacts the overall network performance. With our proposal, streaming video in vehicular networks will provide a better quality of reconstructed video at the receiver side under the same network conditions. Furthermore, this method has particularly proven its benefits when working with Quality of Service (QoS) techniques, not only by increasing the received video quality but also because it avoids starvation of the lower-priority traffic. Full article

Within the power line communication (PLC) network, a large number of electronic devices are connected, and environmental factors can cause unusual behavior, leading to high-amplitude impulse noise in the received signal and, as a result, packet losses and burst errors in the data that are sent. Burst errors make it difficult to send data over power line channels efficiently and accurately. Analyzing error patterns with intelligent techniques can provide valuable insights into data transmission efficiency, enhance transmission quality, and optimize PLC systems. This research proposes a three-state Fritchman–Markov chain-based power line communication error model and develops a software-defined PLC system. The goal is to analyze and model the system’s statistical error process. The PLC system’s fundamental error pattern is deduced from the transmission and reception of data on our software-defined (SD) PLC platform. The system is designed with multi-state quadrature amplitude modulation (M-QAM) data transmission and reception techniques. An error pattern consisting of 50,000 bits is obtained by comparing the bits transmitted with those received using the in-house M-QAM-based PLC transceiver system. The error characteristics of the newly developed M-QAM SD-PLC system are precisely modeled using the error model. Examining the burst error statistics of the reference error sequences of the SD-PLC system and the three-state Fritchman–Markov error model reveals striking similarities. According to the results, the error model accurately represents the error characteristics of the developed M-QAM SD-PLC system. The proposed three-state Fritchman–Markov chain-based error model for PLC has the potential to provide a comprehensive understanding of the error process in PLC. Additionally, it can assess error control strategies with less computational complexity and a shorter simulation time. Full article

The current state of the art in small Unmanned Aerial System (sUAS) testing and evaluation exists mainly in the realm of outdoor flight. Operating small flying sUAS in constrained indoor or subterranean environments places different constraints on their communication links (control links and camera/sensor links). Communication loss in these environments is much more severe due to the proximity of obstacles. This paper examines how correlated packet loss (burst errors) occurring on both the control and camera communication links affects the ability of pilots to fly and navigate small sUAS platforms in constrained Non-Line of Sight (NLOS) environments. A software test bench called AirSim, a UAV simulator, allows us to better understand the effects of correlated packet loss on flyability without damaging multiple sUAS units by flight testing. The simulation was designed to support the design of test methodologies for evaluating the robustness of the communication links and to understand performance without damaging flight tests. Throughout the simulations, it is observed how different levels of packet loss affect the pilot and the number of simulated crashes into the obstacles placed through space. The simulations modeled packet loss both on the video link and the control link to display how packet loss affects ability to pilot and control the sUAS. The utility of using a simulated environment rather than flight testing prevents damage to the fragile and expensive drones being used. Full article

We study the influence of the active queue management mechanism based on the queue size on the serialization of packet losses, i.e., the occurrences of losses in long, consecutive series. We use a traffic model able to mimic precisely the autocorrelation function of traffic, which is known to be far from zero in packet networks. The main contribution is a theorem on the burst ratio parameter, describing the serialization of losses, proven for an arbitrary function assigning drop probabilities to queue sizes. In numerical examples, we show the impact of the autocorrelation strength, drop probability function, and load of the link, on the serialization of losses. Full article

Packet loss is a major problem for wireless networks and has significant effects on the perceived quality of many internet services. Packet loss models are used to understand the behavior of packet losses caused by several reasons, e.g., interferences, coexistence, fading, collisions, and insufficient/excessive memory buffers. Among these, the Gilbert-Elliot (GE) model, based on a two-state Markov chain, is the most used model in communication networks. However, research has proven that the GE model is inadequate to represent the real behavior of packet losses in Wi-Fi networks. In this last category, variables of a single network layer are used, usually the physical one. In this article, we propose a new packet loss model for Wi-Fi that simultaneously considers the temporal behavior of losses and the variables that describe the state of the network. In addition, the model uses two important variables, the signal-to-noise ratio and the network occupation, which none of the packet loss models available for Wi-Fi networks simultaneously take into account. The proposed model uses the well-known Hidden Markov Model (HMM), which facilitates training and forecasting. At each state of HMM, the burst-length of losses is characterized using probability distributions. The model was evaluated by comparing computer simulation and real data samples for validation, and using the log-log complementary distribution of burst-length. We compared the proposed model with competing models through the analysis of mean square error (MSE) using a validation sample collected from a real network. Results demonstrated that the proposed model outperforms the currently available models for packet loss in Wi-Fi networks. Full article

The dropping function mechanism is known to improve the performance of TCP/IP networks by reducing queueing delays and desynchronizing flows. In this paper, we study yet another positive effect caused by this mechanism, i.e., the reduction in the clustering of packet losses, measured by the burst ratio. The main contribution consists of two new formulas for the burst ratio in systems with and without the dropping function, respectively. These formulas enable the easy calculation of the burst ratio for a general, non-Poisson traffic, and for an arbitrary form of the dropping function. Having the formulas, we provide several numerical examples that demonstrate their usability. In particular, we test the effect of the dropping function’s shape on the burst ratio. Several shapes of the dropping function proposed in the literature are compared in this context. We also demonstrate, how the optimal shape can be found in a parameter-depended class of functions. Finally, we investigate the impact of different system parameters on the burst ratio, including the load of the system and the variance of the service time. The most important conclusion drawn from these examples is that it is not only the dropping function that reduces the burst ratio by far; simultaneously, the more variable the traffic, the more beneficial the application of the dropping function. Full article

An Optical Burst Switching (OBS) network is vulnerable to Burst Header Packet (BHP) flooding attack. In flooding attacks, edge nodes send BHPs at a high rate to reserve bandwidth for unrealized data bursts, which leads to a waste of bandwidth, a decrease in network performance, and massive data loss. Machine learning techniques are utilized to detect this attack in the OBS network. In this paper, we propose a particle swarm optimization–support vector machine (PSO-SVM) model for detecting BHP flooding attacks, in which the PSO is used to optimize the parameters of the SVM. We use the dataset provided by the UCI warehouse to train and test the model. The experimental results show that the detection accuracy of the PSO-SVM model reaches 95.0%, which is 9.4%, 9.6%, 20.7%, 8% higher than naïve Bayes, SVM, k-nearest neighbor, and decision tree. Although DCNN outperforms our model, it requires more processing and training time. Collectively, our approach is effective and high-efficiency in detecting flooding attacks in optical burst switching networks and maintaining network stability and security. Full article

This paper is devoted to further strengthening, in the current asymmetric information environment, the informed level of operators about network performance. Specifically, in view of the burst and perishability of a packet loss event, to better meet the real-time requirements of current high-speed backbone performance monitoring, a model for Packet Loss Measurement at the access network boundary Based on Sampled Flow (PLMBSF) is presented in this paper under the premise of both cost and real-time. The model overcomes problems such as the inability of previous estimation to distinguish between packet losses before and after the monitoring point, deployment difficulties and cooperative operation consistency. Drawing support from the Mathis equation and regression analysis, the measurement for packet losses before and after the monitoring point can be realized when using only the sampled flows generated by the access network boundary equipment. The comparison results with the trace-based passive packet loss measurement show that although the proposed model is easily affected by factors such as flow length, loss rate, sampling rate, the overall accuracy is still within the acceptable range. In addition, the proposed model PLMBSF, compared with the trace-based loss measurement is only different in the input data granularity. Therefore, PLMBSF and its advantages are also applicable to aggregated traffic. Full article