The significance of effective on-chip communication has increased due to the increasing integration of computing cores in contemporary systems. Since routing techniques affect latency, throughput, and power consumption while reducing congestion and deadlock, they are essential to Network-on-Chip (NoC) performance. While adaptive and hybrid algorithms like Q-learning (Q), Path-based Randomized Oblivious Minimal (PROM), and Dynamic Adaptive Deterministic (DyAD) promise greater adaptability, deterministic techniques like XY provide simplicity but lack flexibility. The deterministic, adaptive, and hybrid routing algorithms in NoC are assessed in this study under bursty and constant bit rate (CBR) traffic. By combining delay, throughput, and power, a composite Performance Metric (PM) is used to measure routing efficiency. According to the results, PROM outperforms Q-routing by a substantial margin, achieving the highest efficiency under bursty traffic with a PM of 38.42%. DyAD performs best for CBR traffic, with a PM of 34.97%, compared to XY’s PM of 33.64%. The results show that traffic conditions affect the algorithm's applicability. DyAD performs well under constant loads and PROM is well suited for unpredictable traffic.

Mobile Ad Hoc Networks (MANETs) are self-organizing wireless systems without fixed infrastructure, widely applied in military operations, disaster response, and mobile collaboration. Their dynamic topology, high mobility, and varying node densities make routing highly challenging. This study evaluates the performance of the traditional Ad Hoc On- Demand Distance Vector (AODV) protocol and its machine learning–enhanced variant (ML-AODV) under realistic mobility patterns and high-density conditions. Simulations were conducted in NS-2.35 within a 2000 × 2000 m area using node densities of 100, 500, and 1000, three mobility models (Random Waypoint, Random Walk, and Levy Walk), and varying node speeds. Constant Bit Rate (CBR) traffic with 512-byte packets was used, and performance was assessed through Packet Delivery Ratio (PDR), throughput, end-to-end delay, routing overhead, and jitter. Results show that ML-AODV outperforms AODV in Random Waypoint and Random Walk scenarios, achieving up to 23% higher PDR, lower jitter, and more than 50% reduction in routing overhead. However, AODV performs better under the Levy Walk model at medium and high speeds, especially in dense networks, due to its lightweight route discovery mechanism. Overall, ML-AODV is more effective in unpredictable or human-like mobility environments, while AODV remains advantageous in dense and structured conditions.

With the emergence of next-generation video applications and increasing spatial resolutions, delivering high-quality video is still limited by network bandwidth. Adaptive bitrate (ABR) can select the appropriate bitrate for video streaming based on bandwidth, which can mitigate rebuffering caused by insufficient bandwidth. In comparison to Constant Bitrate (CBR), Variable Bitrate’s (VBR) encoding scheme can achieve the same quality with less bandwidth consumption and is gradually being widely used in ABR streaming. However, the quality of the video is still degraded due to a poor network. Recent research utilizes Super-resolution (SR) in ABR streaming to construct neural Video Quality Enhancement (VQE) systems, thereby improving the quality of video segments downloaded due to insufficient bandwidth. However, SR cannot participate in the downsampling encoding process of videos, which results in the effectiveness of existing SR-based VQE systems being inherently limited due to unavoidable information loss during downsampling encoding. Concurrently, SR’s high computational cost restricts neural VQE systems’ deployment on clients without GPUs. In contrast to the unidirectional workflow of SR, Rescaling can be integrated into the downsampling encoding process of videos, allowing favorable information to be retained for VQE. To implement high-quality real-time VQE for ABR streaming of VBR-encoded videos on CPUs, we propose RePC, a novel neural VQE system framework for optimizing existing neural VQE systems based on Rescaling (Re) for the first time, and Patch Content-awareness (PC). In detail, RePC uses Rescaling instead of SR to achieve better VQE by participating in the video downsampling. We also propose a Video Single-Image Rescaling model, VSIR, to indicate the effectiveness of RePC in quality enhancement. To speed up VQE, RePC designs a PC algorithm to mix interpolation and neural computation based on the practical upsampling ability. Our evaluation results demonstrate quality gains of 0.55–2.96 dB in PSNR and 1.79–3.18 in VMAF with fewer parameters, a speed-up of 15×–286× well up to real-time requirements on CPUs, and Quality of Experience (QoE) improvements of 16.58–26.65 are also achieved in an ABR system under various networking conditions.

Substantial growth in the Internet of Things (IoT) has raised the demand for high-performance wireless sensor networks (WSNs) that can gather and send information from remote locations.However, the constrained energy resources of sensor nodes provide substantial hurdles to extending the network's operating lifetime.This paper offers a unique strategy for enhancing energy management and performance in IoT-enabled WSNs that combines the Energy-Efficient Knapsack Algorithm (EEKA) with K-means clustering.EEKA optimizes sensor node transmission power levels while considering energy limits and network topology.Clustering using the K-means algorithm organizes nodes by energy and proximity for effective data transmission and load balancing.Simulation results demonstrate that the proposed EEKA-K-means strategy enhances network performance compared to baseline methods, including EEKA, SMOFCM, PSO-GA and LEACH.Specifically, the network lifetime increased by 1% to 7%, energy consumption decreased by 12%, and throughput improved by 5.4% to 12.5%.Additionally, the Packet Delivery Ratio (PDR) improved by 1.1% to 6.8%, and the number of active nodes increased by 40%.These results were obtained using simulations conducted on a network of 100 sensor nodes randomly deployed in a 100m 100m area, with initial energy set to 2J per node, assuming a heterogeneous energy model and a constant bit rate (CBR) traffic model.

Neural audio coding has emerged as a vivid research direction by promising good audio quality at very low bitrates unachievable by classical coding techniques. Here, end-to-end trainable autoencoder-like models represent the state of the art, where a discrete representation in the bottleneck of the autoencoder is learned. This allows for efficient transmission of the input audio signal. The learned discrete representation of neural codecs is typically generated by applying a quantizer to the output of the neural encoder. In almost all state-of-the-art neural audio coding approaches, this quantizer is realized as a Vector Quantizer (VQ) and a lot of effort has been spent to alleviate drawbacks of this quantization technique when used together with a neural audio coder. In this paper, we propose and analyze simple alternatives to VQ, which are based on projected Scalar Quantization (SQ). These quantization techniques do not need any additional losses, scheduling parameters or codebook storage thereby simplifying the training of neural audio codecs. For real-time speech communication applications, these neural codecs are required to operate at low complexity, low latency and at low bitrates. We address those challenges by proposing a new causal network architecture that is based on SQ and a Short-Time Fourier Transform (STFT) representation. The proposed method performs particularly well in the very low complexity and low bitrate regime.

Existing H.265/HEVC selective encryption (SE) schemes do not take into account the semantic features of input videos, nor do they adjust the encryption syntax elements according to the sensitivity of video content, which greatly limits their applicability. In this paper, we propose a chaos-based tunable H.265/HEVC SE scheme with semantic understanding. First, a deep hashing network is employed to identify content-sensitive videos by analyzing the semantic features of video sequences. Then, the non-sensitive videos and the retrieved sensitive ones are encrypted with different encryption strengths, respectively. Specifically, for non-sensitive videos, seven syntax elements with bypass-coded bins are selected for encryption at a constant bit rate. Hence, the encrypted bitstream keeps exactly the same compression ratio. To provide heavier visual distortion for content-sensitive videos, the regular-coded bins of four syntax elements and the intra prediction mode (IPM) are encrypted based on their corresponding encoding characteristics as well. Additionally, the selected syntax elements are all masked using a keystream generated by a chaotic system to ensure real-time constraints. Experimental results demonstrate that our suggested scheme offers format compatibility and is secure against all common attacks. Meanwhile, it outperforms state-of-the-art SE schemes in terms of security strength. Furthermore, the proposed scheme can be flexibly used in a wide range of applications according to the user’s requirements for encryption strength and bit rate.

SummaryThe expanding popularity of Voice over WiFi (VoWiFi) necessitates a concerted effort to identify novel ways to increase VoWiFi cell capacity. The primary objective of this study is to increase the capacity of VoWiFi cells by means of frame aggregation of aggregate MAC protocol data unit (A‐MPDU) for variable bit rate (VBR) traffic. Taking into account Arbitration Inter‐frame Spacing (AIFS), Compressed RTP (cRTP) and A‐MPDU frames, we devised a formula to calculate an approximate number of concurrent VoWiFi users that can coexist with no detriment to the quality‐of‐service (QoS) of existing VoWiFi calls over the Wireless Fidelity (WiFi) standards. Here, we used AIFS to determine the channel's health before sending Voice over WiFi data and Short Inter‐frame Spacing (SIFS) to transfer frames such as Request To Send (RTS)/Clear To Send (CTS) and Acknowledgement (ACK). We have used our suggested model to analyse the capacity of VoWiFi cells in IEEE 802.11b/g/n/ac/ax/be Wireless Local Area Network (WLAN)s with VBR traffic utilising DCF Inter‐frame Spacing (DIFS) and AIFS. For IEEE 802.11b/g/n/ac/ax/be, we also determined the most number of MAC protocol data unit (MPDU)s that may be combined into a single A‐MPDU. We have also studied the impact of voice packet retransmission on the cell capacity of a WLAN standard that offers VoWiFi service while taking A‐MPDU method into account. We have compared the results gained using IEEE 802.11be with earlier WLAN standards like IEEE 802.11b/g/n/ac/ax considering the constant bit rate (CBR) and VBR traffics.

Abstract The rapid progress in wireless communication technology and proliferation of multimedia applications, including voice over WiFi (VoWiFi), demand exploration of innovative approaches to enhance the network performance and quality of service. We propose a technique for enhancing the cell capacity of wireless local area network (WLAN) access point that provides VoWiFi service in the sixth‐generation WLAN standard. The proposed technique uses the aggregate media access control protocol data unit (A‐MPDU) for frame aggregation with constant bit rate (CBR) traffic in the WiFi 6 standard (i.e., IEEE 802.11ax). On the other hand, the retransmission of voice packets substantially deteriorates the VoWiFi cell capacity. We compare the results obtained from the use of WiFi 6 with currently existing WLAN standards, such as IEEE 802.11b/g/n/ac. This comparison focuses on distributed coordination function interframe spacing (DIFS) and arbitration interframe spacing (AIFS) using CBR traffic. Using our technique, we can increase the VoWiFi cell capacity for CBR traffic by 24.25% and 25.20% when using DIFS and AIFS, respectively, while considering the A‐MPDU frame aggregation technique.

SummaryThe rapid growth in wireless communication technology and the proliferation of multimedia applications, specifically Voice over Wireless Fidelity (VoWiFi), necessitate the exploration of innovative techniques for improving network performance and quality‐of‐service (QoS). This research paper presents an innovative approach to enhance VoWiFi cell capacity using aggregate medium access control protocol data unit (A‐MPDU) frame aggregation for variable bit rate (VBR) traffic. In this research article, we investigated the effect that the retransmission of voice packets has on the cell capacity of a wireless local area network (WLAN) standard that provides VoWiFi service while taking into account the A‐MPDU approach. When taking into consideration the constant bit rate (CBR) and VBR traffics, we compared the results obtained using WiFi 6 (i.e., IEEE 802.11ax) with older WLAN standards such as IEEE 802.11b/g/n/ac.

Deployment for Wireless Local Area Network (WLAN) technology is experiencing substantial growth. The transmission of Voice over WiFi (VoWiFi) is half-duplex due to its single-talk nature. In order to mitigate the inefficient utilization of available bandwidth, the technique of silent suppression is employed. This feature decreases the level of ambient noise and provides Variable Bit Rate (VBR) communication. To enhance the VoWiFi cell capacity, we utilized VBR traffic instead of Constant Bit Rate (CBR) traffic. In this study, we developed a mathematical model to find the number of VoWiFi users possible within a WLAN Access Point (AP) considering G.729 vocoder. Arbitration inter-frame space (AIFS) has been utilized in our study to evaluate the channel state prior to transmitting real-time data between user equipment (UE) devices. Additionally, short inter-frame spacing (SIFS) has been employed to transmit Request To Send (RTS)/Clear To Send (CTS) and Acknowledgement (ACK) frames, enabling an examination of the VoWiFi cell capacity. Again, to increase the VoWiFi cell capacity, we incorporated the compressed RTP (cRTP) protocol. Further, we conducted an analysis on the impact of retransmission on VoWiFi cell capacity. This analysis involved a comparison of the results using VBR traffic with the previous WLAN standards, namely IEEE 802.11b/g/n/ac/ax. We observed that the improvement in VoWiFi cell capacity in VBR traffic is approximately 2.6 times more than the CBR traffic. Further, our research revealed that the enhancement in VoWiFi cell capacity is approximately 1.5% higher when utilizing AIFS compared to Distributed Coordination Function (DCF) inter-frame spacing (DIFS).

Network-on-chip (NoC) is essential for efficient data transmission in system-on-chip (SoC) architectures, particularly as modern integrated circuits (ICs) become increasingly complex.The choice of topology significantly impacts the performance, throughput, and latency of NoC designs.Traditional topologies, such as Mesh and Spidergon, offer unique benefits and limitations.This study introduces a new topology, Mesh of Spidergon (MoS), which integrates the local connectivity advantages of Mesh with the global routing efficiency of Spidergon.We evaluate the network diameter and average distance of the Mesh, Spidergon, and MoS topologies.The assessment focuses on throughput and latency using Transmission Control Protocol (TCP) applications, specifically analyzing File Transfer Protocol (FTP) and Constant Bit Rate (CBR) traffic patterns under varying link failure rates of 5%, 10%, and 15%.Results show that the MoS topology achieves a throughput of 26.96 Mbps, surpassing Mesh's 12.68 Mbps and Spidergon's 23.51 Mbps using FTP protocol.For the CBR protocol, MoS reaches 34 Mbps, compared to 12.68 Mbps for Mesh and 25.23 Mbps for Spidergon, while maintaining comparable latency to Spidergon at 2.37 ms, whereas Mesh exhibits higher latency with 3.78 ms.Furthermore, MoS demonstrates enhanced resilience under link failures.Overall, the MoS topology significantly improves NoC performance, merging the strengths of existing topologies and offering a viable solution for future SoC designs.

In this paper we study the relationship between the TCP packet loss cycle and the performance of time-sensitive traffic in data centers. Using real traffic measurements and analysis, we find that such loss cycles are not long enough to enable most partition-aggregate time-sensitive TCP applications to recover their packet losses via the TCP 3-dup ACKs mechanism. As a result, the Timeout (RTO) mechanism is frequently triggered, leading to the expansion of the flow completion times (FCT) of such applications by orders of magnitude. Hence, we seek an alternative method that does not change the virtual machines and that can effectively expand the loss cycle duration to enable short flows to finish their transfer without incurring the cost of the RTO. To this end, we propose a novel TCP-AQM mechanism that alternates between a slow constant bitrate (CBR) mode and a fast TCP rate via hysteresis switching to expand the loss cycle. We prove the stability of the proposed TCP-AQM via a control theoretic model, then evaluate its performance gains via small and large scale NS2 simulation and by real FPGA implementation of a prototype on the NetFPGA platform. The results show considerable improvements in FCT distribution and reduction of missed deadlines in simulation and real experiments.

High speed data communication is the demanding factor in both commercial and defence applications. Several algorithms are proposed to support the high-speed data exchange while ensuring the quality, performance and reliability. However, there is still a gap, citing various compatibility issues with variety of transceiver technologies. This paper proposes a novel algorithm for enhancing the performance of mobile ad-hoc networks using Free-Space Optics (FSO). The FSO has the natural ability to the interference while capable of large bandwidth and excellent compatibility. Low power and adaptability are the features with which it has contributed to the latest technologies like storage area network, wireless area network etc. The proposed work uses optical spheres with a multi-transceiver system and a cross-layered reconfigurable routing mechanism. Parameters such as delay, residual energy, throughput, and drop are verified for the Crosslayered Reconfigurable Hierarchical Routing Optical Sphere (CRHROS) protocol for varying numbers of optical transceivers. The proposed work also compares the performance of two traffic sources, Constant Bit Rate (CBR) and Variable Bit Rate (VBR), for the proposed algorithm.

Variable Bitrate (VBR) video encoding can significantly improve the quality-of-experience (QoE) of viewing users due to its capability to provide much higher quality-to-bits ratio compared to Constant Bitrate (CBR) video encoding. However, the streaming of VBR-encoded videos suffers from large variance of video chunk size, which may directly result in frequent rebuffering if not properly handled. In this paper, we propose a novel neural-enhanced adaptive streaming framework for VBR-encoded videos called PreSR, which performs selective prefetching of video chunks to achieve a higher QoE for viewers. The design of PreSR is motivated by an important observation obtained from our measurement, namely, the video quality improvement and bandwidth savings brought by neural enhancement are more pronounced for low-resolution video chunks with complex scenes. By taking the above fact and the time required for neural enhancement into account, we formulate the problem into an optimization problem. Given that the problem is NP-hard, we design the PreSR framework, which is based on the model predictive control theory and also considers key features of VBR-encoded videos. PreSR parallelizes the download of video chunks and model inference processes to fully utilize the available compute resources. Finally, we conduct extensive experiments with real traces, and the results show that PreSR outperforms the state-of-the-art algorithms with an improvement up to 11.25% in terms of the average QoE.

Due to the scattered nature of the network, data transmission in a distributed Mobile Ad-hoc Network (MANET) consumes more energy resources (ER) than in a centralized network, resulting in a shorter network lifespan (NL). As a result, we build an Enhanced Opportunistic Routing (EORP) protocol architecture in order to address the issues raised before. This proposed routing protocol goal is to manage the routing cost by employing power, load, and delay to manage the routing energy consumption based on the flooding of control packets from the target node. According to the goal of the proposed protocol technique, it is possible to manage the routing cost by applying power, load, and delay. The proposed technique also manage the routing energy consumption based on the flooding of control packets from the destination node in order to reduce the routing cost. Control packet exchange between the target and all the nodes, on the other hand, is capable of having an influence on the overall efficiency of the system. The EORP protocol and the Multi-channel Cooperative Neighbour Discovery (MCCND) protocol have been designed to detect the cooperative adjacent nodes for each node in the routing route as part of the routing path discovery process, which occurs during control packet transmission. While control packet transmission is taking place during the routing path discovery process, the EORP protocol and the Multi-channel Cooperative Neighbour Discovery (MCCND) protocol have been designed to detect the cooperative adjacent nodes for each node in the routing. Also included is a simulation of these protocols in order to evaluate their performance across a wide range of packet speeds using Constant Bit Rate (CBR). When the packet rate of the CBR is 20 packets per second, the results reveal that the EORP-MCCND is 0.6 s quicker than the state-of-the-art protocols, according to the findings. Assuming that the CBR packet rate is 20 packets per second, the EORP-MCCND achieves 0.6 s of End 2 End Delay, 0.05 s of Routing Overhead Delay, 120 s of Network Lifetime, and 20 J of Energy Consumption efficiency, which is much better than that of the state-of-the-art protocols.

Underwater Wireless Sensor Network, often known as UWSN, is an appealing research zone because of the mysterious aspect of the ocean. A network of sensor nodes and vehicles that work together as part of the UWSN to collect information and carry out activities in collaboration. Because of the sensor nodes and the limited battery capacity, it is essential for UWSN to have an efficient network. The significant delay in propagation, network dynamics, and probability of error all influence underwater communication, making it difficult to exchange or update sensor nodes. This article put up the idea of a Grid-oriented underwater wireless sensor network (GO-UWSN) and carried out an analysis based on the criteria of energy consumption, utilization, average transmission delay, average jitter, average path loss and average E- 2- E delay in various modes.

A quality enhancement network with coding priors for constant bit rate video coding

Constant bit rate (CBR) videos are widely used in streaming playback applications. However, the image quality of the CBR video is often unstable, especially for scenes with large motion. To this end, we design a new model to represent the distortion of High Efficiency Video Coding (HEVC) constant bit rate video, and propose a neural network for a constant bit rate video quality enhancement (CBREN). We propose a dual-domain restoration module (DRM) to jointly learn the prior knowledge in the pixel domain and the frequency domain. To address the degradation resulting from compression, we propose a two-step quantization degradation estimation strategy. The Inverse DCT (IDCT) Translation Unit (ITU) is used to constrain the quantization table of the constant bit rate video to a suitable range, and the Dynamic Alpha Unit (DAU) is used to fine-tune the quantization table according to the content of each frame. In order to effectively reduce the block distortion of different sizes produced in the compression process, we adopt a multi-scale network. Extensive experiments show that our approach can greatly enhance the quality of CBR compressed video. Moreover, our method can also be applied to constant quantization parameter (CQP) video enhancement tasks, and is certainly superior to existing methods.

Resilient quantized control under Denial-of-Service: Variable bit rate quantization

This paper addresses the problem of traffic splitting in the multi-access system for real-time applications in the uplink. We first understand the Long Term Evolution (LTE)-uplink-scheduling mechanism using Network Simulator () and then analyse the LTE-Packet Data Convergence Protocol queue length process at the end of User Equipment (UE). We provide an end-to-end delay jitter analysis on a single LTE network for the Constant Bit Rate (CBR) Traffic. We observe that the delay jitter only depends on the allocation (Transport Block Size) provided by eNodeB but not on the allocation rate. The main finding is the structural dependency of the delay jitter on one of the CBR parameters: inter-burst time. We then utilize this to develop an online-adaptive traffic splitting algorithm in the multi-access system to improve the overall delay jitter performance of the system. We formulate the traffic splitting problem as an optimization problem applicable to different objective functions. We validate and share our real-world experimental results for the video/Real-time Transport Protocol traffic.