Light Detection and Ranging (LiDAR) sensors generate dense, information-rich point clouds that are critical for autonomous driving, but their high data rates create serious bottlenecks in bandwidth-limited vehicle-to-everything (V2X) communication. This paper presents LiTransNet, a deep learning-based framework for efficient, semantic-preserving transmission of LiDAR point clouds in cooperative autonomous vehicle (AV) systems. LiTransNet employs an end-to-end encoder-decoder architecture that learns compact latent representations optimized jointly for rate, distortion, and downstream perception tasks. A communication-aware adaptive bit-rate controller dynamically adjusts encoding behavior under fluctuating V2X channel conditions, while entropy modeling and lightweight attention mechanisms improve compression efficiency and robustness. Extensive experiments achieve compression, reduce end-to-end transmission latency, and improve semantic accuracy with negligible impact on object detection and path planning performance.

Privacy-Preserving Data Sharing (PPDS) masks the individual's collected data (e.g., medical healthcare data) before being disseminated by organizations for analysis and research. Patient data contains sensitive values that must be dealt with while ensuring certain privacy conditions are met. This minimizes the risk of re-identification of an individual record from the group of privacy-preserved data. However, with the advancement in technology (i.e., Big Data, the Internet of Things (IoT), and Blockchain), the existing classical privacy-preserving techniques are becoming obsolete. In this paper, we propose a blockchain-based secure data sharing technique named “SecureChain”, which preserves the privacy of an individual record using local differential privacy (LDP). The three distinguished features of the proposed approach are lower latency, higher throughput, and improved privacy. The proposed model outperforms the benchmarks in terms of both latency and throughput. In terms of precision, the proposed method improves the accuracy to 88.53% compared to its counterparts, which achieved 49% and 85% accuracy. The results of the experiment verify that the proposed approach outperforms its counterparts.

Underwater Wireless Sensor Networks (UWSNs) play a crucial role in various applications, including environmental monitoring, underwater exploration, marine communication, and disaster management. However, effective data transmission in underwater environments poses significant challenges due to high propagation delay, energy constraints, localization difficulties, and void hole issues. As the UWSN nodes are movable underwater, some of them may move far away from the relay, sensor, or sink nodes, which causes the void hole problem. Node localization with multi-hop verification cooperation can help overcome these issues. In this paper, we propose a Cooperation with Four Hop-by-Hop Forwarding Verification (Co-FHHV) scheme in UWSN to avoid the void hole and void node issues. This mechanism ensures that data moves through three intermediate relay nodes before reaching the sink node. By adding a fourth hop, each node transmits over a shorter distance, reducing energy depletion, and improving network lifetime. Each relay node performs a verification process to check for data integrity before forwarding the data. Using the localization approach to avoid the void hole is the basic motive of this article. Four verification approaches have been introduced, which utilize routing to identify the nearest nodes and activate them for communication. Each node in multiple hop-to-hop forwarding verification employs a discovery approach that attempts to avoid the void hole region and the void node. The Co-FHHV scheme employs a method to select routes with maximum residual energy and minimal hops for efficient data delivery. It also utilizes a forwarder selection strategy based on sensor priority and energy thresholds to extend the UWSN lifetime, improve energy efficiency, ensure node accurate localization, and maintain stability. The scheme used sink nodes, sensor nodes, relay nodes, and localization beacons. The proposed work is tested under multiple simulation scenarios, and the obtained results have been evaluated with the existing underwater communication schemes. The obtained results have demonstrated the excellent performance of the Co-FHHV scheme, which is considered the best among all other Schemes.

Battery lifetime prediction has traditionally focused on individual cells or packs, using model-based estimators or data-driven methods. These approaches, however, do not generalize to Electric Vehicle (EV) fleets, where degradation is influenced by shared charging infrastructure, overlapping routes, and correlated thermal and geographic exposures. This work introduces the fleet network lifetime prediction problem, which aims to forecast when a specified fraction of a fleet will cross a critical State-of-Health (SoH) threshold. To address these challenges, we model the fleet as a time-varying operational graph and propose a Physics-Guided Fleet Spatio-Temporal Graph Neural Network (PG-FSTGNN) that jointly learns individual SoH trajectories and produces calibrated fleet-level Time-To-Failure (TTF) metrics. To compensate for the scarcity of long-term fleet data, we develop a modular simulator that integrates calendar and cycle aging, charging-station queuing, thermal exposure, and operational policies such as load balancing, C-rate throttling, and battery swapping. Across 12 baselines, PG-FSTGNN consistently achieves superior prediction accuracy for TTF_20 , TTF_40 , and TTF_60 , maintains robust calibration even under severe sensor missingness, and provides interpretable insights into fleet-level degradation dynamics, evaluated using the NASA Prognostics Center of Excellence (PCoE) battery dataset and the CALCE (Center for Advanced Life Cycle Engineering) Li-ion battery aging dataset. By combining physics-guided degradation modeling, graph-based learning, and realistic fleet simulation, this framework provides a scalable and reliable solution for EV fleet battery management and supports more effective planning, maintenance scheduling, and infrastructure optimization.

This research endeavours to enhance road safety by developing an accurate driver emotion recognition system. A novel model is introduced, incorporating transfer learning principles alongside NasNet-Large CNN and Faster R-CNN, specifically designed for Driver Facial Expression (DFE) analysis. The primary objective is to bolster the recognition accuracy of Driver Facial Expression Recognition (DFER). A noteworthy improvement in the accuracy and efficiency of facial detection is attained by customizing the Faster R-CNN learning module with the Inception V3 model. The capability to accurately detect emotions is of paramount importance, as it facilitates timely interventions to avert potential accidents. To address the challenges associated with DFER accuracy in low-resolution images, this research deploys a myriad of deep learning methodologies. Through a meticulous analysis, the study identifies and implements feasible and superior solutions to enhance DFER accuracy. Additionally, the inherent constraints of low-resolution images are mitigated through the strategic application of data augmentation techniques. The evaluation of this research showcases impressive accuracy levels across diverse datasets, including JAFFE, CK+ , FER-2013, and DFERCD. These findings bear substantial implications for enhancing Advanced Driver Assistance Systems (ADAS) and contribute substantially to the overarching realm of road safety.

In the digital age of the Internet of Things (IoT), there is a significant shift from traditional computing to an ubiquitous, highly connected, and automated world that provides services to anyone, anywhere, and at any time. An IoT-based system is an unstable network characterized by fluctuating dynamics and fragile connections, resulting in lower performance on congestion, latency, and energy consumption. To effectively manage these functional parameters and implement a dynamic scheduling mechanism, this paper presents a novel scheme, “Dynamic and Adaptive Scheduling of Cognitive Sensors (DASCS) for Collaborative Target Tracking in Energy-Efficient IoT Environments”. In this approach, cognitive sensors dynamically schedule their functions according to their role in the wireless mesh grid and adapt to new states by checking the network traffic conditions. Its dual goals involve reducing network traffic to significantly decrease energy consumption and enhancing network performance by equally distributing energy resources throughout the grid. Furthermore, it works in object detection and monitors the direction of movement within the IoT environment. DASCS improves network performance by increasing packet delivery ratios by 2.31% at the base station and 27.92% at the cluster head, while adding more live sensors, it improves network stability by 38.46%. DASCS also enhances energy efficiency by increasing the average residual energy by 68.8% compared to other benchmark schemes while maintaining a high event detection rate and a low false alarm rate.

Wireless Body Area Networks (WBANs) support continuous patient monitoring in clinical and remote settings by enabling low-power sensors to collect and forward physiological data. However, WBAN deployments are constrained by limited battery capacity and challenging on-body propagation, which increase path loss, degrade link reliability, and shorten network lifetime. Moreover, many existing routing/clustering solutions treat these issues separately rather than jointly. To address this, we propose MacroNet-Enhanced Energy-aware Node Clustering Protocol (MEE-NCP), which integrates dual energy-efficiency models with a MacroNet-based clustering design using four cluster heads (CHs). MEE-NCP forwards data to the coordinator node through CHs using a cost function that prioritizes higher residual energy and shorter distance to balance energy consumption and improve delivery reliability. We evaluate MEE-NCP in MATLAB against representative WBAN routing schemes using Packet Error Rate (PER), Packet Generation Rate (PGR), Data Generation Rate (DGR), RSSI, SNR, residual energy, throughput, end-to-end delay, and network lifetime. Simulation results indicate improved energy distribution and lifetime, reduced delay and packet errors, and stronger link quality via better path-loss handling.

In dynamic settings such as security, autonomous driving, and robotics, effective motion detection and classification are crucial for accurate tracking amidst target and background movements. Traditional approaches, typically designed for static environments, face challenges in complex scenes with multiple types of motion. This research presents a robust algorithm for motion detection in fully dynamic scenarios, utilizing the macro block technique to generate motion vectors, followed by motion vector analysis to classify distinct types of motion. These include camera motion, object motion, background motion, and complex motion, where both background and foreground move simultaneously. By segmenting and categorizing these motion types, the proposed approach improves detection precision in cluttered, real-world environments. Furthermore, the algorithm adapts to lighting variations and is independent of specific sensor setups. Moreover, the high agreement with human judgment, achieving a 90% accuracy rate, underscores the model's robustness and potential applicability in real-world scenarios where dynamic backgrounds are prevalent. This establishes a framework for future research in dynamic motion detection and classification.

The sixth-generation (6G) wireless networks are envisioned to deliver unprecedented capabilities, including ultra-high data rates, ultra-low latency, and massive connectivity. Among the emerging technologies, the terahertz (THz) spectrum is a key enabler for achieving terabit-per-second transmission speeds. However, THz communication faces significant obstacles, such as severe path loss, frequent signal blockage, and high channel variability. To address these challenges, this paper introduces an AI-driven resource allocation and beamforming in 6G terahertz networks (AI-DRAB-6G-THz) that integrates dynamic resource allocation and adaptive beamforming for enhanced performance in 6G THz networks. The framework employs deep reinforcement learning (DRL) to intelligently manage spectrum and power resources in real time, thereby optimizing both spectral efficiency and energy consumption. Simultaneously, a neural network-based beamforming model predicts optimal beam angles and alignment strategies by learning from user mobility patterns and channel state information (CSI). A MATLAB-based 0-1000 rounds simulation environment, incorporating a realistic THz channel model and environmental constraints, is developed to assess system performance. Evaluation across multiple key metrics, including throughput, latency, spectral efficiency, and beam alignment accuracy, demonstrates that the proposed AI-driven approach significantly outperforms traditional heuristic methods. Overall, this work underscores the feasibility and effectiveness of integrating AI into the physical layer of 6G systems, paving the way for intelligent, adaptive, and energy-efficient wireless communication in future 6G networks.

Underwater Acoustic Sensor Networks (UASNs) with limited deployment constraints are critical for emergency response, marine monitoring, and mission-centric operations. UASNs communications are severely constrained by high propagation delay, limited bandwidth, and energy scarcity. This paper proposes a 6G-Enabled Quantum-Aware Energy-Efficient Cooperation (6G-QAEEC) multi-agent underwater acoustic-radio frequency (RF) network framework for reliable multimodal underwater communication. The proposed architecture enables cooperative intelligence among submerged sensor nodes, Autonomous Underwater Vehicles (AUVs) relays, and 6G-enabled surface/Non-Terrestrial Networks (NTN) gateways. A Nearest Node Verification (NNV) discovery protocol is introduced to prune unstable links during route formation, reducing redundant flooding and improving routing stability. To further enhance network efficiency, three cooperation mechanisms are developed: adaptive relay rotation with signal amplification, duty-cycled neighbor re-verification, and opportunistic optical burst communication for local high-rate exchanges. Quantum-aware optimization at the 6G edge accelerates routing and resource scheduling decisions under dynamic channel conditions. Simulation results show that the proposed framework improves packet delivery ratio, reduces energy consumption per bit, lowers routing overhead, and decreases end-to-end delay compared with baseline schemes. Overall, the proposed multi-agent multimodal quantum-aware framework enhances reliability, energy efficiency, and spectral utilization in next-generation underwater networks.

Autonomous Vehicles (AVs) depend on LiDAR sensing to construct accurate three-dimensional representations of their surroundings for real-time perception and navigation. However, LiDAR-based point-cloud processing is inherently affected by noise, sparsity, and uncertainty in dynamic environments, which can degrade path selection and lead to unsafe or inefficient maneuvers. This work presents a LiDAR Fusion and Transmission Optimization (LiFTO) framework for path selection in AVs, integrating multi-object detection, motion estimation, collisionrisk assessment, and trajectory optimization into a unified pipeline. The proposed approach combines robust point-cloud preprocessing and cluster-level feature extraction with probabilistic state estimation to account for perception uncertainty during planning explicitly. A formally verified decision-making module provides deterministic guarantees on timing, safety, and collision avoidance. Unlike heuristic or perception-centric methods, the framework evaluates candidate trajectories using multi-objective optimization criteria, including safety, comfort, efficiency, and lane stability. Experimental results demonstrate improved point-to-point Chamfer Distance (CD) and point-toplane PSNR for geometry accuracy, as well as Bits per Point (Bpp) for compression rate and robust operation under sensor noise on the SemanticKITTI and ShapeNet datasets, validating the framework’s effectiveness for autonomous driving applications.

Urban last-mile delivery with multi-UAV fleets is constrained not only by travel distance, but also by temporary landing-exclusion zones created during parcel drop-off. When several UAVs serve nearby delivery points, these exclusion zones can overlap in space and time, forcing hover waiting and increasing fleet makespan. A further challenge is that rooftop, ground-open, and constrained-corridor deliveries impose different landing radii and occupancy durations, while many scheduling models treat delivery points uniformly. This paper proposes an OpenStreetMap-(OSM-) and LLM-augmented spatio-temporal (ST) occupancy-aware multi-UAV scheduling framework for smart-city last-mile delivery. OSM building footprints, road networks, and open-space polygons are used to classify delivery points into three landing-zone types, after which representative landing-mode parameters are assigned for exclusion-aware scheduling. An LLM-based adaptive weighting mechanism (gpt-4o-mini-2024-07-18, $T=0$) maps zone statistics, conflict density, and urban-morphology cues to priority weights for occupancy-, distance-, and conflict-aware task ordering. Experiments on Tokyo and Shenzhen evaluate 24 configurations across two cities, three fleet sizes, four scheduling strategies, and 10 random seeds. The proposed framework reduces makespan by more than 90,% relative to a sequential single-trip upper-bound reference, confirming the benefit of parallel fleet scheduling under landing-exclusion constraints. Against non-LLM heuristics, the LLM-Adaptive strategy remains competitive and achieves the best result in the resource-constrained Shenzhen 12-UAV case. On a reduced 10-task MILP benchmark solved with CPLEX 22.1 to a gap of at most 0.1,%, the LLM-Adaptive strategy closes 4.3-5.2,% of the optimality gap left by the best hand-tuned heuristic at $K=3$. The results show that OSM-derived zone classification and LLM-based priority weighting provide an interpretable and reproducible approach for exclusion-aware multi-UAV scheduling.

The rapid expansion of e-commerce has led to sophisticated fraud tactics that traditional models struggle to detect. While deep learning approaches, particularly Graph Neural Networks (GNNs), offer superior detection, they suffer from the ”black-box” problem, lacking transparency for auditing and user trust. To address this accuracy-interpretability trade-off, we propose EcoGuard-DualX, a security framework integrating multi-modal learning with dual-granularity explainability. The system fuses transactional logs (IEEE-CIS), network traffic (CIC-IDS2017), and interaction graphs (PaySim) to detect camouflaged fraud. A novel dual-granularity XAI router provides security administrators with feature-attribution maps while engaging end users via an LLM that translates risk scores into natural-language explanations. EcoGuard-DualX achieves 97.8% accuracy on IEEE-CIS and an F1-score of 0.950 on PaySim, outperforming baseline methods. A human user study (n=45) suggests LLM-driven explanations improve comprehension and reduce anxiety versus static analytics (p < 0.001), though broader validation is needed. These findings demonstrate that high-precision fraud detection can be combined with stakeholder-aware transparency across Consumer Electronics (CE) platforms, including smartwatches, voice assistants, and mobile devices.

Underwater acoustic sensor networks (UASNs) are vital for monitoring marine environments, but they face challenges such as high energy consumption, limited bandwidth, poor localization accuracy, and void hole issues. The proposed scheme addresses these challenges by optimizing energy usage, improving localization accuracy, and ensuring reliable communication through dynamic scheduling and void hole avoidance. This paper presents an energy-efficient and localization-based dynamic scheduling (EELDS) scheme for UASNs, designed to optimize energy consumption, enhance localization accuracy, and extend network lifetime in dynamic underwater environments. The proposed EELDS protocol integrates multiple localization techniques. The EELDS scheme utilizes localization techniques, including angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), and received signal strength indicator (RSSI), to accurately estimate node positions. At the same time, the five-state model (FSM) (anchor, active, midway, idle, sleep) dynamically adjusts the network's energy consumption based on node states, optimizing both energy efficiency and network performance. The scheme further incorporates void hole avoidance using relay nodes to ensure efficient data transmission in areas prone to communication voids. Results demonstrate that EELDS outperforms existing protocols by reducing energy consumption, extending network lifetime, and improving throughput, particularly in large-scale UASN deployments. Simulation results for 230 nodes over 5000 rounds demonstrate that EELDS achieves a 27-35 % longer network lifetime, 41-67 % higher throughput, and a 30-50 % lower energy-per-bit cost compared to MAC-layer schemes. Moreover, EELDS reduces localization error by 45-57 %, improves connectivity by up to 33 %, and decreases collisions by more than 50 % compared with recent protocols. The results validate the effectiveness of EELDS in optimizing the operation of UASNs, making it a promising solution for real-world applications such as underwater exploration and oceanographic monitoring.

With the rapid rise of e-commerce, the logistics and distribution industry is experiencing unprecedented growth. In particular, intra-city distribution is the crucial “last mile" of logistics and plays a decisive role in determining overall customer satisfaction. This study improves an inclusive vehicle routing optimization framework for intra-city distribution under dynamic demand. The initiative of a novel memetic algorithm that efficiently solves the NP-hard dynamic vehicle routing problem while guaranteeing high service quality and cost reduction. However, modern intercity distribution systems often struggle with low information, unpredictable demand patterns, and high operational costs due to scattered customer locations and dynamic order information. Addressing these challenges, this study suggests a comprehensive and intelligent vehicle routing optimization framework tailored for intracity distribution under dynamic demand conditions. The proposed system begins with a grey prediction model for short-term demand forecasting across many distribution regions, permitting differentiated vehicle loading methods to optimize transportation costs and improve operational effectiveness. Building upon this, a dynamic vehicle routing optimization model is formulated to reduce costs while assuring high levels of customer satisfaction within strict delivery time windows. To competently manage fluctuating demand, a dynamic information processing approach is introduced; prioritizing customer needs based on their urgency and importance, thereby guaranteeing the timely delivery of critical orders with minimal computational overhead. Moreover, a novel memetic algorithm is considered to solve the complex NP-hard dynamic vehicle routing problem. This algorithm integrates an adaptive elite genetic algorithm for global search with improved crossover and mutation operators, improved by local search methods such as 2-opt and swap methods to refine solutions. Numerical experiments validate the feasibility and performance of the proposed method, indicating significant improvements over conventional fully loaded vehicle schemes and regular route update methods. The results highlight the practical value of the system in attractive intra-city logistics efficiency, reducing costs, and inspiring customer service standards.

In today’s era, technology has become an essential part of life and affects in both ways positive and negative. The advancements in technology have made it possible for anyone to access almost all types of contents and video clips, both online and offline, without any substantial restrictions. Cartoons videos, who’s primary focus is children are popular in all ages have also evolved from manual and advanced visual effects. On one hand, cartoon quality and depth of detail improved on the other hand, objectionable content improved. e.g. violence and nudity in cartoon content, can have a long-term harmful impact on a person’s psychology and mental health. As most of the parental filters are designed around the ‘genre’ and the “Cartoon” itself becomes the token of permission, children are more prone to its bad effects. This research focuses on the detection and prediction of nudity and violence in cartoon videos that can lead to an effective filter mechanism. Two major Deep Neural Network models, RestNet50 and VGG-16 are used in this research which are trained using a custom-made dataset. Because of the unavailability of any standard dataset, a special dataset is developed by collecting cartoon videos in three categories: Nude, Violent, and Normal. A total of 340 video clips from 25 different series are collected to classify the objectionable contents in cartoon videos in three categories i.e. Nude, violent and normal. From these video clips a total of 44,430 frames were selected. These frames are used to train and test the proposed model. A 117 layered neural network is designed that are grouped into 11 broad types. At the input layer (100 x 100 x 3). Filter size at the convolutional layer is (1,1) with 256 layers dimension of the activation layer is (50 x 50 x 64). The size of the output layer is 3, i.e., Nude, Violent and Normal. This model achieves an impressive accuracy of 97.25%, which outperformed as compared to other state-of-the-art existing technologies. Additionally, the proposed work is also compared with VGG-16, which achieved 92.31% accuracy.

Underwater Wireless Sensor Network (UWSN) accomplishes the consideration of a few scientists and academicians towards itself. Because of the brutality of the climate lies submerged represents various difficulties, i.e., high transmission delay, outstanding piece mistake rate, more expense in usage, sinks development and energy imperatives, unequal surface highlights of an area and low data transfer capacity, and so forth Void opening evasion is compulsory for to motivation behind limiting the utilization of energy and amplifying throughput and region inclusion. In this exploration work, the creator planned plans for void opening shirking initial one is, Avoiding Void Hole Adaptive Hop by Hop Vector-Based Forwarding (AVH-AHH-VBF) in submerged remote sensor organization and a second plan for limiting utilization of energy and expanding the lifetime of the organization, Sink Mobility-Adaptive Hop by Hop Vector-Based Forwarding (SM-AHH-VBF). Reproduction results show that our plans beat contrasted and standard arrangement as far as normal Packet Delivery Ratio (PDR), energy charge. Our reproduction confirms the effectiveness of our proposed procedure AVH-AHH-VBF equivalents to 0.17 and SM-AHH-VBF equivalents to 0.24 regarding normal PDR, AVH-AHH-VBF equivalents to 24j and SM-AHH-VBF equivalents to 5j for the normal energy charge, AVH-AHH-VBF had a tradeoff of 63% in light of considering two jumps and SM-AHH-VBF approaches 20% tradeoff for normal start to finish.

Increasingly fine meteorological observations place substantial demands on storage and transmission systems. We propose a prediction-guided residual compression framework based on a variational autoencoder (MDC-PPE). A pretrained spatiotemporal predictor estimates the large-scale atmospheric state from historical observations. MDC-PPE then encodes the prediction residual rather than the original field. The residual coder contains a Physics-Aware Group Block, it splits ordered channels into equal groups, applies group-specific Laplacian and multi-scale processing, and then performs gated low-rank cross-group mixing. A multi-domain rate–distortion loss further constrains pixel, gradient, spectral, and latent-sparsity errors. On two ERA5-derived datasets, MDC-PPE achieved favorable rate–distortion performance relative to nine learned compression baselines. On the S72 dataset, it obtained an MSE of 0.0488 at 0.1326 bpp, with encoding and decoding times of 0.0930 and 0.0974 s per patch, respectively. These results support prediction-guided residual coding as a promising approach to meteorological data compression, subject to the availability of synchronized historical inputs and a shared predictor at the decoder. Code will be released at https://github.com/cike0cop/MDC-PPE.

The evolution toward Sixth-Generation (6G) wireless networks is expected to transform the Internet of Health Things (IoHT) by enabling ultra-reliable low-latency communication (URLLC), energy-aware networking, and intelligent resource management for mission-critical healthcare applications. However, achieving these goals in large-scale IoHT deployments remains challenging, due to unbalanced energy consumption from static clustering, increased latency under dynamic traffic loads, reduced packet reliability in high-interference environments, and inefficiencies in maintaining information freshness. To address these challenges, this paper presents a novel 6G-Enabled Next-Generation Energy-Aware Node Clustering (6G-NENC) framework, specifically designed to leverage 6G-native features such as adaptive spectrum utilization, AI-driven resource allocation, and real-time channel-state monitoring. The proposed approach incorporates a dynamic cluster-head selection mechanism based on instantaneous link quality and residual energy, coupled with link-adaptive and load-balanced data-forwarding strategies. These design choices ensure reduced end-to-end delay, improved reliability, and prolonged network lifetime, even in mobility-intensive IoHT environments. The framework’s performance is validated through extensive simulations under diverse network densities, traffic models, and channel conditions, and compared with several state-of-the-art protocols. Key performance indicators such as latency, packet delivery ratio (PDR), energy consumption, throughput, and Age-of-Information (AoI), consistently demonstrate that 6G-NENC delivers superior efficiency, robustness, and scalability. The results highlight its potential as a foundational architecture for next-generation IoHT systems requiring stringent quality-of-service and quality-of-experience guarantees.