Machine learning (ML)–based intrusion detection systems (IDS) often degrade when deployed across heterogeneous networks due to domain shifts in traffic and configuration. To mitigate this degradation, conventional domain adaptation (DA) methods aim to align source and target data distributions; however, they require access to source data during deployment—an impractical constraint that undermines scalability and reusability. To overcome this limitation, we propose TRANSFA-IDS (Transformer Source-Free Adaptation for IDS), which removes the need for source data during adaptation while preserving the knowledge encoded in the source-trained model. TRANSFA-IDS transforms tabular flow records into structured color image embeddings and employs a compact Vision Transformer with a Deep Support Vector Data Description (Deep-SVDD) head to learn domain-invariant representations of benign behavior. During deployment, it adapts to new environments using only a small portion of unlabeled target traffic by fine-tuning the last Transformer block, efficiently realigning feature distributions without retraining. Experiments across cross-dataset settings (CICIDS2018↔UNSW-NB15) show that TRANSFA-IDS achieves AUROC scores up to 0.908 and 0.873, outperforming traditional non-adaptive unsupervised baselines by over 40% while adapting more than twice as fast as conventional adaptive unsupervised. These results demonstrate that source-free adaptation can deliver both high accuracy and deployment practicality for scalable IDS across diverse network environments.

Semantic communications (SEMCOM) offer a paradigm shift by transmitting the intended meaning of messages using neural networks and shared knowledge, rather than relying on raw data. This is essential for next-generation applications such as autonomous driving and holographic telepresence, where accurate semantic interpretation is critical. However, the open and knowledge-centric nature of SemCom makes it susceptible to adversarial attacks, especially during knowledge sharing and decoding. To enhance security, we introduce SECO, a robust learning framework designed to defend against both targeted and untargeted adversarial threats. SECO combines sensitivity-aware noise detection with Huber loss-based filtering to identify and suppress adversarial perturbations, while preserving the integrity of natural signals. Experimental results on speech datasets under Rayleigh and Rician fading channels show that SECO boosts speech-to-distortion ratios by up to 2.2× and 2.5×, respectively, and reduces semantic disruption by up to 17%. It also demonstrates resilience to natural noise and mitigates fixed-pattern attacks by exploiting unique voice characteristics.

Federated Learning (FL) enables multiple client devices to train a global model collaboratively by sharing only local gradient updates, thus preserving data privacy. Despite its advantages, FL remains vulnerable to Gradient Inversion Attack, where attackers exploit gradient updates to reconstruct private data. To counter these advanced threats, we propose Latent Interpolation Data Synthesis (LIDS), a client-side privacy-preserving training scheme that obfuscates per-sample gradient signals via structured batch construction and latent oversampling. LIDS groups semantically similar samples and generates synthesized variants, reducing the uniqueness of each gradient contribution while preserving model convergence. Experimental results show that LIDS reduces attacker reconstruction quality, with Peak Signal-to-Noise Ratio dropping from 27 to 17. On CIFAR-10, the attacker’s reconstruction label accuracy and fraction of successful reconstructions are reduced by up to 64% and 92%; on CIFAR-100, by 68% and 96%. These privacy gains come at a modest cost of only 11% test accuracy degradation in CIFAR-100.

Unmanned Aerial Vehicles (UAVs) have been widely adopted in various applications, including surveillance, search and rescue, cartography, and damage assessment in remote areas. This study aims to enhance the safe navigation of multiple UAVs in adverse environments by integrating computer vision with deep reinforcement learning (DRL) and a route planning algorithm known as REAP. REAP incorporates a real-time object detection module that identifies obstacle positions from surveillance drone footage or satellite imagery. Additionally, A* and tangent point search algorithms are employed to compute the shortest paths while maintaining connectivity and safe distances from threats, such as dynamic obstacles (e.g., birds). Notably, this study also introduces an optimized DRL approach to further refine flight paths and reduce the latency in search result retrieval. Evaluation results show that the proposed method can compute optimal paths in under 10 milliseconds-three times faster than state-of-the-art (SOTA) methods-while ensuring UAV safety from collisions with flying obstacles, tall structures, or bird swarms.

Extremely large-scale reconfigurable intelligent surfaces (XL-RISs) have been recognized as a promising technology to enhance the capabilities of communication systems and mitigate severe path loss. However, due to the large size of XL-RISs, simply adopting the deployment strategies of RIS may result in coverage areas with relatively low performance improvement. To investigate the potential contribution of each position on the designated surface, we propose a Shapley value sampling method to evaluate their Shapley values. Based on the approximated Shapley values, we propose a linear-time algorithm to determine the near-optimal XL-RIS deployment in terms of expected total transmission rate. Finally, simulation results show that the proposed Shapley sampling method effectively predicts the contribution of each XL-RIS module in total capacity, and we identify the scenarios where our deployment algorithm outperforms the conventional rectangular XL-RIS with the same number of RIS elements.

Monocular 3D object detection offers a cost-effective alternative to LiDAR and stereo cameras by determining 3D positions from a single image. DETR-based methods leverage transformers to integrate visual and depth representations globally, achieving state-of-the-art performance and competitive speeds without manual configurations like non-maximum suppression or anchor generation. However, averaging multiple-depth predictions hinders precise accuracy. This work enhances depth prediction through two innovations: (1) improving depth quality using a Shape-aware Foreground Depth Map (SFDM) and (2) Depth Adaptive Weight (DAW) helps the final depth prediction to benefit flexibly from each component’s contribution. Experiments on the KITTI benchmark demonstrate the proposed model’s state-of-the-art performance. The code is available at https://github.com/useracc687/saw-monodetr.

Robustness of reconfigurable intelligent surface (RIS) has been a concern due to potential hardware impairments (HWI) and imperfect channel state information (CSI) measurements caused by the numerous passive elements on board. Recent studies observe that the impairments not only introduce mis-alignment in phase adjustments but also affect the amplitude of reflected signals, which further complicates the issue. To address this issue, we introduce a novel deep reinforcement learning (DRL)-based discrete optimization framework aimed at mitigating various HWI and CSI imperfections in RIS-assisted millimeter-wave (mmWave) multi-input-single-output (MU-MISO) systems. Employing proximal policy optimization (PPO), our method discretely addresses HWI and CSI challenges without continuous relaxation. Simulation results demonstrate the superiority of our approach over the traditional optimal beamforming baseline in minimizing the worst-case mean squared error (MSE) of the signal received by the users. The code has been made open-source on GitHub, serving as a valuable reference for further research and application in RIS-assisted communication systems.

With the increasing prevalence of Internet of Things (IoT) devices, security vulnerabilities and malware infections have emerged as significant risks. To address these challenges, advanced vulnerability detection tools are essential for enhancing IoT security assessments. In this study, we analyzed common vulnerabilities and evolving attack methodologies to develop improved detection techniques. Our research focuses on two key areas: 1) comprehensive vulnerability detection and 2) malware infection testing strategies. Through on-site testing and detailed analysis, we identified prevalent security flaws in IoT devices and developed a suite of tools tailored for detecting these vulnerabilities. Additionally, we discovered that some devices exhibit inherent immunity to specific malware strains, emphasizing the need for novel malware infection detection strategies. Real-world evaluations uncovered previously unknown vulnerabilities and weaknesses, revealed widespread susceptibility to DoS attacks, and demonstrated that not all devices are vulnerable to malware infections. These findings confirm the effectiveness of our approach in identifying risks and enhancing IoT security.

Cyber Threat Intelligence (CTI) reports provide valuable insights into cybersecurity attack techniques, which are essential for understanding threat execution. Identifying the root causes of these techniques is crucial for developing effective defense mechanisms. However, the unstructured nature and inconsistent terminology of CTI reports pose significant challenges in extracting causal factors, such as Common Weakness Enumerations (CWEs) and vulnerable data components, limiting proactive responses and the understanding of attack interdependencies. To address these challenges, we propose TRACE, a novel framework that extracts causal factors linked to adversarial techniques and generates comprehensive causal graphs revealing interdependencies within CTI reports. TRACE combines pattern extraction and tagging methods to address the limitations of existing approaches. Utilizing Sentence-based Bidirectional Encoder Representations from Transformers (SBERT) embeddings enhanced with knowledge mappings and deep learning techniques, TRACE discovers and models causal relationships between attack techniques within the reports. By bridging the gap between attack techniques and their underlying vulnerabilities, TRACE provides actionable insights to enhance cybersecurity defenses. Evaluated on 710 CTI reports, TRACE achieved an F1 score of 0.87, demonstrating its accuracy in extracting causal factors and its potential to advance automated causal analysis in cybersecurity.

Identifying vulnerabilities in complex 5G network protocols is a challenging task. Manual analysis is time-consuming and often inadequate. Modern ML and NLP methods, though effective, are resource-intensive and struggle to find implicit vulnerabilities. In this research, we utilize GPT-4’s advanced language understanding to detect vulnerabilities directly from 5G specifications. To assess GPT-4’s fundamental capabilities in this domain, we first adopt a zero-shot approach that relies solely on the specification text without external guidance. For detecting more sophisticated vulnerabilities that require deep contextual understanding, we introduce a novel domain-aware strategy, where we explicitly teach GPT-4 about security properties and hazard indicators from related works using few-shot learning. We further employ chain-of-thought prompting to guide the model through structured reasoning steps to identify violations or exploitations that may lead to vulnerabilities. A two-tier filtering process ensures that only promising test-cases are retained. Our method has identified 47 potential vulnerabilities in 5G mobility management procedures, including 27 previously unreported issues, and generated corresponding test-cases. Simulating 14 of them, we have found 9 vulnerabilities, five of which are new. The zero-shot approach is effective in detecting procedural and validation flaws, while the domain-aware method excels in finding protocol violations and advanced attack scenarios. These findings validate our methodology and demonstrate its strength in discovering both known and novel vulnerabilities in 5G protocols.

The integration of artificial intelligence (AI) with the Internet of Things (IoT) marks a significant advancement in sixth-generation (6G) networks. The complexity of these AIoT services has promoted an as-a-service model, where service providers offer tailored architectures to meet varied application needs. Despite the critical importance of optimizing both training and inference in service architectures, this aspect remains under-explored. Our study introduces service scenarios such as ‘no shared (NS)’, where tenants manage their data and models independently, ‘data shared (DS)’, where tenants provide data for collective training, and ‘parameter sharing (PS)’, where only model parameters are shared. We utilize a tandem queue model to simulate the communication and computing demands across cloud-edge-fog architectures. Our proposed Cost and Delay Resource Allocation (CDRA) method significantly reduces costs, with edge and fog-based training and inference lowering costs by up to 44% compared to cloud setups. The evaluation shows that the NS scenario is resource-intensive but offers high privacy, DS is cost-effective and improves model accuracy, and PS balances privacy with longer wait times. These findings provide service providers with a comprehensive comparison of service scenarios and architectures, offering guidance for strategic and economically sound decisions in the ever-evolving landscape of AIoT.

Delay-sensitive applications demand ultra-low latency, which can be achieved by leveraging edge and fog computing to provide computation services closer to users. However, the server capacity limitation of edge and fog computing necessitates offloading to balance the computation load across cloud, edge, and fog servers. While previous works typically focus on the average delay of users' requests and employ probabilistic offloading schemes based on certain probabilities, our study introduces a novel approach that considers the QoS violating probability and offloads users' requests based on the queue length of computing servers. We propose an approximate model with closed-form solutions to determine the near-optimal offloading thresholds of the queue lengths at fog and edge servers. Although the performance results of the approximate queueing model do not precisely match the simulation results, our numerical findings demonstrate that they share the same trend, and the approximate queueing model can provide the optimal queue length threshold in most cases. Moreover, our numerical results reveal that the QoS violating probability of the queue-length-based offloading is significantly lower than that of the probabilistic offloading scheme, with potential reductions of up to 60% in QoS violations in large-scale network scenarios.

Convolutional Neural Network (CNN)-based Intrusion Detection System (IDS) incorporated with Federated Learning (FL) facilitates collaborative model training to secure user privacy and enhance data diversity, emerging as a promising solution to defend malicious flows. Yet, FL is vulnerable to trigger backdoor attacks. Existing methods have limited ability on IDS with FL for detecting or repairing a converged attacked model as they either require access to the training process or are dedicated to specific applications. To tackle the limitations, we propose a novel Features Distraction Defense Framework (FDDF) that does not interfere with the model training and requires only a few additional model inferences to protect users from the aforesaid attack. The experiment results show that FDDF can recover the accuracy effectively and preserve the accuracy of normal models.

Currently, online map navigation applications are essential to our daily demands, such as route finding in a big city or booking a taxi trip. Further, the applications also play a pivotal role in finding access to the nearest medical aid in big cities with complex traffic networks. However, existing commercial map programs are unable to provide route suggestions in areas where a continuous network connection is assured, which is essential for distant emergency medical services. This study introduces a highly effective personalized shortest-path algorithm for finding the fastest path of an emergency vehicle (EV) while also ensuring stable network connections for potential remote robotic surgery. By using a customized A-star algorithm, the experimental findings demonstrate that this technique can outperform current search approaches with a 5% shorter path length per 5km. For rescue missions in a large city, the enhancement is remarkable and has the potential to save lives. Due to high compatibility with the existing search algorithms, this method can easily be included as a critical feature for existing commercial map apps.

The rapid advancement of wireless networks towards Artificial Intelligence (AI)-driven solutions attracts many vendors to build resilient and intelligent capabilities for Open Radio Access Networks (O-RAN). However, besides the benefits of achieving flexibility and intelligence, openness in native AI-driven O-RAN functions is also the target of severe AI-related security threats, e.g., adversarial attacks. This work addresses the security matter for the AI-powered solutions in the physical layer of O-RAN, specifically within the context of deep reinforcement learning (DRL)-based resource allocation. We introduce a new adversarial attack variant that manipulates the environment parameters and misleads the agent's observation during the inference phase. The attack can cause incorrect allocation decisions and significant degradation in the transmission data rate. Our evaluation results show that the attack degrades user data and packet delivery rates by up to 40% and 77.74%, respectively, particularly in ultra-low-latency services. We also found that the major weakness of DRL-driven radio resource allocation is the environment observation stage, where a group of compromised users or jammers can spoof noises and signal power to mislead environment interaction. In our context, the proposed policy infiltration attack is the most efficient approach to cause sustained network inefficiencies or reduced throughput for benign users.

Reconfigurable intelligent surfaces (RIS) is a promising technique to improve communication quality by adjusting the phase shift value of the passive reflected elements equipped on RIS. However, the phase shift value controlled by RIS may suffer from inevitable errors brought by hardware impairment and interference during transmission, which might lead to performance degradation. On the other hand, the assumption of uniform amplitude response made by most literature is not practical due to the imperfect reflection efficiency of the material. To jointly address these two issues, we consider a practical amplitude model that is a function of phase shift value and the phase shift value in our system is imposed an additional error following the Von-Mises distribution. To find the optimal solution that minimizes the average mean square error (MSE) of the received signal, we proposed a gradient descent method (GDM)-based phase shift algorithm that iteratively follows the gradient flow until converges. The numerical result is presented to show the superiority of our proposed algorithm over other existing algorithms.

Autonomous cars are already the driving force behind achieving the goal of intelligent mobility in smart cities. However, providing internet access for automobiles introduces additional risks of distributing false information. By broadcasting fake sharing data, an attack vehicle has the potential to mislead surrounding cars and trigger catastrophic accidents. This work introduces a novel incremental learning model for misbehavior detection in vehicular communications. Unlike previous methods which mostly relied on the pre-trained model, in this work, we develop accumulative learning capability for the misbehavior detection engines at vehicles or distributed roadside units. The model can assist in obtaining new knowledge from continuous learning. The simulation results indicate that the proposed model outperforms the state-of-the-art studies by up to 12% in terms of detection accuracy and 45% faster in continuous processing, particularly in the single detector mode.

Reconfigurable intelligent surface (RIS) is a revolutionary passive radio technique to facilitate capacity enhancement beyond the current massive multiple-input multiple-output (MIMO) transmission. However, the potential hardware impairment (HWI) of the RIS usually causes inevitable performance degradation and the amplification of imperfect CSI. These impacts still lack full investigation in the RIS-assisted wireless network. This paper developed a robust joint RIS and transceiver design algorithm to minimize the worst-case mean square error (MSE) of the received signal under the HWI effect and imperfect channel state information (CSI) in the RIS-assisted multi-user MIMO (MU-MIMO) wireless network. Specifically, since the proposed robust joint RIS and transceiver design problem yields non-convex characteristics under severe HWI, an iterative three-step convex algorithm is developed to approach the optimality by relaxation and convex transformation. Compared with the state-of-the-art baselines that ignore the HWI, the proposed robust algorithm inhibits the destruction of HWI while raising the worst-case MSE effectively in several numerical simulations. Moreover, due to the properties of the HWI, the performance loss is notable under the magnification of the number of reflected elements in the RIS-assisted MU-MIMO wireless network.

Vehicle-to-everything (V2X) communication is one of the key technologies of 5G New Radio to support emerging applications such as autonomous driving. Due to the high density of vehicles, Remote Radio Heads (RRHs) will be deployed as Road Side Units to support V2X. Nevertheless, activation of all RRHs during low-traffic off-peak hours may cause energy wasting. The proper activation of RRH and association between vehicles and RRHs while maintaining the required service quality are the keys to reducing energy consumption. In this work, we first formulate the problem as an Integer Linear Programming optimization problem and prove that the problem is NP-hard. Then, we propose two novel algorithms, referred to as “Least Delete (LD)” and “Largest-First Rounding with Capacity Constraints (LFRCC).” The simulation results show that the proposed algorithms can achieve significantly better performance compared with existing solutions and are competitive with the optimal solution. Specifically, the LD and LFRCC algorithms can reduce the number of activated RRHs by 86$\%$and 89$\%$in low-density scenarios. In high-density scenarios, the LD algorithm can reduce the number of activated RRHs by 90$\%$. In addition, the solution of LFRCC is larger than that of the optimal solution within 7$\%$on average.

Cyber Threat Intelligence (CTI) plays a crucial role in understanding and preemptively defending against emerging threats. Typically disseminated through unstructured reports, CTI encompasses detailed insights into threat actors, their actions, and attack patterns. The MITRE ATT&CK framework offers a comprehensive catalog of adversary tactics, techniques, and procedures (TTPs), serving as a valuable resource for deciphering attacker behavior and enhancing defensive measures. Addressing the challenge of time-consuming manual analysis of MITRE TTPs in unstructured CTI reports, this paper presents MITREtrieval, a novel system that leverages deep learning and ontology to efficiently extract MITRE techniques. This approach mitigates issues related to the implicit nature of TTPs, textual semantic dependencies, and the scarcity of adequately labeled datasets, enabling more effective analysis even with limited sample sizes. Our approach combines a sophisticated sentence-level BERT deep learning model with ontology knowledge to address sparse data challenges, using a voting algorithm to merge outcomes. This results in a more accurate classification of MITRE techniques, capturing contextual nuances effectively. Our evaluation confirms MITREtrieval’s effectiveness in identifying techniques, regardless of their representation in training samples. MITREtrieval has surpassed benchmarks, achieving F2 scores of 58%, 62%, and 69% in multi-label technique identification across 113, 46, and 23 CTI reports, respectively, thereby streamlining CTI analysis and improving threat intelligence.

For critical applications such as emergency medical rescue missions or telehealth in remote areas, stable network connectivity is vital for patient state monitoring and proper temporary care. Unexpected connection interruption or network lag can cause trouble for skilled doctors in remote care centers to predict the progress of a patient’s condition. Network quality is variable in many areas because of signal power degradation (zones without purple coverage) in rural areas with many building obstacles. As a result, many current emergency services still rely on on-site first aid efforts. The idea of unmanned aerial vehicles (UAVs) serving as aerial relaying stations to provide connectivity for ground users has received much attention over the years. However, controlling UAVs via cellular networks is still a challenging issue. In this work, we consider the mission of dispatching UAVbased relaying stations as a path-planning scheme, where the UAVs go to planned locations and serve the EVs with a certain connectivity requirement. The core novelty of this work is a novel searching scheme that can suggest a deployment plan for the swarm of UAVs at the time of the EVs’ departure. The search is also robust for path planning with real-time applications or dynamic environments.

Cyber Threat Intelligence (CTI) helps organizations understand the tactics, techniques, and procedures used by potential cyber criminals to defend against cyber threats. To protect the core systems and services of organizations, security analysts must analyze information about threats and vulnerabilities. However, analyzing large amounts of data requires significant time and effort. To streamline this process, we propose an enhanced architecture, BERT-CRF, by removing the BiLSTM layer from the conventional BERT-BiLSTM-CRF model. This model leverages the strengths of deep learning-based language models to extract critical threat intelligence and novel information from threats effectively. In our BERT-CRF model, the token embeddings generated by BERT are directly fed into the Conditional Random Field (CRF) layer for efficient Named Entity Recognition (NER), thus preventing the need for an intermediate BiLSTM layer. We train and evaluate the model with three publicly available threat entity databases. We also collect open-source threat intelligence data from recent years for evaluating the applicability of the constructed model in a real-world environment. Furthermore, we compare our model with the most popular GPT-3.5 and the most downloaded open-source BERT question-and-answer models. Through this study, our proposed model demonstrated robust usability and outperformed other models, signifying its potential for application in CTI. In a real-world scenario, our model achieved an accuracy of 82.64%, while with malware-specific threat intelligence data, it achieved an impressive accuracy of 93.95%. The code for this research is publicly available at https://github.com/stwater20/ner_bert_crf_open_version.

The increasing diversification of network attacks has posed many security threats. Even within a local area network, different hosts may encounter distinct attacks. Leveraging the intrusion data dispersed across various hosts is crucial to achieving more comprehensive intrusion detection. Decentralized learning has emerged as a promising solution by enabling hosts to share information in a peer-to-peer manner. However, the imbalanced nature of intrusion data and varying data distributions between hosts can significantly impact model performance. To address the challenges of imbalanced and non-IID data, we propose a Decentralized Learning-based Intrusion Detection System (DLIDS). It rebalances training data to mitigate the model's bias towards the majority class and periodically substitutes the training model to facilitate knowledge acquisition. Moreover, the ensemble method is incorporated to integrate diverse perspectives and generate unbiased predictions. Finally, the experiment results on CSE-CIC-IDS2018 dataset show that the proposed method performs well even under imbalanced and non-IID data conditions.

The Denial-of-Service (DoS) attacks have rapidly increased over the years, particularly from the Internet of Things (IoT) devices such as connected IP cameras/vehicles and IP door entries. As a result of a lack of strong security implementation, these low-cost IoT devices can become zombie bots by brute force (using a password dictionary) and malware injection. Thousands of such zombies have been a powerful tool to initiate DoS attacks that can consume any target network's bandwidth. Recently, Deep Learning (DL) based methods have achieved admirable performance to mitigate DoS attacks significantly. However, besides the high latency of processing huge volumes of incoming traffic, current D L-based methods are often designed based on crafting a model carefully, which costs the developers significant time and effort. This paper introduces a novel automated deep-learning (autoDL) scheme to automatically generate an efficient DoS defense model. The system can find the best suitable detection model and a detailed configuration that is lightweight enough to deploy at IoT gateways/wireless routers/programmable switches/edge servers near the attack sources. To our knowledge, this is the first attempt to develop such an autoDL platform for DoS filter generation. The evaluation results show that the defense system generated by autoDL can ease 99.7% of malicious traffic before they go out to the Internet with only 0.593 ms for request reaction, a promising performance compared to the literature.

Radio positioning is critical for many indoor applications, such as behavioral monitoring and autonomous robots. Mobile users, however, can also be exposed to surveillance risks due to this capability. This work presents a Spatial-Temporal Angle-Delay Analysis Scheme (STADAS) for massive MIMO wireless networks that can help the attacker to track a user without the need to enter buildings. First, we transform the channel state information (e.g., angle of arrival, time of arrival) from massive MIMO transmission gained over time into living Angle-Delay profiles (ADPs) with fixed objects (building walls, furniture) and a moving object (the mobile user). Second, a generative adversarial network learning model is used to remove distorted data points from Angle-Delay video frames. The processed ADPs are trained with a Deep Convolutional Neural Network (DCNN)-based model on estimating the user's location. Evaluations on an empirical dataset indicate that radio positioning capabilities in emerging wireless communication technologies such as mmWave MIMO can pose severe privacy and surveillance threats.

Directional antenna technologies are crucial to enhance high-speed data transmission in emerging wireless communications such as mmWave. These technologies can enable high-accuracy radio positioning by exploiting spatial-temporal signal processing in wideband beamforming space. However, the radio positioning technique potentially poses surveillance risks to mobile users, particularly being tracked illegally. This work presents a novel scheme to track a user in a building based on passively received signals. The scheme includes a Deep Convolutional Neural Network (DCNN) localization module to train on the accumulated channel impulse responses (CIR) and corresponding Angle-Delay profiles. The user's estimated locations from the DCNN localization are then refined with an Unscented Kalman filter (UKF) data fusion module to eliminate outlier data points. Simulations indicate that the proposed scheme can accurately regenerate the user trajectory, even without the attacker's physical intrusion into the building. This poses a new concern of surveillance risks in directional wireless communications, given their expected popularity in 5G and beyond.

Mobile live video streaming is expected to become mainstream in the fifth generation (5G) mobile networks. To boost the Quality of Experience (QoE) of streaming services, the integration of Scalable Video Coding (SVC) with Mobile Edge Computing (MEC) becomes a natural candidate due to its scalability and the reliable transmission supports for real-time interactions. However, it still takes efforts to integrate MEC into video streaming services to exploit its full potentials. We find that the efficiency of the MEC-enabled cellular system can be significantly improved when the requests of users can be redirected to proper MEC servers through optimal user associations. In light of this observation, we jointly address the caching placement, video quality decision, and user association problem in the live video streaming service. Since the proposed nonlinear integer optimization problem is NP-hard, we first develop a two-step approach from a Lagrangian optimization under the dual pricing specification. Further, to have a computation-efficient solution and less performance loss, we provide a one-step Lagrangian dual pricing algorithm by the convex transformation of non-convex constraints. The simulations show that the service quality of live video streaming can be remarkably enhanced by the proposed algorithms in the MEC-enabled cellular system.

Resolving traffic bottlenecks caused by emergency situations on the freeways has been a challenge. It often takes much time for the vehicles voluntarily to line up and exit the congestion spot quickly. Unlike existing traffic scheduling schemes, which often rely on traffic signal controllers or are specified for known bottleneck areas, this paper introduces an efficient decentralized traffic coordination method, namely ETRACO, for resolving temporary bottlenecks on the multi-lane freeways. Initially, based on the Vehicle-to-Vehicle (V2V) warning notifications about the congestion, the vehicles negotiate with neighbors to determine a suitable configuration (platoon leader, distance gap, velocity, platoon size) for forming platoons. After that, each platoon leader commands the platoon members to change lane under the condition that there is a safe space on the lane next to the current lane so that the platoon can move safely to that lane. The experimental results demonstrate that our approach can reduce up to 22% delay for the last few vehicles driving through the congestion area during the congestion period. Furthermore, the proposed approach also effectively reduce congestion time for new incoming vehicles, and maintain the fairness for vehicles to leave the congestion area.

Dispatching emergency vehicles (EVs) to fatal accidents or fires as fast as possible is vital to save lives; however, minimizing an EV’s travel time to the rescue spot is still an open challenge. This work presents a path planning and traffic clear-out scheduling scheme to lessen the EV’s travel time in the vision of the Internet of Vehicles (IoV). Initially, the system searches a list of candidate paths to the rescue spot with the estimated time of arrival (ETA) at the EVs’ maximum speed, regardless of the traffic conditions. After that, a vehicle clear-out process evaluates the delay time of clearing out the traffic obstacles on each path to identify the fastest driving path. Finally, the system estimates and issues the signal preemption schedules for the junctions of the selected route to coordinate the traffic flows and let the EV pass through smoothly. From the macro perspective, this work seeks tocontrol the dynamic traffic proactively to reserve a lane for the EV– a feasible approach in the future of connected vehicles. This controllable model apparently contrasts with the conventional techniques of finding the least-cost paths with the uncertainty of traffic state prediction or traffic light preemption alone at the intersections. The simulation shows that our approach can outperform the state-of-the-art solutions in terms of the EV’s travel time reduction, particularly if the congestion or heavy load road segments appear on the selected route but far from the departure location of the EV.

Intelligent Transport Systems (ITSs) play an important role in future smart city design to improve traffic safety and traffic congestion by sharing data collected by vehicles. For sharing the traffic data with other vehicles, the vehicular sensory data are usually uploaded to the cloud server. However, existing data sharing systems for VANETs cannot provide selective data with sufficient privacy protection. Moreover, some schemes also cannot ensure stable data accessibility and the integrity of retrieved data. On the other hand, with the improvements such as lower latency, higher capacity, and increased bandwidth, 5G technology brings more possibilities to future applications. The join of the software-defined networks (SDNs) also offers efficient and effective network management. This paper proposes a primitive vehicular communication system named blockchain-based privacy-preserving and sustainable data query service. The proposed scheme is designed to realize stable data accessibility by leveraging smart contracts and blockchain oracle. With the help of 5G technology and P2P file-sharing system, InterPlanetary File System (IPFS), the proposed scheme aims to support video downloading files with searchable capability and fairness. An incentive token mechanism is also equipped. The merit of auditability is ensured by Ethereum blockchain platform to support the accountability. Besides, we also evaluate its networking performance via SUMO and NS-3 simulators. Our simulation results show that the request-response delay of BPSDQS is less than existing blockchain-based proxy re-encryption (PRE) scheme. Our simulation results also showed that the average request-response delay in our scheme can saving up to 98%.

Service Function Chaining (SFC) provides a method of forwarding traffic flows through one or more service functions (SFs). For service providers, chaining SFs across multiple datacenters to deliver end-to-end services not only provides better utilization of computing resources of datacenters, but also achieves scalability and fault tolerance. However, most telecommunication applications are sensitive to latency, which tends to degrade due to both virtualization and the long distances among datacenters. In this paper, we extend the network service header (NSH) protocol and propose a multipath chaining with the partially-ordered NSH (MCPON) mechanism to achieve low-latency, partially-ordered service function chaining. MCPON adopts a proactive multipath installation for commonly-used service function paths (SFP, a sequence of requisite SFs) to eliminate reactive path decision delays and to reduce end-to-end service latency. To increase multipath diversity for better load balancing, we modify the original NSH encapsulation design so that the multiple paths selected for an SFP are not limited to having the same execution orders of some non-order-constrained SFs. MCPON also utilizes an entry-saving forwarding table design which enables forwarding entries to be shared among different SFC requests. Our evaluations show that proactive k-path computation for an SFC of length l at a scale of n SFFs saves time complexity of${\mathrm{ O}}(kl^{3}n^{3})$, and multipath service chaining reduces latency by 33–68% compared to single-path service chaining in our simulation scenarios.

Mobile Edge Computing (MEC) is a promising technique in the 5G Era to improve the Quality of Experience (QoE) for online video streaming due to its ability to reduce the backhaul transmission by caching certain content. However, it still takes effort to address the user association and video quality selection problem under the limited resource of MEC to fully support the low-latency demand for live video streaming. We found the optimization problem to be a non-linear integer programming, which is impossible to obtain a globally optimal solution under polynomial time. In this paper, we formulate the problem and derive the closed-form solution in the form of Lagrangian multipliers; the searching of the optimal variables is formulated as a Multi-Arm Bandit (MAB) and we propose a Deep Deterministic Policy Gradient (DDPG) based algorithm exploiting the supply-demand interpretation of the Lagrange dual problem. Simulation results show that our proposed approach achieves significant QoE improvement, especially in the low wireless resource and high user number scenario compared to other baselines.

Online Decentralized Learning (ODL) is suitable for Internet-of-Things (IoT) devices since only parameter updates are exchanged with neighbors to avoid uploading private data to a central server and the training data is allowed to arrive at the devices sequentially. However, the current ODL frameworks cannot support the emerging Social IoT (SIoT) paradigm favorably since the SIoT devices exchange parameter updates with only trust-worthy neighbors based on specific social relations (e.g., parental object relation and ownership object relation). Conversely, sharing parameter updates with untrustworthy neighbors could speed up the training process but may violate social relations. Differential privacy (DP) is thus used to ensure data security while excessive devices engaging DP may downgrade the training performance. However, most research neglects the effect of neighbor selection for each device based on social networks, physical networks, and DP. Thus, in this paper, we innovate an ODL framework ODLF-PDP to allow only a part of devices to engage DP (i.e., partially DP) to improve training performance. Then, an algorithm BeTTa is proposed to build an adequate communication topology based on the interplay among the social networks, physical networks, and DP. Last, the experiment results manifest that ODLF-PDP saves more than 20% physical training time compared to the current frameworks via the benchmark of MNIST.

Mobile edge computing and fog computing are promising techniques providing computation service closer to users to achieve lower latency. In this work, we study the optimal offloading strategy in the three-tier federated computation offloading system. We first present queueing models and closed-form solutions for computing the service delay distribution and the probability of the delay of a task exceeding a given threshold. We then propose an optimal offloading probability algorithm based on the sub-gradient method. Our numerical results show that our simulation results match very well with that of our closed-form solutions, and our sub-gradient-based search algorithm can find the optimal offloading probabilities. Specifically, for the given system parameters, our algorithm yields the optimal QoS violating probability of 0.188 with offloading probabilities of 0.675 and 0.37 from Fog to edge and from edge to cloud, respectively.

In the next decades, the popularity of Internet of Vehicles (IoV) technologies and autonomous driving promises to fundamentally change the way of handling the traffic flow on the streets. Traffic separation can be entirely carried out from a remote traffic control center without the police. This work introduces a sequential coordination algorithm, namely SCA, to form and sort platoons of vehicles to quickly exit traffic bottleneck areas caused by temporary situations, such as vehicular accidents and a slow tractor. By exploiting maneuver information from IoV data sharing, SCA schedules the vehicles in a queue by their arrival and lane priority and then instructs them to safely drive through in order. The experimental results demonstrate our approach can reduce up to 32% waiting time for the vehicles to exit accident spots.

Vehicle platooning is a promising technology to bring up significant benefits of improved fuel economy and fewer traffic collisions. However, many security attacks such as beacon message falsification have been exposed, creating grave concerns about maintaining a vehicle platoon stably. This work introduces a robust positioning-based verification scheme, namely PVS, to enhance reliability of vehicle platoon control in vehicular networks. By exploiting geographic and maneuver information from 5G radio-based positioning, PVS can detect whether a vehicle is honest in reporting its location for platoon joining preparation or collision avoidance, with up to 96% accuracy.

Recently, autopilot-like vehicles have become more pervasive. To maintain inter-vehicle distance stably, Cooperative Adaptive Cruise Control (CACC) is then proposed to make each autonomous vehicle exchange its dynamic state with neighboring vehicles via vehicle-to-vehicle (V2V) communication. However, longitudinal platooning via CACC systems alone is not enough to improve traffic throughput since each vehicle has its own desired speed and only considers itself to optimize its traveling. Therefore, we develop a novel framework MANA to determine the suitable platoon-merge maneuver, lane-change maneuver, and space-reserve maneuver. Extensive simulation results manifest that MANA can avoid collisions effectively, converge fast, and save fuel consumption and CO2emissions by 24% and 20%.

The popularity of 5G networks has made smart manufacturing not limited to high-tech industries such as semiconductors due to its high speed, ultra-high reliability, and low latency. With the advance of system on chip (SoC) design and manufacturing, 5G is also suitable for data transmission in harsh manufacturing environments such as high temperatures, dust, and extreme vibration. The defect of the screw head is caused by the wear and deformation of the die forming the head after mass production. Therefore, the screw quality inspection system based on the tactile network in this article monitors the production quality of the screw; the system will send a warning signal through the router to remind the technician to solve the production problem when the machine produces a defective product. Sensors are embedded into the traditional screw heading machine, and sensing data are transmitted through a gateway to the voluntary computing node for screw slot quality inspection. The anomaly detection data set collected by the screw heading machine has a ratio of anomaly to normal data of 0.006; thus, we propose a time-series deep AutoEncoder architecture for anomaly detection of screw slots. Our experimental results show that the proposed solution outperforms existing works in terms of efficiency and that the specificity and accuracy can reach 97% through the framework proposed in this article.

Vehicle-to-everything (V2X) is one of the generic services provided by emerging 5G wireless technology. Data traffic in V2X demands both communication and computation which is a challenge to ensure better user experience where communication and computation offloading are an effective scheme. Earlier works have only focused on communication offloading policies and algorithms. However, there have been a few analytical studies, especially on computation offloading and the role of Road-Side Units (RSUs). In this work, we propose an analytical model for communication and computation offloading to RSUs and gNB to minimize average packet delay by finding an optimal offloading probability through a sub-gradient based algorithm. Extensive simulations have been carried out to validate our model. Our results show that deploying RSUs with gNB increases efficiency by as much as 19% than deploying only gNB. Sensitivity analysis also shows that RSU service rate has 10 times more impact on reducing average packet delay than gNB service rate caused by relatively lower communication bandwidth of gNB.

Leveraging mobile cloud computing (MCC) and mobile edge computing (MEC) for offloading computational tasks is a promising approach to enabling delay-sensitive applications executing vehicles. Despite MCC and MEC's ability and complementary characteristics, most of the existing works on offloading focus on only either MCC or MEC. In this paper, we study their cooperation in a three-tier offloading model of a V2X network where a vehicle can offload computational tasks to cloud computing and MEC. Specifically, we investigate the optimal offloading probabilities of three offloading paths, including Vehicle-to- Infrastructure, Vehicle-to-Cloud, and Infrastructure-to-Cloud. Our contribution is twofold. First, we derive a mathematical model of task execution latency and a formulation to find an optimal solution for the minimum latency problem. Second, we propose an approximation algorithm based on the genetic algorithm toward the optimum. The experiment results show that by exploiting both MCC and MEC's complementary advantages, our proposed algorithm in the three-tier model can shorten the delay significantly compared to existing two-tier models. Depending on the traffic load and the number of Road Side Units, our proposal can reduce the delay by 93.75% on the average, and 99.9% in the best case.

Mobile Edge Computing (MEC) is a promising technique in the 5G Era to improve the Quality of Experience (QoE) for online video streaming due to its ability to reduce the backhaul transmission by caching certain content. However, it still takes effort to address the user association and video quality selection problem under the limited resource of MEC to fully support the low-latency demand for live video streaming. We found the optimization problem to be a non-linear integer programming, which is impossible to obtain a globally optimal solution under polynomial time. In this paper, we first reformulate this problem as a Markov Decision Process (MDP) and develop a Deep Deterministic Policy Gradient (DDPG) based algorithm exploiting the supply-demand interpretation of the Lagrange dual problem. Simulation results show that our proposed approach achieves significant QoE improvement especially in the low wireless resource and high user number scenario compared to other baselines.

Local access data network (LADN) is a promising paradigm to reduce latency, enable lowering energy consumption, and improve quality of service (QoS) for the Fifth Generation (5G) radio access network (RAN) supporting vehicle to everything (V2X) communications. To achieve optimum resource allocation and save energy by minimizing the activation of LADN servers in Cloud-RAN, some remote radio heads (RRHs) can be turned on or off depending on the traffic demand. In this paper, we investigate the problem of how to realize effective resource management in 5G RAN supporting V2X communications. More precisely, we first propose a formulation of the resource management problem as an optimization problem with the objective of minimizing the number of RRHs to be turned on subject to the uplink bandwidth constraints. We then use a fully-fledged professional software to solve our optimization problem and propose a solution with heuristic algorithms to deal with the complexity of the problem for large scenarios. Moreover, we analyze the impact of the density of vehicles on the computation time and the influence of the uplink data rate and vehicle densities on the number of active RRHs. Our numerical results show that our proposed model can efficiently utilize the resources and provide optimum vehicles-to-RRHs associations which lead to energy-savings. For instance, to serve 100 vehicles with aggregated uplink data rate equal to 100 [Mbps], the optimal associations save about 70% of the energy comparing to the strongest-signal associations. Furthermore, we obtain optimal results for the small size problem in reasonable computation times, which are around 50 [ms].

The live video streaming services have been suffered from the limited backhaul capacity of the cellular core network and occasional congestions due to the cloud-based architecture. Mobile Edge Computing (MEC) brings the services from the centralized cloud to nearby network edge to improve the Quality of Experience (QoE) of cloud services, such as live video streaming services. Nevertheless, the resource at edge devices is still limited and should be allocated economically efficiently. In this paper, we propose Edge Combinatorial Clock Auction (ECCA) and Combinatorial Clock Auction in Stream (CCAS), two auction frameworks to improve the QoE of live video streaming services in the Edge-enabled cellular system. The edge system is the auctioneer who decides the backhaul capacity and caching space allocation and streamers are the bidders who request for the backhaul capacity and caching space to improve the video quality their audiences can watch. There are two key subproblems: the caching space value evaluations and allocations. We show that both problems can be solved by the proposed dynamic programming algorithms. The truth-telling property is guaranteed in both ECCA and CCAS. The simulation results show that the overall system utility can be significantly improved through the proposed system.

In order to monitor the stability of industrial systems, engineers installed diversified sensors in systems, and used communication devices to transfer the sensed data to the cloud platform for real-time monitoring and event detection. Furthermore, as industry demand for power grows, the scale and quantity of power systems gradually increase, and the original network data transmission architecture cannot bear such large-scale communication, especially the communication bandwidth tolerance isn't allowed for trusted industrial Internet of things. Therefore, this trusted transmission problem will be one of challenges of the industrial Internet of things. In the application of device load recognition, how to create power fingerprinting recognition sample data, reduce the cloud platform computation complexity and the transmission quantity of sensed data without losing detection accuracy are the subjects of this study. Therefore, this study proposes a resilient section selection mechanism of power fingerprinting applied to device load recognition, in order to determine the transmission time and select the power fingerprinting section to be resiliently transferred, and replace the cycle-fixed full power fingerprinting data transfer for trusted industrial Internet of things. According to the experimental results, in the case of multi-load, the power fingerprinting of the first 25% section have the maximum recognition of 87.5%.

Telecommunications service providers (TSPs) previously provided network functions to end users with dedicated hardware, but they are resorting to virtualized infrastructure for reducing costs and increasing flexibility in resource allocation. A representative case is the Central Office Re-architected as Datacenter (CORD) project from AT&T, which aims to deploy virtualized network functions (VNFs) to over 4000 central offices (COs) across the U.S. However, there is a wide spectrum of options for deploying VNFs over the COs, varying from highly distributed to highly centralized manners. The former benefits end users with short response time but has its inherent limitation on utilizing geographically dispersed resources, while the latter allows resources to be better utilized at a cost of longer response time. In this work, we model the TSP's virtualized infrastructure as hierarchical datacenters, namely hierarchical CORD, and provide a resource allocation solution to strike the optimal balance between the two extreme options. Our evaluations reveal that in general, the 3-tier architecture incurs the least cost in case of deploying VNFs under moderate or loose delay constraints. Furthermore, the margin of improvement on the resource allocation cost increases inversely with the overall system utilization rate. Our results also suggest that as heavy request load overwhelms the network infrastructure, the relevant VNFs shall be migrated to lower-tier edge datacenters or to some nearby datacenters with superior network capacity. The evaluations also demonstrate that the proposed model allows highly adaptive VNF deployment in the hierarchical architecture under various conditions.

With the rapid development of cloud technology, many services have been transferred from local computers to the cloud-based platform, which decreases the amount of computation done on the former. The local computer could thus be developed in the direction of portability and power saving. Graphics processing, apart from providing user interfaces featuring diversified special effects, is also significant in terms of application programs and play interactions. It is exactly on the basis of the concept of graphics processing that cloud-support rendering is developed, which is aimed to improve the graphics efficiency in mobile devices, via the graphics processing units in the cloud-based platform. The cloud-based platform and the mobile devices are usually connected by the Internet; however, as remote rendering might call for greater network bandwidth, its efficiency will be compromised if the network bandwidth is not stable. Given this limitation, this paper sets out to propose a quality-of-service-aware resource allocation strategy for mobile 3D graphics rendering, which is a hybrid rendering technology combining the client-side graphics processing capabilities with the graphics processing units in the cloud-based platform. When network bandwidth is not stable, the technology is able to assess the current network bandwidth, and dynamically configure the rendered frames on the client side and cloud-based platforms. Even when the client side could not access the network, it would still be possible to carry out the drawing through the graphics processing units on the local computer. Three applications are tested in this research: the technology can increase the frame rate by an average of 44.99% when the bandwidth is 10% greater than the minimum limit, by an average of 44.57% when the bandwidth is less than the minimum limit, by an average of 30.86% when the bandwidth is 10% less than the minimum limit, and by an average of 33.74% when the bandwidth is not stable.

Location-based services (LBS) which bring so much convenience to our daily life have been intensively studied over the years. Generally, an LBS query processing can be categorized into snapshot and continuous queries which access user location information and return search results to the users. An LBS has full control of the location information, causing user privacy concerns. If an LBS provider has a malicious intention to breach the user privacy by tracking the users' routes to their destinations, it incurs a serious threat. Most existing techniques have addressed privacy protection mainly for snapshot queries. However, providing privacy protection for continuous queries is of importance, since a malicious LBS can easily obtain complete user privacy information by observing a sequence of successive query requests. In this paper, we propose a comprehensive trajectory privacy technique and combine ambient conditions to cloak location information based on the user privacy profile to avoid a malicious LBS reconstructing a user trajectory. We first propose an r-anonymity mechanism which preprocesses a set of similar trajectories R to blur the actual trajectory of a service user. We then combine k-anonymity with s road segments to protect the user's privacy. We introduce a novel time-obfuscated technique which breaks the sequence of the query issuing time for a service user to confuse the LBS so it does not know the user trajectory, by sending a query randomly from a set of locations residing at the different trajectories in R. Despite the randomness incurred from the obfuscation process for providing strong trajectory privacy protection, the experimental results show that our trajectory privacy technique maintains the correctness of the query results at a competitive computational cost.

In cloud computing, resource subscription is an important procedure which enables customers to elastically subscribe to IT resources based on their service requirements. Resource subscription can be divided into two categories, namely long-term reservation and on-demand subscription. Although customers need to pay the upfront fee for a long-term reservation contract, the usage charge of reserved resources is generally much cheaper than that of the on-demand subscription. To provide a better Internet service by using cloud resource, service operators will expect to make a trade-off between the amount of long-term reserved resources and that of on-demand subscribed resources. Therefore, how to properly make resource provision plans is a challenging issue. In this paper, we present a two-phase algorithm for service operators to minimize their service provision cost. In the first phase, we propose a mathematical formulae to compute the optimal amount of long-term reserved resources. In the second phase, we use the Kalman filter to predict resource demand and adaptively change the subscribed on-demand resources such that provision cost could be minimized. We evaluated our solution by using real-world data. Our numerical results indicated that the proposed mechanisms are able to significantly reduce the provision cost.

In recent years, cloud computing has become a promising solution for decreasing the deployment and maintenance costs of Internet services. To provide Internet application service by using cloud resource, a service provider needs to consider the resource subscription cost and Service Level Agreement (SLA) of its users. Several kinds of pricing model of cloud resource subscription have been proposed. In such case, the Internet service provider plays the role of a cloud customer with a need of optimal cloud resource subscription policy to reduce its operation cost. Therefore, how to determine a suitable policy of cloud resource subscription has become a challenging issue. In this work, we proposed a two-phase approach to solve the cloud resource subscription problem. The first phase considered long-term resource reservation. In this phase, we proposed a mathematic model to compute an upper bound of the optimal amount of long-term reserved resource. The second phase was dynamic resource subscription phase. In order to overcome dynamic resource demand, in this phase, we used Hidden Markov Model (HMM) to predict resource demand and allocate VM resource adaptively based on the prediction. We evaluated our solution using real-world resource demand data. Our numerical results indicated that our approach can reduce the cost of cloud resource subscription significantly.

ABSTRACT The IEEE 802.16e standard is thus proposed for supporting high data rate and dynamic mobility in WiMAX. IEEE 802.16e specifies the association handoff mechanism in the MAC layer, i.e., providing contention‐free‐based initial ranging, and thus mobile stations can perform the initial ranging early during the handoff period. An MS executing the association handoff during a scan duration is disallowed to send/receive any packets to/from the serving BS. IEEE 802.16e suffers from not determining a precision scan duration period because of losing the transmission opportunity or the response status of the received ranging response (RNG‐RSP) message. Although the MS can set a longer scan duration to complete the initial ranging procedure, it significantly degrades handoff delay and delay jitter of real‐time service flows. In addition, most handoff studies seldom considered balancing traffic load among neighbor base stations (BSs). This paper thus proposes an efficient Adaptive Load‐balancing Association handoff approach (ALA) consisting of two phases: (1) the Adaptive Association Handoff phase (AAH) and (2) the Predictive Direction‐based Load Balancing phase (PDLB), to overcome above mentioned problems. AAH proposes an adaptive re‐association mechanism to reduce lost synchronizations, and thus improve the grade of service. PDLB adopts the Polynomial Regression‐based RSS prediction algorithm to accurately predict the moving direction of mobile nodes. Numerical results demonstrate that ALA significantly outperform IEEE 802.16e and others in average handoff delay, number of handoffs, dropping probability, GoS, network utilization, and number of lost synchronizations. Copyright © 2011 John Wiley & Sons, Ltd.

Unstructured peer-to-peer (P2P) systems with two-layer hierarchy, comprising an upper layer of super-peers and an underlying layer of ordinary peers, are used to improve the performance of large-scale P2P systems. In order to deal with continuous growth of participating peers, a scalable super-peer overlay topology with a lower diameter is essential. However, there is relatively little research conducted on constructing a scalable super-peer overlay topology. In the existing solutions, the number of connections that super-peers need to maintain is in direct proportion to the total number of super-peers which makes the solutions not scalable as well as not practical. Therefore, in this paper, we propose a scalable hierarchical unstructured P2P system which using a self-similar square network graph (SSNG) to construct and maintain the super-peer overlay topology dynamically. Moreover, a forwarding mechanism over SSNG is presented to enable each super-peer to receive just one flooding message. The analytical results show that the proposed SSNG-based overlay is more scalable and efficient than the perfect difference graph (PDG)-based overlay proposed in the literature.

Abstract The requirement for in‐vehicle passengers to access Internet multimedia services has risen recently. As a consequence, Vehicle Ad hoc NETwork (VANET) has gained much attention, and is regarded as a promising solution for providing in‐vehicle Internet service through inter‐vehicle and infrastructure communication. A new developed wireless network technique, termed WiMAX Mobile Multihop Relay (MMR), provides a good communication framework for a VANET formed from vehicles on high‐speed freeways. Applying MMR WiMAX allows some public transportation vehicles to act as relay vehicles (RVs) to provide Internet access to passenger vehicles. However, the standard handover procedure of mobile or MMR WiMAX suffers long delay due to the lack of information about the next RV. This study presents a cross‐layer fast handover scheme, called vehicular fast handover scheme (VFHS), where the physical layer information is shared with the MAC layer, to reduce the handover delay. The key idea of VFHS is to utilize oncoming side vehicles (OSVs) to accumulate physical and MAC layers information of passing through RVs and broadcast the information to vehicles that are temporarily disconnected, referred to as disconnected vehicles (DVs). A DV can thus perform a rapid handover when it enters the transmission range of one of approaching RVs. The effectiveness of VFHS is verified using ns2 simulations. Simulation results indicate that VFHS significantly decreases handover latency and packet loss. Copyright © 2009 John Wiley & Sons, Ltd.

In this work, we investigate the optimal key generation problem for a threshold security scheme in mobile ad hoc networks. The nodes in these networks are assumed to have limited power and critical security states. We model this problem using a closed discrete-time queuing system with L queues (one per node) randomly connected to K servers (where K nodes need to be contacted to construct a key). In this model, each queue length represents the available security-related credits of the corresponding node. We treat this problem as a resource allocation problem where the resources to be allocated are the limited power and security credits. We introduce the class of Most Balancing Credit Conserving (MBCC) policies and provide their mathematical characterization. We prove, using dynamic coupling arguments, that MBCC policies are optimal among all key generation policies; we define optimality as maximization, in a stochastic ordering sense, of a random variable representing the number of keys generated for a given initial system state.

The IEEE 802.16e standard is proposed for mobile nodes to access high data rate and long transmission radius in WiMAX. For supporting contention-free-based initial ranging and early performing the initial ranging during handoff, IEEE 802.16e specifies the association handoff mechanism in the MAC layer. However, an MS is difficult to determine the scan duration in the association handoff mechanism because it may lose the transmission opportunity or the response status of the received ranging response (RNG-RSP) message. Although the MS can set a longer scan duration to complete the initial ranging procedure for the association handoff, a long duration significantly degrades the QoS parameters of delay and delay jitter of real-time service flows. This paper thus proposes an efficient adaptive load-balancing association handoff approach (namely ALA) that consists of two phases: (1) the Adaptive Association Handoff phase (AAH) and (2) the Predictive-direction-based load balancing phase (PDLB), to overcome above mentioned problems. AAH proposes an adaptive re-association to reduce lost synchronizations, and thus improve the grade of service. PDLB adopts a prediction scheme to accurately predict the moving direction of mobile nodes, and thus achieve the reduction of the number of candidate target BSs in order to minimize the handoff delay and to balance traffic loads. Numerical results demonstrate that the proposed ALA approach obviously outperforms the IEEE 802.16e protocol and other approaches in average handoff delay, number of handoffs, dropping probability, grade of service, network utilization, and number of lost synchronizations.

Along with rapid development of Internet technologies and widespread adoption of broadband residential access, video streaming service becomes a promising killer application. In order to solve device diversity, scalable video coding (SVC) has been standardized by the Joint Video Team of the ITU-T VCEG and the ISO/IEC MPEG. Using SVC, each device is capable of determining which layer should be decoded according to its capacities. On the other hand, comparing with traditional client/server architecture, peer-to-peer (P2P) technology can provide high scalability, high resilience, and prevent single point failure. Many studies have been proposed to improve the video quality under different considerations. However, little work has been done on transmitting SVC-encoded video based on P2P mesh topology. In this paper, a P2P SVC-encoded video streaming based on network coding (NC) is proposed. First of all, we propose a novel coding scheme, SVC-NC, for improving error robustness of SVC-encoded video. Second, we apply three scheduling mechanisms based on SVC-NC, startup request scheduling, priority request scheduling, and priority response scheduling to deliver SVC-encoded video more efficiently. Finally, we demonstrate the performance of our approach via simulation. The simulation results indicate that our approach can achieve low startup latency, smooth playback, and high video quality.

Abstract As digital devices with communication capability become more pervasive, we are entering the era of ubiquitous computing, as predicted by Mark Weiser. In ubiquitous environments, distributed context management servers are deployed everywhere to provide information and computing resources for users anytime and anywhere. Smart handheld computing devices with context‐aware applications may retrieve context information from the nearest server. This study investigates the problem of routing packets to the nearest server in a ubiquitous environment. An anycast routing protocol based on swarm intelligence, referred to as ARPSI, is proposed to route packets dynamically to a nearby server in a mobile,ad hoc, wireless network. Based on swarm intelligence, ARPSI is able to find a short path to a neighboring server efficiently and quickly. Simulations are conducted to evaluate the performance of ARPSI and our simulation results show that ARPSI achieves a higher packet delivery ratio, shorter routing path to anycast servers, and lower control packet overhead than the AODV‐based anycast protocol (A‐AODV) protocol. Copyright © 2009 John Wiley & Sons, Ltd.

The VSF-OFCDM system has been proposed as the forward link interface for achieving high data rate in 4G mobile communications, which allocates orthogonal channelization codes of an OVSF code tree in two-dimension (2D) spreading in the time and frequency domains. However, it suffers from two disadvantages namely moderate utilization and low transmission quality. Moderate utilization is caused by code blocking, and low transmission quality is due to the multicode interference from high channel loading in the time domain of the 2D spreading. A trade-off thus exists between code blocking and multicode interference. Lower code blocking means higher interference. For achieving high utilization while also providing high transmission quality is a critical issue that should be addressed in 4G VSF-OFCDM. Therefore, this paper proposes a 2D spreading approach based on Adaptive Load-balancing with Markov decision process (denoted by ALM). The ALM approach consists of three phases: 1) the adaptive 2D spreading phase to select the 2D spreading combinations, 2) a dynamic re-combination of a 2D spreading to decrease channel load while supporting transmission quality, and 3) the cost-based Markov decision process (MDP) code selection approach to minimize code blocking and to select the least cost channelization code as the optimal solution. Numerical results indicate that the proposed approach outperforms other approaches in transmission quality ratio and fractional reward loss.

This study presents a cross-layer fast handover scheme for VANET, called vehicular fast handover scheme (VFHS), where the physical layer information is shared with the MAC layer, to reduce the handover delay. The key idea of VFHS is to utilize oncoming side vehicles (OSVs) to collect physical and MAC layers information of passing through RVs and broadcast the information to vehicles that are temporarily disconnected, referred to as broken vehicles (BVs). A BV can thus perform a rapid handover when it enters the transmission range of the approaching RVs. The effectiveness of VFHS is verified using ns2 simulations. Simulation results indicate that VFHS significantly decreases handover latency and packet loss.

Integration of messaging services in heterogeneous wireless networks plays an important role in facilitating personal communications. Furthermore, supporting context-awareness drives wireless messaging services to the ubiquitous and human-centric extent. In this article we introduce a context-aware wireless messaging system in ubiquitous networking environment. The key concepts are (1) heterogeneous wireless data are accessed and translated by a gateway with corresponding networking interfaces or terminals; (2) a messaging service with always-best-connected capability is provided according to the context information collected by user's mobile or handheld devices; and (3) a Resource Description Framework (RDF) context server is used to manage all kinds of context information. With our system, users can enjoy the cost-effective and near-real-time communication even when they face network heterogeneity in the ubiquitous networking environment. This paper also describes the implementation of the system prototype to demonstrate the effectiveness of the proposed system.

In this paper, a new cross-layer protocol was developed, called as SmSCTP protocol, for session mobility over WLAN and 3G UMTS heterogeneous networks. Two key issues investigated in this work are the connection broken problem and fleeting-location-collapse problem. The connection broken problem indicates that a connection is broken when a mobile device is roaming to a different network. The connection broken problem incurs the long handoff delay time and cannot provide the seamlessly handoff result for session mobility. Traditional session mobility schemes, such as SIP (layer-7 solution) and MSCTP (layer-4 solution) protocols, only provide non-real time registration when third party user is calling up mobile user. The non-real time registration incurs the well-known fleeting-location-collapse problem and causes additional location update overhead. This paper presents a SIP-based MSCTP (SmSCTP) protocol which is a cross-layer, combination of layer-4 and layer-7, approach. The SmSCTP protocol utilizes the multi-homing mechanism to reduce hand-off delay time and to provide the more seamless handoff scheme. In the SmSCTP protocol, two new SIP signalings for the SmSCTP protocol are designed to simplify the initial handoff procedure and solve fleeting-location-collapse problem. Finally, simulation results are conducted to illustrate the performance achievements of the proposed SmSCTP protocol by improving the signaling cost, the hand-off delay time.

The dynamic nature of MANET causes many challenges in designing robust and scalable P2P system. Although flooding-based techniques are shown to be robust in highly dynamic network, it leads to poor efficiency in terms of bandwidth usage and scalability. In this paper, we propose an efficient and scalable P2P file sharing system based on swarm intelligence for MANET, referred to as P2PSI. By applying the behavior of the real ant colonies, P2PSI owns the capability of adaptive learning and is able to cope with mobility problem without flooding. Moreover, we also present a cross-layer architecture for P2PSI to reduce the redundant message overhead and query latency. Performance of our cross-layer design P2PSI is compared with two existing cross-layer design service discovery protocols, namely, CLdsrand CLdsrthrough simulations. The simulation results show that our cross-layer design P2PSI achieves better performance in terms of control overhead, request success ratio, and path length.

In a pervasive environment, regional servers with local information may be deployed everywhere and pervasive computing devices may require connecting to the nearest service server to download the most up-to-date information. This study investigates the problem of routing packets to the nearest server in a pervasive environment. An anycast routing protocol based on Swarm Intelligence and anycast service, called Anycast Routing Protocol using Swarm Intelligence (ARPSI), is proposed to route packets dynamically to a nearby server in a mobile, ad hoc, wireless network. ARPSI applies the behavior of the real ant colonies to find a shorter path to a neighboring server efficiently and quickly. Simulation results show that ARPSI achieves a higher packet delivery ratio, shorter routing path to anycast servers, and lower control packet overhead than A-AODV protocol. To our knowledge, this is the first work to integrate swarm intelligence and anycast service to solve anycast routing in a mobile ad hoc network with servers deployed at stationary locations.

This paper presents an integrated solution for code management, assignment, and reassignment problems in UMTS. We propose a new architecture for code management and, based upon this new architecture, a code assignment strategy, referred to as "crowded-group first strategy". Our system architecture and code assignment strategy represent significant improvements both in the time complexity and the maintenance complexity. Moreover, the code blocking probability of the crowded-group first strategy is competitive to that of the other strategies. In this paper, we also propose a new code reassignment strategy, called the "crowded-branch first strategy". The main objective of this reassignment strategy is to reduce reassigned call probability with low computation overhead and extend this strategy for the general case. In order to systematically analyze the performances of the code assignment strategy, we implement a simulator to analyze the code selection behavior and code blocking probability of each strategy. Moreover, we propose some new performance metrics, named "weighted code blocking", "reassigned call probability", and "ratio of actual code reassignments", in order to precisely measure the performance obtained by different strategies. From the simulation results, we show that our proposed strategies efficiently utilize the OVSF codes with low computation overhead.

Based on discrete wavelet transformation and block truncation coding, in this paper, a system is presented to cope with a tampered digital image. Without contrast with the original image, this system has its great advantage to detect any tampered possibility, and then restore that tampered part. Experiments have indicated that restored version is extremely close to the original one.

The IPv6-enabled network architecture has recently attracted much attention. In this paper, we address the issue of connecting MANETs to global IPv6 networks while supporting IPv6 mobility. Specifically, we propose a self-organizing, self-addressing, self-routing IPv6-enabled MANET infrastructure, referred to as IPv6-based MANET. The proposed self-organization addressing protocol automatically organizes nodes into tree architecture and configures their global IPv6 addresses. Novel unicast and multicast routing protocols, based on longest prefix matching and soft state routing cache, are specially designed for the IPv6-based MANET. Mobile IPv6 is also supported such that a mobile node can move from one MANET to another. Moreover, a P2P information sharing system is also designed over the proposed IPv6-based MANET. We have implemented a prototyping system to demonstrate the feasibility and efficiently of the IPv6-based MANET and the P2P information sharing system. Simulations are also conducted to show the efficiency of the proposed routing protocol and the P2P file sharing system.

In 3G WCDMA, several multi-code assignment mechanisms have proposed to reduce the waste rate. Nevertheless, these methods bring two inevitable drawbacks including, high complexity of handling multiple codes, and increasing the cost of using more rake combiners at both the base stations and mobile nodes. Therefore, we propose an adaptive grouping code assignment herein to provide a single channelization code for any possible rate of traffic, even though the required rate is not powers of two of the basic rate. Based on the dynamic programming algorithm, the adaptive grouping approach forms several calls into a group. Then it allocates a subtree to the group and adaptively shares the subtree codes for these calls in the concept of time-sharing of slots during a group cycle time. Numerical results indicate that the proposed adaptive grouping approach reduces significantly the waste rate and thus increases the system utilization.

The Internet protocol version 6 (IPv6)-enabled network architecture has recently attracted much attention. In this paper, we address the issue of connecting mobile ad hoc networks (MANETs) to global IPv6 networks, while supporting IPv6 mobility. Specifically, we propose a self-organizing, self-addressing, self-routing IPv6-enabled MANET infrastructure, referred to as IPv6-based MANET. The proposed self-organization addressing protocol automatically organizes nodes into tree architecture and configures their global IPv6 addresses. Novel unicast and multicast routing protocols, based on longest prefix matching and soft state routing cache, are specially designed for the IPv6-based MANET. Mobile IPv6 is also supported such that a mobile node can move from one MANET to another. Moreover, a peer-to-peer (P2P) information sharing system is also designed over the proposed IPv6-based MANET. We have implemented a prototyping system to demonstrate the feasibility and efficiency of the IPv6-based MANET and the P2P information sharing system. Simulations are also conducted to show the efficiency of the proposed routing protocols.

This paper presents an integrated solution to the code management and assignment problem in the UMTS system. We propose a new architecture for code management, and based upon this new architecture, a new code assignment strategy, referred to as 'crowded-group first strategy'. Our system architecture and code assignment strategy represent significant improvements in the time complexity and in the complexity of the storage operation. Moreover, the code blocking probability of the crowded-group first strategy can be as good as that of the crowded-first strategy. In order to systematically analyze the performance of the code assignment strategies, we implement a simulator to analyze the code selection behavior of each strategy, and the performance of the code blocking can be anticipated based on the analysis. We also propose a new performance metric, named 'weighted code blocking', in order to precisely measure the performance obtained by different strategies. From the simulation results, we show that our strategy efficiently utilizes the OVSF codes with the low computation overhead.

Since the orthogonal characteristic of the orthogonal variable spreading factor (OVSF) code tree in wideband CDMA (WCDMA) systems, code blocking increases as traffle load or required rate increases. This causes inefficient utilization of channelization codes. Hence, how to efficiently manage the resource of channelization codes of OVSF code tree in WCDMA is an important issue and has been studied extensively. Therefore, in this paper we focus on how to assign channelization code efficiently. Additionally, most researches did not consider the analysis of tree state with dynamic traffic load and lack of systematic call admission control (CAC) mechanism. Therefore, in this paper, we first propose the Markov decision process (MDP) based analysis to assign channelization codes efficiently. Next, we extend the MDP-based approach as the call admission control mechanism to maximize system revenue while reducing blocking. Numerical results indicate that the proposed MDP approach yields the best fractional reward loss and code blocking reward loss as compare to that of others, including the random, left most, and crowded first schemes, and the MDP-based CAC outperforms the capacity-based CAC significantly.

The reduced load approximation technique has been extensively applied to flat networks, but the feasibility of applying it to hierarchical network model has seldom been described. Hierarchical routing is essential for large networks such as the Internet inter/intra-domain routing hierarchy and the Private Network to Node Interface (PNNI) standard. Therefore, this paper proposes an efficient and accurate analytical model for evaluating the performance of hierarchical networks with multiple classes of traffic. A performance analysis model with considering multiple classes of traffic, the complexity of analytical and explosion of computation will be extremely increased, and hence, result in inaccurate analytical. The issue of multiple classes of traffic has to be addressed in performance analysis model. In this paper, we first study the reduced load approximation model for loss networks, and then propose a novel performance evaluation model for large networks with multirate hierarchical routing. The hierarchical evaluation model is based on decomposing a hierarchical route into several analytic hierarchical segments. Once the blockings of these hierarchical segments are accurately determined, the blocking of the hierarchical path can be estimated accurately from these segments blocking. Numerical results indicate that the proposed hierarchical reduced load approximation yields quite accurate blocking probabilities as compared to that of simulation results. Furthermore, the accuracy of the proposed hierarchical reduced load approximation heuristic is independent of the blocking or the offered traffic load. Finally, we also draw some remarks on the convergence of the reduced load based approximation analysis model.

The Internet protocol, based on packet switching technique, provides an efficient sharing of network bandwidth. However, fairness is always an important issue when resources are shared among users. For networks with quality of service guarantee, the fairness criteria will be different from that of best effort networks. In particular, due to bandwidth reservation and call admission control, connection's blocking probability becomes a more important fairness measures based on the blocking probability. We then propose three admission control schemes that try to improve fairness while maintaining high network throughput and revenue. The proposed schemes are evaluated via simulations as well as analytical models. Our numerical results show that the proposed schemes yield good fairness with only slightly degradation in network throughput and revenue.

This paper describes P2broadcast, a novel live video streaming system for P2P networks which organizes peers into hierarchical clusters to reduce startup latency and the service interruption probability. P2broadcast has two key features: highly available and efficient join, and low service interruption probability. The highly available and efficient join algorithm uses RTT of two peers as a hint of available bandwidth between them. As a consequence, the startup latency can be shortened and overhead can be reduced. In addition, P2broadcast constructs a "short and wide" overlay tree which reduces the probability of service interruption due to the leave or failure of a peer. Our simulation results show that P2broadcast outperforms in startup latency and service interrupt probability over existing approaches in the literature.

Although the reduced load approximation technique has been extensively applied to flat networks, the feasibility of applying it to hierarchical network model has seldom been described. However, hierarchical routing is essential for large networks, such as the Internet inter/intra-domain routing hierarchy and the PNNI standard. Furthermore, most of research focused on the performance evaluation with fixed routing. Hence, this paper proposes an efficient and accurate analytical model for evaluating the performance of adaptive hierarchical networks with multiple classes of traffic. The evaluation model is based on decomposing a hierarchical route into several analytic hierarchical segments, therefore the blocking of the hierarchical path can be computed from these segments blocking. Numerical results indicate that the proposed adaptive hierarchical reduced load approximation yields quite accurate blocking probabilities as compared to that of simulation results. We also investigate the speed of the convergence rate of the reduced load approximation analysis in both of OD pair level and alternative hierarchical path level.

One of the most important features of the next generation Internet is the ability to provide quality of service (QoS) guarantee. Recent developments in the Internet provide at least two types of service. For example, guaranteed service and controlled load service in Integrated Services networks, and expedited forwarding and assured forwarding in Differentiated Services networks. Providing guaranteed service and expedited service, referred to as bandwidth-guaranteed traffic, requires reservation of a fixed amount of bandwidth while controlled load service and assured forwarding service, referred to as fair-shared traffic, requires reservation of a minimum amount of bandwidth to ensure finite queue length. Routing in a network with these two types of service should take their traffic and QoS characteristics into consideration. In this paper, we propose two routing algorithms that use different cost functions and routing strategies when routing different classes of traffic. Our simulation results show that the proposed soft routing algorithm is able to yield low blocking probability for bandwidth-guaranteed traffic and high max-min fair share rate for fair-shared traffic under various traffic conditions.

Describes a novel framework, which supports scalable and efficient multicast transmission for distributed multimedia applications, referred to as SEMA. SEMA has two key features: scalable and efficient multicasting, and information classification. In the proposed SEMA, the scalable and efficient multicast mechanism takes time constraints of multimedia transmission into account. Hence, it is suitable for multimedia transmission having the real-time properties. Simulations are performed to justify the scalability and efficiency features. Additionally, information classification can encourage multimedia servers to decompose original multimedia streams into several layered sub-streams such that each sub-stream has part of the presentation capability and can be transmitted to media clients on a chosen multicast group. Media clients then can choose what sub-streams to receive according to their playback capability.

The reduced load approximation technique has been investigated for flat networks successfully. Nevertheless, less attention has been focused on hierarchical network model. However, hierarchical routing is essential for large networks. For example, the ATM PNNI standard and the Internet inter/intra-domain routing hierarchy. Hence, the goal of this paper is to propose an efficient and accurate analytical model for evaluating the performance of hierarchical networks with multiple classes of traffic. We first study the reduced-load approximation model for multirate loss networks, and then propose a new model for networks with hierarchical routing. Our numerical results show that the proposed hierarchical reduced-load approximation yield quite accurate blocking probabilities as compared to that of simulation results.

Recently, QoS routing has been studied intensively. In QoS routing, an essential issue is routing granularity. Most of the research adopts per-flow granularity in the forwarding table. Some research advocates per-source-destination pair granularity in the forwarding table with route pinning. The flow based approach has finer granularity, thus is more efficient in traffic engineering and resource utilization. However, the computation overhead and storage overhead are also higher. On the other hand, source-destination based granularity is more efficient on packet processing and forwarding, but has higher blocking probability. We propose the concept of forwarding with routing marks. With a limited number of routing marks, the proposed routing algorithm reduces the forwarding complexity and storage overhead significantly while yielding very competitive performance in terms of fractional reward loss.

For reducing routing information to achieve scalability in large ATM networks, ATM private network-to-network interface (PNNI) adopts hierarchical routing. In order to provide efficient routing, a large ATM network is decomposed into subnetworks, called peer groups (PG), and peer groups will advertise aggregated routing information only. Because the aggregated information is not very precise, a call set up message may be rejected on a chosen route. When an ATM node discovers a call set up message cannot proceed due to insufficient resource, it initiates a back-tracking procedure called "crankback" against call blocking. Although crankback reduces blocking probability, it also causes additional overhead, such as longer setup delay. Therefore, in this paper, we propose two approaches to reduce crankback overhead, The first approach adds additional information into the set up message, referred to as CIS (crankback information stack), to reduce crankback overhead. The other approach, referred to as CT (cost threshold), uses aggregated path cost and the cost information of a previously rejected call set up message to determine whether call setup on the next alternate path should be tried. Our simulation results show that both of the proposed approaches reduce crankback overhead significantly. Especially, the combination of CIS and CT approach achieves further improvement.

The multi-level hierarchical network architecture has been shown to be a scalable and cost efficient solution for large video-on-demand (VOD) systems. The predominant operation cost of a hierarchical VOD system consists of network transmission cost and video storage cost. How to minimize the operation cost under several operating constraints is an important issue. In this paper, we proposed several fast video placement algorithms that can achieve near optimal operating cost. We have also proposed a time-variant arrival traffic model with arrival rate which matches the statistics gathered from commercial systems.

For achieving scalable and QoS-ware in large ATM networks, the ATM Private Network-to-Network Interface (PNNI) adopts hierarchical routing which reduces nodal and link information. In order to perform hierarchical routing efficiently, the network will consist of subnetworks, called peer groups, and peer groups will advertise the aggregated information periodically which is based on the time-based update interval or on an event driven basis. The time-based update policy is not adequate to cope with dynamic network traffic. Therefore, we propose a dynamic update policy, referred to as the cost-based update (DCU) policy, to enhance the accuracy of aggregated information and the performance of hierarchical routing, while decreasing the frequency of re-aggregation and information distribution and the overhead of communication. In our simulations, we compare the DCU policy with PNNI time-based update (DCU) policy, full update (FU) policy, and logarithm of residual bandwidth update (LRBU) policy. Our simulation results indicate that the proposed DCU policy yields better performance while significantly reduces the frequency of re-aggregation and the amount of distributed aggregation information.

In this paper we study the path-constrained-cost-optimized routing problem, which tries to minimize path cost while satisfying the QoS requirements of a connection. In previous research, each link of the network is associated with a set of QoS metrics, which represents the QoS metrics that can be provided by this link. However, in a real network, ATM switches are able to provide a finite number of QoS classes, instead of just one QoS class. Therefore, in this paper, we studied the path-constrained-cost-optimized routing problem where each link of the network is associated with several sets of QoS metrics. Our solution to this problem consists of two tasks: QoS decomposition and QoS-constrained least cost routing. We propose a greedy algorithm to decompose the end-to-end QoS constraint to local QoS constraints such that the path cost can be minimized. For QoS-constrained least cost routing, we first study three approaches for defining link costs. We then propose two routing algorithms for finding QoS-constrained paths.

In the past three years, we have been implementing a VOD system over ATM networks based on a hierarchical three-level network architecture. One of the most important design problems is how to organize video programs stored at different level of servers. We propose an optimal video placement strategy which gives the optimal number of video copies should be stored at each level of server such that the cost function can be minimized while a lower bound of the request rejection probability for each video is guaranteed. Our major contribution is that we model the time-variant-request arrivals as a non-homogeneous Poisson process.

In future BISDN networks, significant burdens will be placed on the processing elements in the network since call routing and admission policies will be more computationally intensive than those in present day networks. Thus, the bottleneck in future networks is likely to shift from the communication links to the processing elements. The delays at these elements are influenced by their processing capacity and factors such as; routing algorithms, propagation delays, admission control functions, and network topology. The goal of this paper is to characterize the behavior of these factors on the call setup time and accepted call throughput. This behavior is examined for three sequential routing schemes and two flooding routing schemes under various network parameters and different forms of admission control. The results of our study indicate that processing capacity and the admission control function can affect the call setup time and accepted call throughput significantly while propagation delay does not affect these performance measures significantly.

The virtual path (VP) concept has been proposed to simplify traffic control and resource management in future B-ISDN. In particular, call setup processing can be significantly reduced when resources are reserved on VPs. However, this advantage is offset by a decrease in statistical multiplexing gains of the networks. The focus of this paper is on how to improve bandwidth efficiency through adaptive routing when capacity is reserved on all VPs. The authors first examine two VP capacity reservation strategies. They then design and evaluate computationally feasible Markov decision process-based routing algorithms and show that the network blocking probability can be significantly reduced by MDP routing.>

The authors examine the effects of call processing delay, propagation delay, the admission control function due to quality of service (QOS) requirements, and routing algorithms on the call setup time in future B-ISDN networks. Three routing schemes from circuit-switched networks and two parallel versions of these routing schemes are investigated under various network parameters and different forms of admission control. Analytic models for different routing algorithms are developed and were validated by simulation results. The results of the study indicate that call processing delay associated with the admission control function affects the network performance significantly while propagation delay does not affect the performance significantly.>