Mobile Internet-of-Things (IoT) applications increasingly demand low-latency service delivery and efficient content dissemination, yet conventional cloud-centric solutions often suffer from excessive backhaul delay.We present an energy-aware floating architecture that enables peer-assisted edge computing and cooperative content caching across mobile IoT devices. The framework comprises three key components: (i) Floating Service, where selected user nodes temporarily act as local servers and adapt in real time to node mobility to sustain seamless service provisioning; (ii) Cooperative Caching, which supports dynamic, proximity-based content exchange among neighboring devices; and (iii) a mobility-aware service-migration predictor that forecasts user movement to guide timely service handover and cache preloading. A radio duty-cycle energy model records transmit, receive, and idle activity to estimate device-side power expenditure. Extensive simulations show that the proposed system reduces end-to-end latency by up to 87.5% for more than 90% of service requests, while the cooperative caching component lowers retrieval delay by up to 58.1% for 28% of communications. Crucially, the floating mechanism increases the average device radio duty cycle by only 1–3 percentage points, corresponding to an incremental power cost of just 3.6–24.6 mW per node. These results underscore the practicality of energy-aware, peer-assisted edge collaboration for next-generation mobile IoT deployments.

Transformer-based architectures have introduced end-to-end solutions for Multiple Object Tracking (MOT), seamlessly integrating object detection and association. However, their high computational demands—such as the need for feature map fusion across multiple frames—pose significant challenges to real-time deployment, limiting their practicality. In this paper, we present WT-MOT (Weight Table-based Multiple Object Tracking), a novel framework that addresses these limitations by leveraging the underutilized potential of attention weight tables for efficient object similarity evaluation. WT-MOT employs self- and cross-attention mechanisms to assess object similarity and directly assign identifications, integrating spatial, appearance, and temporal dimensions. By introducing the “frame embedding” concept, WT-MOT enhances the ability to distinguish objects across frames without relying on motion models or post-processing steps. Experimental results on the MOT17 and MOT20 benchmarks demonstrate the effectiveness of WT-MOT, achieving MOTA scores of 76.9% and 73.2%, respectively, setting new performance standards for Transformer-based MOT solutions. These findings highlight WT-MOT as a computationally efficient and robust tool for real-time MOT applications, paving the way for broader adoption of Transformer-based tracking methods in practical environments.

The Cloud Continuum Framework (CCF) extends computing capabilities across near-edge, far-edge, and extremeedge nodes beyond the traditional edge to meet the diverse performance demands of emerging 6G applications. While Deep Reinforcement Learning (DRL) has demonstrated potential in automating Virtual Network Function (VNF) migration by learning optimal policies, centralized DRL-based orchestration faces challenges related to scalability and limited visibility in distributed, heterogeneous network environments. To address these limitations, we introduce MARC-6G (Multi-Agent Reinforcement Learning for Distributed Context-Aware Service Function Chain (SFC) Deployment and Migration in 6G Networks), a novel framework that leverages decentralized agents for distributed, dynamic, and service-aware SFC placement and migration. MARC-6G allows agents to monitor different portions of the network, collaboratively optimize network control policies via experience sharing, and make local decisions that collectively enhance global orchestration under time-varying traffic conditions. We show through simulations that MARC-6G improves SFC deployment efficiency, reduces migration costs by $\mathbf{3 4 \%}$, and lowers energy consumption by $\mathbf{1 2. 5 \%}$ compared to the state-of-the-art centralized DRL baseline.

Advanced beamforming techniques enable stable vehicular communication and address mmWave limitations by accurately directing the signal. However, traditional beamforming techniques struggle in high-speed vehicles due to time-intensive codebook processing and image-based feedback adjustments. Multi-modal beamforming using real-time data like GPS, cameras, and LiDAR to train the Deep Learning (DL) models can provide adaptive beam steering, improving reliability in dynamic conditions. Despite this, centralized systems involving large raw data transmission are vulnerable to saturation and malicious interference, and they neglect privacy concerns, necessitating a new framework. This paper proposes a novel federated attentionbased fusion learning framework named FedAttention for multimodal beamforming in the Internet-of-Vehicle (IoV). FedAttention further improves the model generalization ability by utilizing the CNN-Transformer architecture and making full use of the Multi-access Edge Computing (MEC) servers for the potential federated split learning to enhance efficiency. Based on the realworld datasets, FedAttention achieves 98.16 % in Top-5 accuracy and 82.09 % in Top-1 accuracy, a 26.86 % improvement compared to the current FLASH framework with less wall clock time, showing its training efficiency and robustness.

In this work, we aim to address the challenge of slice provisioning in edge-based mobile networks. We propose a solution that learns a service function chain placement policy for Network Slice Requests, to maximize the request acceptance rate, while minimizing the average node resource utilization. To do this, we consider a Hierarchical Multi-Armed Bandit problem and propose a two-level hierarchical bandit solution which aims to learn a scalable placement policy that optimizes the stated objectives in an online manner. Simulations on two real network topologies show that our proposed approach achieves 5% average node resource utilization while admitting over 25% more slice requests in certain scenarios, compared to baseline methods.

The orchestrators’ deployment problem presents numerous challenges in 6G Network Radio Access Networks due to their large-scale, dynamic conditions, and variable user demands. Most works propose single- or hierarchical-orchestrator solutions, which offer poor resiliency, high signaling overhead, and slow adaptation to variable network dynamics. To tackle these challenges, we propose an online, data-driven, fully decentralized, Multi-Agent Reinforcement Learning (MARL)-based, self-organization orchestrator deployment system for 6G networks, which jointly optimizes the tradeoff between user throughput and fairness, based on time-varying system conditions. In the proposed approach, a flexible variable number of decentralized, cooperative, peer self-organization agents autonomously adapt their associated orchestrator’s deployment location and activity to optimize network operation, without requiring centralized coordination. Simulations show improvements of up to 77% in user throughput compared to Hierarchical and Single Orchestrator baselines in a broad range of realistic scenarios.

Effective channel estimation (CE) is critical for optimizing the performance of 5G New Radio (NR) systems, particularly in dynamic environments where traditional methods struggle with complexity and adaptability. This paper introduces GraphNet, a novel, lightweight Graph Neural Network (GNN)-based estimator designed to enhance CE in 5G NR. Our proposed method utilizes a GNN architecture that minimizes computational overhead while capturing essential features necessary for accurate CE. We evaluate GraphNet across various channel conditions, from slow-varying to highly dynamic environments, and compare its performance to ChannelNet, a well-known deep learning-based CE method. GraphNet not only matches ChannelNet’s performance in stable conditions but significantly outperforms it in high-variation scenarios, particularly in terms of Block Error Rate. It also includes built-in noise estimation that enhances robustness in challenging channel conditions. Furthermore, its significantly lighter computational footprint makes GraphNet highly suitable for real-time deployment, especially on edge devices with limited computational resources. By underscoring the potential of GNNs to transform CE processes, GraphNet offers a scalable and robust solution that aligns with the evolving demands of 5G technologies, highlighting its efficiency and performance as a next-generation solution for wireless communication systems.

In Vehicle-to-Everything (V2X) communication, advanced beamforming techniques address signal attenuation caused by mmWave, which provides high bandwidth and low latency. Multi-modal beamforming using Federated Learning (FL) can leverage resources like GPS, Lidar, and image data, significantly accelerating beam searching while enhancing data privacy. The heterogeneity of vehicles, however, affects the availability of computing resources for training machine learning models. Moreover, the multi-modal fusion network may contain billions of parameters, leading to extended training time for FL. To address these challenges, this paper proposes a novel Deep Reinforced Federated Split Learning framework (DRFSL) tailored for multi-modal beamforming with different sub-model architectures. DRFSL efficiently utilizes MEC computing and adapts the collaborative and distributed training to dynamic network conditions and system heterogeneity by incorporating deep reinforcement learning and split learning with FL. Experimental evaluation using real-world datasets demonstrates that DRFSL minimizes average training time by 49.45% and inference time by 24.43% and can achieve higher accuracy within the same timeframe compared to the existing FLASH framework.

Beamforming techniques use massive antenna arrays to formulate narrow Line-of-Sight signal sectors to address the increased signal attenuation in millimeter Wave (mmWave). However, traditional sector selection schemes involve extensive searches for the highest signal strength sector, introducing extra latency and communication overhead. This paper introduces a dynamic layer-wise and clustering-based federated learning (FL) algorithm for beam sector selection in autonomous vehicle networks called enhanced Dynamic Adaptive FL (eDAFL). The algorithm detects and selects the most important layers of a machine learning model for aggregation in FL process, significantly reducing network overhead and failure risks. eDAFL also consider an intra-cluster and inter-cluster approach to reduce overfitting and increase the abstraction level. We evaluate eDAFL on a real-world multi-modal dataset, demonstrating improved model accuracy by approximately 6.76% compared to existing methods, while reducing inference time by 84.04% and model size up to 52.20%.

Open-Radio Access Network (O-RAN) facilitates the scalability of cellular networks by introducing a RAN Intelligent Controller (RIC) component whose functions can be flexibly distributed over large-scale 6G networks. Artificial Intelligence (AI) is effective in optimizing RIC placement in 6G O-RAN, mitigating the limited adaptability of non-data-driven methods in complex time-varying network conditions. However, the centralized orchestration of current approaches for RIC placement hinders scalability. This work introduces a data-driven DEcentralized Reinforced RAN Intelligent Controller orchestration (DERRIC) method for 6G networks, leveraging the online learning capabilities of decentralized multi-agent Reinforcement Learning (RL) orchestration to solve the RAN Intelligent Controller Placement Problem (CPP). DERRIC is a two-layer network management scheme with decentralized orchestrators that adapt to network conditions, deploy controllers, and allocate resources. These orchestrators manage distributed controllers to optimize RAN parameters, such as user transmission power. DERRIC's main goal is to increase the system's overall user Packet Delivery Ratio (PDR) by optimal controller deployment and operation. Optimal controller deployment reduces controller-user latency and accelerates user-transmission-power control decisions, leading to further enhancement to user PDR. We show that DERRIC reduces the controller-user latency and power consumption by up to 66% and 29% and increases user PDR by up to 14% compared to state-of-the-art baselines in a broad range of simulated scenarios.

The advent of virtual reality and immersive communication technologies is effecting a transformation in user experiences, enabling high-quality, low-latency interactions. In order to meet these requirements, edge caching, in particular for the transmission of virtual reality content, has become an efficient strategy for the mitigation of transmission latency and the decrease of backhaul traffic loads. This paper provides an introduction to a two-stage hybrid caching framework developed to manage the typical obstacles related to the caching of 360° video. The proposed framework comprises two stages: a learning stage, which employs a Deep Q-Network to predict cache replacement actions, and a solving stage, which utilizes Integer Linear Programming to refine and optimize caching decisions. Furthermore, an L2 edge cache architecture is designed with the goal of enhancing cache utilization and further alleviating backhaul traffic. Performance evaluations illustrate that the proposed framework significantly enhances the cache hit ratio and reduces latency and backhaul usage compared to other methods.

Immersive communication is regarded as a key driver in the evolution of Virtual Reality technologies. To offer immersive experiences, 360° video edge caching has become an effective solution for minimizing latency to popular content. In order to find the optimal placement for 360° video caching, numerous conventional optimization approaches or deep learning methods have been proposed. However, conventional optimization is computationally expensive and unsuitable for online decision-making, while relying solely on deep learning methods can not guarantee the optimality and feasibility of the solutions, such as whether solutions satisfy the constraints of the original caching problem. In this paper, a dual-engine intelligent caching framework that combines operations research and deep reinforcement learning is proposed for 360° video edge caching and content prefetching. This framework introduces a shared hierarchical caching architecture and formulates a global shared caching placement problem. Adopting a cutting-edge pruning approach, the local cache replacement algorithm is proposed. This utilizes deep reinforcement learning to predict cache boundaries, which are then used to prune the search space of integer linear programming efficiently. Numerical results show that the proposed scheme provides significantly improved performance relative to alternative caching schemes.

Digital twins (DT) offer a low-overhead evaluation platform and the ability to generate rich datasets for training machine learning (ML) models before actual deployment. Specifically, for the scenario of ML-aided millimeter wave (mmWave) links between moving vehicles to roadside units, we show how DT can create an accurate replica of the real world for model training and testing. The contributions of this paper are twofold: First, we propose a framework to create a multimodal Digital Twin (DT), where synthetic images and LiDAR data for the deployment location are generated along with RF propagation measurements obtained via ray-tracing. Second, to ensure effective domain adaptation, we leveragemeta-learning, specificallyModel-Agnostic Meta-Learning(MAML), withtransfer learning(TL) serving as a baseline validation approach. The proposed framework is validated using a comprehensive dataset containing both real and synthetic LiDAR and image data for mmWave V2X beam selection. It also enables the investigation of how each sensor modality impacts domain adaptation, taking into account the unique requirements of mmWave beam selection. Experimental results show that models trained on synthetic data using transfer learning and meta-learning, followed by minimal fine-tuning with real-world data, achieve up to 4.09× and 14.04× improvements in accuracy, respectively. These findings highlight the potential of synthetic data and meta-learning to bridge the domain gap and adapt rapidly to real-world beamforming challenges.

Federated Learning (FL) is widely applied in privacy-sensitive domains, such as healthcare, finance, and education, due to its privacy-preserving properties. However, implementing FL in dynamic wireless networks poses substantial communication challenges. Central to these challenges is the need for efficient communication strategies that can adapt to fluctuating network conditions and the growing number of participating devices, which can lead to unacceptable communication delays. In this article, we propose Stochastic Client Selection for Tree All-Reduce Federated Learning (CSTAR-FL), a novel approach that combines a probabilistic User Device (UD) selection strategy with a tree-based communication architecture to enhance communication efficiency in FL within densely populated wireless networks. By optimizing UD selection for effective model aggregation and employing an efficient data transmission structure,CSTAR-FLsignificantly reduces communication time and improves FL efficiency. Additionally, our approach ensures high global model accuracy under scenarios where data distribution is heterogeneous from User Device (UD)s. Extensive simulations in dynamic wireless network scenarios demonstrate thatCSTAR-FLoutperforms existing state-of-the-art methods, reducing model convergence time by up to 40% without losing the global model accuracy. This makesCSTAR-FLa robust solution for efficient and scalable FL deployments in high-density environments.

The rapidly evolving realm of Extended Reality (XR) demands high bandwidth and low-latency communication to support immersive experiences such as high-resolution 360-degree videos and real-time interactions in virtual reality gaming. In this study, “resources” are defined as digital assets essential for XR applications, divided into “static resources” (immutable media files such as textures and video segments) and “dynamic resources” (real-time user data and interactive elements crucial for user interactions). A primary challenge in XR environments is optimizing the delivery and caching of these resources within existing network infrastructures to enhance the Quality of Experience (QoE) for users. We introduce a novel hybrid network architecture that integrates resource caching, user-to-user communication, and central server oversight. This architecture not only ensures reliable delivery but also significantly reduces communication latency. Preliminary experiments, conducted under conditions where each node in the network has a 10% chance of failing at any given time, demonstrate that our approach enhances the delivery efficiency of static resources by 68%, affecting 38% of communications, with an increase in latency observed in 4% of cases by 22%. For dynamic resources, it reduces latency in 89% of the cases by an average of 30%, though 8% of cases experienced a 36% increase in latency. These results affirm the effectiveness of our architecture in enhancing user experience in XR environments under challenging network conditions.

The next generation of Virtual Reality (VR) applications is expected to provide advanced experiences through Six Degree-of-Freedom (6DoF) technology. However, 6DoF VR applications require latency and throughput guarantees. This article presents a novel intra-domain routing algorithm with throughput guarantees for minimizing the overall end-to-end (E2E) latency for all flows deployed in the network.We investigate the Joint Flow Allocation (JFA) problem to find paths for all flows in a network such that it determines the optimal path for each flow in terms of throughput and latency. The JFA problem is NP-hard. We use a mixed integer linear programming to model the system, along with a heuristic, Flow Latency and Throughput Aware Sensitive Routing (FLATWISE), which is one order of magnitude faster than optimally solving the JFA problem. FLATWISE introduces an adaptive routing approach that dynamically adjusts the path calculation based on E2E latency. The novelty of FLATWISE lies in its unique ability to precisely tune the routing path by either constraining or relaxing the path criteria to align the E2E latency of the selected path with the latency demands of each VR flow. This approach ensures that the latency of the calculated path approximates the latency of each VR flow, enabling more flexible and efficient network routing to meet diverse latency requirements. Our evaluation considers 6DoF VR application flows, which demand high throughput and ultra-low E2E latency. Extensive simulations demonstrate that FLATWISE significantly reduces flow latency, over-provisioned latency, E2E latency, and algorithm execution time when network flows are processed randomly. FLATWISE improves flow throughput and frame rate compared to related work approaches.

Fifth-generation (5G) mobile networks are expected to support the dynamic provisioning of services with heterogeneous Quality of Service requirements through Network Slicing. However, the uncertainty in the resource requirements of the tenant’s future Network Slice Requests raises the problem of how Network Slices can be admitted onto the mobile network infrastructure. To address this, we investigate the Slice Admission Control problem in virtualization-enabled mobile networks. Specifically, we focus on the scenario in which a controller needs to select an Admission Control policy (or algorithm) based on the patterns of previous Network Slice Requests and formulate such a problem as a Multi-Armed Bandit problem. By leveraging Online Learning (OL), we propose a framework, Drift-AwaRe upper confIdence bOund (DARIO), that adaptively selects and learns the performance of online Slice Admission Control (SAC) policies by monitoring for changes in the underlying patterns of Network Slice Request (NSR) features. We evaluate the performance of our framework in terms of the relative gains in average revenue, acceptance ratio, and average resource utilization when compared to both static and adaptive baselines and show that we outperform the considered baselines for the considered metrics.

As a mature indoor positioning solution, fingerprint-based positioning has been widely applied. However, traditional fingerprint positioning schemes still face the problems of limited hidden spatial feature extraction ability and insufficient fingerprint calibration with unlabeled crowdsourcing data. In order to address the above problems, we refer to the transformer-based deep learning model in natural language processing (NLP) and propose a crowdsourcing indoor positioning model via semi-supervised bidirectional encoder representation for transformer with masking, namely CrowdBERT. First, we tokenize the fingerprint data to adapt to the input form of the model. Then, we design a spatial fingerprint attention encoder as a feature extractor, which internal multihead attention mechanism combined with three-layer spatial feature embedding can fully capture the spatial features of fingerprint sequences. Meanwhile, we propose received signal strength-token masking to help the model perform bidirectional feature extraction so that the pretraining can more efficient use of hidden features from unlabeled crowdsourcing fingerprints. Finally, the limited labeled fingerprints is used to fine tune the downstream network structure to further improve the positioning accuracy. To evaluate our proposed positioning system, we conduct a set of comprehensive experiments on the three different data sets and evaluation results demonstrate that the CrowdBERT model significantly outperforms the other traditional positioning algorithms, such as K nearest neighbor, DNN, residual network, stacked autoencoder, and variational autoencoder.

The millimeter-wave (mmWave) spectrum has become a core of wireless communication, which has the advantages of richer spectrum resources, larger communication bandwidth, and smaller spectrum interference. Human activity recognition (HAR) by mmWave radar based on point cloud attracts significant attention due to its nature of privacy-preserving, which is an important task of realizing integrated sensing and communication (ISAC). This article proposes a framework of spatial–temporal point cloud transformer (ST-PCT) to realize high precision of HAR, based on sequential point cloud after preprocessing from mmWave radar without voxelization. In ST-PCT, it consists of four enhanced components: 1) a framewise spatial neighbor embedding module to extract the local feature; 2) a temporal and spatial attention mechanism module to find connections within and across frames; 3) an optimized attention mechanism to improve the efficiency of feature extraction; and 4) a sensor fusion module with more motion information to improve the difference between activities. We experimentally evaluate the efficiency of our framework compared with several approaches based on the voxelization or point cloud directly. The experimental results have demonstrated that the proposed ST-PCT network greatly outperforms the other approaches in terms of overall accuracy (oAcc), achieving 99.06% and 99.44%, respectively, on two data sets.

The key impediments to deploying deep neural networks (DNN) in IoT edge environments lie in the gap between the expensive DNN computation and the limited computing capability of IoT devices. Current state-of-the-art machine learning models have significant demands on memory, computation, and energy and raise challenges for integrating them with the decentralized operation of heterogeneous and resource-constrained IoT devices. Recent studies have proposed the cooperative execution of DNN models in IoT devices to enhance the reliability, privacy, and efficiency of intelligent IoT systems but disregarded flexible finegrained model partitioning schemes for optimal distribution of DNN execution tasks in dynamic IoT networks. In this paper, we propose DISNET, a distributed micro-split deep learning scheme for heterogeneous dynamic IoT. DISNET accelerates inference time and minimizes energy consumption by combining vertical (layer-based) and horizontal DNN partitioning to enable flexible, distributed, and parallel execution of neural network models on heterogeneous IoT devices. DISNET considers the IoT devices’ computing and communication resources and the network conditions for resource-aware cooperative DNN Inference. Experimental evaluation in dynamic IoT networks shows that DISNET reduces the DNN inference latency and energy consumption by up to 5.2× and 6×, respectively, compared to two state-of-the-art schemes without loss of accuracy.

The next generation of Virtual Reality (VR) applications is expected to provide advanced experiences through Six Degrees of Freedom (6DoF) content, which requires higher data rates and ultra-low latency. In this article, we refactor 6DoF VR applications into atomic services to increase the computing capacity of VR systems aiming to reduce the end-to-end (E2E) of 6DoF VR applications. Those services are chained and deployed across Head-Mounted Displays (HMDs) and Multi-access Edge Computing (MEC) servers in high mobility scenarios over realedge network topologies. We investigate the Distributed Service Chain Problem (DSCP) to find the optimal service placement of services from a service chain such that its E2E latency does not exceed 5 ms. The DSCP problem is NP-hard. We provide an integer linear program to model the system, along with a heuristic, namely disTributed sErvice chaiN orchEstraTor (TENET), which is one order of magnitude faster than optimally solving the DSCP problem. We compare TENET to DSCP implementation and well-known service migration algorithms in terms of E2E latency, power consumption, video resolution selection based on E2E latency, context migrations, and execution time. We observe a significant reduction of E2E latency and gains in more advanced video resolution selection and accepted context service migrations when using TENET’s deployment strategy on VR services.

Network Slicing has emerged as a powerful technique to enable cost-effective, multi-tenant communications and services over a shared physical mobile network infrastructure. One major challenge of service provisioning in slice-enabled networks is the uncertainty in the demand for the limited network resources that must be shared among existing slices and potentially new Network Slice Requests. In this paper, we consider admission control of Network Slice Requests in an online setting, with the goal of maximizing the long-term revenue received from admitted requests. We model the Slice Admission Control problem as an Online Multidimensional Knapsack Problem and present two reservation-based policies and their algorithms, which have a competitive performance for Online Multidimensional Knapsack Problems. Through Monte Carlo simulations, we evaluate the performance of our online admission control method in terms of average revenue gained by the Infrastructure Provider, system resource utilization, and the ratio of accepted slice requests. We compare our approach with those of the online First Come First Serve greedy policy. The simulation's results prove that our proposed online policies increase revenues for Infrastructure Providers by up to 12.9 % while reducing the average resource consumption by up to 1.7% In particular, when the tenants' economic inequality increases, an Infrastructure Provider who adopts our proposed online admission policies gains higher revenues compared to an Infrastructure Provider who adopts First Come First Serve.

Federated Learning (FL) is gaining popularity in trajectory prediction field due to its privacy-preserving and scalability capabilities. However, deploying FL on resource-constrained devices and varying wireless network conditions can be challenging. Moreover, the design of FL’s distributed neural architectures is complex requiring expert knowledge. To address these limitations, we propose the network-adaptive FEDerated learning for reinFORCEd mobility prediction (FedForce) system. FedForce uses reinforcement learning to design a transformer neural network that jointly optimizes prediction accuracy, training time, and transmission time based on the unique features of the mobility dataset, the client’s computing capacity, and the available network throughput. FedForce achieves an average displacement error of 0.20m on the ETH+UCY dataset and 76% accuracy on the Orange dataset (−0.02m and 10% better than the best-performing existing baselines, respectively), while reducing FL training and transmission time by a factor of 3. FedForce can save up to 80% of computational resources and 96% of communication overheads with negligible accuracy decrease.

Multi-User Augmented Reality (MUAR) is gaining popularity and enabling interactions collaboratively with mobile users in the virtual 3D world. In this way, decomposing MUAR into elements with specialized ordered Service Functions (SFs) to form a SF chain with decoupled source dynamically allows the SF distribution into multiple edge computing servers and executes SFs of MUAR services in parallel while improving its quality level. However, orchestrating distributed SFs at the network edges and at multiple mobile clients while minimizing latency and meeting resource-hungry requirements is still a research challenge. This paper proposes a mobility-aware SF chaining orchestration scheme for MUAR services called MSF. MSF improves the usage of processing, storage, and networking resources and reduces the latency of MUAR services by optimizing SF chaining in real-time and reinstantiating services into optimal routes. The results show that MSF outperforms state-of-the-art approaches regarding MUAR session acceptance ratio, CPU and bandwidth utilization, and latency in different mobile scenarios.

The deployment of Internet of Things (IoT) temperature sensors in urban areas is essential for the monitoring and understanding of the thermal environment. However, accurate temperature measurements can be compromised by factors such as direct sunlight, leading to overheating and inaccurate readings. We propose a Machine Learning-based approach that addresses this challenge by dynamically ventilating the sensor environment using small fans, enabling accurate and energy-efficient temperature measurements. This paper focuses on two interconnected problems: predicting steady-state temperature using a limited window of initial temperature measurements and investigating the impact of ventilation time. We employ various DNNs suitable for low-power IoT sensor devices to predict temperature using multivariate time series from different sensors and compare their accuracy. Furthermore, we highlight the tradeoff between prediction accuracy, which is correlated to the length of the observed input sequence, and energy consumption dependent on ventilation time. By adopting advanced prediction techniques, we can develop efficient IoT systems for accurate and energy-efficient environment monitoring in smart cities.

Edge Computing architecture provides computing capacity closer to users to fulfill the requirements of Future Internet services and applications, such as low latency. However, as users move at the edge and connect to different access points, service instances have to be migrated in order to keep service levels constant. Considering this issue, we introduce a graphbased algorithm to distribute data over networks considering time cost budgets. We then use this algorithm to develop a mobility-aware latency-constrained solution to position userspecific service instances, i.e., services that use application state and user session data, called Data Covers Framework (DCF). We compare DCF with other approaches for service provisioning at the edge that use fixed hosts or active service placement considering user mobility. Our experiments show that DCF achieves similar or better performance in terms of the percentage of packets delivered under latency requirements for different application classes while reducing the number of service migrations by 21% and the service interruption time by 41%.

Positioning estimations of wireless sensors can be enhanced via sensor collaboration. To enable this, various methods have been proposed; yet, most do not leverage the entire collective knowledge, which also involves the estimation’s uncertainty. In this article, we introduce Anchor-free Ranging-Likelihood-based Cooperative Localization (ARLCL); a novel anchor-free and technology-agnostic localization algorithm that utilizes inter-exchanged ranging signals from sensors to enable their simultaneous positioning. Ranging technologies with easy-to-model propagation properties, such as UWB or LiDAR are among the first beneficiaries that ARLCL is targeting. To examine its applicability, however, even to signals that are noisier and often unsuitable for ranging, we assess ARLCL with real-world BLE RSS measurements. At the same time, we consider deployments that typically induce flip-ambiguity, being a major problem in cooperative localization. We provide an extensive comparison against the most widely-adopted optimization method (Mass-Spring) but also against the recent likelihood-based approach (Maximum Likelihood - Particle Swarm Optimization). The results showed that ARLCL outperformed the baselines in almost all scenarios. Our gain in positioning accuracy is also found to be positively correlated to both the swarm’s size and the signal’s quality, reaching an improvement of 40%.

With the fast development of location-based services, an ubiquitous indoor positioning approach with high accuracy and low calibration has become increasingly important. In this work, we target on a crowdsourcing approach with zero calibration effort based on visible light, magnetic field, and WiFi to achieve submeter accuracy. We propose a CrowdFusion simultaneous localization and mapping (SLAM) composed of coarse-grained and fine-grained trace merging, respectively, based on the iterative closest point (ICP) SLAM and GraphSLAM. ICP SLAM is proposed to correct the relative locations and directions of crowdsourcing traces and GraphSLAM is further adopted for fine-grained pose optimization. In CrowdFusion SLAM, visible light is used to accurately detect loop closures and magnetic field to extend the coverage. According to the merged traces, we construct a radio map with visible light and WiFi fingerprints. An enhanced particle filter fusing inertial sensors, visible light, WiFi, and floor plan is designed, in which visible light fingerprinting is used to improve the accuracy and increase the resampling/rebooting efficiency. We evaluate CrowdFusion based on comprehensive experiments. The evaluation results show a mean accuracy of 0.67 m for the merged traces and 0.77 m for positioning, merely replying on crowdsourcing traces without professional calibration.

Large Machine Learning (ML) models require considerable computing resources and raise challenges for integrating them with the decentralized operation of heterogeneous and resource-constrained Internet of Things (IoT) devices. Running ML tasks on the cloud can introduce network delay, throughput, and privacy concerns, whereas running ML tasks on IoT devices is penalized by their constrained resources. For this reason, recent research proposed cooperative execution of ML tasks over IoT networks but disregarded resource variability and the IoT devices’ energy constraints simultaneously. In this paper, we propose Early Exit of Computation (EEoC), an adaptive, energy-efficient, low-latency inference scheme over IoT networks. EEoC adaptively distributes the inference computation load between the IoT device and the edge server, based on estimated communication and computation resources, to jointly minimize prediction latency and energy consumption. We evaluate our solution’s latency and energy profile on a real testbed running two widely used neural networks. Results show that EEoC can reduce latency and energy consumption up to 24.6% and 46.5%, respectively, compared to other state-of-the-art solutions without sacrificing accuracy.

Perceptual studies show that the Quality of Service (QoS) of large-scale Mobile Virtual Reality (MVR) applications is positively correlated to video frame rate and the duration of the immersive experience. These metrics depend on the sum of computation and network latency needed to generate and deliver a video frame to the Head-Mounted Display (HMD) and the power consumption on the HMD. Recent research shows that Multi-access Edge Computing (MEC) can support mobile HMDs to reduce their computing latency, but its potential to maintain the acceptable end-to-end (E2E) latency and reduce power consumption under high mobility conditions remains unexplored. This paper proposes Service Chaining Graph (SCG), an orchestrator to split VR applications into atomic services and deploy them across HMDs and MEC servers according to an optimization problem that aims to jointly minimize latency and energy consumption. Through simulations, we show that SCG reduces E2E latency by up to 74% in three high-mobility user-dense scenarios compared to four widely used service-migration strategies against a moderate increase in power consumption. Unlike other approaches, SCG can find a balance between average latency and energy consumption by migrating services between MEC servers and HMDs according to a policy depending on application requirements.

Immersive entertainment based on Multi-User Virtual Reality (MUVR) is gaining popularity to enable in-game interactions with multiple users. However, computing and network-intensive utilization of resources require changes in cloud-based and traditional monolithic (single machine) deployments towards distributed edge computing architecture to enable high-quality interaction. Decomposing MUVR into elements with specialized Service Functions (SF) in order and organizing it into a Service Function Chaining (SFC) requests orchestration enables MUVR to reuse frame processing between users and improves MUVR scalability. However, orchestrating distributed edge computing resources and multiple destination SFCs while minimizing delay and meeting resource requirements is a challenging task. This article proposes a multi-criteria SFC orchestration scheme for MUVR services, called MuSFiCO. MuSFiCO maps edge computing resources and instantiates SFCs on distributed servers based on delays threshold, CPU and memory resources, and bandwidth. We developed a constrained-based heuristic to minimize delay and compare it with the baseline monolithic deployment and cloud-based SFC algorithms. Results demonstrate the efficiency of MuSFiCO compared to other approaches in terms of latency, CPU, memory, bandwidth utilization, as well as orchestration decision-time.

Future Internet applications, such as the vehicular use cases, impose requirements that challenge current networking paradigms. Hence, other networking paradigms have been proposed, such as Software-Defined Networking (SDN) and Information-Centric Networking (ICN), aiming to change network management and operation fundamentals to meet those requirements. Leveraging these networking paradigms, in this paper we present the Mobility-aware Service-Centric Networking (MSCN), an ICN-inspired solution to mitigate mobility-related networking issues by focusing on service provisioning rather than content. Due to the requirement of installing specialized hardware, ICN-based solutions face adoption issues. Still, SDN features can be used to emulate ICN behaviour without the requirement for deployment of new infrastructure. Therefore, we present an SDN-based implementation of MSCN (SD-MSCN), which relies on already installed SDN-enabled switches and the OpenFlow protocol. We evaluate the performance of our proposal by comparing SD-MSCN with other SDN-enabled ICN and IP protocols. Simulations show that our proposal outperforms IP-based solutions in the presence of user mobility events. Nevertheless, it is possible to produce proactive IP-based solutions that achieve similar performance as our proposal. However, our proposal has a significantly better performance than IP-based solutions in environments with frequent service mobility events.

Information-Centric Network (ICN) architectures, such as Named Data Networking (NDN), can improve content delivery on the Internet by deploying in-network caching techniques. Replacing the entire established Internet with a novel architecture is a non-trivial task, which is why this work develops a layered network architecture consisting of several smaller NDN-based mobile networks (resp., domains), interconnected using a Distributed Hash Table (DHT)-based network running as an overlay on top of existing Internet infrastructures. Using simulations, we model real-world network characteristics to evaluate the proposed architecture’s performance successfully.

Anticipating future locations of mobile users plays a pivotal role in intelligent services supporting mobile networks. Predicting user trajectories is a crucial task not only from the perspective of facilitating smart cities but also of significant importance in network management, such as handover optimization, service migration, and the caching of services in a mobile and edge-computing network. Convolutional Neural Networks (CNNs) have proven to be successful to tackle the forecasting of mobile users’ future locations. However, designing effective CNN architectures is challenging due to their large hyper-parameter space. Reinforcement Learning (RL)-based Neural Architecture Search (NAS) mechanisms have been proposed to optimize the neural network design process, but they are computationally expensive and they have not been used to predict user mobility. In large urban scenarios, the rate at which mobility information is generated makes it a challenge to optimize, train, and maintain prediction models for individual users. However, considering that user trajectories are not independent, a common trajectory-prediction model can be built and shared among a set of users characterized by similar mobility features. In the present work, we introduce Reinforcement Convolutional Transfer Learning (RC-TL), a CNN-based trajectory-prediction system that clusters users with similar trajectories, dedicates a single RL agent per cluster to optimize a CNN neural architecture, trains one model per cluster using the data of a small user subset, and transfers it to the other users in the cluster. Experimental results on a large-scale dataset show that our proposed RL-based CNN achieves up to 12% higher trajectory-prediction accuracy, with no training speed reduction, over other state-of-the-art approaches on a large-scale, real-world mobility dataset. Moreover, RC-TL’s clustering strategy saves up to 90% of the computational resources needed for training compared to single-user models, in exchange for a 3% accuracy reduction.

Predicting mobile users' trajectories accurately is essential for improving the performance of wireless networks and autonomous systems. In this paper, we tackle the problem of tra-jectory prediction in a multi-agent scenario where the social inter-action among users is taken into consideration. We propose Intra-Cluster Reinforced Social Transformer (INTRAFORCE), a novel system to design and train Social-Transformer neural networks that learn the spatio-temporal interactions among neighboring mobile users and predict their joint future trajectories. Unlike state-of-the-art social-aware trajectory predictors that either miss the large-distance interactions or are computationally expensive due to the pooling of all users' interactions, INTRAFORCE clusters users with similar trajectories and learns their interactions. INTRAFORCE performs Neural Architecture Search to optimize each transformer's architecture to fit each cluster's user mobility features using Reinforcement Learning. Through experimental validation, we show that INTRAFORCE outperforms several state-of-the-art trajectory predictors on five widely used small-scale pedestrian mobility datasets and one large-scale privacy-oriented cellular mobility dataset by achieving lower prediction error. training time, and computational complexity.

Distributed training of Machine Learning models in edge Internet of Things (IoT) environments is challenging because of three main points. First, resource-constrained devices have large training times and limited energy budget. Second, resource heterogeneity of IoT devices slows down the training of the global model due to the presence of slower devices (stragglers). Finally, varying operational conditions, such as network bandwidth, and computing resources, significantly affect training time and energy consumption. Recent studies have proposed Split Learning (SL) for distributed model training with limited resources but its efficient implementation on the resource-constrained and decentralized heterogeneous IoT devices remains minimally explored. We propose Adaptive REsource-aware Split-learning (ARES), a scheme for efficient model training in IoT systems. ARES accelerates training in resource-constrained devices and minimizes the effect of stragglers on the training through device-targeted split points while accounting for time-varying network throughput and computing resources. ARES takes into account application constraints to mitigate training optimization tradeoffs in terms of energy consumption and training time. We evaluate ARES prototype on a real testbed comprising heterogeneous IoT devices running a widely-adopted deep neural network and dataset. Results show that ARES accelerates model training on IoT devices by up to 48% and minimizes the energy consumption by up to 61.4% compared to Federated Learning (FL) and classic SL, without sacrificing model convergence and accuracy.

In modern 5G and Beyond (B5G) networks, the number of users and devices consuming highly-demanding services in terms of latency and throughput. Due to their high dynamicity and fine-grainess, such services must be supported by a joint management and integration effort between technologies such as Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAVs), and novel radio and energy transfer techniques. The notion of Flying Edge Computing (FEC) arises as a prominent solution to provide a deeper level of integration and capabilities to UAV networks in collaboration with traditional edge computing and B5G infrastructure. FEC constitutes a highly elastic computation layer in modern networks, which can quickly adapt to surges in demand. This paper dives into FEC’s main opportunities and motivations in modern scenarios and presents some of the important design aspects of FEC. Experimental results show that the coupling of traditional MEC with FEC can deliver significantly better Quality of Service (QoS), improve service availability, and user satisfaction. Furthermore, FEC can adapt to user mobility patterns more efficiently, delivering contents and services.

Accurate prediction of urban traffic flows brings enormous advantages to big cities. Therefore, many urban traffic flow predictors have been developed in recent years. Urban traffic flow predictors aim to identify complex mobility patterns and capture urban traffic flow characteristics from large-scale historical datasets. Afterward, trained models are used to predict the future traffic volume in terms of the number of moving objects (e.g., vehicles). Convolutional Neural Networks (CNN) and other deep learning approaches are brilliant choices because of their ability to learn Spatio-temporal dependencies. Nevertheless, the extensive set of hyper-parameters tends to make these neural networks overly complex and challenging to design. In this work, we introduce RL-CNN, a framework based on Reinforcement Learning whose objective is to autonomously discover highperformance CNN architectures for the given traffic prediction task without human intervention. We examine the proposed RL-CNN model as a traffic flow estimator on a real-world and large-scale vehicular network dataset. We observe improvements of 5% - 10% in the average traffic flow prediction accuracy over the state-of-art approaches.

Predicting traffic flow in large cities is beneficial for a wide range of applications, including vehicle navigation services, vehicle routing, and traffic congestion management. In this scenario, deep learning approaches such as Recurrent Neural Networks (RNN) and its variant Long Short Term Memory (LSTM) are excellent alternatives due to their ability to learn long-term dependencies. However, these neural networks only learn the temporal traffic information for each trajectory (moving object), failing to take advantage of spatial information shared by neighboring trajectories. This paper introduces MTL-LSTM (Multi-Task Learning-based LSTM) traffic flow estimator, which attempts to explore both temporal and spatial dependencies among adjacent trajectories. Specifically, we employ LSTM predictors with the MTL approach to explore traffic flow patterns across urban trajectories. To examine the proposed model, we predict traffic flow in Porto's city using a data set from buses and taxies. Our experiments show improvements of 10% to 15% over the state-of-the-art.

Trajectory and traffic flow prediction will play an essential role in Intelligent Transportation Systems (ITS) to enable a whole new set of applications ranging from traffic management to infotainment applications. In this scenario, deep learning approaches such as Recurrent Neural Networks (RNN) and its variant Long Short Term Memory (LSTM) are excellent alternatives due to their ability to learn spatiotemporal dependencies. However, these neural networks tend to be over-complex and hard to design due to the broad set of hyper-parameters. We propose an automated framework to predict future trajectories and traffic flows in urban areas without human interventions. We employ Reinforcement Learning (RL) and Transfer Learning (TL) to generate high-performance LSTM predictors, which is referred as RL-LSTM. In addition, we introduce HERITOR (High ordE r tR affI c convoluTiOn R 1-lstm), a novel deep learning algorithm for traffic flow prediction. Specifically, HERITOR attempts to capture pure spatiotemporal features of urban traffic. The extracted features are fed into the RL-LSTM to realize a high performance LSTM for traffic flow prediction. We examine the proposed trajectory and traffic flow predictors on two real-world, large-scale datasets and observe consistent improvements of 15% - 25% over the state-of-the-art.

Future vehicular applications will rely on communication between vehicles and other devices in their vicinity. Technologies, such as LTE-V2X, are awaited to operate under the Cellular Vehicle-to-Everything (C-V2X) standard to make this communication possible. However, current LTE technology has to go through transformations to enhance its performance in vehicular communications. One possible enhancement for LTE is the usage of the latest handover schemes, such as RACH-less and Make-before-break (MBB), to create seamless mobility. In the current study, we propose a RACH-less MBB handover scheme using Software-Defined Networks (SDN). Our main contributions are: (i) unifying lower layer handover operations with controller network updating procedures; and (ii) creating a signaling protocol that allows base stations and controllers to exchange information needed for timing alignment of the UE without executing a RACH procedure. Simulation results show that our proposed handover scheme has a shorter execution time and reasonable signaling overhead when compared to baseline schemes from the literature.

Next-generation networks are expected to provide higher data rates and ultra-low latency in support of demanding applications, such as virtual and augmented reality, robots and drones, etc. To meet these stringent requirements of applications, edge computing constitutes a central piece of the solution architecture wherein functional components of an application can be deployed over the edge network to reduce bandwidth demand over the core network while providing ultra-low latency communication to users. In this article, we provide solutions to resource orchestration and management for applications over a virtualized client-edge-server infrastructure. We investigate the problem of optimal placement of pipelines of application functions (virtual service chains) and the steering of traffic through them, over a multi-technology edge network model consisting of both wired and wireless millimeter-wave (mmWave) links. This problem is NP-hard. We provide a comprehensive “microscopic” binary integer program to model the system, along with a heuristic that is one order of magnitude faster than optimally solving the problem. Extensive evaluations demonstrate the benefits of orchestrating virtual service chains (by distributing them over the edge network) compared to a baseline “middlebox” approach in terms of overall admissible virtual capacity. Moreover, we observe significant gains when deploying a small number of mmWave links that complement the Wire physical infrastructure in high node density networks.

In modern networks, edge computing will be responsible for processing and learning from the critical network- and user-generated data, such as wireless link usage, mobility information, application requests, and many others. The presence of Artificial Intelligence-based (AI) applications at the edge of the network will enable the network to predict necessary user behavior and its impact on network infrastructure, such as base station overloading. One of the main strategies for offloading users and base stations is to deploy UAV base stations, or flying base stations, which can dynamically provide service and connectivity. In this article, we introduce a framework for distributed learning over Multi-access Edge Computing (MEC), which manages data applications in a fully distributed setting across edge servers, thus reducing the cost of collecting user information in a centralized server. We couple the proposed distributed learning with a novel similarity metric for user trajectories, which can aggregate neural network models with similar costs as other model aggregation techniques. However, the aggregation technique can achieve much higher accuracy. Furthermore, we apply the proposed distributed learning scheme to manage and deploy flying base stations to areas that experience high demand or poor user connectivity, thus optimizing connectivity in terms of user satisfaction, delay, and network throughput.

Traditional WiFi positioning relies on a predefined radio map, which is labor-intensive and time-consuming for professionals. Recently, crowdsourcing has emerged as a promising solution for facilitating WiFi positioning. To crowdsense a radio map, traces collected from normal users are merged to recover the original walking paths. In this work, we design a robust iterative trace merging algorithm called WiFi-RITA based on WiFi access points as signal-marks. The algorithm formulates the trace merging problem as an optimization problem in which each trace is translated and rotated to minimize the limitation of distances among traces defined by WiFi access points. WiFi-RITA is further enhanced by removing outliers. WiFi-RITA is robust to the rotation errors of traces and efficient for a large number of short traces. According to the crowdsensed radio map, a sensor fusion approach based on particle filter by fusing inertial sensors and a multivariate Gaussian fingerprinting is proposed to enhance the accuracy of crowdsourcing indoor positioning. The experiment results in two large-scale environments demonstrate that WiFi-RITA positioning with zero-effort calibration achieves high positioning accuracy, which outperforms Pedestrian Dead Reckoning (PDR) and fingerprinting with K Nearest Neighbor.

L-SCN is a two-layered Service-Centric Networking (SCN) architecture. The L-SCN design splits the network into domains and specifies communication protocols for service provider information propagation. Nodes in a domain receive substantial knowledge about the available resources (e.g., CPU, RAM) and available services within the domain, while the communication between different domains is realized through supernodes. We extend L-SCN with new communication mechanisms, which improve the processing time and provide lower protocol overhead for service request processing. The two proposed mechanisms are named event-driven and provider-driven. The event-driven mechanism propagates service provider information based on an event (e.g., high overload). The provider-driven mechanism propagates service provider information periodically.

Vehicular ad-hoc Networks (VANETs) are characterized by intermittent connectivity caused by the mobility of vehicles. To deal with these path breaks, studies propose the use of the Named Data Networking (NDN) architecture. In NDN the content is requested and retrieved based on its name and not on location of hosts. Hence, NDN poses as a suitable candidate for VANETs. In NDN-VANETs broadcasting every message is considered the most reliable way for content retrieval to avoid path breaks. In this work, to reduce the usage of network resources, we unicast all messages and we limit the dissemination area of messages by installing directional antennas in every vehicle. Directional antennas assist us to control the network traffic in particular areas. We developed an algorithm for choosing the appropriate directional antenna to send a message, allowing us to decrease the network traffic in vehicles located outside of the spreading area of the message. Furthermore, we use a contention-based algorithm to satisfy unfulfilled requests, when the content is not retrieved after a specific time period. Our results show improvements on the application performance. We retrieve more Data messages faster in the same time period. Finally, we improve the load in terms of sent and received messages on each node.

This paper presents a complete framework for content discovery and retrieval in Information-Centric Networks. For content discovery, we implement a method similar to our previously developed pull-based BFR [1], which uses Bloom filter-based signaling to inform servers about the name prefixes of available requests. For content retrieval, we propose in this paper a feedback-based cooperative protocol implementing network coding-based forwarding. The proposed network coding-based protocol provides a distributed solution to control the multisession codeblock size, i.e., the number of variables that are combined into network coded packets, by setting a capacity constraint on each node and by piggybacking the available capacity as feedback on messages sent to neighbors. The network codes are decided using linear programming. We compare the proposed network coding-based protocol with push-based BFR [2] and pull-based BFR [1]. The results show that the proposed protocol outperforms both push-based BFR and pull-based BFR in terms of content discovery overhead and average content block retrieval delay.

In Connected Autonomous Vehicles scenarios or CAV, ubiquitous connectivity will play a significant role in the safety of the vehicles and passengers. The extensive amount of sensors in each car will generate vast amounts of data that cannot be processed promptly by onboard units. Edge and fog computing are emerging solutions for remote data processing for autonomous vehicles, offering higher computing power, as well as the low latency required by autonomous driving. However, due to the highly distributed nature of fog and edge computing servers, CAV mobility may pose a challenge to keep services close to end-users and maintaining QoS. In this paper, we propose MOSAIC, service migration, and resource management algorithm for intra-tier and inter-tier communication in edge and fog computing. The proposed solution performs proactive migration of services based on mobility information, server resources, QoS, and network conditions. Simulation results show the efficiency of the proposed algorithm in terms of latency, migration failures, and network throughput.

A service-centric network requires a routing protocol to route service requests towards service providers. Routing operations can be divided into intra-domain and inter-domain routing. In the proposed approach, a so-called supernode is responsible for managing its own domain as well as for communicating with the supernodes of other domains to perform inter-domain routing. In order to appoint appropriate nodes as supernodes in the network topology, in this paper, we use Dominating Sets (DS) and Connected Dominating Sets (CDS). We propose fully distributed algorithms for constructing DS as well as CDS over the network topology. To prepare routing information, the nodes of each domain inform their supernodes about their available service names and resources (e.g., CPU, RAM). To this aim, the nodes use Bloom filters which reduce bandwidth and storage overhead. The performance evaluation shows that the required bandwidth overhead for DS and CDS construction algorithms increases with the topology size. The results also show that for large network topologies, CDS-based routing requires significantly less bandwidth overhead than both DS-based routing and Named Data Networking with multicast forwarding strategy. Finally, from the results we can observe that both DS-based and CDS-based routing have significantly lower service retrieval time than NDN multicast strategy.

Although many target applications in VANETs are information-centric, the performance of Named Data Networking (NDN) in vehicular ad-hoc networks is severely hampered by persistent network partitioning, typical of many vehicular scenarios. Existing approaches try to address this issue by relying on opportunistic communications. However, they leave open the crucial issue of how to guarantee content persistence and tight QoS levels while optimizing the resource utilization in the vehicular environment. In this work we propose DeepNDN, a communication scheme based on the joint application of NDN and of probabilistic spatial content caching, which enables content retrieval in fragmented and dynamic network topologies with tight delay constraints. We present a data-based approach to DeepNDN management, based on locally modulating content replication and delivery in order to achieve a target hit ratio in a resource-efficient manner. Our management algorithm employs a Convolutional Neural Network (CNN) architecture for effectively capturing the complex relations between spatio-temporal patterns of mobility and content requests and DeepNDN performance. Its numerical assessment in realistic, measurement-based scenarios suggest that our management approach achieves its target set goals while outperforming a set of reference schemes.

Video-on-Demand (VoD) services create a demand for content orchestrator mechanisms to support Quality of Experience (QoE). Fog computing brings benefits for enhancing the QoE for VoD services by caching the content closer to the user in a multi-tier fog architecture, considering their available resources to improve QoE. In this context, it is mandatory to consider network, fog node, and user metrics to choose an appropriate fog node to distribute videos with QoE support properly. In this article, we introduce a content orchestrator mechanism, called of Fog4Video, which chooses an appropriate fog node to download video content. The mechanism considers the available bandwidth, delay, and cost, besides the QoE metrics for VoD, namely number of stalls and stalls duration, to deploy VoD services in the opportune fog node. Decision-making acknowledges periodical reports of QoE from the clients to assess the video streaming from each fog node. These values serve as inputs for a real-time Analytic Hierarchy Process method to compute the influence factor for each parameter and compute the QoE improvement potential of the fog node. Fog4Video is executed in fog nodes organized in multiple tiers, having different characteristics to provide VoD services. Simulation results demonstrate that Fog4Video transmits adapted videos with 30% higher QoE and reduced monetary cost up to 24% than other content request mechanisms.

Vehicular traffic re-routing is key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System, which intends to improve not only mobility but also driving experience and safety of drivers and passengers. Context-aware and multi-objective re-routing approaches will play an important role in traffic management. However, most of these approaches are deterministic and can not support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, and, thus, degrade the overall traffic efficiency. In this work, we introduce Safe and Sound (SNS), a non-deterministic multi-objective re-routing approach for improving traffic efficiency and reduce public safety risks (based on criminal events) for drivers and passengers. SNS employs a hybrid architecture and a cooperative re-routing approach for improving system scalability and computation efforts. SNS uses a recurrent neural network to both predict future safety risks dynamics and enable a personalized re-routing in which each vehicle decides the risks it wants to avoid. Simulation results revealed that when compared to state-of-the-art approaches, SNS reduces the CPU time of the re-routing algorithm in approximately 99% and decreases the average safety risk for drivers and passengers in at least 30% while keeping efficient traffic mobility.

Future mobile networks will enable the massive deployment of mobile multimedia applications anytime and anywhere. In this context, mobility management schemes, such as handover and proactive multimedia service migration, will be essential to improve network performance. In this article, we propose a proactive mobility management approach based on group user trajectory prediction. Specifically, we introduce a mobile user trajectory prediction algorithm by combining the Long-Short Term Memory networks (LSTM) with Reinforcement Learning (RL) to automate the model training procedure. We further develop a group user trajectory predictor to reduce prediction calculation overheads of users with similar movement patterns. To validate the impact of the proposed mobility management approach, we present a virtual reality (VR) service migration scheme built on the top of the proactive handover mechanism that benefits from trajectory predictions. Experiment results validate our predictor's outstanding accuracy and its impacts on enhancing handover and service migration performance to provide quality of service assurance.

In Named Data Networking (NDN), there is a need for routing protocols to populate Forwarding Information Base (FIB) tables so that the Interest messages can be forwarded. To populate FIBs, clients and routers require some routing information. One method to obtain this information is that network nodes exchange routing information by each node advertising the available content objects. Bloom Filter-based Routing approaches like BFR [1], use Bloom Filters (BFs) to advertise all provided content objects, which consumes valuable bandwidth and storage resources. This strategy is inefficient as clients request only a small number of the provided content objects and they do not need the content advertisement information for all provided content objects. In this paper, we propose a novel routing algorithm for NDN called pull-based BFR in which servers only advertise the demanded file names. We compare the performance of pull-based BFR with original BFR and with a flooding-assisted routing protocol. Our experimental evaluations show that pull-based BFR outperforms original BFR in terms of communication overhead needed for content advertisements, average roundtrip delay, memory resources needed for storing content advertisements at clients and routers, and the impact of false positive reports on routing. The comparisons also show that pull-based BFR outperforms flooding-assisted routing in terms of average round-trip delay.

In Named Data Networking (NDN) content is retrieved based on names instead of locations of hosts. Therefore, NDN is a suitable candidate for content retrieval in Vehicular ad-hoc Networks (VANETs), which are characterized by path breaks due to intermittent connectivity. In an NDN-VANET to avoid path breaks broadcasting every message is considered the most reliable way for content retrieval. In this work, we unicast messages to reduce the usage of network resources, when it is possible. Our work limits the dissemination area of messages by installing directional antennas in vehicles. We develop an algorithm to choose the appropriate directional antenna to unicast a message, allowing vehicles outside of the spreading area of the message to perform other tasks. Moreover, each vehicle performs route discovery to nodes that store content, when the content is not retrieved after a specific time period. Therefore, when necessary, paths are reconfigured to include new vehicles.

Seamless outdoor-indoor positioning plays a critical role in many emerging applications, e.g., large-coverage user navigation in cities, smart buildings, and analytics of user spatial location big data. It is still challenging to construct a large-scale seamless outdoor-indoor positioning system due to the limited coverage of indoor positioning. In this paper, we propose a seamless outdoor-indoor crowdsensing positioning (SoiCP) system in which a radio map is automatically constructed based on crowdsourcing pedestrian dead reckoning (PDR) traces without professional site surveying. The constructed radio map is robust to inaccurate PDR traces and does not rely on prior knowledge of floor plans. In SoiCP, the crowdsensed radio map is obtained by a proposed three-step trace matching algorithm. This algorithm leverages building gates and WiFi fingerprints as landmarks to merge the noisy crowdsourcing traces and accurately construct the user walking paths. Moreover, following the crowdsensed radio map, SoiCP uses an enhanced particle filter to fuse PDR, GPS, and WiFi fingerprinting for seamless outdoor-indoor positioning with high accuracy. The comprehensive real-world experiments in two large-scale shopping malls demonstrate that SoiCP can effectively crowdsense the walking paths and track moving users with high accuracy.

Positioning is envisioned as an essential enabler of future fifth generation (5G) mobile networks due to the massive number of use cases that would benefit from knowing users' positions. In this work, we propose a particle filter-based reinforcement learning (PFRL) approach for the robust wireless indoor positioning system. Our algorithm integrates information of indoor zone prediction, inertial measurement units, wireless radio-based ranging, and floor plan into an particle filter. The zone prediction method is designed with an ensemble learning algorithm by integrating individual discriminative learning methods and Hidden Markov Models. Further, we integrate the particle filter approach with a reinforcement learning-based resampling method to provide robustness against localization failure problems such as the kidnapping robot problem. The PFRL approach is validated on a two-tier architecture, in which distributed machine learning tasks are hosted at client and edge layer. Experiment results show that our system outperforms traditional terminal-based approaches in both stability and accuracy.

Crowdsensing indoor walking paths based on crowdsourcing traces collected from normal users has recently become an emerging topic for indoor positioning, which can reduce the labor effort of building radio maps and improve the positioning accuracy when a floor plan is unavailable. In this work, we design an indoor walking path crowdsensing system with massive noisy crowdsourcing traces. In this system, we propose a robust iterative trace merging algorithm based on WiFi access points as markers (named 'WiFi-RITA') to merge massive noisy traces. The algorithm formulates the trace merging problem as an optimization problem in which each trace is controlled to translate and rotate to minimize the limitation of distances among traces defined by WiFi access points as markers. WiFi-RITA is robust to the rotation errors and uncertain absolute locations of user traces, and can efficiently work for a large number of user traces. We further adopt a landmark matching algorithm to match the merged traces to the target building and adopt a 2-dimensional histogram approach to remove outlier traces. With such procedures, we generate walking paths of a large-scale building with a mean accuracy of 2.1m.

Bloom filter-based routing protocols for Named Data Networking (NDN) aim at facilitating content discovery in NDN. In this paper, we compare the performance of two Bloom filter-based routing protocols, namely BFR and COBRA. BFR is a push-based routing protocol that works based on Bloom filter-based content advertisements, while COBRA is a pull-based routing protocol that operates based on route traces left from previously retrieved content objects, which are stored in Stable Bloom Filters. In this paper, we show that BFR outperforms COBRA in terms of average memory needed for storing routing updates, average round-trip delay, normalized communication overhead, total Interest communication overhead, and mean hit distance.

Location aware services in the Internet of Things are essential for smart environments. Location awareness enables operational systems to deliver useful information for supplying context-aware applications. We propose an efficient probabilistic model to provide good and stable localization accuracy in smart building environments for smartphones. Our proposed localization method fuses zone detection, radio-based ranging, inertial measurement units and floor plan information into an enhanced particle filter. Zone detection is designed with an ensemble learning algorithm by combining Hidden Markov Models and discriminative learning methods. We first apply ensemble learning models to achieve zone detection. Further, we integrate zone detection and an enhanced ranging model to achieve high and stable localization performance. Experiment results in an office-like indoor environment show that our system outperforms traditional localization approaches considering stability and accuracy. The localization method can achieve performance with an average localization error of 1.26 meters.

The growing ubiquity of smart-phones equipped with built-in sensors and global positioning system (GPS) has resulted in the collection of large volumes of mobility data without the need of any additional devices. The large size of heterogeneous mobility data gives rise to rapid development of location-based services (LBSs). The predictability of mobile users' behavior is essential to enhance LBSs. To predict human mobility, many techniques have been proposed. However, existing techniques require good data quality to guarantee optimal performance. In this paper, we proposed a hybrid Markov chain to predict mobile users' future locations. Our model constantly adapts to available user trace quality to select either the first order or the second order Markov chain. Compared to existing solutions, our model is adaptive to discrete gaps in data trace. To help us understanding complex user behaviors, we have also proposed a technique benefiting both temporal and spatial parameters to extract Zone of Interests (ZOIs). To evaluate the algorithm's performance, we use a real-life dataset from the Nokia Mobile Data Challenge (MDC) collected around Lake Geneva region from 180 users. We found a satisfactory future user location prediction accuracy of 70 - 84%.

An accurate room localization system is a powerful tool for providing location-based services. Considering that people spend most of their time indoors, indoor localization systems are becoming increasingly important in designing smart environments. In this work, we propose an efficient ensemble learning method to provide room level localization in smart buildings. Our proposed localization method achieves high room-level localization accuracy by combining Hidden Markov Models with simple discriminative learning methods. The localization algorithms are designed for a terminal-based system, which consists of commercial smartphones and Wi-Fi access points. We conduct experimental studies to evaluate our system in an office-like indoor environment. Experiment results show that our system can overcome traditional individual machine learning and ensemble learning approaches.

Vehicles such as cars are expected to use communication technologies for retrieving different kinds of information and exchanging information with other vehicles for safety and infotainment purposes. This results in vehicular networks, where vehicles can connect to other vehicles or communication infrastructures such as Road Side Units. The Recursive Inter- Network Architecture (RINA) has been proposed as a Future Internet architecture. This paper investigates and analyses how vehicular networks can be supported by RINA and how a RINA based vehicular network architecture can be designed to support efficient management of mobile vehicles.

The prevalence of smartphones equipped with global positioning system has enabled researchers to excavate users mobility patterns in the cities. The knowledge of users' behavior, such as their locations, plays a significant role in location-based services, resource management, logistic administration and urban planning. To understand complex behavior of humans we utilize spatio-temporal analysis on collected geo-location points to exploit Individual Zone of Interests in urban areas. In addition, we designed a hybrid Markov chain model to forecast future locations of pedestrians. Compared to existing mobility prediction methodologies, our predictor can adapt it's behavior constantly based on the quality of existing traced data to switch between first-order or second-order Markov chain. Moreover, we propose a model to predict city area congestion. The model predicts the number of users in a specific area of a city by discovering the regular mobility patterns of a group of users. We conducted comprehensive empirical experiments using a real-life dataset, namely the Mobile Data Challenge dataset, which was collected in the city of Lausanne in Switzerland with around 180 participants. We found a satisfactory user future location prediction accuracy of 70-84% and area congestion prediction accuracy of 65-73% for the users.

Vehicular Ad-Hoc Networks (VANETs) include services such as video streaming for automated safety precautions and autonomous driving. NDN is proposed in VANETs as a solution for the connection breaks, since NDN decouples the content exchange from the location of the host. The combination of NDN and VANETs leads to autonomous ad-hoc network architectures that are self-managed. In this paper, we apply the NDN architecture in VANETs, to retrieve information from other vehicles and to save network resources. We present a Vehicle to Infrastructure (V2I) communication architecture for NDN-VANETs, which consists of vehicles and Road Side Units (RSUs). For installed RSUs along the roads we develop two communication techniques: First, in the centralized approach every node that requests content sends its Interests to the nearest RSU. RSUs are responsible for routing the Interests to the content source. In our second approach, a hybrid communication technique uses RSUs as a backup mechanism and forwards packets to them, if a route to the content source is unavailable. We compare our approaches with our previous work iMMM-VNDN, flooding and AODV. Our results show that we outperform previous works in terms of Interest Satisfaction Ratio and the total amount of Delivered Data in the requester node.

Content prefetching brings contents close to end users before their explicit requests to reduce the content retrieval time, which is crucial for mobile scenarios, such as vehicular ad-hoc networks (VANETs). In order to make intelligent prefetching decisions, three questions have to be answered: which content should be prefetched, when and where it should be prefetched. This paper answers these questions by proposing a vehicle mobility prediction-based over-the-top (OTT) content prefetching solution. We proposed a vehicle mobility prediction module to estimate the future connected roadside units (RSUs) using data traces collected from a real-world VANET testbed deployed in the city of Porto, Portugal. We designed a multi-tier caching mechanism with an OTT content popularity estimation scheme to forecast the content request distribution. We implemented a learning-based algorithm to proactively prefetch the user content to VANET edge caching at RSUs. We implemented a prototype using Raspberry Pi emulating RSU nodes to prove the system functionality. We also performed large-scale OpenStack experiments to validate the system scalability. Extensive experiment results prove that the system can bring benefits for both end-users and OTT service providers, which help them to optimize network resource utilization and reduce bandwidth consumption.

We propose a novel network coding (NC) enabled named data networking (NDN) architecture for scalable video delivery. Our architecture utilizes NC in order to address the problem that arises in the original NDN architecture, where optimal use of the bandwidth and caching resources necessitates the coordination of the Interest forwarding decisions. To optimize the performance of the proposed NC-based NDN architecture and render it appropriate for transmission of scalable video, we devise a novel rate allocation algorithm that decides on the optimal rates of Interests sent by clients and intermediate nodes. The flow of Data packets achieved by this algorithm maximizes the average quality of the video delivered to the client population. To support the handling of Interest and Data packets when intermediate nodes perform NC, we introduce the use of Bloom filters, which store efficiently additional information about the Interest and Data packets, and modify accordingly the standard NDN architecture. We also devise an optimized Interest forwarding strategy that implements the target rate allocation. The proposed architecture is evaluated for transmission of scalable video over PlanetLab topologies. The evaluation shows that the proposed scheme exploits optimally the available network resources.

In this paper, we present L-SCN, a new routing architecture for Service-Centric Networking (SCN), which makes use of a two-layer forwarding scheme composed of inter-domain and intra-domain communication. Unlike existing SCN routing architectures relying on a flat organization, our design splits the network into domains. Nodes within a domain possess significant knowledge about existing services and available resources within the domain. Supernodes provide a significant advantage in comparison to other architectures. They assure the inter-domain communication and make use of a pull and push mechanism combined with Bloom filters. It allows us to minimize the protocol overhead and optimize sharing of information about available services and resources in the network.

People-centric sensing is a research topic that aims to obtain and analyze urban data from crowdsourcing, such as participatory and opportunistic sensing. Data provided by these sources increase our knowledge about different aspects of our lives in urban scenarios, which can help us to understand and address issues that cities face. Thus, the sustainable people participation is crucial to the development of this sensing paradigm. In this direction, we focus on a central element for the deployment of people-centric sensing applications: guarantee sustainable participation of users. For this, we discuss the existing challenges at the main components of an architecture to support people-centric sensing. In order to enrich this discussion, we also evaluate the incentive mechanisms used by Foursquare, mechanisms that could be used, with proper adaptation, in several types of sensing systems. Among the results, we found evidence that a specific type of incentive (mayorship-based) could be very effective to increase users' engagement. Moreover, we present a set of policies to be incorporated into an existing or new people-centric sensing architecture to complement traditional incentive mechanisms.

Network Function Virtualization involves implementing network functions (e.g., virtualized LTE component) in software that can run on a range of industry standard server hardware, and can be migrated or instantiated on demand. A prediction service hosted on cloud infrastructures enables consumers to request the prediction information on-demand and respond accordingly. In this paper we introduce MOBaaS, which is a network function of Mobility and Bandwidth prediction cloudified over the cloud computing infrastructure. We implemented the service orchestration framework of MOBaaS, which can easily be setup and integrated with any other cloud-based LTE entities to provide prediction information about the future location of mobile user(s) as well as the network link(s) bandwidth availability. This information can be used to generate required triggers for on-demand deployment or scaling-up/down of virtualized network components as well as for the self-adaptation procedures and optimal network function configuration. We also describe the performance evaluation of the MOBaaS cloudification procedures and present an example of the benefit of such a prediction service.

Smart environments interconnect indoor building environments, indoor wireless sensor and actuator networks, smartphones, and human together to provide smart infrastructure management and intelligent user experiences. To enable the "smart" operations, a complete set of hardware and software components are required. In this work, we present Smart Syndesi, a system for creating indoor location-aware smart building environments using wireless sensor and actuator networks (WSANs). Smart Syndesi includes an indoor tracking system and a WSAN for environmental monitoring and actuator activation, interconnected via a gateway with mobile users. The indoor positioning system tracks the real-time location of occupants with high accuracy, which works as a basis for indoor location-based sensor actuation automation. To show how the multiple software/hardware components can be integrated, we implemented a system prototype and performed intensive experiments in indoor office environments.

Autonomic communications aim to provide the quality-of-service in networks using self-management mechanisms. It inherits many characteristics from autonomic computing, in particular, when communication systems are running as specialized applications in software-defined networking (SDN) and network function virtualization (NFV)-enabled cloud environments. This paper surveys autonomic computing and communications in the context of software-driven networks, i.e., networks based on SDN/NFV concepts. Autonomic communications create new challenges in terms of security, operations, and business support. We discuss several goals, research challenges, and development issues on self-management mechanisms and architectures in software-driven networks. This paper covers multiple perspectives of autonomic communications in software-driven networks, such as automatic testing, integration, and deployment of network functions. We also focus on self-management and optimization, which make use of machine learning techniques.

The fast and huge increase of Internet traffic motivates the development of new communication methods that can deal with the growing volume of data traffic. To this aim, named data networking (NDN) has been proposed as a future Internet architecture that enables ubiquitous in-network caching and naturally supports multipath data delivery. Particular attention has been given to using dynamic adaptive streaming over HTTP to enable video streaming in NDN as in both schemes data transmission is triggered and controlled by the clients. However, state-of-the-art works do not consider the multipath capabilities of NDN and the potential improvements that multipath communication brings, such as increased throughput and reliability, which are fundamental for video streaming systems. In this paper, we present a novel architecture for dynamic adaptive streaming over network coding enabled NDN. In comparison to previous works proposing dynamic adaptive streaming over NDN, our architecture exploits network coding to efficiently use the multiple paths connecting the clients to the sources. Moreover, our architecture enables efficient multisource video streaming and improves resiliency to Data packet losses. The experimental evaluation shows that our architecture leads to reduced data traffic load on the sources, increased cache-hit rate at the in-network caches and faster adaptation of the requested video quality by the clients. The performance gains are verified through simulations in a Netflix-like scenario.

Passive positioning systems produce user location information for third-party providers of positioning services. In this paper, we provide a passive tracking system for WiFi signals with an enhanced range-only particle filter using finegrained power. Our proposed particle filter, WVT-bootstrap particle filter, provides improved observation likelihood and is equipped with a single coordinated turn model to address the challenges in passive positioning. The anchor nodes for WiFi signal sniffing use software defined radio techniques to extract channel state information for multipath mitigation and a nonlinear regression method is used for the path-loss model. Our tracking system produces measured positioning errors that, in the 80th percentile, are equal to or less than 2 m; this represents a 33% improvement over the traditional bootstrap particle filter. Additionally, it requires (0.12 s for 1000 particles) only half of the computation efforts as a multi-model particle filter.

The surging interest in autonomous coordinateddriving and in proactive safety services, exploiting the wealth ofsensing and computing resources which are gradually permeatingthe urban and vehicular environments, is making provisioning ofhigh levels of QoS in vehicular networks an urgent issue. At thesame time, the spreading model of a smart car, with a wealthof infotainment applications, calls for architectures for vehicularcommunications capable of supporting traffic with a diverse setof performance requirements. So far efforts have been revolvedtowards enabling a single specific QoS level. But the issues of howto support traffic with tight QoS requirements (no packet loss, and delays inferior to 1ms), and of designing a system capable atthe same time of efficiently sustaining such traffic together withtraffic from infotainment applications, are still open. In this paper we present the approach taken in the SNFCONTACT project in order to tackle these issues. The goal ofthe project is to investigate how an architecture for vehicularcommunications which integrates content-centric networking, software-defined networking as well as context aware floatingcontent schemes can properly support the very diverse set ofapplication and services currently envisioned for the vehicularenvironment.

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as a fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range-based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranges and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1.3m for mean accuracy and 2.2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.

Content-Centric Networking (CCN) naturally supports multi-path communication, as it allows the simultaneous use of multiple interfaces (e.g. LTE and WiFi). When multiple sources and multiple clients are considered, the optimal set of distribution trees should be determined in order to optimally use all the available interfaces. This is not a trivial task, as it is a computationally intense procedure that should be done centrally. The need for central coordination can be removed by employing network coding, which also offers improved resiliency to errors and large throughput gains. In this paper, we propose NetCodCCN, a protocol for integrating network coding in CCN. In comparison to previous works proposing to enable network coding in CCN, NetCodCCN permits Interest aggregation and Interest pipelining, which reduce the data retrieval times. The experimental evaluation shows that the proposed protocol leads to significant improvements in terms of content retrieval delay compared to the original CCN. Our results demonstrate that the use of network coding adds robustness to losses and permits to exploit more efficiently the available network resources. The performance gains are verified for content retrieval in various network scenarios.

The rapid growth of ubiquitous applications and location-based services has made indoor navigation an interesting topic. Some indoor localization solutions exploit radio information and Inertial Measurement Units (IMUs), which are embedded in most of the modern smartphones. In this paper, we present a real-time indoor localization approach that fuses WiFi Receiving Signal Strength Indicator (RSSI) readings, IMUs, and floor plan information in an enhanced particle filter. The algorithms are designed and implemented into a terminal-based system, which uses commercial smartphones and WiFi access points. Extensive real-world experiment results show that our tracking method can achieve the average tracking error of 1.7 meters and 90% accuracy of 3.2 meters.

Networking (SCN) extends Information-Centric Networking (ICN) from content to services, enabling clients to request services without having them coupled to specific servers. This brings new authentication challenges, since legacy authentication techniques do not apply. We propose and evaluate three generic SCN use cases and their corresponding authentication methods.

The location of data centres is crucial when mobile network operators are moving towards cloudified mobile networks to optimize resource utilization and to improve performance of services. Quality of Experience (QoE) can be enhanced in terms of content access latency, by placing user content at locations where they will be present in the future. The Follow-Me Cloud (FMC) concept aims at optimising operations of moving Mobile Network Operators Services towards cloudified environments, where Information Centric Networking (ICN) and the appropriate content migration policies are of paramount importance. However, several factors need to be considered, including user movements and mobility prediction (MP), content popularity, and migration. This paper addresses all these aspects by implementing a fully integrated multi-criteria FMC and mobility prediction mechanisms (MP-FMC) on a cloud infrastructure. Experimental evaluation shows that MP-FMC can be orchestrated on-demand within a reasonable time frame, and it could deliver ≈ 33% improvement of content retrieval time.

The rapid growth area of ubiquitous applications and location-based services has made indoor localization an interesting topic for research. Some indoor localization solutions for smartphones exploit radio information and Inertial Measurement Units (IMUs), which are embedded in most of the modern smartphones. In this work, we propose to fuse WiFi Receiving Signal Strength Indicator (RSSI) readings, IMUs, and floor plan information in an enhanced particle filter to achieve high accuracy and stable performance in the tracking process. We provide an efficient double resampling method to mitigate errors caused by off-the-shelf IMUs and WiFi sensors embedded in commodity smartphones. The algorithms are designed in a terminal-based system, which consists of commercial smartphones and WiFi access points. We evaluate our system in two complex environments along moving paths. Experiment results show that our tracking method can achieve the average tracking error of $1.01$ meters and $90\%$ accuracy of $1.7$ meters.

This paper presents a hybrid approach for real-time indoor localization without interaction with end users or network operators. The proposed solution uses signal strength information from multiple Radio Interfaces (RIs) to estimate locations of target devices. This solution benefits from different characteristics of radio signals at each RI to overcome challenges such as multipath propagation. The proposed algorithms may combine signal information prior to or after the localization process. The system operates blindly without a priori knowledge of the environment layout or target devices' radio settings. Results of real indoor experiments with both WiFi and GSM show an improvement by the proposed hybrid solution with a median error of 1.6m compared to 2.3m for WiFi and 3.0m for GSM.

Cloud computing enables the on-demand delivery of resources for a multitude of services and gives the opportunity for small agile companies to compete with large industries. In the telco world, cloud computing is currently mostly used by mobile network operators (MNO) for hosting non-critical support services and selling cloud services such as applications and data storage. MNOs are investigating the use of cloud computing to deliver key telecommunication services in the access and core networks. Without this, MNOs lose the opportunities of both combining this with over-the-top (OTT) and value-added services to their fundamental service offerings and leveraging cost-effective commodity hardware. Being able to leverage cloud computing technology effectively for the telco world is the focus of mobile cloud networking (MCN). This paper presents the key results of MCN integrated project that includes its architecture advancements, prototype implementation, and evaluation. Results show the efficiency and the simplicity that a MNO can deploy and manage the complete service lifecycle of fully cloudified, composed services that combine OTT/IT- and mobile-network-based services running on commodity hardware. The extensive performance evaluation of MCN using two key proof-of-concept scenarios that compose together many services to deliver novel converged elastic, on-demand mobile-based but innovative OTT services proves the feasibility of such fully virtualized deployments. Results show that it is beneficial to extend cloud computing to telco usage and run fully cloudified mobile-network-based systems with clear advantages and new service opportunities for MNOs and end-users.

Cloudification of the Centralized-Radio Access Network (C-RAN) in which signal processing runs on general purpose processors inside virtual machines has lately received significant attention. Due to short deadlines in the LTE frequency division duplex access method, processing time fluctuations introduced by the virtualization process have a deep impact on C-RAN performance. This paper evaluates bottlenecks of the OpenAirInterface (OAI is an open-source software-based implementation of LTE) cloud performance, provides feasibility studies on C-RAN execution, and introduces recommendations for cloud architecture that significantly reduces the encountered execution problems. In typical cloud environments, the OAI processing time deadlines cannot be guaranteed. Our proposed cloud architecture shows good characteristics for OAI cloud execution. As an example, in our setup more than 99.5% processed LTE subframes reach reasonable processing deadlines close to performance of a dedicated machine of a single core CPU.

Information-centric networking (ICN) has been proposed to cope with the drawbacks of the Internet Protocol, namely scalability and security. The majority of research efforts in ICN have focused on routing and caching in wired networks, while little attention has been paid to optimizing the communication and caching efficiency in wireless networks. In this work, we study the application of Raptor codes to Named Data Networking (NDN), which is a popular ICN architecture, in order to minimize the number of transmitted messages and accelerate content retrieval times. We propose RC-NDN, which is a NDN compatible Raptor codes architecture. In contrast to other coding-based NDN solutions that employ network codes, RC-NDN considers security architectures inherent to NDN. Moreover, different from existing network coding based solutions for NDN, RC-NDN does not require significant computational resources, which renders it appropriate for low cost networks. We evaluate RC-NDN in mobile scenarios with high mobility. Evaluations show that RC-NDN outperforms the original NDN significantly. RC-NDN is particularly efficient in dense environments, where retrieval times can be reduced by 83% and the number of Data transmissions by 84.5% compared to NDN.

Time-based indoor localization has been investigated for several years but the accuracy of existing solutions is limited by several factors, e.g., imperfect synchronization, signal bandwidth and indoor environment. In this paper, we compare two time-based localization algorithms for narrow-band signals, i.e., multilateration and fingerprinting. First, we develop a new Linear Least Square (LLS) algorithm for Differential Time Difference Of Arrival (DTDOA). Second, fingerprinting is among the most successful approaches used for indoor localization and typically relies on the collection of measurements on signal strength over the area of interest. We propose an alternative by constructing fingerprints of fine-grained time information of the radio signal. We offer comprehensive analytical discussions on the feasibility of the approaches, which are backed up by evaluations in a software defined radio based IEEE 802.15.4 testbed. Our work contributes to research on localization with narrow-band signals. The results show that our proposed DTDOA-based LLS algorithm obviously improves the localization accuracy compared to traditional TDOA-based LLS algorithm but the accuracy is still limited because of the complex indoor environment. Furthermore, we show that time-based fingerprinting is a promising alternative to power-based fingerprinting.

Cloud Computing enables provisioning and distribution of highly scalable services in a reliable, on-demand and sustainable manner. However, objectives of managing enterprise distributed applications in cloud environments under Service Level Agreement (SLA) constraints lead to challenges for maintaining optimal resource control. Furthermore, conflicting objectives in management of cloud infrastructure and distributed applications might lead to violations of SLAs and inefficient use of hardware and software resources. This dissertation focusses on how SLAs can be used as an input to the cloud management system, increasing the efficiency of allocating resources, as well as that of infrastructure scaling. First, we present an extended SLA semantic model for modelling complex service-dependencies in distributed applications, and for enabling automated cloud-infrastructure management operations. Second, we describe a multi-objective VM allocation algorithm for optimised resource allocation in infrastructure clouds. Third, we describe a method of discovering relations between the performance indicators of services belonging to distributed applications and then using these relations for building scaling rules that a CMS can use for automated management of VMs. Fourth, we introduce two novel VM-scaling algorithms, which optimally scale systems composed of VMs, based on given SLA performance constraints. All presented research works were implemented and tested using enterprise distributed applications.

Information-centric networking (ICN) is a new communication paradigm that aims at increasing security and efficiency of content delivery in communication networks. In recent years, many research efforts in ICN have focused on caching strategies to reduce traffic and increase overall performance by decreasing download times. Since caches need to operate at line-speed, they have a limited size and content can only be stored for a short time. However, if content needs to be available for a longer time, e.g., for delay-tolerant networking or to provide high content availability similar to content delivery networks (CDNs), persistent caching is required. We base our work on the Content-Centric Networking (CCN) architecture and investigate persistent caching by extending the repository implementation in CCNx. We show by extensive evaluations in a YouTube and web server traffic scenario that repositories can be efficiently used to increase content availability by significantly increasing the cache hit rates.

Information-centric networking (ICN) enables communication in isolated islands, where fixed infrastructure is not available, but also supports seamless communication if the infrastructure is up and running again. In disaster scenarios, when a fixed infrastructure is broken, content discovery algorithms are required to learn what content is locally available. For example, if preferred content is not available, users may also be satisfied with second best options. In this paper, we describe a new content discovery algorithm and compare it to existing Depth-first and Breadth-first traversal algorithms. Evaluations in mobile scenarios with up to 100 nodes show that it results in better performance, i.e., faster discovery time and smaller traffic overhead, than existing algorithms.

Commoditization and virtualization of wireless networks are changing the economics of mobile networks to help network providers (e.g., MNO, MVNO) move from proprietary and bespoke hardware and software platforms toward an open, cost-effective, and flexible cellular ecosystem. In addition, rich and innovative local services can be efficiently created through cloudification by leveraging the existing infrastructure. In this work, we present RANaaS, which is a cloudified radio access network delivered as a service. RANaaS provides the service life-cycle of an on-demand, elastic, and pay as you go 3GPP RAN instantiated on top of the cloud infrastructure. We demonstrate an example of real-time cloudified LTE network deployment using the OpenAirInterface LTE implementation and OpenStack running on commodity hardware as well as the flexibility and performance of the platform developed.

Many location-based services target users in indoor environments. Similar to the case of dense urban areas where many obstacles exist, indoor localization techniques suffer from outlying measurements caused by severe multipath propaga­tion and non-line-of-sight (NLOS) reception. Obstructions in the signal path caused by static or mobile objects downgrade localization accuracy. We use robust multipath mitigation techniques to detect and filter out outlying measurements in indoor environments. We validate our approach using a power-based lo­calization system with GSM. We conducted experiments without any prior knowledge of the tracked device's radio settings or the indoor radio environment. We obtained localization errors in the range of 3m even if the sensors had NLOS links to the target device.

Low quality of wireless links leads to perpetual transmission failures in lossy wireless environments. To mitigate this problem, opportunistic routing (OR) has been proposed to improve the throughput of wireless multihop ad-hoc networks by taking advantage of the broadcast nature of wireless channels. However, OR can not be directly applied to wireless sensor networks (WSNs) due to some intrinsic design features of WSNs. In this paper, we present a new OR solution for WSNs with suitable adaptations to their characteristics. Our protocol, called SCAD - Sensor Context-aware Adaptive Duty-cycled beaconless opportunistic routing protocol is a cross-layer routing approach and it selects packet forwarders based on multiple sensor context information. To reach a balance between performance and energy-efficiency, SCAD adapts the duty-cycles of sensors according to real-time traffic loads and energy drain rates. We compare SCAD against other protocols through extensive simulations. Evaluation results show that SCAD outperforms other protocols in highly dynamic scenarios.

Clock synchronization in the order of nanoseconds is one of the critical factors for time-based localization. Currently used time synchronization methods are developed for the more relaxed needs of network operation. Their usability for positioning should be carefully evaluated. In this paper, we are particularly interested in GPS-based time synchronization. To judge its usability for localization we need a method that can evaluate the achieved time synchronization with nanosecond accuracy. Our method to evaluate the synchronization accuracy is inspired by signal processing algorithms and relies on fine-grain time information. The method is able to calculate the clock offset and skew between devices with nanosecond accuracy in real time. It was implemented using software defined radio technology. We demonstrate that GPS-based synchronization suffers from remaining clock offset in the range of a few hundred of nanoseconds but the clock skew is negligible. Finally, we determine a corresponding lower bound on the expected positioning error.

Indoor localization systems become more interesting for researchers because of the attractiveness of business cases in various application fields. A WiFi-based passive localization system can provide user location information to third-party providers of positioning services. However, indoor localization techniques are prone to multipath and Non-Line Of Sight (NLOS) propagation, which lead to significant performance degradation. To overcome these problems, we provide a passive localization system for WiFi targets with several improved algorithms for localization. Through Software Defined Radio (SDR) techniques, we extract Channel Impulse Response (CIR) information at the physical layer. CIR is later adopted to mitigate the multipath fading problem. We propose to use a Nonlinear Regression (NLR) method to relate the filtered power information to propagation distances, which significantly improves the ranging accuracy compared to the commonly used log-distance path loss model. To mitigate the influence of ranging errors, a new trilateration algorithm is designed as well by combining Weighted Centroid and Constrained Weighted Least Square (WC-CWLS) algorithms. Experiment results show that our algorithm is robust against ranging errors and outperforms the linear least square algorithm and weighted centroid algorithm.

This paper addresses the problem of service development based on GSM handset signaling. The aim is to achieve this goal without the participation of the users, which requires the use of a passive GSM receiver on the uplink. Since no tool for GSM uplink capturing was available, we developed a new method that can synchronize to multiple mobile devices by simply overhearing traffic between them and the network. Our work includes the implementation of modules for signal recovery, message reconstruction and parsing. The method has been validated against a benchmark solution on GSM downlink and independently evaluated on uplink channels. Initial evaluations show up to 99% success rate in message decoding, which is a very promising result. Moreover, we conducted measurements that reveal insights on the impact of signal power on the capturing performance and investigate possible reactive measures.

Energy is of primary concern in wireless sensor networks (WSNs). Low power transmission makes the wireless links unreliable, which leads to frequent topology changes. Resulting packet retransmissions aggravate the energy consumption. Beaconless routing approaches, such as opportunistic routing (OR) choose packet forwarders after data transmissions, and are promising to support dynamic features of WSNs. This paper proposes SCAD - Sensor Context-aware Adaptive Duty-cycled beaconless OR for WSNs. SCAD is a cross-layer routing solution and it brings the concept of beaconless OR into WSNs. SCAD selects packet forwarders based on multiple types of network contexts. To achieve a balance between performance and energy efficiency, SCAD adapts duty-cycles of sensors based on real-time traffic loads and energy drain rates. We implemented SCAD in TinyOS running on top of Tmote Sky sensor motes. Real-world evaluations show that SCAD outperforms other protocols in terms of both throughput and network lifetime.

Recent advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing environmental conditions and number of users, application performance might suffer, leading to Service Level Agreement (SLA) violations and inefficient use of hardware resources. We introduce a system for controlling the complexity of scaling applications composed of multiple services using mechanisms based on fulfillment of SLAs. We present how service monitoring information can be used in conjunction with service level objectives, predictions, and correlations between performance indicators for optimizing the allocation of services belonging to distributed applications. We validate our models using experiments and simulations involving a distributed enterprise information system. We show how discovering correlations between application performance indicators can be used as a basis for creating refined service level objectives, which can then be used for scaling the application and improving the overall application's performance under similar conditions.

This paper presents a survey on the usage, opportunities and pitfalls of semantic technologies in the Internet of Things. The survey was conducted in the context of a semantic enterprise integration platform. In total we surveyed sixty-one individuals from industry and academia on their views and current usage of IoT technologies in general, and semantic technologies in particular. Our semantic enterprise integration platform aims for interoperability at a service level, as well as at a protocol level. Therefore, also questions regarding the use of application layer protocols, network layer protocols and management protocols were integrated into the survey. The survey suggests that there is still a lot of heterogeneity in IoT technologies, but first indications of the use of standardized protocols exist. Semantic technologies are being recognized as of potential use, mainly in the management of things and services. Nonetheless, the participants still see many obstacles which hinder the widespread use of semantic technologies: Firstly, a lack of training as traditional embedded programmers are not well aware of semantic technologies. Secondly, a lack of standardization in ontologies, which would enable interoperability and thirdly, a lack of good tooling support.

In this paper, we investigate content-centric data transmission in the context of short opportunistic contacts and base our work on an existing content-centric networking architecture. In case of short interconnection times, file transfers may not be completed and the received information is discarded. Caches in content-centric networks are used for short-term storage and do not guarantee persistence. We implemented a mechanism to extend caching on persistent storage enabling the completion of disrupted content transfers. The mechanisms have been implemented in the CCNx framework and have been evaluated on wireless mesh nodes. Our evaluations using multicast and unicast communication show that the implementation can support content transfers in opportunistic environments without significant processing and storing overhead.

Integrating physical objects (smart objects) and enterprise IT systems is still a labor intensive, mainly manual task done by domain experts. On one hand, enterprise IT backend systems are based on service oriented architectures (SOA) and driven by business rule engines or business process execution engines. Smart objects on the other hand are often programmed at very low levels. In this paper we describe an approach that makes the integration of smart objects with such backends systems easier. We introduce semantic endpoint descriptions based on Linked USDL. Furthermore, we show how different communication patterns can be integrated into these endpoint descriptions. The strength of our endpoint descriptions is that they can be used to automatically create REST or SOAP endpoints for enterprise systems, even if which they are not able to talk to the smart objects directly. We evaluate our proposed solution with CoAP, UDP and 6LoWPAN, as we anticipate the industry converge towards these standards. Nonetheless, our approach also allows easy integration with backend systems, even if no standardized protocol is used.

Opportunistic routing (OR) employs a list of candidates to improve reliability of wireless transmission. However, list-based OR features restrict the freedom of opportunism, since only the listed nodes can compete for packet forwarding. Additionally, the list is statically generated based on a single metric prior to data transmission, which is not appropriate for mobile ad-hoc networks. This paper provides a thorough performance evaluation of a new protocol - Context-aware Opportunistic Routing (COR). The contributions of COR are threefold. First, it uses various types of context information simultaneously such as link quality, geographic progress, and residual energy of nodes to make routing decisions. Second, it allows all qualified nodes to participate in packet forwarding. Third, it exploits the relative mobility of nodes to further improve performance. Simulation results show that COR can provide efficient routing in mobile environments, and it outperforms existing solutions that solely rely on a single metric by nearly 20-40 %.

A reliable and robust routing service for Flying Ad-Hoc Networks (FANETs) must be able to adapt to topology changes. User experience on watching live video sequences must also be satisfactory even in scenarios with buffer overflow and high packet loss ratio. In this paper, we introduce a Cross-layer Link quality and Geographical-aware beaconless opportunistic routing protocol (XLinGO). It enhances the transmission of simultaneous multiple video flows over FANETs by creating and keeping reliable persistent multi-hop routes. XLinGO considers a set of cross-layer and human-related information for routing decisions, as performance metrics and Quality of Experience (QoE). Performance evaluation shows that XLinGO achieves multimedia dissemination with QoE support and robustness in a multi-hop, multi-flow, and mobile network environments.

Wireless Mesh Networks (WMNs) are increasingly deployed to enable thousands of users to share, create, and access live video streaming with different characteristics and content, such as video surveillance and football matches. In this context, there is a need for new mechanisms for assessing the quality level of videos because operators are seeking to control their delivery process and optimize their network resources, while increasing the user’s satisfaction. However, the development of in-service and non-intrusive Quality of Experience assessment schemes for real-time Internet videos with different complexity and motion levels, Group of Picture lengths, and characteristics, remains a significant challenge. To address this issue, this article proposes a non-intrusive parametric real-time video quality estimator, called MultiQoE that correlates wireless networks’ impairments, videos’ characteristics, and users’ perception into a predicted Mean Opinion Score. An instance of MultiQoE was implemented in WMNs and performance evaluation results demonstrate the efficiency and accuracy of MultiQoE in predicting the user’s perception of live video streaming services when compared to subjective, objective, and well-known parametric solutions.

Current advanced cloud infrastructure management solutions allow scheduling actions for dynamically changing the number of running virtual machines (VMs). This approach, however, does not guarantee that the scheduled number of VMs will properly handle the actual user generated workload, especially if the user utilization patterns will change. We propose using a dynamically generated scaling model for the VMs containing the services of the distributed applications, which is able to react to the variations in the number of application users. We answer the following question: How to dynamically decide how many services of each type are needed in order to handle a larger workload within the same time constraints? We describe a mechanism for dynamically composing the SLAs for controlling the scaling of distributed services by combining data analysis mechanisms with application benchmarking using multiple VM configurations. Based on processing of multiple application benchmarks generated data sets we discover a set of service monitoring metrics able to predict critical Service Level Agreement (SLA) parameters. By combining this set of predictor metrics with a heuristic for selecting the appropriate scaling-out paths for the services of distributed applications, we show how SLA scaling rules can be inferred and then used for controlling the runtime scale-in and scale-out of distributed services. We validate our architecture and models by performing scaling experiments with a distributed application representative for the enterprise class of information systems. We show how dynamically generated SLAs can be successfully used for controlling the management of distributed services scaling.

Linking the physical world to the Internet, also known as the Internet of Things, has increased available information and services in everyday life and in the Enterprise world. In Enterprise IT an increasing number of communication is done between IT backend systems and small IoT devices, for example sensor networks or RFID readers. This introduces some challenges in terms of complexity and integration. We are working on the integration of IoT devices into Enterprise IT by leveraging SOA techniques and Semantic Web technologies. We present a SOA based integration platform for connecting WSNs and large enterprise business processes. For ensuring interoperability our platform is based on Linked Services. These are thoroughly described, machine-readable, machine-reasonable service descriptions.

Opportunistic routing (OR) takes advantage of the broadcast nature and spatial diversity of wireless transmission to improve the performance of wireless ad-hoc networks. Instead of using a predetermined path to send packets, OR postpones the choice of the next-hop to the receiver side, and lets the multiple receivers of a packet to coordinate and decide which one will be the forwarder. Existing OR protocols choose the next-hop forwarder based on a predefined candidate list, which is calculated using single network metrics. In this paper, we propose TLG - Topology and Link quality-aware Geographical opportunistic routing protocol. TLG uses multiple network metrics such as network topology, link quality, and geographic location to implement the coordination mechanism of OR. We compare TLG with well-known existing solutions and simulation results show that TLG outperforms others in terms of both QoS and QoE metrics.

For smart applications, nodes in wireless multimedia sensor networks (MWSNs) have to take decisions based on sensed scalar physical measurements. A routing protocol must provide the multimedia delivery with quality level support and be energy-efficient for large-scale networks. With this goal in mind, this paper proposes a smart Multi-hop hierarchical routing protocol for Efficient VIdeo communication (MEVI). MEVI combines an opportunistic scheme to create clusters, a cross-layer solution to select routes based on network conditions, and a smart solution to trigger multimedia transmission according to sensed data. Simulations were conducted to show the benefits of MEVI compared with the well-known Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. This paper includes an analysis of the signaling overhead, energy-efficiency, and video quality.

The application of pesticides and fertilizers in agricultural areas is of prime importance for crop yields. The use of aircrafts is becoming increasingly common in carrying out this task mainly because of its speed and effectiveness in the spraying operation. However, some factors may reduce the yield, or even cause damage (e.g. crop areas not covered in the spraying process, overlapping spraying of crop areas, applying pesticides on the outer edge of the crop). Climatic conditions, such as the intensity and direction of the wind while spraying add further complexity to the control problem. In this paper, we describe an architecture based on unmanned aerial vehicles (UAVs) which can be employed to implement a control loop for agricultural applications where UAVs are responsible for spraying chemicals on crops. The process of applying the chemicals is controlled by means of the feedback obtained from the wireless sensor network (WSN) deployed on the crop field. The aim of this solution is to support short delays in the control loop so that the spraying UAV can process the information from the sensors. We evaluate an algorithm to adjust the UAV route under changes in wind intensity and direction. Moreover, we evaluate the impact of the number of communication messages between the UAV and the WSN. Results show that the adjustment of the route based on the feedback information from the sensors could minimize the waste of pesticides.

Research in the area of Wireless Sensor Networks (WSNs) has become more and more driven by real-world experimental evaluations rather than network simulation. Numerous testbeds of WSNs have been set up in the past decade, often with very much differing architectural design and hardware. The Testbed Management Architecture for Wireless Sensor Networks (TARWIS) presented in this paper provides the most crucial management and scheduling functionalities for WSN testbeds, independent from the testbed architecture and the sensor node's operating systems. These functionalities are: a consistent notion of users and user groups, resource reservation features, support for reprogramming and reconfiguration of the nodes, provisions to debug and remotely reset sensor nodes in case of node failures, as well as a solution for collecting and storing experimental data. We describe the workflow of using a TARWIS on a WSN testbed over the entire experimentation life cycle, starting from resource reservation over experiment definition to the collection of real-world experimental data.

Wireless Multimedia Sensor Networks (WMSNs) play an important role in pervasive and ubiquitous systems. The multimedia content in such networks has the potential of enhancing the level of information collected, enlarging the range of coverage, and enabling multi-view support. For WMSN applications, the multi-tier network architecture has proven to be more beneficial than a single-tier in terms of energy-efficiency, scalability, functionality and reliability. In this context, a multimedia intrusion detection application appears as a promising application of multi-tier WMSNs, where the lower tier can detect the intruder using scalar sensors, and the higher tier camera nodes will be woken up to send real time video sequences from the detected area. The transmission of multimedia content requires a certain quality level from the user perspective, while energy consumption and network overhead should be minimized. Among the existing mechanisms for improving video transmissions, Forward Error Correction (FEC) can be regarded as a suitable solution to improve video quality level from the user point-of-view. In this work, we propose a Quality of Experience (QoE)-aware FEC mechanism for WMSNs, which creates redundant packets based on impact of the frame on the user experience. According to the simulation results, our proposed mechanism achieved similar video quality level compared with standard FEC, while reducing the transmission of redundant packets, which will bring many benefits in a resource-constrained system.

Over the past several years, a number of design approaches in wireless mesh networks have been introduced to support the deployment of wireless mesh networks (WMNs). We introduce a novel wireless mesh architecture that supports authentication and authorisation functionalities, giving the possibility of a seamless WMN integration into the home's organization authentication and authorisation infrastructure. First, we introduce a novel authentication and authorisation mechanism for wireless mesh nodes. The mechanism is designed upon an existing federated access control approach, i.e. the AAI infrastructure that is using just the credentials at the user's home organization in a federation. Second, we demonstrate how authentication and authorisation for end users is implemented by using an existing web-based captive portal approach. Finally, we observe the difference between the two and explain in detail the process flow of authorized access to network resources in wireless mesh networks. The goal of our wireless mesh architecture is to enable easy broadband network access to researchers at remote locations, giving them additional advantage of a secure access to their measurements, irrespective of their location. It also provides an important basis for the real-life deployment of wireless mesh networks for the support of environmental research.

Costly on-site node repairs in wireless mesh networks (WMNs) can be required due to misconfiguration, corrupt software updates, or unavailability during updates. We propose ADAM as a novel management framework that guarantees accessibility of individual nodes in these situations. ADAM uses a decentralised distribution mechanism and self-healing mechanisms for safe configuration and software updates. In order to implement the ADAM management and self-healing mechanisms, an easy-to-learn and extendable build system for a small footprint embedded Linux distribution for WMNs has been developed. The paper presents the ADAM concept, the build system for the Linux distribution and the management architecture.

This paper describes how Quality of Service (QoS) support can be introduced to Overlay Multicast in a Chord Peer-to-Peer network. We support the concept of QoS classes (to support various hop-by-hop QoS parameters such as bandwidth requirements) as well as node to root Round Trip Time (RTT) constraints. Our evaluations show that we can guarantee QoS without changing the basic properties of Chord significantly.

We propose a novel methodology to generate realistic network flow traces to enable systematic evaluation of network monitoring systems in various traffic conditions. Our technique uses a graph-based approach to model the communication structure observed in real-world traces and to extract traffic templates. By combining extracted and user-defined traffic templates, realistic network flow traces that comprise normal traffic and customized conditions are generated in a scalable manner. A proof-of-concept implementation demonstrates the utility and simplicity of our method to produce a variety of evaluation scenarios. We show that the extraction of templates from real-world traffic leads to a manageable number of templates that still enable accurate re-creation of the original communication properties on the network flow level.

We propose NetICE9, a novel landmark-less method for embedding RTTs into virtual spaces. NetICE9 is inspired by VIVALDI, the most commonly used landmark-less approach. VIVALDI chooses its neighbors randomly and optimizes only towards one neighbor at a time. With NetICE9, we propose a solution to those drawbacks. NetICE9 significantly improves both the stability of the simulation and the precision of how RTTs are embedded into a virtual space. Our evaluation based on RTTs measured in the Internet show that NetICE9 significantly outperforms VIVALDI in terms of stability and precision of RTT prediction. NetICE9 improves RTT prediction also in comparison to GNP.

Constructing a topology aware overlay network is an open research topic. In this paper we propose a novel protocol for building overlay networks - a distributed fisheye view. Similar to round trip time (RTT) prediction approaches, we consider the end systems to be embedded in a virtual metric space. Unlike other approaches, we use only the distances (measured RTTs) to build an RTT proximity aware overlay network. Therefore, we are able to construct a fisheye view without performing the embedding. At the same time we are still able to guarantee the geographical diversity of the neighbors. Once built, the fisheye views on the end systems are continuously refined as information about new potential neighbors is available. This makes our overlay network adaptive to changes in the network topology. To evaluate our approach, we compared it with an existing topology aware overlay network construction approach - binning. We based this comparison on RTT measurements obtained using the King RTT measurement method and from the Planet Lab “all site ping” experiment. Our evaluation shows that the overlay network built using our approach outperforms binning in terms of relative RTT stretch. We also show that for increasing number of neighbors the performance of our approach converges towards an optimal solution.

In this paper we propose a routing backbone construction mechanism that exploits and uses the synchronization messages exchanged by synchronized contention-based MAC protocols. Due to the usage of synchronization messages no additional control traffic is required to setup the routing backbone. Every node running a synchronized contention-based MAC protocol follows a given listen/sleep cycle. Because routing is supported by the backbone, non-backbone nodes can temporarily turn off their radios for multiple listen/sleep cycles. Thus, additional energy can be saved. Accordingly, non-backbone nodes do not have to wake up in every listen/sleep cycle to synchronize with other nodes, but wake up only if required, i.e., if they have to report some sensor readings to a base station. In this case, they synchronize to the backbone, send their data, and go back to sleep after successful transmission. Our approach is applicable to rather static networks with mainly source-to-sink traffic. Most monitoring applications are of this kind.

Numerous round trip time (RTT) prediction schemes use the least squares method to embed hosts in virtual euclidean spaces. The least squares method minimizes the residuals between measured data (measured RTTs) and their approximation (euclidean distances between the host position and fixed points, to which the distance was measured). This is achieved by minimizing an objective function, which is defined as a sum of square differences between measured distances to fixed points (landmarks) and euclidean distances to those landmarks in a virtual space. Since there is no direct way (closed form) for finding minima of the objective function, numerical function minimization must be used. In this paper we identify the problem of existence of multiple local minima of objective functions and their impact on resulting RTT predictions. To overcome this problem, we propose an algorithm for finding all local minima of the objective function. By finding all minima, we are able to identify the global minimum of the objective function, and thus ensure the optimal embedding of a host in the virtual space. To evaluate our algorithm we compare it with standard methods for function minimization using data collected by the Planet-Lab all-pings experiment.

This paper describes how Quality of Service (QoS) enabled Overlay Multicast architectures using Peer-to-Peer (P2P) networks can enhance the experience of end-users in Networked Virtual Environments (NVE). We show how IP Multicast, which offers an easy to use API for implementing NVE but is not widely deployed, can be made available to end-users by bridging it transparently with P2P networks. We describe how different P2P and Application Level Multicast (ALM) architectures can be extended with QoS mechanisms using our proposed OM-QoS (Overlay Multicast QoS) architecture. The presented approach allows users to experience QoS for NVE such as group-based multimedia broadcasting and distributed multiplayer games.

In this paper the classification of discrete events, computed on tiny wireless sensor nodes, is investigated. Three different classifiers are evaluated: a Bayesian classifier, a fuzzy logic controller (FLC), and a neural network approach. The target applications pose several requirements on the classifiers. No a priori knowledge about the event classes is available. Events are only observable as collections of raw sensor data. Accordingly, event classes need to be learned from that raw (training) data. As a consequence, pre-labeling of the events is not possible either. In our work, event classes are learned by a k-means clustering algorithm. Any subsequent classifier training is based on these extracted event classes. Thus, the resulting classifiers are completely self-learning. Event classes are learned from emitted signal strength estimations, which are collected and processed by dynamically established tracking groups. The resulting event estimates are reported to a base station, where the classifiers are trained. The learned classifier parameters are then downloaded onto the sensor nodes, where any subsequent classification and filtering is performed.

Cross-layer design has been proposed as a promising paradigm to tackle various problems of wireless communication systems. Recent research has led to a variety of protocols that rely on intensive interaction between different layers of the classical layered OSI protocol architecture. These protocols involve different layers and introduce new ideas how layers shall communicate and interact. In existing cross-layer approaches, the violation of the OSI architecture typically consists in passing information between different adjacent or non-adjacent layers of one single station's protocol stack to solve an optimization problem and exploiting the dependencies between the layers. This paper proposes to go a step further and to consider cross-layer information exchange across different layers of multiple stations involved in multi-hop communication systems. It outlines possible application scenarios of this approach, and trades off between advantages and disadvantages of the proposed \emph {multi-hop cross-layer design}. It examines an application scheme in a scenario of a wireless sensor network environment operating with a recent energy-efficient power saving protocol.

In this paper we analyze the positioning of landmarks in coordinates-based Internet distance prediction approaches with focus on Global Network Positioning (GNP). We show that one of the major drawbacks of GNP is its computational overhead for a large number of landmarks and dimensions. In our work we identify two factors, which have a great impact on the computational overhead. The first one is being able to determine the optimal number of dimensions for embedding a given set of landmarks into a Euclidean space. The second factor is the selection of a good starting point for minimizing the total error of embedding. We propose an algorithm based on the simplex inequality (a generalized form of the triangle inequality) to extract the optimal number of dimensions based on distance measurements between landmarks. We also provide methods to compute a good starting point for the minimization problem and to reduce the number of variables involved in the minimization. We performed experiments with data obtained from the PlanetLab all-sites-pings experiment to verify the correctness and performance gains of our algorithm. The experimental results show that our enhancements to GNP landmark positioning are able to find the optimal number of dimensions for embedding the landmarks. These enhancements also accelerate the function minimization.

Abstract — In this paper we present a concept for providing QoS to multicast in structured P2P networks. We show on the example of Scribe / Pastry how to enforce QoS aware tree construction in a structured P2P network. We achieve this by modifying the ID assignment method of Pastry based on the QoS requirements of peers. As a result, the multicast tree holds the QoS (bandwidth) requirements on each of its end-to-end paths. We have evaluated the proposed concept by comparing default random Pastry ID assignment with our proposed method. The results of the evaluation show that using our method all endto-end paths in the multicast tree fullfill the bandwidth QoS requirements, which is usually not the case for default Pastry. I.

"The tutorial addresses communication protocols networking issues in wireless sensor networks. After an introduction to into general features of sensor nodes such as platforms and energy consumption as well as a comparison of wireless sensor networks with mobile ad-hoc networks, we study lower layer communication protocols in wireless sensor networks. In particular, we present medium access control protocols and compare them with protocols used in wireless local area networks. Then, we discuss the issue of topology control and present routing protocols proposed for wireless sensor networks. Reliable transport protocols as required for management and reprogramming of sensor nodes will be compared. Finally, security mechanisms to ensure confidentiality, authentication and network availability will be presented."

In this paper we present our solution for providing IP Multicast on end systems in the Internet. The goal of the proposed solution is not to replace IP Multicast, but to provide an IP Multicast interface to applications on end systems in the current Internet environment, where IP Multicast is not available. Our solution, called Multicast Middleware, is a software, which is based on using Application Level Multicast (ALM) for transporting IP Multicast traffic. The use of the Multicast Middleware is transparent for applications on end systems, since our Multicast Middleware uses a virtual network interface to intercept native IP Multicast communication. In this paper we also present a performance evaluation of our Multicast Middleware. The results of this evaluation show that our Multicast Middleware is able to provide high bandwidth throughput to applications. This makes our Multicast Middleware a viable solution for supporting multimedia streaming services, etc.

Reliable data transfer is one of the most difficult tasks to be accomplished in multihop wireless networks. Traditional transport protocols like TCP face severe performance degradation over multihop networks given the noisy nature of wireless media as well as unstable connectivity conditions in place. The success of TCP in wired networks motivates its extension to wireless networks. A crucial challenge faced by TCP over these networks is how to operate smoothly with the 802.11 wireless MAC protocol which also implements a retransmission mechanism at link level in addition to short RTS/CTS control frames for avoiding collisions. These features render TCP acknowledgments (ACK) transmission quite costly. Data and ACK packets cause similar medium access overheads despite the much smaller size of the ACKs. In this paper, we further evaluate our dynamic adaptive strategy for reducing ACK-induced overhead and consequent collisions. Our approach resembles the sender side's congestion control. The receiver is self-adaptive by delaying more ACKs under nonconstrained channels and less otherwise. This improves not only throughput but also power consumption. Simulation evaluations exhibit significant improvement in several scenarios

Abstract — Nowadays heterogeneity of communication technologies would allow nodes to be optimally connected nearly anytime and anywhere. Unfortunately, the different technologies are not designed for seamless interworking. The heterogeneity is often perceived as a hurdle instead of an enabler for being always best connected. The dynamic selection and configuration of the most appropriate technology is by far too complex for the end user, especially when considering ad-hoc connections. The concept of Cellular Assisted Heterogeneous Networking (CAHN) provides a framework to offer convenient and secure management of heterogeneous end-to-end sessions between nodes. Furthermore, the proposed out-of-band signaling enables the seamless integration of ad-hoc links to offer best performance whenever nodes are within vicinity. The introduced separation of the signaling and the data plane allows to switch on power demanding broadband interfaces like GPRS, UMTS, or even WLAN only if actually required, i.e., data has to be sent or received). In this paper we present the potential benefits resulting from these two features enabled by CAHN. Extensive simulations show that both, the integration of ad-hoc links and the selective activation of high power broadband interfaces, can significantly increase the efficiency of heterogeneous sessions in terms of throughput and energy consumption. I.

Distributed systems such as peer-to-peer networks and distributed servers can optimize their performance by adapting to the underlying network. End-to-end measurements are an important basis for such adaptivity. Although most applications measure similar properties of the network, the measurements are mostly done in application-specific ways. In this paper we propose a general peer-to-peer measurement service based on clusters of endpoints that show virtually identical QoS properties when observed from outside the cluster. We discuss the clustering concept as well as its use in the measurement service, and we present a measurement-based method for the remote identification of clusters. This method allows for detecting clusters that are not part of the peer-to-peer network. Our evaluation shows that the presented method is able to reliably detect clusters using measurements of round-trip time or of available bandwidth.

In this paper we present a simple and stateless broadcasting protocol called Dynamic Delayed Broadcasting (DDB) which allows locally optimal broadcasting without any prior knowledge of the neighborhood. As DDB does not require any transmissions of control messages, it conserves critical network resources such as battery power and bandwidth. Local optimality is achieved by applying a principle of Dynamic Forwarding Delay (DFD) which delays the transmissions dynamically and in a completely distributed way at the receiving nodes ensuring nodes with a higher probability to reach new nodes transmit first. An optimized performance of DDB over other stateless protocols is shown by analytical results. Furthermore, simulation results show that, unlike stateful broadcasting protocols, the performance of DDB does not suffer in dynamic topologies caused by mobility and sleep cycles of nodes. These results together with its simplicity and the conservation of network resources, as no control message transmissions are required, make DDB especially suited for sensor and vehicular ad-hoc networks.

Communication over multiple hops, such as in hybrid wireless networks, can only work if the individual hops cooperate by forwarding packets from other hops. We present the Linux implementation of our previously proposed cooperation and accounting strategy for hybrid wireless networks called CASHnet. We describe our implementation as well as our testbed, where we performed different evaluations regarding introduced delay and packet processing time. We identify the limitations of our scheme in a real-life scenario and discuss possible improvements

Multihop wireless networks based on the IEEE 802.11 MAC protocol are promising for ad hoc networks in small scale today. The 802.11 protocol minimizes the well-known hidden node problem but does not eliminate it completely. Consequently, the end-to-end bandwidth utilization may be quite poor if the involved protocols do not interact smoothly. In particular, the TCP protocol does not manage to obtain efficient bandwidth utilization because its congestion control mechanism is not tailored to such a complex environment. The main problems with TCP in such networks are the excessive amount of both spurious retransmissions and contention between data and acknowledgment (ACK) packets for the transmission medium. In this paper, we propose a dynamic adaptive strategy for minimizing the number of ACK packets in transit and mitigating spurious retransmissions. Using this strategy, the receiver adjusts itself to the wireless channel condition by delaying more ACK packets when the channel is in good condition and less otherwise. Our technique not only improves bandwidth utilization but also reduces power consumption by retransmitting much less than a regular TCP does. Extensive simulation evaluations show that our scheme provides very good enhancements in a variety of scenarios.

Despite the availability of high bandwidth Internet access for end-users, video broadcasting over the Internet is not widely spread. Multicast communication decreases the network load by eliminating redundancy of the data transfer. However IP multicast was never widely accepted by commercial Internet service providers (ISP). Existing solutions solving this problem, like MBONE tunneling, are not available for end-users accessing the Internet via xDSL or TV cable. Application layer multicast using peer-to-peer (P2P) overlay networks could solve the problem of sparse IP multicast support in the Internet. A limitation of this approach is the lack of standardized interfaces for existing IP multicast applications. We propose a solution, which bridges application layer multicast and IP multicast and uses a P2P (overlay) network to transport multicast data. Our solution - including a "proof-of-concept" prototype - enables video broadcasting over the Internet using existing IP multicast applications without requiring additional service deployment.

Wireless communication technologies enabled the possibility of building spontaneous networks between two or more users to exchange data. The problem in the establishment of such networks lies in the configuration that has to be agreed on and in the way the communicating parties can be identified. In prior publications we have presented our vision of convenient networking in a heterogeneous environment. In this paper, we describe an implementation that offers a dashboard-like tool, which can, with the help of a cellular network, ease the formation of spontaneous networks among heterogeneous nodes. Furthermore, the provided implementation is able to secure the acquired communication links in the spontaneous network and therefore protect the exchanged information against possible abuse.

Multi-hop cellular networks (also called hybrid networks) appears to be a promising combination of the dynamics of mobile ad hoc networks and the reliability of infrastructured wireless networks. However, several known weaknesses of mobile ad hoc networks still persist. Besides the security and routing issues, the cooperation among nodes is of great importance. We propose a highly decentralized accounting and security architecture that provides a solid foundation for a cooperation scheme based on rewards and is applicable to multi-hop cellular networks. This scheme incorporates a security architecture which is based on public key cryptography and uses digital-signatures and certificates.

In recent years, a great deal of effort has been devoted to make the TCP protocol more resilient to the random packet losses inherent in the wireless channels in ad hoc networks. In this paper, we investigate the use of fuzzy logic theory for assisting the TCP error detection mechanism in such networks. An elementary fuzzy logic engine is presented as an intelligent technique for discriminating packet loss due to congestion from packet loss by wireless induced errors. The architecture of the proposed fuzzy-based error detection mechanism is also introduced and discussed. The full approach, for inferring the internal state of the network, relies on round trip time (RTT) measurements only. Hence, this is an end-to-end scheme which requires only end nodes cooperation. Preliminary simulation evaluations show how viable this approach may be.

Setting up experimental networks of a sufficient size is a crucial element for the development of communication services. Unfortunately, the required equipment, like routers and hosts, is expensive and its availability is limited. On the other hand, simulations often lack interoperability to real systems and scalability, which limits the scope and the validity of their results. Therefore, an intermediate approach between these two alternatives that allows for setting up testbeds on a cluster of computers is needed. This paper presents an intermediate approach based on the emulation of IP routers and evaluates the concept. In a first set of experiments the impact of various parameters on the packet delay was investigated, while further experiments compare the performance of differentiated services run on the network emulator with the results obtained by the well known network simulator ns.

With the increasing complexity of network management activities due to naturally limited human involvement, carriers and service providers are looking to migrate from manual, static provisioning models to the more dynamic service-oriented automated provisioning models to meet customer demands for rapid service turn-up and obtain more customers and maximize revenue opportunities. We propose a novel distributed architecture where highly mobile and intelligent agents can take the responsibilities of not only provisioning, but also configuration audit management in a timely fashion. Although previous research has focused on using mobile agents for network monitoring or simple push-based device configuration in a distributed architecture, their ability have not been exploited in dynamic IP service provisioning. Using a simple push-based model to configure network services while ignoring dependencies among configuration elements may easily lead to configuration inconsistencies resulting in failure or inefficiencies. In this paper, we have taken a new approach to configuration modeling that is device neutral and based on which any existing or emerging IP services can be presented by encapsulating service semantics, including service-specific data. We have developed new mobile intelligent provisioning and audit agent architectures that use the knowledge built upon configuration dependency modeling. Examples of intelligent agents are presented to complement the proposed management architecture.

This paper describes a concept to support scalable multicast communications for small audio/video conferencing groups on the Internet. The solution presented in this paper is based on extensions of IPv6 and the session description protocol (SDP). A goal of the concept called multicast for small conferences (MSC) is the smooth deployment in the Internet.

Differentiated services (DiffServ) are not yet fully adapted and integrated with mobile environments, especially when mobile IP is used as the mobility management protocol in the Internet. When a mobile node visits a foreign network and requests services based on service level agreements (SLAs), the DiffServ Internet service providers (ISPs) must care about how to charge the mobile user. In addition, SLAs have to be renegotiated between home/foreign links and their ISPs. Authentication, authorization and accounting (AAA) procedures must be provided. This paper proposes a concept to combine the service location protocol and the mobile IP AAA based architecture. It is independent of the availability of a foreign agent (FA) and works for IPv4 or IPv6 in a uniform manner. The architecture supports mobile telephony over packet-based IP networks without requiring GSM-like mobility management and accounting schemes.

This paper describes a solution called secure mobile IP (SecMIP) to provide mobile IP users secure access to their company's firewall protected virtual private network. The solution requires neither the introduction of new protocols nor the insertion or modification of network components. It only requires a slight adaptation of the end system communication software in order to adapt the mobile IP and IP security protocol implementations to each other. The paper describes the concept, prototype implementation, and initial performance measurement results.

To support quality-of-service (QoS) provisioning in highly dynamic mobile environments, we propose a signaling protocol allowing mobile users to contact a Differentiated Services bandwidth broker for QoS negotiation. The protocol can also be used for QoS negotiations between bandwidth brokers.

We previously proposed a range-based service level agreement (SLA) approach and edge provisioning in DiffServ capable virtual private networks (VPNs) to customers that are unable or unwilling to predict the load between VPN endpoints exactly. With range-based SLAs customers specify their requirements as a range of quantitative values rather than a single one. Various suitable policies and algorithms dynamically provision and allocate resources at the edges for VPN connections. However, we also need to provision the interior nodes of a transit network to meet the assurances offered at the boundaries of the network. Although a deterministic guaranteed service (single quantitative value approach) provides the highest level of QoS guarantees, it leaves a significant portion of network resources on the average unused. We show that with range-based SLAs providers have the flexibility to allocate bandwidth that falls between a lower and upper bound of the range only, and therefore, take advantage of this to make multiplexing gain in the core that is usually not possible with a deterministic approach. But dynamic and frequent configurations of an interior device is not desired as this will lead to scalability problems and also defeats the purpose of the DiffServ architecture which suggests to drive all the complexities towards edges. We, therefore, propose virtual core provisioning that only requires a capacity inventory of interior devices to be updated based on VPN connection acceptance, termination or modification at the edges.

Currently two approaches to provide quality of service in the Internet are being discussed. An early one is the Resource Reservation Protocol, RSVP, (IETF RFC 2205) based on an end to end approach. Recent and ongoing activities in the IETF's Differentiated Service Working Group are focused on methods for providing quality of service in backbones. This paper presents a concept for the integration of both integrated and differentiated services, describes a prototype implementation, and presents evaluation results. Additionally the paper discusses business aspects arising from this service translation.

Customers of virtual private networks (VPN) over differentiated services (Diffserv) infrastructure are most likely to demand not only security but also guaranteed quality of service (QoS) as there is a desire to have leased line like services. However, it is expected that they will be unable or unwilling to predict load between VPN endpoints. In this paper, we propose that customers specify their requirements as a range of quantitative services in the service level agreements (SLAs). To support such services ISPs would need to have an automated provisioning system that can logically partition the capacity at the edges to various classes (or groups) of VPNs and manage them efficiently to allow resource sharing among the groups in a dynamic and fair manner. While with edge provisioning, a certain amount of resources based on SLAs (traffic contract at edge) are allocated to VPN connections, we also need to provision the interior nodes of a transit network to meet the assurances offered at the boundaries of the network. We therefore propose a two-layered model to provision such VPN-Diffserv networks where the top layer is responsible for edge provisioning and drives the lower layer in charge of interior resource provisioning with the help of a bandwidth broker (BB). Various algorithms, with examples and analysis, are presented to provision and allocate resources dynamically at the edges for VPN connections. We have developed a prototype BB performing the required provisioning and connection admission.

As today's network infrastructure continues to grow and Differentiated Services IP backbones are now available to provide various levels of quality of service (QoS) to VPN traffic, the ability to manage increasing network complexity is considered as a crucial factor for QoS enabled VPN solutions. There is growing trend by corporate customers to outsource such complicated management services to Internet service providers (ISP) not only to avoid the for economic reasons. We present methods to provide end-to-end capacity allocation to VPN connections in a single ISP domain and show the implementation of a bandwidth broker managing the outsourced VPNs for corporate customers that have service level agreements (SLAs) with their ISPs. We also present practical configuration examples of commercial routers for enabling QoS enabled VPN tunnels and show how the bandwidth broker can dynamically establish tunnels when users send connection requests from the WWW interface.

The differentiated services (DiffServ) architecture for the Internet implements a scalable mechanism for quality-of-service (QoS) provisioning. Bandwidth brokers represent the instances of the architecture, that automate the provisioning of a DiffServ service between network domains. Although several bandwidth broker implementations have been proposed, the alternatives and trade-offs of the different viable approaches of inter-broker communication were not studied up to now. This paper presents the broker signaling trade-offs considered in the context of a DiffServ scenario used by the Swiss National Science Foundation project CATI, and it presents results gathered by simulations.

This paper describes an architecture for the management of QoS-enabled virtual private networks (VPNs) over the Internet. The architecture focuses on two important issues of VPNs: security and quality-of-service (QoS). The security achieved in VPNs is based on IPSec tunnels, while QoS can be supported by mechanisms as proposed by the differentiated services currently being defined by the IETF. We describe an architecture that is based on the concept of service brokers. These service brokers are used for communication between different domains (such as ISP and customer networks) as well as within domains. The architecture described in the paper is currently being implemented as part of the CATI project funded by the Swiss National Science Foundation (SNF).

This paper describes the design and the prototyping of a compiling tool for the automated implementation of distributed applications: ALFred. This compiler starts from the formal specification of an application written in ESTEREL and then integrates end-to-end communication functions tailored to the application characteristics (described in the specification); it finally produces a high performance implementation. The paper describes the communication architecture associated with the approach. The compiler consists of a control compiler, also called ALF compiler, and a data manipulation compiler (the ILP compiler) that combines data manipulation functions in an efficient way (the ILP loop). The ALFred compiler has been designed to allow the development and the analysis of non-layered high performance communication architectures based on ALF and ILP.

Integrated layer processing (ILP) is an implementation concept that "permits the implementor the option of performing all the (data) manipulation steps in one or two integrated processing loops". To estimate the achievable benefits of ILP, a file transfer application with an encryption function on top of a user-level TCP has been implemented and the performance of the application in terms of throughput and packet processing times has been measured. The results show that it is possible to obtain performance benefits by integrating marshalling, encryption, and TCP checksum calculation. The experiments yielded in a throughput gain of only 10-20% in contrast to the 50% gain achieved for simple loop experiments. Simulations of memory access and cache hit rate show that the main benefit of ILP is reduced memory access rather than an improved cache hit rate. ILP reduced the number of memory accesses up to 30% in the experiment, but the relative amount of cache misses could not be reduced compared to a carefully designed non-ILP implementation. The results also show that data manipulation characteristics may significantly influence the cache behavior and the achievable performance gain of ILP. Considering these results, ILP can only be recommended in cases where the the ILP loop consists of several, but very simple data manipulations without complex calculations over the data.

Integrated Layer Processing (ILP) is an implementation concept which "permit[s] the implementor the option of performing all the [data] manipulation steps in one or two integrated processing loops" [1]. To estimate the achievable benefits of ILP, a file transfer application with an encryption function on top of a user-level TCP has been implemented and the performance of the application in terms of throughput and packet processing times has been measured. The results show that it is possible to obtain performance benefits by integrating marshalling, encryption and TCP checksum calculation. They also show that the benefits are smaller than in simple experiments, where ILP effects have not been evaluated in a complete protocol environment. Simulations of memory access and cache hit rate show that the main benefit of ILP is reduced memory accesses rather than an improved cache hit rate. The results further show that data manipulation characteristics may significantly influence the cache b...

Abstract Requirements of emerging applications together with rapid changes in networking technology towards gigabit speeds require new adequate transport systems. Integrated designs of transport services, protocol architecture and implementation platforms are required by forthcoming applications in high‐speed network environments. The transport subsystem PATROCLOS (parallel transport subsystem for cel/ based high‐speed networks) is designed with special emphasis on a high degree of inherent parallelism to allow efficient implementations on multiprocessor architectures combined with specialized hardware for very time critical functions. The paper presents the new parallel protocol architecture of PATROCLOS, an appropriate implementation architecture based on transputer networks, and performance evaluation results, which indicate high throughput values.

Requirements of emerging applications together with rapid changes in networking technology towards gigabit speeds require new adequate transport systems. Integrated designs of transport services, protocol architecture, and implementation platforms are needed for the requirements of forthcoming applications in high-speed network environments. The transport subsystem PATROCLOS (Parallel Transport subsystem for cell based high speed networks) is designed with special emphasis on a high degree of inherent parallelism to allow efficient implementations on multiprocessor architectures combined with specialised hardware for very time critical functions. The paper presents transport system design guidelines based on experiences gained with parallel implementations of transport and network layer protocols on transputer networks, an implementation architecture for PATROCLOS based on transputer networks and results of a performance evaluation, which indicate promising throughput values.>

Interconnections of remote local area networks (LANs) across public networks are of growing interest. The paper presents an OSI gateway to interconnect LANs across ISDN. The OSI connectionless network protocol (CLNP) supports interconnection of different subnetworks. The most important component of the gateway is an adaption layer to integrate ISDN into the gateway structure. The main task of this adaption layer is mapping the service provided by the connection oriented ISDN subnetwork to the service expected by CLNP and vice versa. It is designed to fulfil requirements on fairness, low costs, and acceptable performance. The gateway components are implemented on a multiprocessor platform built with transputers.>

High performance communication systems have to provide very high data rates of more than 100 Mbit/s to new applications such as multimedia applications. To overcome the bottleneck in communication systems, the protocol processing of the layers above media access control layer, light weight protocols such as XTP (Xpress Transfer Protocol) are used. XTP is a protocol with functionality of layers three and four and was designed for protocol processing in reliable high speed networks such as FDDI, XTP is based on parallel finite state machines (FSMs) to support an efficient realization in VLSI. This paper presents a software implementation of XTP for a multiprocessor system. The implementation is based on these FSMs, which are mapped onto processes running on the processors of a multiprocessor architecture and communicating with each other by message passing.>

The use of parallelism for protocol processing in a parallel architecture of an internetworking unit is presented. This architecture consists of pipelines and arrays of processors and supports multiple memory concepts (local and global memory). A high-performance parallel implementation of the internetworking protocol in a gateway is discussed, and selected performance results are presented. The results show that requirements of high-speed networks with throughputs of more than 100 Mb/s can be fulfilled with the proposed parallel architecture and implementation.>